# BugBook: Genetics of insects as food and feed

C Sandrock, T N Generalovic, K Paul, G E L Petersen, Eli Sellem, M B Smith, M Tapio, W Yakti, L W Beukeboom, D Deruytter, et al.

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## To cite this version:

C Sandrock, T N Generalovic, K Paul, G E L Petersen, Eli Sellem, et al.. BugBook: Genetics of insects as food and feed. Journal of Insects as Food and Feed, 2025, 166 (3), pp.1 - 59. 10.1163/23524588-bja10260. hal-05285561

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BRILL
WAGENINGEN
ACADEMIC

JOURNAL OF INSECTS AS FOOD AND FEED (2025) DOI:10.1163/23524588-BJA10260

Journal of
Insects
as Food and Feed

brill.com/jiff

RESEARCH ARTICLE

# BugBook: Genetics of insects as food and feed

C. Sandrock¹* ID, T.N. Generalovic²,³ ID, K. Paul⁴ ID, G.E.L. Petersen⁵ ID, E. Sellem⁴ ID, M.B. Smith⁶ ID, M. Tapio⁷ ID, W. Yakti⁸ ID, L.W. Beukeboom⁹ ID, D. Deruytter¹⁰ ID, C.D. Jiggins² ID, T. Lefebvre⁴ ID, P. Librado¹¹ ID, B.A. Pannebakker¹² ID, C.J. Picard⁶ ID, C. Rhode¹³ ID, J.G. Sørensen¹⁴ ID, A.C. Bouwman¹⁵ ID, L.S. Hansen¹⁴,¹⁶ ID and J. Obšteter¹⁷ ID

¹Department of Livestock Sciences, Research Institute of Organic Agriculture (FiBL), Ackerstrasse 113, 5070 Frick, Switzerland; ²Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK; ³Better Origin, Entomics Biosystems Limited, Cambridge CB21 5DA, UK; ⁴RandD Biotech Innovations, Žnsect, Evry 91000, France; ⁵Laves Institute for Apiculture Celle, Herzogin-Eleonore-Allee 5, 29221 Celle, Germany; ⁶Department of Biology, Indiana University-Indianapolis, 723 W Michigan Street, Indianapolis, IN 46202 USA; ⁷Production Systems - Genomics and Breeding, Natural Resources Institute Finland (LUKE), FI-31600 Jokioinen, Finland; ⁸Urban Plant Ecophysiology Division, Faculty of Life Sciences, Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Lentzeallee 55, 14195 Berlin, Germany; ⁹Groningen Institute for Evolutionary Life Sciences, University of Groningen, P.O. Box 11103, 9700 CC Groningen, The Netherlands; ¹⁰Insect Research Centre, Inagro, Ieperseweg 87, 8800 Rumbeke-Beitem, Belgium; ¹¹Institut de Biologia Evolutiva (CSIC - Universitat Pompeu Fabra), Barcelona, Spain; ¹²Laboratory of Genetics, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands; ¹³Department of Genetics, Stellenbosch University, Private Bag X1, Matieland 7602, Stellenbosch, South Africa; ¹⁴Department of Biology, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark; ¹⁵Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands; ¹⁶Center for Quantitative Genetics and Genomics, Aarhus University, CF Møllers Allé 3, DK-8000 Aarhus C, Denmark; ¹⁷Agricultural institute of Slovenia, Hacquetova ulica 17, 1000 Ljubljana, Slovenia; *christoph.sandrock@fibl.org

Received 2 December 2024 | Accepted 16 June 2025 | Published online 9 July 2025

## Abstract

Just like traditional livestock, farmed insects harbour genetic variation and can be selectively bred to optimise traits of interest. This BugBook article presents a comprehensive overview of how genetics can contribute to improving insect production for food and feed. Molecular genetics and genomics approaches for generating the data essential for understanding species biology are presented, as well as their implementation in dedicated selective breeding programmes, and options for health and quality control of mass rearing operations. To harness the breeding potential of farmed insects, methods to investigate population genetic diversity and structure through population and evolutionary genetic principles are provided as well as tools for monitoring genetic variation and assessing genetic consequences of captive breeding to adequately manage populations. An overview is given on quantitative genetics of farmed insects, how to record phenotypes and pedigrees, estimate genetic parameters, and design optimal breeding programmes. Lastly, the role of functional genetics in insect production is discussed, the biological link between DNA and phenotypic variation, and key to effectively apply genetic improvement strategies through selective breeding. This article identifies knowledge gaps in insect breeding and provides recommendations for application and future research. Major challenges in the field of genetics of farmed insects include how to phenotype large numbers of individual insects over generations; how environmental factors affect

Published with license by Koninklijke Brill BV | DOI: 10.1163/23524588-BJA10260

© C. SANDROCK ET AL., 2025 | ISSN: 2352-4588 (online)

This is an open access article distributed under the terms of the CC BY 4.0 license.
trait expression, including interaction with genetics; and how to translate results from laboratory settings to mass rearing environments. This article will contribute to further develop the area of genetics of insects for food and feed.

Keywords

demographic inference – insect breeding – population management – review – selection scheme

# 1 Introduction

Insects have long been a traditional component of the human diet in many regions worldwide, particularly in Africa, Asia, and Central and South America (Chakravarthy *et al.*, 2016; Omuse *et al.*, 2024; van Huis, 2013). Recently, there has also been a rapidly growing interest in insect production for animal feed and human food in the Western world (van Huis *et al.*, 2021, 2025). Whereas traditional edible insects were predominantly beetle larvae, caterpillars, grasshoppers, crickets, and termites (Chakravarthy *et al.*, 2016), novel farmed insects are mostly flies and Tenebrionid beetles. Adults or larvae are either harvested whole or processed for ingredients, such as purified proteins and fats for human food (Bellezza Oddon *et al.*, 2025; Dunkel and Payne, 2016; Hazarika and Kalita, 2023) and animal feed (Heuel *et al.*, 2022b; Stadtlander *et al.*, 2017; van Huis *et al.*, 2025). Nutritional profiles of insects are favourable for omnivorous and carnivorous diets (Heuel *et al.*, 2022a; Makkar *et al.*, 2014) and feeding live insects could be a veritable enrichment component in animal production (Bellezza Oddon *et al.*, 2021). A smaller focus lies on the production of insect components for technical applications (Rehman *et al.*, 2023), for cosmetics and paints (Franco *et al.*, 2022; Triunfo *et al.*, 2021), and the agricultural use of insect frass (Barragán-Fonseca *et al.*, 2022; Poveda, 2021).

The potential for reducing ecological footprints of food chains, for instance, by replacing barely sustainable feed components like soy and fishmeal with insect-based products in livestock and aquaculture production (Heuel *et al.*, 2021; Hua, 2021) is widely acknowledged (e.g. Smetana *et al.*, 2021; van Huis, 2013; van Huis and Oonincx, 2017). Yet, the field faces diverse challenges, e.g. regulatory restrictions in several regions complicate large-scale production (Lähteenmäki-Uutela *et al.*, 2021), and food and feed safety issues (Heuel *et al.*, 2023; Wynants *et al.*, 2019) deriving from waste stream bioconversion (Bosch *et al.*, 2019; Ewusie *et al.*, 2018; Gold *et al.*, 2018). Other questions concern insect nutritional requirements (Oonincx *et al.*, 2025), infections

and disease vectoring (Joosten *et al.*, 2020; Vogel *et al.*, 2022), reproduction and welfare under mass rearing conditions (Tomberlin *et al.*, 2025) and data analyses (Smetana *et al.*, 2025). As insect production can only be considered an emerging industry at this point, economic concerns are at the forefront of the stakeholders' considerations. Optimisation of production processes can be approached along various lines, including the technical production facilities and environmental conditions (Coudron *et al.*, 2025; Deruytter *et al.*, 2025), but also genetic improvement through selective breeding. Only very recently is the sector becoming aware of the pervasive and palpable impact of the genetic makeup of insects farmed for food and feed and opportunities for breeding to support the sector's maturation comparable to the state-of-the-art in traditional livestock (Eriksson and Picard, 2021; Hansen *et al.*, 2024b; Jensen *et al.*, 2017; Sellem *et al.*, 2024).

For decades, livestock populations have been tailored to the requirements of their specific production system. Animal breeding has grown into a highly specialised and complex research field covering sophisticated phenotyping and performance testing systems, statistical models for genetic evaluation and prediction in various species, and the integration of genetics and economics into robust breeding programme designs (e.g. Gianola and Rosa, 2015; Miglior *et al.*, 2017; Pérez-Enciso and Steibel, 2021). However, for farmed insects, and especially insects for food and feed, systematic selective breeding is still in its infancy, and its establishment will require a great deal of optimisation. Insect production systems have unique biological and operational characteristics that make the task of genetic improvement particularly challenging. These include the existence of multiple life stages (egg, larva or nymph, pupa, and adult), usually short lifespans, small size of individuals, complex reproductive behaviour and the sheer scale of populations leading to a very poor and/or unpredictable representativeness of a single individual, which complicates the evaluation of individual phenotypes. These challenges demand the development of new paradigms, potentially inspired by aquaculture breeding, where
similar complex systems exist. Adapting selective breeding to insect production also requires amalgamating the different fields of farmed insect genetics to align priorities and achieve a common goal. Researchers and insect breeders currently involved in the field of farmed insect genetics come from a range of backgrounds, each bringing unique perspectives, methodologies, and areas of expertise, which, while enriching, can also create silos and challenges in communication and integration.

The necessary harmonisation of breeding programmes and goals faces the challenge of the diversity of insect species produced for food and feed at present and in the future. Among more than one million insect species described (and many more estimated) (Stork, 2018), numerous are highly promising to explore as food, feed, and technical products (van Huis, 2020). However, the number of insect species used for production purposes is currently restricted to a few dozen (Francuski and Beukeboom, 2020; van Huis and Tomberlin, 2017). For most primary species there is a striking lack of knowledge on their evolutionary history and species-wide population genetic structure (up to complete ignorance of the existence of wild congeners of some production insects). This BugBook article collates available insights for four thematic sections – molecular, evolutionary, quantitative and functional genetics – for the prominent (traditional and novel) insects for food and feed. Examples include the black soldier fly (BSF) (*Hermetia illucens* L.; Diptera: Stratiomyidae), the house fly (*Musca domestica* L.; Diptera: Muscidae), the yellow mealworm (*Tenebrio molitor* L.; Coleoptera: Tenebrionidae), the lesser mealworm (*Alphitobius diaperinus* L.; Coleoptera: Tenebrionidae), the house, the tropical house, and the Mediterranean field cricket (*Acheta domesticus* L., *Gryllodes sigillatus* L., and *Gryllus bimaculatus* De Geer; Orthoptera: Gryllidae), as well as migratory and desert locusts (*Locusta migratoria* L. and *Schistocerca greガria* Forsskål; Orthoptera: Acrididae). We complement the conceptual framework and relevant approaches for genetics research by several comparatively more detailed or advanced insect examples. These include model insects in the academic field, such as the vinegar fly (*Drosophila melanogaster* L.; Diptera: Drosophilidae), the parasitoid wasp *Nasonia vitripennis* (Walker; Hymenoptera: Pteromalidae) and the red flour beetle (*Tribolium castaneum* L.; Coleoptera: Tenebrionidae), insects farmed for other purposes than food and feed, such as the honeybee (*Apis mellifera* L.; Hymenoptera: Apidae) and the silkworm (*Bombyx mori*

L.; Lepidoptera: Bombycidae), but also pest and biocontrol insects.

In this BugBook article, we provide comprehensive overview of genetic principles, processes and tools as well as recommendations for further genetic studies and applications to advance the field of insects for food and feed. We aim to unify knowledge and foster cooperation to accelerate progress in insect breeding programmes and address the unique challenges of insect production systems, ultimately advancing sustainability and productivity. Throughout thematic Sections 2-5 (Figure 1), we review the wider field of insect genetics, ranging from molecular methods commonly used for insect genetic research, to quantitative genetic approaches, to selection of breeding candidates with superior traits. New approaches and methodologies, as well as those established in animal breeding, are critically considered for their application in academic research and the insect industry, along with the identification of specific technological demands, challenges and opportunities. We outline central concepts of population genetics and evaluations of genetic diversity and dynamics of genetic variation in space and time, which is crucial for deciphering hierarchical metapopulation structure across field and production contexts, as well as demographic patterns relevant for inferring domestication processes and conservation strategies. We also discuss the deep knowledge of population structure and evolutionary history required to make informed selection and crossing decisions and effectively manage farmed insect populations in a sustainable manner, as well as biological relationships between genetic variation and functional phenotypic variation. Finally, we discuss conceptual links between the topic areas (Figure 1) to identify interdisciplinary challenges and synergies, and make concluding recommendations for future directions to implement progress in the field of farmed insect genetics.

## 2 Molecular genetics

A comprehensive understanding of the basic biology and evolutionary history of any given farmed insect is key to inform and improve selective breeding programmes, and ensure high health and quality standards in mass rearing operations. This section describes various molecular genetics and genomics analyses, selection and preparation of samples, and the optimal DNA/RNA extraction methods for addressing specific
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Graphical index contextually capturing thematic Sections 2-5.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>FIGURE 2 Potential bidirectionally interpretable and mutually non-exclusive research pathways to address molecular genetics in insects for food and feed.</figcaption></figure>

questions from barely equipped applied research to large-scale bioinformatics endeavours.

## Genotyping methods

The polymerase chain reaction (PCR) is a fundamental technique in modern molecular biology. Delineated by specific primers, it amplifies a target region in the nuclear or mitochondrial genome. PCR facilitates the study of genomic regions from a limited amount of starting material, enabling genetic analysis of virtually any organism without constraints on the sampling environment (Freeland, 2020; Rowe *et al.*, 2017). Genotyping techniques can be categorised into PCR-based and non-PCR-based methods. Within each category, various assays can be performed on mitochondrial DNA (mtDNA), nuclear DNA, or messenger RNA (mRNA) that serves as the intermediate molecule between DNA and proteins. These methods range from simple techniques, such as elucidating evolutionary relationships through PCR-based amplification followed by Sanger sequencing,

to the assessment of total genome diversity, variability, and structural variation through whole genome sequencing (Figure 2) (Holliday *et al.*, 2019; Hoy, 2019).

## Mitochondrial barcoding

One of the most common, and relatively inexpensive, methods of genotyping is to sequence different regions of mtDNA. The small, circular, double-stranded mannerally inherited DNA molecules (approx. 16 kb) are abundant (many copies per cell), stable, suitable for non-invasive sampling, and easily purified for genetic analyses. mtDNA contains coding regions for highly conserved, essential genes involved in energy metabolism (Hwang and Kim, 1999; Wallace and Chalkia, 2013) allowing the design of PCR primers that work universally across entire phyla such as arthropods. Consequently, mtDNA remains the most accessible genetic marker for assessing phylogenetic and phylogeographic relationships (Dowling and Wolff, 2023; Wu *et al.*, 2022). Accurate classification of an organism's evolutionary
origin is crucial, as unresolved taxonomic uncertainties, like unrecognised sibling taxa within a cryptic species complex, would impede the definition of management units necessary for tailored breeding in insect livestock (Section 4).

Typically, short fragments of several hundred base pairs comprising individual genes are sequenced rather than entire mitogenomes, such as the commonly used ‘barcode’ region (Pentinsaari et al., 2016), which encompasses most of the cytochrome oxidase I (COI). Analysing additional genes within the mitochondria does not always provide significant advantages due to the linked nature of the loci (Sandrock et al., 2011b) even though assembling mitogenomes of farmed insects is affordable (Homchan et al., 2024; Yu et al., 2022). Although COI barcoding is barely useful to address intra-population variation, it can represent a minimal approach to discriminate strains of different origin in comparative studies, as demonstrated in the house fly (Pastor et al., 2014). Further comparison with a comprehensive phylogeography, as available for the BSF and so far capturing approximately 60 distinct COI haplotypes worldwide (Guilliet et al., 2022; Khamis et al., 2020; Nguyen et al., 2023; Pazmiño et al., 2023; Stähls et al., 2020), is particularly meaningful in this context (see Section 3: Mito-nuclear phylogeographic patterns). Remarkably, BSF haplotypes exhibit divergences of up to 4.9%, which may be indicative of a transition to cryptic taxon delimitations (Hebert et al., 2003; Lavinia et al., 2017), suggesting the need for further investigation (Generalovic et al., 2023). Based on few available studies, divergence in the yellow mealworm remains way shallower, merely reaching 2% (Song et al., 2022). Importantly, all amplified and sequenced genes must be verified as mitochondrial, and not pseudocopies present in the nucleus of many insect species called nuclear mitochondrial DNA segments (NUMTs). This is typically verified via translation of the gene sequence (Hazkani-Covo et al., 2010).

## Microsatellites

Microsatellites, synonymously referred to as ‘simple sequence repeats (SSR)’ or ‘short tandem repeats (STR)’, are short DNA sequences of 2-6 bp that are repeated few-to-many times. They tend to be widespread in eukaryotic genomes and usually located within non-coding parts such as intergenic or intronic regions. Repetitive motifs experience comparatively high mutation rates at evolutionary scales (Weber and Wong, 1993). Hence, their presumed neutral evolution allows these markers to accumulate high allelic diversity, often

comprising dozens of alleles per locus. Conversely, microsatellites frequently reside in ‘junk’ DNA regions which challenges designing primers within respective flanking regions that are locus-specific and conserved for universal intraspecific amplification. Microsatellites have been and still are widely used for genetic profiling since the 1990s, especially in forensics and paternity testing (Butler, 2014), yet also for population genetics (Section 3) and evolutionary ecology research (Brede et al., 2006; Buellesbach et al., 2023; Sandrock et al., 2007).

Using previously established microsatellite markers is particularly useful if standardised reference data sets are openly accessed for subsequent cross-study comparison. Microsatellites are for example available for BSF (Kaya et al., 2021; Rhode et al., 2020). If no published markers exist, the basic development of new microsatellites can be inexpensively accomplished by generating whole genome sequence reads. Using available pipelines (e.g. msatcommander, (Faircloth, 2008)), reads can be screened for repetitive motifs complemented by adequate primer design swiftly yielding many new microsatellite markers (Abe and Pannebakker, 2017). Next, establishing and validating a robust microsatellite marker set involves screening for meaningful polymorphisms as well as evaluating possibly segregating “null alleles”. These are alleles for which amplification fails due to polymorphisms in a given primer binding region. Such drop out alleles are problematic, and their frequencies may vary among populations. Therefore, newly developed microsatellite loci require a thorough population survey to assess the quality of the designed primers in their ability to amplify across populations. If primer re-design is no option (e.g. due to other amplification artefacts), candidate markers standing out because of null allele homozygotes should be excluded from sets of routinely implemented loci. Further, hypervariable as well as barely polymorphic loci both have their merits, depending on relative scopes to resolve relatedness within and across populations (Section 3).

Microsatellite analysis basically uses conventional PCR amplification. For convenience, multiple loci may be co-amplified in multiplex PCR reactions, if annealing temperatures of individual primers allow or were co-designed for that accordingly. It is imperative to label the forward primers of markers that amplify sequences of possibly overlapping fragment length using (up to six) different fluorescent dyes, and purposive primer design in the flanking regions ensures non-overlapping ranges of finally amplified sequences when the same dye is used. In this way, co-amplification of 5 to 8 loci
is readily possible within in one reaction (Sandrock *et al.*, 2010) but combining up to 24 loci e.g. in human genotyping kits is feasible (Williams *et al.*, 2023). Following the amplification of polymorphic microsatellite loci via PCR, allelic discrimination is performed based on capillary electrophoresis in relation to an internal size standard. This allows for the resolution of fragments that differ by 1 nucleotide or more. Several software tools exist to handle the raw data and visualise fluorescence signals that effectively capture fragment length (e.g. GeneMapper (Thermo Fisher Scientific, Waltham, MA, USA) or Geneious (Dotmatics, Boston, MA, USA)). In diploid genotypes, the sizes of the fragments allow to differentiate between homozygotes and heterozygotes, i.e. sizes of the two alleles are either the same or different, respectively. Not to be generalised, amplification of repetitive motifs can exhibit stutter signals, which requires careful allele calling for some markers.

Alternatively, sequence-based microsatellite genotyping (SSRseq) combines next-generation sequencing techniques with multiplex PCR amplified microsatellite loci (Lepais *et al.*, 2020; Zhan *et al.*, 2017). Because this yields direct access to allele sequences, up to 60 loci can simultaneously be amplified. SSRseq also resolves the issue of homoplasy, where alleles that are genotyped to the same size using electrophoresis represent two alleles due to masked SNPs or indel variation. One requirement of SSRseq is reference genome data, which is currently available for many insects for food and feed, including the BSF (Cai *et al.*, 2024; Costagli *et al.*, 2024; Generalovic *et al.*, 2021; Zhan *et al.*, 2020); yellow mealworm (Eleftheriou *et al.*, 2022a; Eriksson and Picard, 2021; Kaur *et al.*, 2023; Oppert *et al.*, 2023); house cricket (Dossey *et al.*, 2023); house fly (Scott *et al.*, 2014); migratory locust (Wang *et al.*, 2014); or can be newly generated from low coverage genome sequences. By means of efficient processing pipelines and automated data analysis, this approach can be readily normalised across laboratories to yield high-quality microsatellite data, supplemented with SNP and indel variation, for many individuals (Choi *et al.*, 2022; Lepais *et al.*, 2020).

## Single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are alternative markers for genotyping. In contrast to the multiallelic nature of microsatellites, most SNPs are biallelic. Each SNP locus therefore provides less information per locus than a single microsatellite (e.g. Liu *et al.*, 2017), but SNPs have a much higher density in the genome and readily extend into relevant coding regions, making them more informative with regards

to genome coverage and locus representation (Thalamuthu *et al.*, 2005). Furthermore, as SNPs are mutationally more stable, it makes them less prone to homoplasy that might confound genetic analysis (Zimmerman *et al.*, 2020). With the recent technical ease of high-throughput genotyping technologies, SNPs are becoming the markers of choice in genomic applications, such as QTL-mapping, genome-wide association studies (GWAS), marker-assisted selection, genomic prediction, and resolving evolutionary relationships (Sections 3 and 4).

Several methods exist for genotyping SNPs, the choice of which depends on numbers of samples and SNPs required. For genotyping a limited number of SNPs in a limited number of samples, Sanger sequencing of PCR amplified regions of up to 1000 base pairs long is most often used (Hawkins, 2017). In contrast to fragment analysis of microsatellites, Sanger sequencing of PCR amplicons provides complete information (all polymorphisms as well as heterozygous sites). If only a few SNPs need to be genotyped, for instance those known to be linked to a phenotype from previous studies (Section 4), methods that directly target a specific SNP are more useful. Of these, both TaqMan probes and Kompetitive Allele-Specific PCR (KASP) assays are frequently used (He *et al.*, 2014; Woodward, 2014). For genotyping a single bi-allelic SNP, TaqMan uses two allele-specific probes labelled with different fluorescent dyes, each hybridising to a different allele in quantitative PCR (qPCR) also known as real-time PCR (RT-PCR) amplification (see Section 2: PCR-based RNA assays). Alleles are then determined by measuring the intensity of the fluorescent dyes as they bind to the template. While sensitive and reliable, TaqMan technology is limited by the use of the specialised allele-specific probes (Woodward, 2014). Conversely, KASP uses unlabelled allele-specific primers with a unique tail sequence that binds to different fluorescent dyes, allowing the determination of genotypes based on end-point PCR products (He *et al.*, 2014). It is more cost-effective than TaqMan, and therefore suitable for high-throughput analyses typically required by breeding programmes.

Genotyping a larger number of SNPs distributed throughout the genome, particularly for GWAS, typically requires either next-generation sequencing (NGS) methods (e.g. low-coverage Illumina sequencing (Lou *et al.*, 2021)), targeted enrichment strategies using a set of DNA probes for the targeted SNPs (then sequenced using Illumina technologies), or pre-made microarray systems including required SNPs. Although the bioinformatics approaches differ between these methods,
all depend on genotypes being compared to reference genomes. Due to the large sample sizes needed to associate specific traits with populations in GWAS, using NGS can be both costly and time-consuming. While SNP arrays are also expensive, they are generally more practical for such large-scale studies. Currently, there is only one high density genotyping array dedicated to farmed insects, which covers 679 205 SNPs and over 99% of the yellow mealworm genome (Axiom® YNS_Mol1).

There are also ‘in-between methods’ that focus on sequencing only genome subsets, rather than the entire genome. Examples include restriction-site associated DNA sequencing (RADseq), which sequences only the fragments near restriction enzyme sites, and exome sequencing, which targets only the coding regions of the genome (Zhang et al., 2024). Alternatively, very low coverage sequencing of an entire population can be used, or alternatively pooled sequencing (Pool-seq) (Donkpegan et al., 2022; Gmel et al., 2023) for applications that do not require individual genotype information. While these methods are excellent for population differentiation by leveraging existing nucleotide diversity (Fuentes-Pardo and Ruzzante, 2017), they often lack the resolution needed for trait associations and other industrially important questions (i.e. GWAS). This is because most complex traits are polygenic, with each gene typically having a low effect (Section 4), necessitating massive sample sizes to detect rare and often subtle signals in the genome with clearly defined genotypes not typically generated from low coverage SNPs.

Finally, more advanced methods for understanding genetic diversity and its correlation with phenotypes increasingly rely on deep sequencing, typically applied to a smaller number of samples. Pangenomics represents the next frontier, enabling not only the association of nucleotide diversity across populations, but also the assessment of the role of structural variants in genome organisation and phenotypes (Tong et al., 2022). These methods require a substantial amount of high-quality DNA, and in some cases, such as with chromatin-capture techniques like Hi-C for scaffolding (Yamaguchi et al., 2021), fresh tissue is needed to capture subtle yet informative associations. Currently, both PacBio and Nanopore sequencing technologies generate long reads (>10kb) that can detect smaller structural variants, while Hi-C can capture long-range structural variants such as inversions, insertions, and duplications (Figure 2).

We end this section with the following recommendations. Next to basic COI barcoding, microsatellite genotyping is cheap, quick and reliable to characterise and

monitor population genetic diversity indices or assign individuals to genetic clusters or admixed ancestry to inform experimental and breeding designs (Section 3). As an extension, the future goal, however, is to validate and implement high to medium density SNP arrays when starting the selection scheme and subsequently monitor generations in industrial breeding. SNP genotyping or sequencing is required to highlight interesting SNPs, linked to traits of interest by means of GWAS, and to design small density arrays or SNP panels (low-cost SNP sequencing) aiming to convey a genomic selection scheme (Section 4). SNP panels and the use of imputation, however, are sensitive to the reference population. This can be problematic without long range information on the presence of structural variants contributing to the loss of amplification of alleles or haplotypic linkage of various SNPs (Kapun et al., 2021).

## Sample collection

### Secure sampling storage

Reliable genotyping and genomic results necessitate sampling and storage conditions tailored to the intended use of extracted materials. DNA can be extracted from all insect stages, including hemi- and holometabolous species. When collecting specimens for subsequent molecular methods, their initial storage conditions, if not being used immediately, are critical to downstream success. The optimal storage option for any insect at any stage is to flash freeze in liquid nitrogen and store at -80 °C environment. The next most effective method is to euthanise the insect as rapidly as possible (freezing) and then store it in a nucleic acid storage solution (e.g. RNAlater or a similar chemical solution) or >70% ethanol, preferably at lower temperatures (4 °C or -20 °C). Ethanol dehydrates the specimens, effectively neutralising the enzymatic activities that would degrade nucleic acids; however, it is crucial that the solution remain >70%. Thus, after 24-48 hours of storage, it is important to replace the ethanol anew, given that the original solution will be diluted with moisture from the insects, and particularly larvae. Air-dried or pinned specimens can be utilised for DNA extractions, but the DNA can be degraded, likely yielding only small amplicons (Fong et al., 2023). While DNA is constant across life stages, tissue and environmental conditions, mRNA is much more sensitive and more volatile depending on the context. The expression of genes depends on the exact conditions prior to sampling. Some genes, e.g. heat shock genes, respond by up- or down-regulation within minutes of changed environmental conditions (Sørensen et al., 2003). Furthermore,
RNA is much faster degraded than DNA. Therefore, sam-
pling design is important for the samples to serve the
purpose of the study, and fast handling and storage
(preferably at –80 °C or in dedicated solutions designed
to preserve RNA) is crucial to ensure integrity of the
sample.

Choice of life stage and tissue

The choice of life stage and tissue type will also impact
downstream analyses. Vice versa, to pursue a certain
research question and successive methodological path-
ways, contingent upon accessible technology, investi-
gators ideally define specific targets, as schematically
illustrated in Figure 2. If generating extracts for indi-
vidual gene sequencing (e.g. mtDNA COI), then a very
small amount of DNA is needed. If NGS methods (PCR-
free) are desired, then a much higher concentration is
needed (>100 ng/µl). For example, approx. 50 yellow
mealworms eggs must be pooled to obtain between
20 and 50 ng/µl of DNA. This is an important factor
to consider when standardising DNA extraction proto-
cols. From larval, pupal, or adult stages, tissue quantity
is largely sufficient to obtain high DNA quantity; how-
ever, the tissue source plays a pivotal role for DNA qual-
ity and quantity when working with small amounts of
starting material. As larvae get larger, fat body tissue
becomes increasingly extensive throughout the body,
which results in lower quantity and often quality of
DNA due to the difficulty of separating lipids during the
extraction procedure. Adult tissue, specifically muscle
tissue, is an excellent tissue type, and should be con-
sidered whenever possible, especially for genome-wide
sequencing methods. It is necessary to use a DNA source
with sufficient cell density from which a non-negligible
quantity can be sampled without risks.

Design and timing

The sampling design for collecting insects is crucial
for accurately assessing genetic variation in population
studies, whether the focus is on wild or farmed popula-
tions (Meirmans, 2015). Representative sampling (tim-
ing of collection and numbers of individuals) avoids
bias, as well as inadvertently pooling subpopulations
that have different allele frequencies leading to misin-
terpretation of genetic structure (Section 3). This can
arise if samples are collected from different develop-
mental stages at the same time and place, or if samples
from nearby sites are pooled together. Additionally, spe-
cial care is needed in farmed populations where genetic
drift can become significant, particularly if batch pro-
duction involves discrete, non-overlapping generations.

For Evolve and Resequencing studies (Section 4), recom-
mendations suggest sampling over enough generations
to detect selection, with simulations indicating that 10-
20 generations are often optimal. This allows for the
accumulation of genetic changes due to selection while
controlling for drift. Replication is also key in these stud-
ies, as it helps distinguish true selective responses from
random variation. Moreover, the strength of selection
should be carefully considered, as stronger selection
can lead to more detectable changes over fewer gener-
ations at the expense of an elevated risk of genetic drift
and hitch-hiking of genetically linked loci, while weaker
selection may require longer observation periods to dis-
cern its effects but with less drift due to higher effective
population size (see Section 3: Population differentia-
tion and its causes) and allow more recombination.

Non-destructive sampling

Current methods for DNA collection and extraction
from insects are destructive, as the most common tech-
niques macerate entire individuals (Figure 2) (Huanca-
Mamani et al., 2015). Indeed, two limiting factors in pro-
cessing non-destructive extractions remain; the limited
amount of extracted DNA derived from the small tissue
quantities and the effects on the insect that likely affects
either the phenotype of breeding candidates (see Sec-
tion 4: Phenotyping), and/or fitness. Few protocols have
been developed to extract insect DNA from frass (Kidd
et al., 2003) and exuviae (Kranzfelder et al., 2016), but
this method cannot identify individuals, resulting in a
‘population’ sample. This approach can be particularly
useful for insects at the larval stage, where large quanti-
ties of frass and exoskeleton sheds are produced. How-
ever, the quality of DNA suffers from degradation by
nucleases when collected in the field resulting in insuffi-
cient DNA quantity (Peng et al., 2018). High-quality DNA
was also extracted by sampling 2-3 mm² wings clips
from red flour beetles (Châline et al., 2004), and honey-
bee queens, as well as from their exuviae and faeces
(Bubnič et al., 2020). Legs seem interesting due to their
large quantity of muscle tissue as a suitable source of
DNA (Ožana et al., 2020). Another promising and quan-
titatively efficient DNA source is haemolymph. How-
ever, collecting the latter two could be particularly diffi-
cult without huge individual impairments, e.g. through
immune responses affecting overall performance (Vogel
et al., 2022).
## DNA/RNA extraction methodology

### Sample processing

Methods for obtaining nucleic acids depend on the intended downstream applications, with each method presenting its own advantages and disadvantages in terms of ease of procedure, cost, and quality of the extracted nucleic acids (Figure 2). In some cases, kit-based methods are used, which provide all the necessary components, while in other cases, chemical-based methods are employed, which require the purchase of all components.

DNA/RNA extraction, in particular, involves three distinct steps: cell membrane and nuclear membrane lysis, stabilisation of DNA/RNA, and precipitation and washing. To effectively isolate DNA/RNA, it is necessary to break down the cell membrane and nuclear membrane, which are composed of roughly equal parts glycoproteins and lipids. To liberate DNA/RNA from chitinous materials such as legs and wings, buffers containing relatively high levels of dithiothreitol (DTT), proteinase K, and detergent are typically used. Following isolation, the next step is to remove contaminants without compromising the stability of the DNA/RNA. DNA/RNA is then precipitated and washed to remove other protein debris.

It is important to note that DNA extraction methods can vary significantly from one another, with broad categorisation into chemical vs silica-based and mechanical methods. Chemical methods are further divided into organic extraction techniques, such as the phenol-chloroform-isoamyl alcohol (PCI) method, and inorganic extraction techniques, including cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulphate (SDS) extraction, salting out, and solid-phase extraction techniques. Finally, physical extraction includes magnetic bead-based methods and silica-based spin column extractions. Figure 2 outlines available and recommended DNA extraction methods based on the desired analyses/outcomes.

### PCR-based assays

PCR-based methods (mtDNA and other DNA sequencing as well as microsatellite and SNP genotyping), require uninhibited DNA. Traditional, less expensive, and time-saving chemical-based methods are suitable for these applications, as they are effective in many sample assays. The quickest method, which utilises Chelex beads to chelate DNA from various materials and lyses cells using heat, is not necessarily the most cost-effective. The yield of small DNA fragments is not ideal for many applications. While Chelex extraction may outperform other methods in terms of DNA quan-

tity, purity is often problematic, which, in conjunction with single-stranded DNA, negatively affects storage stability, except for shorter fragments up to a few hundred base pairs (Al-Griw *et al.*, 2017). Despite these limitations, Chelex extraction is effective in removing PCR inhibitors and is commonly used in PCR-based methods, such as mtDNA, microsatellite, or amplified sequencing. Alternative methods include chemical extraction procedures, such as PCI and CTAB, as well as 'salting out' methods using a high concentration of salts. PCI extraction uses organic solvents to separate DNA from proteins and other cellular debris, yielding high quality DNA. However, it is labour-intensive, and requires careful handling of hazardous chemicals, and often introduces residual phenol that can inhibit downstream PCR analysis. CTAB and other salting out methods are less toxic, but yield can vary widely depending on the amount of tissue used (Figure 2).

### Native genomic DNA assays

For both whole genome sequencing (WGS) and whole exome sequencing (WES) it is important to start with high quality starting material, which is fresh or flash-frozen at ultra-low temperatures (–80 °C) which effectively rules out frass and exuviae as a reliable source material for these purposes (Fuentes-Pardo and Ruzzante, 2017). Several methods are commonly used for DNA extraction, each with their own advantages and disadvantages. A traditional method is the PCI extraction. Further, silica-based column extraction kits, such as Qiagen DNeasy Blood and Tissue Kit (Qiagen) or the Monarch PCR and DNA Clean-up Kit (New England Bio-labs) are widely used and are usually the first method of choice for beginners due to their simplicity, efficiency, and minimised contaminants carrying through. However, yields can vary depending on the sample type and specific kit used. Magnetic bead-based extraction methods are another popular method, especially given their propensity to be automated, resulting in high purity extractions. However, residues from the magnetic beads can interfere with library preparation and sequencing procedures.

Each of these methods can be tailored to specific sample types, downstream applications, and sequencing methodology, making them versatile tools in genomic research. For example, if long-read sequences are desired (i.e. PacBio HiFi or ONT Minion), kits and other methods exist to ensure gentle digestion and limited mechanical shearing to ensure long intact fragments of DNA (Figure 2).
## PCR-based RNA assays

RNA extractions required for qPCR and PCR-based transcriptomics are undeniably more complicated and require extensive planning due to the prevalence of RNases in the environment. The initial step involves collecting the desired tissue and ensuring it is free of RNase activity, which involves sterilising instruments with a cleanser like RNase Away or other decontamination reagents. Once the sample is prepared, the procedure is remarkably similar to DNA extraction, with options to use solid-phase methods (silica-based kits), acid phenol methods like Trizol, and magnetic beads methods. As for DNA extraction, dedicated kits are commercially available from all main supplies, e.g. RNeasy Kit (Qiagen) or E.Z.N.A.® Total RNA Kit (Omega Bio-tek). Additionally, a DNase treatment is often added to the purified RNA extractions to eliminate any contaminating DNA fragments. To assess specific changes in expression for specific genes using qPCR on mRNA transcripts, cheaper methods that allow for the analysis of many samples with replication, such as Trizol or automated magnetic bead systems, would suffice (Figure 2). Alternatively, RNA can be converted to complementary DNA (cDNA) using reverse transcription (e.g. Omniscript RT, Qiagen) to generate more stable templates for downstream qPCR gene expression analyses (Section 5). For standard short-read based RNAseq experiments, silica-based kits, Trizol, and magnetic beads all work well. If the goal is to sequence only mRNA, using a method that enriches for polyadenylated RNA (i.e. using oligo (dT) beads) or depletion of rRNA is necessary. Newer technologies have been developed that remove the need for RNA extraction and instead take the tissue directly to qPCR (e.g. Scientific's Cells-to-Ct kit, ThermoFisher). Although efficient, these methods are generally more costly (Section 5).

## DNA/RNA-protein-based assays

The interaction of DNA/RNA-protein complexes (without altering the DNA/RNA), i.e. epigenetic regulation, should also be considered important for the future of the insects as food and feed industry. Modification of histone complexes causes structural changes that regulate gene expression and repression (Glastad et al., 2019). Therefore, phenotypes of interest, maintained by simple or complex gene regulatory networks, may not differ between populations whilst the epigenetic regulatory control may vary (see Section 5: Gene expression analysis). Typically, bead- and column-based extraction methods are required (Figure 2). Unlike DNA/RNA extractions, the ability to efficiently extract histone

complexes requires specialised equipment for optimised fragmentation via sonication. Several sequencing methods exist for studying the epigenome e.g. histone modifications (ChIP-seq), DNA Methylation (MeDIP), chromatin conformation (Hi-C), chromatin accessibility (ATAC-seq), etc., as reviewed extensively by Li (2021).

# 3 Evolutionary and population genetics

While arguably one of the most important aspects for understanding species biology and implementing effective genetic management strategies, defining a population is not a trivial task. Characteristics such as its effective population size, genotype and allele frequency distributions, and the degree of linkage disequilibrium determine the extent of phenotypic variation within the population, and thus its potential for adaptive evolution, extinction risk, and ‘susceptibility’ to stochastic processes (i.e. genetic drift) under natural and man-made selection pressures. Here, we outline why and how to assess these parameters, how to evaluate single- and multi-population structure, and how to reconstruct evolutionary relationships and demographic history.

For a summary of relevant measures and tools for evolutionary and population genetics analyses we refer to dedicated reviews, including Excoffier and Heckel (2006), Bourgeois and Warren (2021), Saravanan et al. (2020), Casillas and Barbadilla (2017) and Sethuraman et al. (2020), that are also collated in Table A1 in the Supplementary material.

### Basic organisation of genetic variation

The population concept

Although one of the central concepts in biology, the definition of a population depends on the context. Two broad conceptual understandings of biological populations exist: (i) the ecological view that defines a population as a group of organisms of the same species that co-inhabit a space in time; and (ii) the evolutionary view that emphasises the reproductive cohesion of a group of organisms (Waples and Gaggiotti, 2006). Through the application of assisted reproductive technologies (e.g. artificial insemination and embryo transfer) and the continuous displacement of farmed species by human activities, these two definitions have become quite separated from each other, because individuals isolated by both geographical space and time can still contribute to a single gene pool. Throughout this BugBook article, the term population refers to units of individuals that are genetically and spatially related, either natural pop-
ulations in a certain geographic location or managed
breeding populations including production stocks and
laboratory strains, and that together may form a geneti-
cally and otherwise subdivided metapopulation.

Effective population size ($N_e$)

In population genetics, a distinction is made between
the census population size ($N_c$), the estimated ‘absolute’
number of individuals in a biological population, and
the effective population size ($N_e$), the number of the-
oretical, genetically distinct individuals that contribute
to the next generation (Frankham *et al.*, 2004). As such,
the $N_e$ of a population is a critical parameter deter-
mining the strength of evolutionary forces acting on
that population (see Section 3: Evolutionary forces) and
is in most cases much smaller than $N_c$ (Waples and
Gaggiotti, 2006). Populations with smaller $N_e$ harbour
reduced genetic diversity due to inbreeding and allelic
loss by random drift, making such populations suscepti-
ble to inbreeding depression, low evolvability, and even-
tual extinction (Hedrick and Garcia-Dorado, 2016; Pal-
stra and Ruzzante, 2008). Estimating $N_e$ is important
for insect farming operations to ensure that (i) founding
populations are large enough to sustain a robust breed-
ing pool, (ii) the population remains viable and fit over
successive generations, and (iii) sufficient genetic vari-
ation persists in the population to enable response to
selection (e.g. Hull *et al.*, 2024; Rhode *et al.*, 2020).

Unfortunately, $N_e$ is a notoriously difficult parame-
ter to estimate, as it depends strongly on demographic
parameters, including the harmonic mean of $N_c$ over
successive generations, sex ratios, and varied reproduc-
tive success of mate-pairs. As no single method con-
siders all demographic factors, true $N_e$ is believed to
be often overestimated (Luikart *et al.*, 2010). With the
advent of molecular genetics and the use of DNA mark-
ers (Section 2), $N_e$ is increasingly estimated using molec-
ular approaches (Hohenlohe *et al.*, 2021). These molecu-
lar approaches can be grouped into two broad concepts:
(i) inbreeding $N_e$ ($N_{ei}$) and (ii) variance $N_e$ ($N_{ev}$) (Luikart
*et al.*, 2010) based, wherein $N_{ei}$ considers primarily the
presence of heterozygosity within a population, and $N_{ev}$
considers the change in allele frequencies over genera-
tions. Due to the temporal component, the $N_{ev}$ approach
is particularly sensitive to the early detection of loss of
genetic diversity and gives an approximation of short-
term $N_e$ in a population. Due to their relatively short
lifecycles and rapid generational turnover, insects over-
come one of the major limitations of computing $N_e$
that require two sampling timepoints, such as $N_{ev}$ (e.g.
Rhode *et al.*, 2020). However, as levels of heterozygos-

ity usually only stabilise in the generations following a
bottleneck event, $N_{ev}$ can still be considered superior to
$N_{ei}$ for giving an indication of mid- to long-term $N_e$. For
estimating $N_e$ from a single sample, the linkage disequi-
librium method (LD-$N_e$) has been shown to be the most
robust (Waples, 2024; Waples and Do, 2010). As a pop-
ulation becomes more inbred and thus more homozy-
gous, LD between loci increases in inverse proportion to
$N_e$ (e.g. Kaya *et al.*, 2021).

Genetic equilibrium and diversity

Measurements of genetic diversity are important for
understanding the genetic health of a population. This
is especially true in managed populations with short
generation intervals, such as insects, which can experi-
ence rapid and irreversible loss of diversity (e.g. Rhode
*et al.*, 2020). Yet this also applies to their wild congeners,
whose ephemeral habitats might influence population
dynamics (e.g. DiLeo *et al.*, 2024). The two primary mea-
sures of genetic diversity are allelic diversity and het-
erozygosity (Greenbaum *et al.*, 2014).

Allelic diversity is defined by the number of alleles present ($A_n$), which can be impacted by the number and nature of the genetic markers assessed (Section 2). It is reported as numbers of alleles per locus, total alleles across loci, or average number of alleles over a set of loci. Unfortunately, this parameter is subject to bias in sample size – the more individuals, the more rare or low frequency alleles will be detected, meaning that results cannot be directly compared between samples of different sizes. To mitigate this, effective number of alleles ($A_e$) and allelic richness ($A_r$) have been developed to correct for rare alleles and allow comparison of estimates across samples. Standardisation is hereby often based on the sample containing the fewest individuals (e.g. Kaya *et al.*, 2021). Other commonly used estimates of ‘allelic’ diversity include nucleotide diversity that measures the fraction of mismatches between pairs of DNA sequences drawn from the same population (Korunes and Samuk, 2021), and haplotypic diversity that exploits LD across a number of SNPs (e.g. Albu-raki *et al.*, 2023).

Heterozygosity measures how alleles segregate into
genotypes. For a locus harbouring two alleles, it reaches
its maximum at 0.5 if both alleles segregate at equal
frequency. With more than two alleles, expected het-
erozygosity is calculated as one minus the sum of all
squared allele frequencies, so generally the more alle-
les at a locus, the more heterozygosity is expected. In
a population that is in Hardy-Weinberg Equilibrium
(HWE), where no evolutionary forces act on a particu-
lar locus, each allele in the gene pool should have equal (frequency-dependent) probability to combine with any other allele in the gene pool in subsequent diploid generations (Frankham et al., 2004). This equates to random mating, and mathematically to allele and genotype frequencies remaining at equilibrium after one generation (Mayo, 2008). This measure is called the expected heterozygosity ($H_{exp}$) and is one of the primary statistics for evaluating evolutionary potential of a population due to its more universal algebraic accessibility compared to allelic richness (Frankham et al., 2004). When non-random mating occurs in a population, including selection subsets of individuals for breeding purposes (Section 4), deviation from expected genotypic frequencies (under HWE) can lead to inflated or deflated heterozygosity estimates. This is captured by the observed heterozygosity ($H_{obs}$), which is directly determined from counts of the genotypic data. Deviation from HWE is most frequently measured using *F*-statistics, that is fixation indices (Wright, 1965). One of the fixation indices, $F_{IS}$, quantifies the relationship between observed and expected heterozygosity within a population as $F_{IS} = (H_{exp} - H_{obs})/H_{exp}$, and gives an indication of heterozygous excess or deficiency. Capturing potential heterozyous deficiency, the $F_{IS}$-value is also often interpreted as a molecular analogue of the inbreeding coefficient, but should be done so with caution (e.g. Hoffmann et al., 2021).

Based on $H_{obs}$ compared to $H_{exp}$ for individual or combined loci, we can formulate the inbreeding coefficient ($F$) of (i) an individual (I) relative to the total (T) population ($F_{IT}$); (ii) an individual relative to the subpopulation ($F_{IS}$); and (iii) a subpopulation compared to the total population ($F_{ST}$); and $1 - F_{IT} = (1 - F_{IS})(1 - F_{ST})$ (Conner and Hartl, 2004). The $F$-statistics are thus of high importance for population genetics analyses and can be conceptually extended to identify population subdivision and metapopulation structure (see Section 3: Detecting (meta)population (sub)structure).

A population in equilibrium will have an $F$-value ≈ 0, where $F$ is defined as the inbreeding coefficient of the relative hierarchical population structure ($F_{IS}$ for within subpopulations and $F_{ST}$ between sub-populations). This ultimately measures deficiency or excess of observed heterozygous genotypes across loci, relative to the expected heterozygosity under random mating. Heterozygous deficiency ($H_{exp} > H_{obs}$) leads to positive $F$-values, possibly due to positive assortative mating, where individuals with similar genotypes mate more frequently, and/or inbreeding. Negative $F$-values through excess of heterozygosity compared to random

mating can eventually result from negative assortative mating or outcrossing of individuals from genetically distinct populations.

At the individual level, classical inbreeding coefficients describe the probability that two alleles at any locus are identical by descent (Hartl, 2020). When based on pedigree evaluation ($F_{PED}$), the coefficients are calculated using Wright's formulae and under the assumption of a 0.5 chance of inheritance of each allele between generations. However, pedigree tracking in insects is challenging, especially the paternal side, due to their mating systems. Alternatively, $F$ may then be inferred from genotypic frequency data or the length of runs of homozygosity ($F_{ROH}$) across the genome (Gmel et al., 2023; Kanaka et al., 2023; Purfield et al., 2012; Rodríguez-Ramilo et al., 2019).

## Evolutionary forces

Beyond characterising patterns of genetic diversity, understanding the evolutionary processes is pivotal for successfully managing wild and farmed populations. These processes involve a complex interplay of forces (De Meeûs et al., 2007), which can be broadly classified as (i) demographic, which relate to effective population size changes, mating systems, and gene flow, and (ii) locus-specific, which include uneven mutation and recombination rates along the genome, as well as selection. Demographic forces are expected to entail genome-wide impacts on diversity and can be considered neutral background variation (Casillas and Barbadilla, 2017; Lewontin and Krakauer, 1973). Locus-specific evolutionary forces, however, generate region-specific patterns of diversity.

Evolution is defined as the change in allele frequencies within a population, induced by the evolutionary forces. Furthermore, in the absence of evolutionary forces, a population is considered ideal (i.e. infinite size, isolated, panmictic reproduction, with no selection or mutation) and remains in HWE. However, in reality, all populations are influenced by the four evolutionary forces: mutation, genetic drift, migration, and selection.

Mutation is the ultimate source of new genetic variation (Loewe and Hill, 2010). Heritable mutations occur in the germ line, with the introduction of errors during DNA replication (i.e. substitution, deletion, or insertion of nucleotides). As fidelity in DNA replication is important for survival, mutation rates are extremely small. Moreover, the mutation rate is variable along the genome, between individuals, populations and species. Chromosomal rearrangements also represent a form of mutation, and particularly recombination shuffles pre-
existing genetic variation within each population, cre-
ating new allelic combinations, potentially leading to
phenotypic variation.

According to the neutral theory of evolution (e.g. see Casillas and Barbadilla, 2017), the fate of mutations is largely determined by genetic drift, i.e. the unpredictable, random changes in allele frequencies. Genetic drift depends on N<sub>e</sub> (Kimura, 1968) and can lead to either random loss or fixation of some variants (Masel, 2011), and generally to reduced genetic diversity. Particularly small populations are prone to strong drift that can lead to population decline and extinction due to a higher risk of randomly fixing deleterious alleles (Smith and Haigh, 1974).

Low levels of genetic diversity in a population can be rescued by migration that causes gene flow from external individuals, i.e. transferring alleles from one population to another. Even relatively small numbers of migrants per generation can equalize allele frequencies between the source and the sink populations, and result in an effectively interbreeding panmictic population. In that sense, gene flow can counteract genetic drift by increasing *N*<sub>e</sub>. In natural populations, migration is constrained by geographic distances or physical barriers. Populations in closer proximity exchange individuals more frequently, and accordingly, tend to be more closely related. Conversely, captive populations are maintained in isolation and experience little to no gene flow from outside. Combined with elevated levels of drift due to reduced *N*<sub>e</sub>, these populations can rapidly diverge from their source populations, thereby facilitating overall formation of population genetic structure (see Section 3: Genetic clustering, admixture and introgressive hybridisation).

Both migration and genetic drift affect the estab-
lishment of new populations by a limited number of
individuals from the original population, which is con-
sidered a founder effect (Hartl, 2020). Controlling for
the putative impact of such population bottlenecks is
highly relevant for maintaining variation in popula-
tions of insects farmed for food and feed, which usually
only contain a fraction of the diversity present in their
wild counterparts, with possible health consequences
as increased susceptibility to pathogens (Croze *et al.*,
2016).

Selection acts on genetic variation through the fit-
ness of individual phenotypes, according to their repro-
ductive success in particular environments. To enable
selection to act on a trait (natural or artificial), the trait
must exhibit phenotypic variation within the popula-
tion that to some extent must stem from genetic vari-

ation between individuals (Falconer and MacKay, 1996). New genetic variants introduced through mutation or migration are generally neutral, less frequently deleterious, and only rarely advantageous. Individuals carrying mutations that reduce fertility will reproduce less, and their alleles will gradually disappear from the population (known as negative or purifying selection, with a negative selection coefficient; s < 0). Conversely, individuals carrying mutations underlying desirable traits will reproduce more frequently, spreading their alleles in the population (positive selection; s > 0), which progressively adapts to the environment (Fisher, 1958). The latter represents the sole adaptive mechanism and forms the basis of any breeding scheme (Section 4).

The time required for removing or fully spreading a non-neutral allele in the population depends on its initial frequency and its selection coefficient (s), but also on its dominance coefficient (h) and on N<sub>e</sub>. Even highly beneficial mutations may be lost forever if N<sub>e</sub> is small and genetic drift is high. The degree of dominance also determines the phenotypic effect of an allele in heterozygous state and can range from recessive (no effect on fitness; h = 0), additive (intermediate effect on fitness; h = 0.5), to dominant (full effect on fitness; h = 1). Individuals with a heterozygous genotype may also exhibit superior or inferior fitness than their homozygous counterparts due to overdominance (h > 1) or under-dominance (h < 0). As a mechanism of balancing selection, overdominance can maintain both alleles segregating in the population, making trait improvement through selective pure breeding less efficient. Maintaining diversity is however beneficial in fluctuating and unpredictable environments, e.g. with varying predator and pathogen pressure (Croze *et al.*, 2016). Except for balancing selection, selection leads to a loss of genetic diversity on the loci under selection or even in a wider region. A fundamental consideration when designing breeding schemes is that dominance and selection coefficients are not independent but rather negatively correlated: since harmful mutations are often recessive, consanguinity usually entails deleterious effects, the so-called inbreeding depression (Charlesworth and Charlesworth, 1999; Hedrick and Garcia-Dorado, 2016), see Section 4: Maintaining genetic diversity. Therefore, by selecting a small proportion of individuals with desirable traits for contributing to the next generation, breeders inevitably add a drift component as well, with possibly unwanted fitness and performance effects on their farmed stock.

Overall, natural and artificial selection are powerful,
context-dependent evolutionary forces. Yet, in real pop-
ulations, selection tends to be a complex process featuring interplays between selection regimes (e.g. Hill and Robertson, 1966) and genetic architectures of traits (Section 4). Multiple alleles with variable effects may segregate at any given locus, traits are frequently governed by multiple loci which may epistatically interact, and can be correlated with other traits, that may themselves be subject to context-dependent selection.

*Geographical and evolutionary genetics: allele frequency changes in space and time*

Population differentiation and its causes

Individual and combined evolutionary forces may act differently on populations distributed across space and time, both including single or multiple traits, and may ultimately homogenise or subdivide populations at different hierarchical levels.

Founder effects are common during colonisation of new habitats or recolonisation after population contraction (Pannell and Charlesworth, 2000; Szűcs et al., 2017). Such processes usually involve comparatively small sub-samples, not necessarily representative of the original population, and can hence translate into significant genetic drift or a genetic bottleneck characterised by a severely reduced overall number of alleles (Kanaka et al., 2023). The same applies to the establishment of a captive laboratory or production population from one or multiple existing production or laboratory strains (Cai et al., 2024; Kaya et al., 2021), or from a wild population (Ewusie et al., 2019; Rhode et al., 2020). Extended gene flow between different natural or managed populations may result in a unilateral increase of diversity, mutual genetic homogenisation, or assimilation of one population by another, whereas inbreeding coupled with negligible migration likely increases differentiation among subpopulations (Simões et al., 2010). Adaptive processes via natural selection can counteract or reinforce the effects of migration or drift.

Regardless of any selective forces, in large natural populations, genetic differentiation can also result from unequal mating probability between individuals as a function of geographic distance, i.e. isolation-by-distance (Aguillon et al., 2017; Suárez et al., 2022). However, isolation-by-distance patterns are sensitive to sampling design (Meirmans, 2015) and may entail a phenomenon called ‘rare-allele-surfing’ at the colonisation front as a consequence of comparatively rapid population expansions (Excoffier and Ray, 2008), as seems the case for BSF non-native range expansions (Kaya et al., 2021).

Possibly “unexpected” structure may also reflect a Wahlund effect (De Meeûs et al., 2007): genotypes drawn from a sample of individuals which are effectively not in mating equilibrium will collectively exhibit heterozygote deficiency (Hartl, 2020). Detection thereof may indicate an unnoticed sampling artefact, e.g. when field sampling captured seasonally non-overlapping generations at a given site, which can include sampling adult and immature insects at the same time (Section 2). Alternatively, genetic substructure in sympatry may reveal previously unrecognised ecological associations and/or phylogenetically derived cryptic taxa (Castillo and Barbash, 2017; Hagberg et al., 2022; Sánchez-Guillén et al., 2016).

Indication for subpopulation structure in supposedly standardised farming conditions can have serious consequences (see Section 4: Monitoring and managing genetic diversity). For example, widely traded BSF populations of mixed North American and Asian origin exhibit deviations from genetic equilibrium regardless of geographical area they were sourced from (Kaya et al., 2021). This suggests poorly understood pre- and/or post-mating mechanisms influencing reproductive success and particularly sexual conflicts (Tomberlin et al., 2025) between distinct evolutionary lineages (Generalovic et al., 2023). Management itself may induce substructure over time, e.g. through discrete restocking of breeding parents where restricted geneflow between successive cycles possibly reinforces divergence, which can hamper breeding progress if undetected (Section 4).

### Detecting (meta)population (sub)structure

Deciphering allele frequency variation in space and time is not only informative to reconstruct past influences on present (meta)population structure (Bradburd and Ralph, 2019). Exploring substructure helps to disentangle population relationships and inform both future monitoring of wild populations and managing breeding populations, yet requires comprehensive sampling of wild and captive populations from the entire, possibly global range. Utilising multiple complementary methods for combined analysis of metapopulation structure is generally recommended.

The above-mentioned *F* statistics are routinely used to characterise subdivision at different hierarchical levels of population structure. By accounting for allele frequency differences, the fixation index *F*<sub>ST</sub> (Wright, 1965) is widely used to quantify pairwise genetic differentiation between populations, and the overall extent of partitioning due to population structure within metapopulations (De Meeûs et al., 2007; Kanaka et al., 2023;
Meirmans and Hedrick, 2011). Its values range from 0 meaning complete panmixia and free interbreeding, to 1 meaning complete differentiation and no alleles shared between subpopulations. Extensive matrices of pairwise $F_{ST}$ can be visualised via heat-maps (Eleftheriou *et al.*, 2022b) or by constructing unrooted genetic dendrograms using e.g. unweighted pair group method with arithmetic mean or neighbour-joining approaches (Supplementary material, Table 1A).

Another marker-independent approach for a better general overview of inter-population comparison is Analyses of Molecular Variance, AMOVA (Excoffier *et al.*, 1992; Kanaka *et al.*, 2023). AMOVA partitions the genetic variance into proportions explained by within and among components of individuals and populations in a non-hierarchical manner, as well as by levels of one or multiple hierarchical (nested) factors, such as geographic origin or captive versus wild-sourced, as relevant for a given insect study. The extent to which pairwise $F_{ST}$ reflect isolation-by-distance patterns among populations can be tested by correlating matrices of genetic and geographic distances using a Mantel test, usually upon adequate data linearization/transformation (Rousset, 1997). Moreover, as geography in nature may be confounded with associated factors of ecological relevance (e.g. Sandrock *et al.*, 2011b), a partial Mantel test can extend the concept of AMOVA by assessing genetic differentiation between levels of a given sampling factor (e.g. habitat) while controlling for geographic distance (see Diniz-Filho *et al.*, 2013). Allele frequency relationships of individual multilocus genotypes or entire populations can be visualised in dendrograms using geometric approaches (Cavalli-Sforza and Edwards, 1967; De Meeûs *et al.*, 2007). Alternatively, individuals or groups defined according to sampling populations, geography, captivity status, or modelled genetic clusters (see Section 3: Reconstructing demographic history) can be illustrated along the most explanatory dimensions of a multivariate space using principal component analysis (PCA) (Abegaz *et al.*, 2019; De Meeûs *et al.*, 2007; François *et al.*, 2010; Kanaka *et al.*, 2023), or via derived algorithms as implemented in discriminant analysis of principal components (DAPC) (Thia, 2023).

Evaluating population structure in farmed insects is best accomplished through a combination of population genetic indices, statistical procedures, and visual approaches. For example, geometric visualisation is principally blind to diversity components behind genetic structures. Complementary inspection of allelic

richness, e.g. via mixed models (Kaya *et al.*, 2021), can then help identifying diversity hot spots.

Genetic clustering, admixture and introgressive hybridisation

Metapopulation genetic structure can further be inferred through clustering-based methods. This is particularly helpful for large datasets with numerous populations, e.g. sampled across a species' distribution range, or for allocating previously uncharacterised populations in the context of published genotypic data. The ancestry of each individual genotype is modelled as descending from a pre-defined number of potential genetic clusters, *K*. Ancestry coefficients can reveal pure assignment or admixture between different clusters.

Multiple implementations of model-based clustering exist (Supplementary material, Table 1A), essentially differing in their statistical procedures to find the optimal solution, conditioned on *K*. Virtually all implementations provide visualising relative cluster contributions to individuals within populations or geographic origin (e.g. Zhang *et al.*, 2009). Bayesian modelling using Markov Chain Monte Carlo (MCMC) techniques are popular for estimating distinct clusters from multilocus genotype frequencies, as properly accounting for the uncertainty associated with limited data (Pritchard *et al.*, 2000). With full genome sequences, maximum likelihood (ML) approaches provide much faster optimisation procedures, at limited costs in accuracy (Alexander *et al.*, 2009). More recent developments allowed to further speed up computations, including mathematical derivatives for safer and faster ML optimisation (Beugin *et al.*, 2018; Cheng *et al.*, 2021).

Caution is advised when inferring distinct clusters, because numbers of *K* inflated beyond biological meaning result in overfitted models and misleading estimates of admixture. It is thus crucial to refine model choice based on respective model log-likelihoods (Evanno *et al.*, 2005), cross-validation, or information criteria (Akogul and Erisoglu, 2016). This is important for both, accepting panmixia with modest isolation-by-distance across larges scales, as found in house flies (Bahrndorff *et al.*, 2020) or aphids (Sandrock *et al.*, 2011a), but also for appropriate inference of complex population structure resulting from dynamic evolutionary history (Kaya *et al.*, 2021; Nunez *et al.*, 2024). Moreover, all model-based implementations assume that population genetic principles, such as HWE, are met. Consequently, despite some robustness to violations (Falush *et al.*, 2003; Meirmans, 2012), they tend to assign unique genetic clusters to strongly diverged populations, although these
may have originated from ancient admixture events. Likewise, recent bottlenecks may inflate natural population structure (Chapuis et al., 2014) and challenge traceability of heavily drifted production strains to a common yet unsampled source. Unknown demographic history, ecological associations, and insufficiently representative sampling (or extinct missing link populations) bear some risk of misleading conclusions (Lawson et al., 2018; Meirmans, 2015), especially when analysing intensively managed populations and farmed stocks. Alternative models, insensitive to inbreeding or time-differences between samples, have been developed, based on factor analysis (François and Jay, 2020) or on the so-called f4-statistics, although the latter is computationally feasible up to a few hundred genomes only (Librado and Orlando, 2022). Genetic cluster analyses may thus be used as explanatory rather than a diagnostic tool, whereby inspecting results along a range of *K* itself can provide insights into population stratification and help scaling relative impacts in space and time (Gilbert, 2016; Gilbert et al., 2012).

Explicitly describing the population history behind clustering results involves co-inferring population splits alongside historical episodes of gene flow. These population histories are often summarised as admixture graphs (Nielsen et al., 2023), or inferred through coalescent modelling (Kamm et al., 2020). Monitoring ongoing geneflow between production strains or with local wild populations is in fact crucial to mitigate unintended introgression in either direction, possibly challenging breeding progress of farmed strains or genetic integrity of wild populations (see Section 4: Monitoring and managing genetic diversity). Reconstructing historic and recent admixture is particularly interesting because human traffic and trade likely facilitated secondary contact between genetically distant populations, or their (repeated) translocation into uncolonised yet suitable ranges, from where further dispersal and naturalisation around the globe was possibly initiated even before the advent of modern farming of insects for food and feed. Comparable to population genetic patterns in invasive pest insects (Blumenfeld et al., 2021; Sethuraman et al., 2020), the global metapopulation structure of the BSF reflects decisive historic admixture events, which fuelled large scale dispersal including colonisation of multiple non-native continents through secondary and tertiary admixture (Kaya et al., 2021).

To assess whether introgressed genetic material provides a selective advantage (see Section 3: Signatures of selection), as documented in multiple instances (Clarkson et al., 2014; Moest et al., 2020; Svedberg et al., 2021),

tracing its ancestral origin and fate is key. Common methods to pinpoint introgressed tracts involves variations of the Pattersons *D* statistic, or ABBA BABA test (Durand et al., 2011; Green et al., 2010). This includes the $f_d$ (Martin et al., 2014), and *f*-branch ($f_b$) statistics (Malinsky et al., 2018), the latter of which can be used with a known phylogeny to identify which populations were involved in the hybridisation event (donor or receiver). Using the *f*-branch statistics, up to 15.2% gene flow has been detected from wild to captive populations of BSF, highlighting potentially useful strategies for maintaining diversity in closed populations (Generalovic et al., 2023).

High congruent support was found for admixture in certain BSF production strains as well as for admixture between wild BSF populations e.g. from Australia and Central Europe, when comparing microsatellite data (Kaya et al., 2021) and applying *f*-branch methods to genome-wide SNPs (Generalovic et al., 2023). This highlights the possibility for efficient complementary screening strategies, where basic qualitative evidence for admixture could precede more costly measures to quantify proportion of gene sharing and identify loci involved. In-depth genomic characterisations of relative contributions to an admixed population can be relevant to inform population management.

## Reconstructing demographic history

Disentangling ancient population structure from relatively contemporary population genetic dynamics (primarily reflecting anthropogenic influences) and delimitating native as well as non-native ranges is challenging. Classifying diversity patterns, such as indigenous genetic hot spots vs secondary admixture, is important, but calls for inferring relative ranking of possibly confounding spatial and temporal effects. Exhaustive sampling of wild and captive populations from the entire distribution range increases the chance to infer and date a given species' demographic trajectories appropriately, including delimitation of possible domestication centres. The emerging discipline of museomics, involving DNA sequencing from historical specimens housed in entomological collections, also opens a window into the genetic past and helps in reconstructing population structure before the more recent advent of insect farming (Wandeler et al., 2007). A versatile tool for comparing models of competing demographic scenarios is Approximate Bayesian Computation (ABC) (Beaumont, 2010; Csilléry et al., 2010). When deep evolutionary history remains uncertain, possibly due to strong masking by contemporary human-mediated signals, formulating
and statistically assessing models based on plausible biological hypotheses is a critical step. Careful construction plus correct specification and evaluation of all reasonable demographic hypotheses, including underlying parameters and relevant summary statistics, is advised. ABC analyses have been used for microsatellite data to infer native range expansions of the BSF in the Americas, as well as its colonisation origins and courses on different non-native continents (Kaya et al., 2021). To account for increasingly complex genomic data sets (e.g. Blumenfeld et al., 2021), machine learning tools have already (Pudlo et al., 2016) and continue to strongly improve computational efficiency for demographic history inference (Bourgeois and Warren, 2021; Fortes-Lima et al., 2021; Tran et al., 2024), including edible insects (Chapuis et al., 2020). Recently advanced algorithm efficiency led to the development of software for inferring Ancestral Recombination Graph (ARG) that encodes the complete genealogical history of samples (Supplementary material, Table 1A). The inferred ARG is represented with a sequence of trees, where each tree represents the genealogical history of a part of the genome that was broken up by recombination. The tree sequence represents an evolutionary encoding of the data that compresses the file and enables efficient computation of relevant statistics.

## Mito-nuclear phylogeographic patterns

Complementing nuclear-genetic biogeographic patterns with phylogenetically informative mitochondrial markers adds valuable insights (Edwards et al., 2022), see Leite et al. (2014) for an insect example. Geographic associations of genealogical lineages can shed light on evolutionary cradles, vicariance effects, processes like secondary contact or directionality of range expansions. Compared to diploid nuclear loci, mitochondrial haplotypes are maternally inherited and more prone to demographic stochasticity. Accordingly, phylogeographic patterns based on mtDNA alone may not be overinterpreted. However, comparing them to demographic inferences based on nuclear markers can itself represent a highly informative evaluation for reconstructing evolutionary fates. Conclusions drawn from different marker systems may both be congruent with a certain hypothesis (e.g. diversity hot spots and dispersal routes), complexity may increase beyond individual marker perspectives (e.g. relative contributions to admixture), or a seemingly plausible narrative may be challenged by conflicting signals. For instance, nuclear genetic structure among populations of the migratory locust is substantially lower among subpopulations in

areas with frequent outbreaks, despite similar levels of diversity and effective population sizes (Chapuis et al., 2009). However, despite its eponymous mobility, its native range is phylogeographically highly structured. Dispersal from African hotspots established a Southern and a Northern major lineage across Eurasia and Australia that each comprise two distinct yet shallower latitudinally structured mitochondrial clades (Ma et al., 2012). Further, the stronger mitochondrial than nuclear genetic structure of house fly populations in their non-native ranges (Krafsur et al., 2005) likely reflects that originally colonising maternal lineages prevailed despite ongoing nuclear geneflow. The intriguing case of the BSF highlights the potential relevance of global phylogeographic data for breeding strategies. After deducting mitochondrial patterns mediated by historic translocations due to human commensalism as well as current trade for farming worldwide, reconciling native phylogeographic structure (Generalovic et al., 2023; Guilliet et al., 2022; Ståhls et al., 2020) points at two major mtDNA clades from South and North America, respectively, separated by up to 5% COI divergence. Apart from experimental disproof of reproductive isolation of both evolutionary lineages (Ståhls et al., 2020), admixture between them was decisive to initiate global non-native naturalisations (Kaya et al., 2021). However, the phylogenetically older dating of the North American mitochondrial lineage (Guilliet et al., 2022) appears to contrast northwards declining nuclear genetic diversity across the Americas (Kaya et al., 2021). Recent genomic evidence (Generalovic et al., 2023) corroborates deep phylogenetic signals similarly reflected in both mitochondrial and nuclear genomes and moreover identifies a Central American zone of mito-nuclear discordance (e.g. Toews and Brelsford, 2012). Hypotheticalising the BSF represents a cryptic species complex shaped by ancient hybridisation might have profound consequences on interpreting domestication history and optimising future breeding.

## Signatures of selection

Population structure reflects cause and consequence of the interplay of ecological and evolutionary processes shaped by population genetics and demography, adaptive and non-adaptive forces, and their interactions (Lowe et al., 2017). Differentiating between the effects of demography and selection on genomic signals is notoriously difficult (Bourgeois and Warren, 2021; Li et al., 2012). Signals of selection can be confounded by genetic drift (Jensen et al., 2005), most notably through small founding populations, as random fluctuations of allele
frequencies can lead to loss of genetic diversity genome wide. Founder effects can generate a strong genetic bottleneck that shows randomised signals of reduced genetic diversity, somewhat similar to that of selective sweeps (Nielsen, 2005). Adequate modelling of population demography can go a long way towards distinguishing between signals of selection and drift. Statistical methods for detecting intra-population selection, such as sweeps, may use the Site Frequency Spectrum (SFS), Linkage Disequilibrium (LD), and measures of genetic diversity (π). Cross-population selection scans similarly rely on single site markers or haplotype information (Supplementary material, Table 1A).

Hard selective sweeps are more easily identifiable (Saravanan et al., 2020), and occur when a rare beneficial mutation rapidly increases in frequency via selection due to an increase in fitness (Nielsen et al., 2005). Regardless of the reason, e.g. local adaptation in heterogenous environments (Tiffin and Ross-Ibarra, 2014), transfer to captivity (Montgomery et al., 2010; Rhode et al., 2020) or selective breeding (Hull et al., 2024), this process causes surrounding linked alleles to “sweep” along with the beneficial allele by a process called genetic hitchhiking. This reduces genetic variation in the flanking regions and generates a pronounced signal of large homozygous regions in linkage disequilibrium (Smith and Haigh, 1974). In contrast, soft selective sweeps arise from standing genetic variation, which becomes selected due to sudden environmental changes, e.g. in feedstock (Hermisson and Pennings, 2005). Fixation of selected alleles can be reached but segregating on different haplotype backgrounds around the selected site. This does not produce a pronounced reduction in diversity and limits our potential to identify soft sweeps, despite they may represent the prevalent mode of adaptation (Saravanan et al., 2020). Common methods of selective sweep analysis include the Composite Likelihood Ratio (CLR) test that compares the neutral demographic model for SFS with a selective model that considers a selective sweep in each genomic window. This method identified regions with sweep-like patterns associated with honeybee adaptation (Ji et al., 2020) and BSF domestication (Generalovic et al., 2023). Furthermore, selective sweep analysis has resolved loci involved in butterfly wing patterning, size and shape (Moest et al., 2020; Montejo-Kovacevich et al., 2021), and insecticide resistance (Barnes et al., 2017). Under domestication, for example, up to 3% of the gene repertoire of the silkworm is impacted by selection through sweeps (Tong et al., 2022).

In addition to selective sweeps, we discuss several key methods for detecting selection as outlined in Supplementary material and reviewed in Saravanan et al. (2020). As sweeps reduce genetic diversity locally, around selected alleles, general measures of genetic diversity are based on nucleotide diversity (π). Motivated by the same rationale, another common method to identify more recent selection signatures is to search for Runs of Homozygosity (ROH), continuous homozygous regions of the genome typically classified into short (<1 Mb), intermediate (1-5 Mb), and long (>5 Mb) runs (Peripolli et al., 2017). Short runs are products of shared ancestry broken down by recombination whereas long ROH can be used to identify patterns of recent inbreeding or selection, as exemplified for BSF (Generalovic et al., 2021), honeybees (Gmel et al., 2023), and aquaculture species (Paul et al., 2022). LD-based methods provide additional power in deciphering between drift and selection with selective signals providing a more structured pattern of linkage decay surrounding the beneficial allele whereas drift generates a more widespread non-specific pattern of LD. Methods as the integrated Haplotype Score (iHS) (Voight et al., 2006) are more robust to demographic artefacts and can be combined with SFS-based methods where possible (Hull et al., 2024). For inter-population detection of selection, both single-site differentiation using $F_{ST}$ has been previously outlined above and discussed further in Section 4. Alternatively, haplotype-based methods can be used to cross compare populations for genomic regions that reached intermediate-to-high frequencies as blocks of extended haplotype homozygosity (XP-EHH), a signal that captures population-specific selective sweeps (Cai et al., 2024; Montero-Mendieta et al., 2019).

Further, confounding effects of sample size, $N_e$, and population history (including time since domestication or selection) should be considered when performing selection analyses. It is also important to note that products of selection may arise due to influences from alternative evolutionary forces such as hybridisation and adaptive introgression (Calfee et al., 2020). Completing several methods to detect selection with phenotypic analysis, e.g. GWAS or QTL (Section 4), can provide strong evidence for candidate causal genes under selection linked with the association of specific traits of interest (Hayes et al., 2008; Szmatoła et al., 2016). However, these metrics are still unknown for the main species of insects as food and feed despite available genomic resources.

Lastly, structural variants have significant potential to influence phenotypic differences across populations
or strains by locally suppressing recombination. What
may appear as regions of the genome diverging due to
selection could, in fact, be influenced or driven by struc-
tural constraints. In this context, the vinegar fly is a
prime example of how substantially structural genomic
variation can shape adaptation processes (Kapun et al.,
2023), which could be relevant in farming contexts, as
known from conventional livestock (Liang et al., 2024;
Yang et al., 2024).

4 Quantitative genetics

Most traits of importance for production and fitness
(e.g. crude weight, growth rates, nutrient composi-
tion, feed conversion efficiency, egg clutch sizes, etc.)
are multifactorial and complex quantitative traits con-
trolled by multiple genes, the environment, and inter-
actions between genotype and environment. Hence,
phenotypic values of quantitative traits are often con-
tinuous, and follow a normal distribution with a specific
mean and variance. This variance can be exploited by
selecting and mating the best-performing individuals
with each other, thereby shifting the population mean
in a desirable direction over successive generations.
Which traits are relevant is determined by the require-
ments from production systems and society. A breed-
ing programme aims at genetically improving traits of
interest through breeding, preferably avoiding negative
effects on other important fitness or production traits at
the population level. The expected response to selection
(R) is represented by the breeder's equation; R = h²S,
which depends on the heritability of the trait (h²) (see
Section 4: Estimating genetic parameters), and the dif-
ference between the mean of the selected individuals
and mean of the population (selection differential, S)
(Figure 3) (Hansen et al., 2024b). These parameters are
dependent on the amount of genetic variation present
in the population, the accuracy of breeding values (esti-
mated genetic potential of individual), and the selection
intensity. In this section, we discuss the main parts
required to set up a breeding programme to exploit
genetic variation in quantitative traits for selection in
production insects.

Breeding objectives

To construct and implement a sustainable breeding
programme, some practical considerations include the
choice or establishment of the (sub-)population to be
improved (see Section 4: Monitoring and managing
genetic diversity), the production system the animals

will exist in, and the overarching goal of the breed-
ing and selection activities (with all these steps being
dependent on one another). Insect performance is
impacted by the environment, including both exter-
nal environmental factors as well as, to a larger extent,
factors inherent to the production system. Insect pro-
duction systems can vary hugely in size and complexity,
from single-batch, single-substrate systems to systems
using multiple different substrate streams across indi-
vidual life stages and/or generations of the animal.
Nonetheless, most production systems can be defined
within the parameters of the following key components:
(i) the insects themselves (including community aspects
such as density, sex ratio, and the genetic makeup
of the population); (ii) the physical environment; (iii)
fixed resources; (iv) economics; and (v) management.
Once the production system for the breeding popula-
tion has been described, the next step towards sustain-
able genetic improvement is the clear definition of a
breeding goal. Having a defined breeding goal is a firm
prerequisite of any breeding programme as it allows
for the inclusion of several biological traits into one
expression of breeding worth and thus, enables progress
in selection (Nielsen et al., 2014). Due to the variable
nature of insect production systems, the definition of

<figure><img src="image_6.png" /><figcaption>FIGURE 3 Illustration of the change in mean ($\bar{X}_O$) representing the expected response to selection (R) in the offspring generation. The difference between the mean of the individuals selected as parents ($\bar{X}_S$) for the next generation and mean of the parent generation ($\bar{X}$)) represents the selection differential (S).</figcaption></figure>
one central breeding goal is difficult to achieve. Breeding goals can either be descriptive with a set of ideal trait descriptions, or mathematical expressions of the ideal individual with each trait weighted based on perceived (or measured) economic value, as has been exemplified in BSF (Zaalberg *et al.*, 2024). Although breeding goals are often mixed in large livestock, farmed insects have a much higher potential for clear breeding goals entirely limited to measurable traits. This is a crucial benefit that can off-set difficulties in e.g. measuring the performance of individual insects.

Relevant traits in a breeding goal for insects for food and feed typically include fertility, reproductive traits, growth, larval and pupal developmental time, and larval and pupal survival and pupation rates. These are traits that enhance production efficiency by optimising output with minimal input, reducing time to harvest, and contributing to profitability and sustainability. The traits included in the breeding goals also depend on market requirements, where body composition traits become relevant to produce insect oil and insect meal, or even fatty acid and amino acid profiles. Furthermore, traits like disease resistance or resilience could become important to maintain healthy and productive populations in the future.

## Information collection

### Identification systems

A selection of target traits is defined based on the breeding goal, and relevant information on the traits need to be collected to make selection decisions. For most selection strategies, pedigree recording is an essential part of information collection. To record the pedigree, a unique individual identification system is essential, as it allows recording of information in a mass rearing environment. Dust, dyes, and mutilation marking, have been widely applied in insect studies (Hagler and Jackson, 2001), which allows tracking of individuals or groups at a given time in their life cycle. The challenge arises when an individual moults and the exoskeleton is shed along with any physical identifiers, and when holometabolous insects go through metamorphosis, complicating the tracking of individuals over an extended period. Consequently, tracking through isolated or grouped rearing has been the method of choice in insect breeding so far. Individuals can be reared individually in a small, labelled cup, which allows individual selection (see Section 4: Selection). Alternatively, groups of siblings can be reared in small containers to perform family-based selection. Isolated rearing of breeding animals can introduce differences in performance between

the breeding and production population (see Section 4: Genotype-by-environment interactions) and introduces confounding between genetic and environmental influences on trait variance (see Section 4: Environmental variance).

## Phenotyping

A major bottleneck in insect breeding schemes includes obtaining sufficient phenotypic records for accurate selection (e.g. Klein, 1974). The short life cycle of many production insects complicates trait recording on all individuals and evaluation within available timeframes and poses requirements for phenotyping throughput, labour, and space. Additionally, random measurement errors will lead to poor reproducibility of the phenotype, leading to less successful genetic improvement of the trait. Development of high-throughput automated phenotyping methods using computer vision, sensors, and machine learning to predict phenotypes automatically could enhance phenotyping capacity and accuracy (Nawoya *et al.*, 2024) and allow phenotyping of a sufficient number of individuals for selection in a timely manner for a range of relevant traits, such as body size (Laursen *et al.*, 2021) and protein content (Cruz-Tirado *et al.*, 2023). Moreover, sex identification is not always possible in juvenile insects, imposing requirements of isolated rearing from phenotyping until selection for mating. Promising developments in e.g. computer vision to identify sex at the larval stage in BSF (Nawoya *et al.*, 2025) could support direct selection after juvenile phenotyping.

Phenotypes can be collected on selection candidates themselves; named own performance, or can be collected on family members, such as sibs or offspring. Data collection on offspring to evaluate parent performance, so called progeny testing, requires overlapping generations, hence is not suitable for insect species with discrete generations. Data collection on sibs can easily be exploited in insects and is of additional interest for recording complex traits that require sacrificing of individuals, such as detailed body composition (Bouwman *et al.*, 2024). Besides measures on individuals, group records can be utilised to obtain information on many groups while sacrificing some within-group information (e.g. Adamaki-Sotiraki *et al.*, 2023; Scieuzo *et al.*, 2023).

## Genotyping

Genotypes can be a useful information source in insect breeding (Eriksson and Picard, 2021) (Section 2). The advancement in genotyping technology has increased the availability of genomic data that can be used to
reconstruct pedigrees, correct pedigree errors, or identify previously unidentified familial relatives. Genomic information can also be used to evaluate the level of inbreeding in the population for breeding management purposes. Last, genotyping phenotyped individuals is required to establish a reference population for genomic selection (see Section 4: Selection), to detect regions in the genome associated with the trait of interest, and to determine the genetic architecture of the trait (Xia, 2020) (see Section 4: Genetic architecture of quantitative traits).

## Genetic architecture of quantitative traits

To elucidate the genomic architecture of quantitative traits, loci in the genome that affect the traits can be identified. These loci are called quantitative trait loci or QTLs. Once identified, genomic markers associated with the trait of interest could be used for marker assisted selection (see Section 4: Selection criterion). In insects, the vinegar fly has been the model species to study genetic architecture (Flatt, 2020; Mackay, 2004). Medically and agriculturally important QTLs have been identified and mapped in insect pests (Behura, 2006), the silkworm (Lu et al., 2004) and the honeybee (Guichard et al., 2021), and extending such studies to other farmed insect species should be relatively straightforward. Identifying naturally segregating loci that affect quantitative traits can be done using QTL mapping based on linkage, genome-wide association studies (GWAS), or by evolve and resequencing studies.

## QTL mapping via linkage analysis

In QTL mapping, phenotypes and marker genotypes are combined from a cross between two strains that differ with respect to one or more traits. Genomic regions between markers highly associated with the trait in the offspring of the cross can be identified as the QTL underlying these trait differences (Heckel, 2003). A linkage map of the markers needs to be available. A common method for QTL mapping in the vinegar fly is creating recombinant inbred lines (RILs) (Mackay, 2010). Such lines are created by inbreeding the second generation of a cross between two lines, which allows associating phenotypes to QTLs. The downside of QTL mapping via linkage analysis is that only sites that show allelic differences between the two parental lines can be detected (Mackay, 2001), and that the genomic regions identified are usually rather broad and require additional testing to fine map the region (Mackay, 2010). An alternative is therefore association testing using genome-wide SNPs in linkage disequilibrium with the causal loci.

## Genome-wide association

In GWAS, a large population of phenotyped and genotyped individuals can be used to estimate the effect of each genotype marker on the phenotype, possibly leading to candidate causal variants or genes. For GWAS, it is important to have sufficient power to detect associations, which depends on the trait's heritability, its genetic architecture, and the sample size. The set of sampled individuals should ideally be unrelated and contain several hundreds to thousands of individuals. GWAS data is commonly analysed with linear mixed models to estimate effects of each SNP. The inclusion of a relationship matrix accounts for spurious association due to family structure or population stratification (Price et al., 2010; Sahana et al., 2023). Alternatively, the principal components from the genotype data can be used to correct for population structure (Price et al., 2010; Sahana et al., 2023). As each SNP is tested individually, a correction for multiple testing needs to be applied to avoid false positive associations (Lander and Kruglyak, 1995). For example, a Bonferroni threshold (α / #SNPs) or false discovery rate threshold (Benjamini and Hochberg, 1995; Storey and Tibshirani, 2003) can be applied. Examples of GWAS in insects can be found in Hull et al. (2024) and Xia (2020). With the advancement of SNP arrays for insects for food and feed GWAS will become much easier to perform.

## Evolve and resequencing

In evolve and resequencing studies, the genome of (pools of) individuals from a base population is compared to the genome of (pools of) individuals from a line split off from that base population that has undergone artificial selection, to identify genomic responses. The response to selection can be observed by comparing allele frequency differences between the base population and the selected line to identify selection signatures as reviewed in Schlötterer et al. (2015) and Turner et al. (2011). Multiple replicates under the same conditions, either in the same or in opposite direction, can confirm responsive alleles related to the selection criteria (Schlötterer et al., 2015; Turner et al., 2011). The advancement of high-throughput sequencing techniques (Section 2) combined with reduced cost for resequencing, and the possibility to use pooled samples to identify allele frequencies of each line and its base has made this an attractive method in insect species with short generation intervals (Generalovic et al., 2025b).
## Genetic parameters

Efficient genetic improvement requires an understanding of how much of the phenotypic variation observed is attributable to genetic (i.e. heritability) and how much to environmental factors. This information is essential for an accurate breeding value estimation as well as for developing an efficient breeding objective. Before any systematic selective breeding can be initiated, the major variance components and genetic parameters of the traits in the breeding goal need to be estimated.

The use of linear mixed models with Restricted Maximum Likelihood (REML), correcting for all systematic effects, including all available information from lineal and collateral relatives and a residual term as random effects, allows for estimation of additive genetic ($\sigma_a^2$) and residual ($\sigma_e^2$) variance (see the next three paragraphs). The model typically includes the random animal effect (the additive genetic effect) and can be extended with additional random effects, such as those originating from common environment or maternal genetics. Other covariates can be included, such as other recorded traits to include their effect on the trait of interest, or the inbreeding coefficient to evaluate the inbreeding effect on performance (Paul et al., 2022). The phenotypic records can be individual or group records, where the use of group records for genetic parameter estimation and genetic evaluation naturally has consequences for the accuracy of estimates (Hansen et al., 2025; Olson et al., 2006).

## Additive genetic variance

In breeding, the major variance component of interest originates from differences in additive genetics between individuals in the population, as offspring will directly inherit only the additive genetic effects from their parents. Estimating the additive genetic variance requires relatedness information to build a relationship matrix. Until now, the proportion of phenotypic variance in production traits made up of additive genetic variance has been estimated in farmed insects based on parent/offspring or full/half sibling records. Examples include traits in the house cricket (Castillo, 2005; Ryder and Siva-Jothy, 2001), desert locust (Chapuis et al., 2021), yellow mealworm (Morales-Ramos et al., 2022; Prokkola et al., 2013; Sellem et al., 2024), house fly (Boatta et al., 2023; Hansen et al., 2024a), and BSF (Bouwman et al., 2022; Generalovic et al., 2025a). Molecular data (e.g. SNPs) is being increasingly used to infer the genetic relationship between individuals in a population, which in turn is used to estimate the additive genetic variance if

phenotypic data is also known (Srivastava et al., 2023), as exemplified in BSF (Hull et al., 2024).

## Indirect genetic variance

Indirect genetic effects can represent an additional source of phenotypic variation. An example is maternal genetic effects, the genetic value of the mother's genome for her offspring's phenotype. In insects, maternal genetic effects have been primarily studied in honeybees (Brascamp and Bijma, 2014), but have not yet been thoroughly evaluated in insects produced for food and feed. The estimation of maternal genetic effects requires identity information of the mother of the phenotyped offspring. When variance components are derived using full-sib families, the maternal genetic effect is confounded with additive as well as common and maternal environmental effects. Genomic instead of pedigree data can help disentangling the confoundment (Lee et al., 2010). Grouped animals are also affected by social indirect genetic effects, i.e. the effect of the genetic value of an individual on phenotypes of others in the group. Social indirect genetic effects were shown to influence female choice and aggressiveness in crickets (Bailey and Zuk, 2012; Santostefano et al., 2017), mating rate in the burying beetle (Nicrophorus vespilloides) (Carter et al., 2019), and position within a social network and locomotion in the vinegar fly (Signor et al., 2017; Wice and Saltz, 2023). Studying and quantifying indirect genetic effects includes recording phenotypes of individuals from genetically diverse groups, which requires a system to identify individuals from different genetic backgrounds in a group (Ellen et al., 2016).

## Environmental variance

Environmental effects can be major sources of phenotypic variation and need to be properly accounted for when estimating genetic parameters and breeding values. Although production insects can be reared in controlled environments, common environmental effects can cause animals reared in the same container to perform more similar. When families are reared in the same container, the common environmental effect can only be disentangled from the additive genetic effect by splitting family groups over multiple environments (i.e. containers) (Bouwman et al., 2022; Hansen et al., 2024a). The confounding between additive genetic and common environmental components can also be disentangled by increasing genetic connectedness between management units such as families reared in separate containers (Kuehn et al., 2007; Yu et al., 2017). Genetic connectedness can be increased with repetitive use of one
parent with multiple mates, or by identifying unknown relatives with either more meticulous pedigree keeping or by using genomic data retrospectively. Additionally, maternal environmental effects, defined as the non-genetic contribution of females to their offspring's phenotype (Reznick, 1991), have been shown to affect the number and size of eggs, sex ratio, developmental time, growth rate, etc., in insects (Mousseau and Dingle, 1991). To estimate the maternal environmental effect it would be necessary to mix offspring across different mothers and environments. However, for genetic improvement, the priority is to separate additive genetic and environmental components, whereas it is less critical to differentiate various sources of environmental variance from one another.

## Estimating genetic parameters

(1) Heritability: The evolvability of a trait is dependent on the extent of phenotypic variance that can be explained by additive genetic variance in a given population in a particular environment, i.e. the narrow-sense heritability ($h^2$) of a trait (Falconer and MacKay, 1996). The heritability is estimated as $\frac{\sigma_a^2}{\sigma_a^2 + \sigma_e^2}$. Fitness traits, such as fertility, clutch size, or hatchability, commonly have very low heritabilities. On the other hand, non-vital morphological traits often exhibit high heritabilities, indicating significant underlying genetic variation (Mousseau and Roff, 1987). An overview of studies reporting estimates of $h^2$ for a range of traits in insects farmed for food and feed can be found in (Hansen et al., 2024b).

(2) Correlation: Using multivariate mixed models to obtain estimates of trait covariances, the genetic and phenotypic correlation between two or more traits can be estimated as $\frac{\text{cov}(x,y)}{\sqrt{\sigma_x^2 \sigma_y^2}}$ where x and y are either phenotypic or genetic values of the two traits. The traits need to be measured on related or preferably the same individuals but could also be on half or full siblings. Correlating traits at e.g. full-sib family level, using group sums or averages as the phenotype, enables estimation of correlations without maintaining information at the individual level over time (e.g. Hansen et al., 2024a). Estimates can be improved if a group record of one trait is correlated to individual records of another (Ma et al., 2021), for example by maintaining individual-level information at the adult stage.

## Selection

### Selection criterion

Selection of the best individuals as parents of the next generation can be based on several criteria. Until now, genetic improvement in insects produced as food and feed has primarily been done by selection on phenotypic performance of an individual itself, or through environment-induced selection using experimental evolution. Another strategy is to select individuals based on estimated breeding values (EBVs) that capture only the genetic part of the phenotypic value.

### Phenotypes

The simplest form of selection is selection on the observed phenotype, commonly known as mass selection or phenotypic selection. Phenotypic selection does not require any relatedness information and is thus straightforward to implement in insects (Facchini et al., 2022; Ma et al., 2024; Slagboom et al., 2024; Song et al., 2022). Using phenotypes challenges multi-trait selection, because some traits are tightly integrated through genetic mechanisms, and selection on one trait might result in undesired responses in another (correlated responses) (e.g. Armitage and Siva-Jothy, 2005; Flatt, 2020). Additionally, some trait recordings require sacrificing the individual, rendering phenotypic selection only possible when selection is on directly observable traits. Although random mating should prevent high levels of inbreeding, insects usually have large full-sib family structures, hence phenotypic selection runs the risk of increasing inbreeding as relatives are more likely to perform well and be co-selected in a given environment.

## Pedigree-based breeding values

Breeding values (BVs) reflect the average performance of the progeny of an individual expressed as (twice) their deviation from the population average performance. Prediction of BVs requires phenotypic information, a relationship matrix, and estimates of variance components. BV estimation uses linear mixed models and is most commonly done using Best Linear Unbiased Prediction (BLUP) (Henderson, 1950). Estimates of the fixed effects (Best Linear Unbiased Estimates, BLUE) provide valuable knowledge about the systematic variables that are constant across individuals, such as sex, age, or treatment group, and can be used to make informed decisions on management. Simultaneously, these effects are separated from the genetic effects that are of primary interest in selective breeding. BLUPs are estimated for all random effects, which would include
the additive genetic effect (either from individuals or their parents), and other variables which are not constant for all individuals, such as common environment effects. Selection on EBVs allows multi-trait selection schemes where covariance between traits are accounted for. Furthermore, weights, either arbitrary or based on economic importance (Zaalberg *et al.*, 2024), can be placed on each trait to compute a combined EBV for a group or an individual. Maintaining information on individual phenotypes in a multigenerational pedigree poses profound challenges in insect populations. Short and discrete generations imply obtaining and maintaining phenotypic and relatedness information in large quantities and computing EBVs before selection decisions can be made, which, for some species, could be just a few days. Re-identifying selection candidates when EBVs have been computed once again circles back to the challenge of tracking insects over time. If rearing full sibs in isolated groups is feasible, family EBVs can be used as the selection criterion (e.g. Hansen *et al.*, 2025).

## Genomic breeding values

Alternatively, EBVs can be predicted using genomic data, for example marker assisted selection (MAS) (Fernando and Grossman, 1989). In MAS, the selection criterion is based on identifying the genotype at markers which are in linkage disequilibrium with candidate or known causal loci (QTLs; see Section 4: Genetic architecture of quantitative traits). However, a significant limitation of MAS is the lack of comprehensive knowledge of QTLs for traits of interest, or the fact that the markers used may only account for a minimal proportion of the genetic variance associated with these traits (Collard and Mackill, 2008). A step further is predicting EBVs using high density genomic markers, called genomic BLUP (GBLUP) (Meuwissen *et al.*, 2001). In GBLUP, the relationship matrix is constructed using only genomic data. GBLUP requires a reference population of phenotyped and genotyped individuals. Having this population, one can predict EBVs of unphenotyped animals based on their genotypes with higher accuracy than in the pedigree model (Schaeffer, 2006). Accuracy of genomic prediction decreases with increasing distance between reference and target population, wherefore frequent update of the reference population is crucial to maintain high accuracy (Pszczola *et al.*, 2012), especially in insects with quick generation turnaround. The use of genotypes for EBV prediction in insects is still limited, however, there have been some attempts. Genomic prediction has been performed in honeybees (Bernstein *et al.*, 2023)

and a proof-of-principle study was done in the parasitoid wasp *Nasonia vitripennis*, showing its potential and highlighting considerations for practical application of genomic prediction in insects (Xia *et al.*, 2024). Although accuracies of these predictions did not reach accuracies observed in traditional livestock, both have shown a positive effect of including genomic data in predicting EBVs for insects. Additionally, the prediction can be strengthened by the combined use of pedigree and genomic data in the so called single-step GBLUP (Aguilar *et al.*, 2010; Legarra *et al.*, 2009).

## Selection scheme

The selection strategy is often chosen based on the unit for which information is acquired. Phenotypic selection (Facchini *et al.*, 2022; Morales-Ramos *et al.*, 2019) and selection on individual EBVs are fitting for scenarios where the available information is collected on individuals, or where individuals are units that can be selected independently. When information is collected on families, e.g. full-sibling groups, family-based selection is a fitting strategy. This holds especially for insects, as keeping family phenotype and pedigree information is easier than collecting individual information. Two types of family-based selection include between-family selection and within-family selection, but a combination of the two can also be applied (Hansen, 2024). Between-family selection selects the best performing families to produce the next generation. Although easy to implement, it requires starting with a large number of families (>100) as in every generation families are excluded from breeding and hence inbreeding increases rapidly. Within-family selection maintains the number of families and selects the best individuals within each family to generate the next generation.

If information can only be collected on siblings due to phenotyping being invasive or sex-specific, sib-selection is an appropriate strategy. In advanced breeding programmes, several selection methods and selection rounds can be combined, which is advantageous when the breeding goal includes an array of traits with different requirements for phenotyping (invasiveness, life-stage, sexual dimorphism). The selection criterion depends on whether relatedness information is collected (EBVs) or not (phenotypes). Multi-trait breeding can also be accomplished by selecting different lines on different single-trait breeding goals and ultimately cross them to obtain crossbred offspring which outperform the parental average (Meyermans *et al.*, 2025). Other strategies, which have not been formally investigated in
production insects, include progeny selection and optimum contribution selection.

Finally, if information includes genotypes, genomic selection can be applied. This strategy allows for selection of juveniles even before they express the phenotypes or even if they do not express the phenotype (sex-specific trait). The main benefit of genomic selection in livestock arises from the shortened generation interval. This is of limited value in insect populations that already have short generation intervals and could even complicate the recording of sufficient information before selection decisions. The main benefit of genomic selection in insects might come from identifying genetic relatedness and increasing accuracy for hard- or expensive-to-measure traits, traits with low heritability, and traits that are expressed later in the animal's life, since they only need to be measured on the reference population (Calus et al., 2013), similar to the situation in poultry (Wolc et al., 2016) and aquaculture (Castillo-Juárez et al., 2015; Lillehammer et al., 2020; Luo et al., 2022). For most insects, getting sufficient DNA for genotyping is generally done by sacrificing the individual, although less invasive approaches might be applicable (Section 2, e.g. Bubnič et al. (2020)). If sacrificed, the individual is then no longer available as a selection candidate to produce the next generation, and if damaged its suitability as a parent could be compromised; however, its DNA could be of value to predict the genetic merit of its full siblings.

## Mating design

The choice of selection scheme and production method is highly dependent on the species' mating system (monogamy, polygyny, polyandry) that supports different pedigree structures (full-sib families, maternal- or paternal half-sib families) and enables different mating designs (controlled-pair mating, familial group mating, random group mating). Recent studies have investigated the possibilities of utilising different mating designs in different production insects (Hansen et al., 2024a; Hoffmann et al., 2021; Laursen et al., 2024) and unveiled how those might differ between wild and captive rearing environments (Jensen et al., 2025). Mating designs are frequently used to not only optimise genetic improvement, but also to limit inbreeding (Slagboom et al., 2024). When rearing is done in random batches, rotating batches over each other at the time of mating in every generation can be used to mitigate inbreeding. In the case of family selection, males can be rotated over different families. If a high degree of mating control is achievable, optimum contribution selection (OCS) can

be employed. OCS maximises the mean breeding value in the offspring while controlling the rate of inbreeding by optimising individuals'/families' contribution and constraining the increase in mean kinship in the breeding population (Meuwissen, 1997). Another approach would be to minimise the increase in mean inbreeding of the population with a fixed constraint on the desired genetic progress (Colleau and Moureaux, 2006) which has already been applied in trout and insects. The final combination of information source, selection criterion, and selection strategy are thus dependent on the possibility to control the mating strategy and mating conditions (e.g. isolated mating pairs). As knowledge is sparse, research efforts for different farmed insects will be valuable for the development of species-specific insect breeding schemes.

## Experimental evolution

Experimental evolution intends to change an organism's trait of interest by applying an environmental selective regime under controlled conditions and is particularly convenient in organisms with short generation intervals, such as insects. Adaptive evolution may occur by new mutations in individuals or by a change in the population's allele frequency. The speed and effect size of the adaptive process depends on the experimental population's initial genetic variation, the number of generations, and the strength of selection. "Experimental evolution" in a narrow sense is the adaptive response of a population under controlled laboratory or field conditions to an applied selective environment. In a broader sense, it includes more controlled forms of artificial selection based on selecting specific individuals that most strongly express the trait of interest for breeding the next generation (Kawecki et al., 2012). One major advantage of having an experimental population evolving is that tracking individual phenotypes every generation is bypassed by scoring phenotypes after several generations (e.g. Gligorescu et al., 2023). This also spares data collection on relatedness between individuals to construct relationship matrices. Consequently, this approach sacrifices the ability to deliberately select on specific phenotypes while accounting for trait correlations, track population inbreeding, and, in some cases, control selection intensity. Another, more recent, approach is to use whole genome sequencing to compare individuals from evolved versus control populations, which permits to identify genomic loci responding to adaption.

Important aspects when setting up experimental evolution studies include replication of starting popula-
<figure><img src="image_7.png" /><figcaption>FIGURE 4 Forms of genotype-by-environment interaction, with the phenotype on the y-axis, environments on the x-axis, and the reaction norm gradient between them. Solid black and dashed lines represent different genotypes. (A) absence of $G \times E$ when all genotypes have the same response in the different environments; (B) non-crossover interaction when there is a change in the magnitude of the response without crossover (scale $G \times E$); (C) crossover interaction when there is a reranking of genotypes in the considered environments ($G \times E$).</figcaption></figure>

tions to control for stochastic processes and possibly alternative evolutionary responses, including genetic redundancy (Generalovic *et al.*, 2025b). Control populations that are not subjected to the applied selection regime need to be set up simultaneously to control for allele frequency changes independent of the selective pressure, such as domestication effects and genetic drift (e.g. Hull *et al.*, 2024). The starting population requires sufficient standing genetic variation for the trait of interest. This can be accomplished by mixing several field-collected populations prior to the selection experiment. However, this may bear disadvantages through outbreeding depression that breaks up co-adapted gene complexes (Frankham *et al.*, 2011). Population size is another important factor, specifically the number of individuals contributing to the next generation ($N_e$, see Section 3: Effective population size and Section 4: Monitoring and managing genetic diversity). A final factor to consider is the number of generations. A response to selection is sometimes already visible after 5-10 generations in experimental insect populations, but it may also require longer (Lirakis and Magalhães, 2019).

An important question is whether a selected trait is maintained in commercial mass rearing conditions. Not only may the mass rearing conditions cause relaxation of selective pressure applied in the laboratory, but it may also trigger other adaptive responses that interfere with the selected trait. Due to limited studies that applied experimental evolution to insects for food and feed (but see Boatta *et al.*, 2024), information remains scarce.

### Genotype-by-environment interactions

Genotype-by-environment interactions ($G \times E$) occur when different genotypes (e.g. populations, groups of relatives) respond to environmental variation (abiotic factors, diet, microbiota, scale, etc.) in different ways, i.e. when trait expression depends on which genotype

is evaluated in which environment (Figure 4) (e.g. Sandrock *et al.*, 2022). Two types of $G \times E$ can be distinguished (Falconer and MacKay, 1996): (i) crossover $G \times E$, where genotypes can change ranking between environments (Figure 4C); (ii) non-crossover $G \times E$, where genotypes retain ranking, but the differences are larger in one environment compared to another (Figure 4B), or a combination of both. $G \times E$ can reduce efficiency of selection when exchanging individuals across environments since it implies that genotypes differ in genetic potential to adapt to different environments (Falconer and MacKay, 1996). This is especially true for the case of reranking, where there is no single superior genotype across environments, and hence selection in one environment will lead to a lower genetic gain than expected in another. In addition, under $G \times E$, not only mean performance of a given genotype may change depending on environment, but also respective variances, which holds equal importance for consistency in production (Laursen *et al.*, 2024).

$G \times E$ interactions have been widely demonstrated being a key driver in evolutionary ecology, with plenty of examples in entomology research (Gamboa and Watanabe, 2019; Sandrock *et al.*, 2010; Santos *et al.*, 1994), conventional livestock (e.g. Fodor *et al.*, 2023; Wackchaure *et al.*, 2016) and aquaculture (e.g. Sae-Lim *et al.*, 2016). Well-characterised examples are known from insect models such as the honeybee (Costa *et al.*, 2012), or the vinegar fly, including a plethora of life history traits and metabolic phenotypes (Flatt, 2020; Reed *et al.*, 2010). Several available studies suggest this aspect is also relevant in insects for food and feed (Generalovic *et al.*, 2025a; Greenwood *et al.*, 2021; Laursen *et al.*, 2024; Sandrock *et al.*, 2022; Silvaraju *et al.*, 2024; Zhang *et al.*, 2024).

Two major types of models are used to study $G \times E$ interactions: multi trait model (MTM) and reaction
norm model (RNM). The multi trait model is mostly used when few categorically classified environments are studied. The phenotypic performance of the studied genotype recorded in different environments are considered as a separate, but potentially correlated traits (Falconer, 1952). The reaction norm model uses a continuous variable to describe environments that allows to classify genotypes in this gradient and to identify environmental factors causing G × E (Hayes et al., 2016; Sae-Lim et al., 2016).

Awareness of the presence and the extent of G × E is key, for ensuring selection response. If not accounted for, performance and reproductive fitness cannot be predicted for any production setting that differs from the selection regime of the nucleus population (Mulder and Bijma, 2005). In insects, the main challenge are thus environmental differences within the nucleus population compared to rearing conditions on production farms, e.g. including climate control, diet quality and provision, insect density, etc. Moreover, in evolutionary diverse taxa like the BSF where mitochondrial haplotypes comprise substantial coding differences (Guilliet et al., 2022), complex interactive effects of diet and mitonuclear epistatis may be involved, as shown in the vinegar fly (Mossman et al., 2016).

## Monitoring and managing genetic diversity

### Establishing a breeding population

Insect species farmed for food and feed generally lack a long history of domestication or closed captive rearing, with few exceptions. As a result, well-characterised breeds, strains, or lines that are specifically adapted or bred for particular purposes are not readily available and must be established. The establishment of a breeding population begins with the careful selection of founder individuals that can be (i) initiated from an already commercially farmed population; (ii) collected from wild populations; or (iii) created from a combination of wild and/or captive origins; and it continues with a stringent monitoring of its population genetic structure. Establishing a large breeding population that encompasses high genetic variation is more likely to capture diversity that can be leveraged in breeding while simultaneously reducing the risk of stochastic loss of genetic variants (Frankham, 2005). Conversely, reproductive success within the founder population is equally important to prevent bottlenecks and resulting founder effects. To mitigate this risk, it is essential to avoid assortative mating among founders, which does not seem to be relevant in genetically uniform populations (Laudani et al., 2024) yet appears to become

relevant when crossbreeding substantially differentiated subpopulations (Hoffmann et al., 2021; Kaya et al., 2021). Despite an absence of general recommendations regarding the census and effective population size of farmed insect populations, guidelines for effective population size in livestock generally suggest a low range of 50-100 (Wool Williams et al., 1998). To maintain the evolutionary potential, an effective population size of >5000 could be needed (Franklin and Frankham, 1998) which would entail a census population size above this number.

### Maintaining genetic diversity

Long-term genetic improvement depends not only on the establishment of diverse populations, but equally on maintaining genetic variation to improve populations' robustness and production. Captive, isolated populations are at risk of genetic erosion due to average relationship between individuals increasing every generation. The life-history characteristics of many insect species, including short life cycles, high fecundity, high juvenile mortalities, large variances in family size, and skewed progeny contributions, renders them particularly susceptible to drastic fluctuations in population size. This, combined with inbreeding, results in an increase in homozygosity that reduces genetic variation over generations. Increased inbreeding can lead to inbreeding depression, decreasing the phenotypic value for fitness and possibly other economically important traits (Hedrick and Garcia-Dorado, 2016; Leung et al., 2025; Woodworth et al., 2002). Increasing homozygosity further causes the loss of variability for currently neutral traits that can become economically important in the future (Meuwissen et al., 2020). Primary strategies for maintaining genetic diversity beyond establishing a diverse breeding population and ensuring high effective population size entail: (i) Managing the rate of inbreeding below 1% per generation (Bentsen and Olesen, 2002) through careful mate choice decisions that minimises mating between relatives. Such a strategy requires pedigree records or use of molecular markers (see Section 4: Information collection); and (ii) Population augmentation, which is the introduction of unrelated individuals to a population to restore genetic diversity and counteract the effects of inbreeding depression through outcrossing (Kronenberger et al., 2018).

Little is known about genetic erosion and effects of inbreeding in captive insect populations beyond recent indication of substantial fitness reductions upon repeated sibling mating (Laudani et al., 2024). Counter
to livestock breeding experience, insects exhibit many fold the generational turnover, resulting in rapid loss of genetic variation (Rhode *et al.*, 2020). Conversely, insects might tolerate higher levels of inbreeding than livestock (Cai *et al.*, 2022; Swindell and Bouzat, 2006). Purging of recessive deleterious alleles through inbreeding could mitigate negative effects of inbreeding depression (Caballero *et al.*, 2017; Pérez-Pereira *et al.*, 2021). However, such a management strategy comes with a risk of population collapse and is not guaranteed to be efficient in small populations (Frankham, 2005). Inbreeding depression can even be environmentally dependent, wherefore purging of deleterious recessives might only be effective in specific environments (Bijlsma *et al.*, 1999). The general recommendation is to avoid inbreeding and maintain high levels of genetic variation through management practices and continuous monitoring and evaluation of the breeding population.

## Outbreeding depression

Introducing new genetic material to a breeding population is highlighted both as a breeding strategy (cross-breeding) and as an inbreeding management tool (population augmentation). Such strategies can be beneficial but must be used with caution. Although they can positively impact phenotypes through hybrid vigour and introduction of new genetic variation, they may also result in the loss of genetic gains achieved through selective breeding for production traits. Detrimental loss of fitness in the population due to outbreeding depression may also be an outcome (Kurbalija *et al.*, 2010; Peer and Taborsky, 2005), likely due to disruption of co-adapted gene complexes and/or genomic incompatibilities manifesting in the first or later generations (Edmands and Timmerman, 2003). Intentionally seeking heterosis to enhance fitness or production traits may be highly sensitive to the parental counterparts involved in interbreeding. In some insect species, outbreeding can yield beneficial effects (Singh *et al.*, 2002; Szűcs *et al.*, 2017), while in others, it may have no significant impact (Fountain *et al.*, 2015; Leung *et al.*, 2025). Complex metapopulation structure with highly diverged evolutionary lineages or even unrecognised cryptic taxa (Generalovic *et al.*, 2023; Hagberg *et al.*, 2022) may indicate possible risks of outbreeding depression. Yet, prediction thereof solely based on genetic distance is difficult. While neutral genetic drift can have strong impact on genome-wide differentiation (Cai *et al.*, 2024), the segregation of non-recombining structural genomic variations was indeed shown to mediate and maintain insect ecotype differentiation through envi-

ronmental adaptation (Kapun *et al.*, 2023). Hence, the effect, whether positive or negative, on desired traits or fitness when employing crossbreeding is likely source and context dependent. Consequently, unmonitored interbreeding intended to randomly increase genetic diversity may lead to counterintuitive unwanted consequences for fitness in the short term and ultimately affect the population's genetic makeup in the long term.

## Protecting wild populations

The existence of wild alongside managed populations of several farmed insect species calls for an increased awareness for conservation genetics responsibilities. Although there are regional differences in regulating insect productions, as well as in motivations for their implementation, the insect production sector should develop strategies to safeguard the integrity of local wild populations across both indigenous and non-native ranges. This will warrant the preservation of unique genetic profiles for future breeding purposes. For the same reason, continuous monitoring of introgression from insect farms into the wild is recommended to evaluate and trace regional invasion potentials (Bang and Courchamp, 2021). Even if largely maladapted under natural selection, introgression from strains selected in mass-rearing contexts has the potential to change life history traits of wild populations possibly broadening capacities for ecological adaptation (Beaurepaire *et al.*, 2024; San Jose *et al.*, 2023). Invasiveness could significantly destabilise natural ecosystems and worsen public perception of insect farming (Lourenço *et al.*, 2022). Such unwanted developments need to be proactively prevented. Possible strategies beyond physical containment and geographical isolation include genetic monitoring of both farmed strains and wild populations, reproductive control through genetic modifications, selective breeding for low fitness in the wild, as well as development of industry guidelines to minimise escapes and ensure responsible management practices across the sector.

# 5 Functional genetics

Functional genetics refers to the study of the genetic architecture of organismal variation. Technological developments have facilitated the transition from targeted studies on few genetic markers to applying whole genome scans. The focus in the context of breeding insects for food and feed is to identify biological relationship between genes (and variants) and phenotypes
of interest, such as reproduction, growth, resilience, or
resistance to disease. In its simplest form, variation in
protein coding regions of the DNA sequences lead to
variation in mRNA sequences, which are translated to
the active molecules. Therefore, it is generally expected
that all variation in phenotypic performance is based
on segregating variants of protein coding genes, and
regions that regulate their expression.

For traits with a simple genetic architecture, where
single or few loci with large effect determine the phe-
notype, progress has been made, especially within the
field of human health and disease research (Reed et
al., 2011; Slavkin, 2014). Generally, however, pin-pointing
genes and genetic variants underlying organismal traits
remains difficult. This is partly due to many traits being
highly polygenic and controlled by many genes, each
with a minor effect, but also since phenotypes are
affected by numerous additional (environmental) fac-
tors (Hansen, 2024) (Section 4). Multiple intermedi-
ate processes and specifically interfering factors con-
tribute to the final phenotype. Thus, the direct asso-
ciation between gene variant and trait value is often
hard to identify and predict. The strength or direction
of a genotypic effect on a phenotype often depends on
the environmental condition (see Section 4: Genotype-
by-environment interactions). Recent investigations of
farmed insects have observed differential performance
and composition of different strains based on admin-
istered diets pointing out phenotyping challenges due
to G × E interactions (Generalovic et al., 2025a; Glig-
orescu et al., 2023; Greenwood et al., 2021; Sandrock et
al., 2022).

The main mission for functional genomic studies is
to (i) generate biologically meaningful interpretation
of large datasets including genetic/genomic informa-
tion, but also transcriptomic (gene expression) and sim-
ilar large scale ‘omic’ data, and (ii) identify biological
relationships between genetic/genomic variation and
organismal (functional) phenotypic variation. Intricate
knowledge of the biological system being investigated
is required to achieve clear and robust interpretation,
including functional annotation of genes and molec-
ular pathways and a deep understanding of the ecol-
ogy and/or requirements and conditions experienced
by the studied organism (Muchina et al., 2025). In the
following, we discuss applications and achievable goals
of available techniques and highlight common pitfalls.
Illustrated by examples, we provide general recommen-
dations how to apply functional genomic tools.

***Current trends in insect functional genetics/genomics***

Currently, functional genomics publications related to insect farming are heavily skewed towards two commercial species: the yellow mealworm and the Mediterranean field cricket make up over 70% of the literature pool. This is because they have long been studied as model organisms for molecular mechanisms of development, regeneration, immunity, and host-pathogen interactions, and in part, due to their comparatively easy rearing in the laboratory (Mito and Noji, 2008; Petronio Petronio *et al.*, 2022). While research on these organisms provided a solid foundation of fundamental research, there has been a shift towards more applied research aimed at understanding gene function and regulation that could advance the field of insects as food and feed. This shift is primarily driven by numerically strongly increasing literature records on the BSF (Athanassiou *et al.*, 2025; English *et al.*, 2021; Siddiqui *et al.*, 2024), fuelled by technological developments that allow functional genomic studies and molecular manipulation with little prior information (Figure 5). While high quality genomes are still much desired (Oppert *et al.*, 2023), many studies can be performed as sequencing allows the *de novo* construction of annotated genomes and transcriptomes, permitting targeted RNAi and gene editing.

Beyond the focus on progress directly on commer-
cially farmed insects, there is a wealth of knowledge
on other model species systems like red flour beetle
(Campbell et al., 2022; Kumar et al., 2018) and vinegar fly
(Mackay and Huang, 2018), which can serve as inspira-
tion for adopting approaches to tackle yet unattempted
work in the target species for food and feed.

***Techniques and their applications***

The first step in identifying the genetic architecture that underlies a trait of interest is a choice between investigating associations between genotype (DNA-sequence) and/or expression level (mRNA abundance) as these approaches provide different insight. Following the generation of lists of candidates, these should be interpreted via bioinformatics analysis and then functionally verified. To achieve this, it is also required that accurate phenotyping is performed to support the analysis. Ideally functional genomic studies include all these steps.

Associating genotype with phenotype

The first step is typically some form of genotype-
phenotype correlation analysis (e.g. QTL mapping or
GWAS, see Section 4: Genetic architecture of quantita-
tive traits) to identify a statistical association. Yet, these
<figure><img src="image_8.png" /><figcaption>FIGURE 5 Publications (up to April 2024) related to functional genomic studies done on three groups of commercially farmed insects (data curated from Web of Science, excluding publications related to insect-fed livestock). Cricket refers to five species commonly reared for food and feed (*Brachytrupes membranaceus*, *Gryllus assimilis*, *Gryllus bimaculatus*, *Gryllotalpa orientalis* and *Acheta domesticus*).</figcaption></figure>

approaches do not identify the underlying molecular mechanisms causing the observed phenotypic variation. QTL mapping studies identify large effect loci which can then be scanned for genic content. Resolution is coarse and unlikely to yield unique candidate genes and gene variants, however, promising candidates can then be further characterised through analysis of gene functions and pathways/networks. Re-sequencing these genes may further highlight DNA sequence variants that can be explored in candidate gene fine-mapping association studies (e.g. Hull et al., 2024). The impact on sequence variants (e.g. SNPs and indels) can be investigated through *in silico* strategies to find, for example, if a base pair substitution leads to an alteration in amino acid sequence and peptide folding. Various *in silico* approaches have also been developed to find conserved regulatory elements (e.g. promoter region motifs and other ‘enhancer’ sequences in the 5’- or 3’-untranslated regions or intron splicing sites).

## Gene expression analysis

Transcriptome analyses is a complementary approach to screen for genes showing expression differences associated with a phenotype, providing insights into when and where mRNA of different genes is accumulated in an organism (Singh and Verma, 2022). For example, the increase in mRNA levels of specific genes in different tissues of silkworms served to identify and describe the regulation of silk gland activity (Masuoka

et al., 2022; Yokoi et al., 2021), while RNA sequencing (RNAseq) under stress conditions served to link Dipteran transcriptomics to phenotypes and indicate a potential role of genes responding to the stressor (de Oliveira et al., 2021; Hull et al., 2023; Yadav et al., 2017). Similarly, transcriptional profiling in BSF has proven useful to explore chemoreception of different developmental stages (Scieuzo et al., 2021) or in a sex-specific context during mating or oviposition (Xu et al., 2020), the regulatory basis of nutrient, energy and particularly lipid metabolism during larval growth stages (Giannetto et al., 2020; Peng et al., 2023; Sukmak et al., 2024; Zhu et al., 2019), or specific immune responses (Moretta et al., 2020; Vogel et al., 2018) that possibly induce production trade-offs (Shah et al., 2024), as well as gene expression differentials according to selection for increased growth (Hull et al., 2023) or temperature tolerance (Ma et al., 2024). The relevance of gene regulation is illustrated in the silkworm (Tong et al., 2022) where large parts (55%) of the structural variation found in pan-genomes affected regulatory regions. Importantly, transcriptomes do not necessarily identify causal variants underlying a change in phenotype – for example alteration in expression of a transcription factor could affect expression levels of many downstream genes. Therefore, QTL/GWAS approaches are highly complementary to transcriptome experiments.

Measuring gene expression via transcription is one of the most accessible and widely utilised approaches
for investigating gene function and regulation, with a wide array of tools from high-throughput RNAseq of whole transcriptomes (De Wit et al., 2012) to targeted techniques such as qPCR. Proteins are in most cases functional molecules and can be measured using Western blots, ELISA, and mass spectrometry-based methodology. These approaches do not always produce concordant results, in part because abundance of mRNA does not necessarily translate to abundance of proteins: abundance does not reveal much about turnover dynamics (transcription rate, translation rate, degradation rate). For example, measuring the expression of *vitellogenin*, a gene crucial for egg development and oogenesis, in the yellow mealworm in response to a molecule secreted by a parasite (Warr et al., 2006) found significantly higher mRNA abundance in infected beetles compared to uninfected controls, nevertheless, associated protein levels were unexpectedly lower.

Given the rapid advancement of sequencing technologies, differential gene expression studies using whole transcriptome sequencing (via RNAseq) is the most common approach. For example, Hull et al. (2023) used RNAseq to investigate differential gene expression between selected and unselected lines of BSF to find genes associated with larval growth. RT-PCR can be applied to candidates to expand sampling and validate the correlation between transcription and phenotype. Measuring gene expression using RT-PCR comes with substantial technical and biological variation (as gene expression is extremely sensitive to environmental conditions). Thus, care must be taken to randomise and normalise intermediate steps in the procedures, by investigating and validating reference genes specifically for each treatment (Gao et al., 2019; Rhode and Greenwood, 2023) or applying other measures to account for systematic, technical and random variance (Heckmann et al., 2011).

Careful interpretation of the generated data is needed. A major challenge of linking transcriptomic data to gene function is that data can be noisy due to random interactions among genes, their products, and environmental factors. The existence of segregating gene variants might not necessarily be manifested as differential gene expression in a transcriptomic study, and differential gene expression may not be detected in studies of genetic diversity. Redundancy in genetic pathways poses another challenge, as multiple genes can compensate for each other's functions (Generalovic et al., 2025b). Finally, gene variants and differential gene expression with critical effects in specialised tissues or developmental stages might easily be overlooked in

whole body extractions of single stages (Oppert et al., 2023; Scieuzo et al., 2021).

Fully dissecting the functional impact of genotypes on phenotypes necessitates a wider look at multiple tiers of biological organisation. Integrating transcriptomic data with other ‘omics’ – such as proteomics, metabolomics, and epigenomic scans – helps constructing a comprehensive view of gene functions (Gallagher and Chen-Plotkin, 2018; Maji and Garg, 2013). The number of proteomics studies in BSF has increased (e.g. Bose et al., 2023; Lu et al., 2021; Rabani et al., 2019). Other studies propose or apply a systems genomics approach with the integration of various multi-omics data (Aagaard et al., 2022; Kadarmideen, 2014; Suravajhala et al., 2016). Next to successful studies (Lecheta et al., 2020), in some cases the integration of data across levels of biological organisation does not provide clear functional answers (Malmendal et al., 2013). Enzyme concentration does not necessarily predict enzyme activity, and resulting metabolites (collectively the metabolome) is complex in function and devoid of info on the dynamics. Accordingly, the decrease of a metabolite under certain conditions can signify decreased production (less need) or increased consumption (more need). As such, no change (at any level) could cover a range of vastly different conditions with different biological consequences but is unlikely to be detected.

Linking gene expression changes directly, e.g. to a response to a stressor relative to wider mechanistic consequence of the treatment can be challenging. For example, while high fold change induction of heat shock proteins correlates well with exposure to increased temperature, causal interpretation is less obvious. Gene expression up-regulation could reflect capability to accommodate stress, or that the organism is severely stressed and barely able to withstand (Morfin et al., 2023; Sørensen, 2010). Finally, though tempting, equating fold changes with biological significance is not necessarily meaningful, as modest regulation of key genes could have huge effects.

To be expressed, a coding region must be accessible for transcription. Epigenetic processes modulate gene expression, with phenotypic consequences. These modifications can be genetically controlled or induced by the environment; for instance, DNA methylation patterns in the house cricket upon exposure to graphene oxide results in multigenerational oxidative stress (Flasz et al., 2023a,b). Polyurethane foam-feeding yellow mealworm results in stage-specific changes in mitochondrial DNA methylation patterns associated with decreased
ATP synthesis (Guo et al., 2019). Further, leg regeneration in Mediterranean field crickets is epigenetically regulated by histone H3K27 methylation (Hamada et al., 2015). Silkworms have exhibited increased DNA methylation during domestication and reduced hatchability when a DNA methyltransferase (Dnmt1) is knocked down suggests important adaption to managed environments (Xiang et al., 2013). Epigenetic profiling studies in insects identified increased expression of epigenetic modification enzymes in the reproductive organs of domesticated silkworm populations (Gao et al., 2020) and the role of epigenetic control of growth in the red flour beetle (George et al., 2019). Given the supposed high relevance particularly in production insects (Mukherjee and Vilcinskas, 2019), further investigations into the epigenome of the core insects as food and feed species will develop our understanding of the high phenotypic plasticity observed (de Carvalho, 2023) that is rarely directly ascribable to DNA sequence variation (Glastad et al., 2019). Epigenetics and methylation studies through whole genome bisulphite sequencing (commonly used to detect DNA modification), are still relatively scarce in insects, except for social insects (Adusumalli et al., 2015; Yagound et al., 2020). Generally being lower than in mammals, levels of cytosine methylation vary substantially across arthropods, and Dipterans (e.g. BSF) seem to have lost this main methylation mechanism, suggesting other mechanisms operate in this order (Bewick et al., 2017). In a breeding context, to mitigate unwanted 'maternal' effects, e.g. in G × E studies, it is recommended to rear experimental populations under equal conditions for multiple generations prior to the trial (Sandrock et al., 2022).

## Bioinformatic prediction of function

Several bioinformatic tools can improve our understanding of candidate gene roles and assist experimental verification of gene functions. Functional interpretation of candidate genes can be restricted by poor annotations. Gene functions can be predicted by comparison to genes of interest with known sequences in databases (e.g. BLAST). Numerous specialised databases are available to support insect genomics research. For example, the Insect Cytochrome P450 Database contains 66 513 P450 genes from more than 680 insect species serving as a comprehensive resource for predicting gene function and aiding to understand their evolution in insects (Wu et al., 2024). Other resources include the CAZy (Cantarel et al., 2009) and iCAZyGFADB databases which integrate multiple annotation tools for genomic and transcriptomic data to identify carbohydrate active enzymes

(Fu and Yang, 2023), the InsectBase 2.0 which provides data on more than 800 insect genome and 25 000 transcriptomes covering more than a million of annotated genes (Mei et al., 2021), and the AlphaFold prediction tool for protein structure and function (Jumper et al., 2021). Generally, the integration of multiple databases can enhance accuracy of prediction (Marques de Castro et al., 2022; Shim et al., 2017), yet bioinformatics-based prediction has limitations: databases capture only publicly available information. Even though a lack of functional evidence in databases does not necessarily mean a computational prediction is incorrect, it necessitates further "wet" functional genomics studies to characterise gene activity and possible roles in cellular processes (Kikuchi et al., 2017; Urzúa-Traslaviña et al., 2021).

## Functional characterisation of candidate loci

The ultimate goal is to experimentally verify the effect of a particular gene or genetic variant on function. One approach is RNAi, which exploits a naturally occurring biological process wherein small RNA molecules inhibit protein expression by neutralising targeted mRNA. Researchers can leverage this molecular machinery to knock down genes of interest and gain insight into their function (Bellés, 2010). RNAi was utilised to explore metallothionein genes in BSF larvae, potentially responsible for metabolising cadmium, to better understand mechanisms relevant for bioremediation (Zhang et al., 2021). By systematically silencing three of these genes upon cadmium-exposure, knock out of one gene (BSFMT2B) decreased larval weight and increased mortality, suggesting its role in cadmium detoxification and tolerance.

In addition to RNAi, CRISPR/Cas9 technology has been widely used in insect functional genomics (Shirai et al., 2022), including the vinegar fly (Bier et al., 2018; Sun et al., 2015), the fall armyworm, Spodoptera frugiperda (Wang et al., 2024), and the Mediterranean field cricket (Matsuoka et al., 2024). The technology enables targeted mutagenesis and can be used to precisely knock out a gene to understand its function (Hillary and Ceasar, 2024). It has also been used to insert a reporter gene into specific loci enabling visualisation of expression patterns (Lo and Matthews, 2023). CRISPR gene editing has been successfully carried out in BSF, yellow mealworm, and various cricket species (Bai et al., 2023; Chen et al., 2023; Deng et al., 2023; Dossey et al., 2023; Extavour et al., 2019; Gunther et al., 2024; Inoue et al., 2023; Nakamura et al., 2022; Oppert et al., 2023; Sui et al., 2024; Zhan et al., 2020). One application of this technology, in both research and breeding
efforts, are visible marker genes, e.g. affecting pigmentation. Easily observable traits can be linked to genes of interest to track genetic modifications and breeding outcomes. To date, two genetic markers have been investigated in *BSF*: mutations in members of the yellow gene family resulting in a pale-yellow phenotype (Dong *et al.*, 2024) and heritable mutation of the white gene, commonly used as a marker in other insect models, resulting in a white-eye phenotype in larvae and adults (Sui *et al.*, 2024).

Prior to CRISPR, most genetic modification involved insertion of genetic constructs into random genomic locations. A common approach is to use the piggyBAC vector to insert a gene of interest with a promoter sequence, and commonly also a marker such as GFP. Recent work has established a system for over expression of target genes in BSF (Kou *et al.*, 2023). Various overexpression systems have been reported and include viral transduction systems such as the *Anopheles gambiae* densovirus (AgDNV), which allowed high gene expression levels in the major malaria mosquito (Suzuki *et al.*, 2014). In the vinegar fly, overexpression of targeted genes can also be achieved using the GAL4-UAS system: female flies carry the gene of interest under the UAS (Upstream Activation Sequence) promoter and are crossed with males expressing the GAL4 transcription factor. GAL4 binds specifically to the UAS which acts as a promoter only in the presence of GAL4 leading to the overexpression of the targeted gene (Brand and Perrimon, 1993; Duffy, 2002). The system does not only allow the over-expression of *D. melanoagster* genes but has also been used to express human genes in the vinegar fly model (Ma *et al.*, 2003).

Animals generally tolerate genome changes much less than plants, and there is a risk that downregulating or knocking out a gene will result in inferior performance, due to the manipulation making the organism generally weaker rather than the gene being directly related to a phenotype in question. Approaches to accommodate this problem include knock-down/knock-out of additionally randomly selected genes, screening additional non-targeted phenotypes, or showing that the phenotype can be improved by over-expression. Ultimately, compared to easier knockout experiments, using knock-ins to replace natural variants and investigate their effect on the phenotype are biologically more meaningful. Gene editing aids characterising functional causalities to inform selective breeding on causal genes or even natural variants relevant for variation in specific production traits. However, natural gene variants could also be uniquely altered or

interchanged depending on genetic architectures and population genetic background, respectively. In this latter case, genetic modification can represent a form of genetic improvement, synonymous to the goal in classical breeding.

*The challenge of high throughput phenotyping*

Both initial QTL/GWAS screens and subsequent phenotyping of functional experiments such as gene knock-outs rely on statistical associations between phenotype and genotype. One challenge is to obtain large samples of phenotyped individuals required. In insect breeding research, the generation of fast, precise, and high-throughput phenotyping is the major bottleneck in experimental and commercial workflows, in comparison to obtaining high quality genome sequence data, which is now relatively straightforward. Technical developments allowing high throughput phenotyping approaches are especially needed for quantitative traits, where numerous genes are involved and large samples are needed to identify associations with phenotypes, especially for traits that are expressed differently in different contexts (Houle *et al.*, 2010).

However, promising research in phenomics in other high density and high-throughput animal production systems, like aquaculture, might inspire solutions for insect farming (e.g. Freitas *et al.*, 2023; Fu and Yuna, 2022). High-throughput phenotyping in insect models has been demonstrated (Hansen *et al.*, 2024a; Laursen *et al.*, 2021; MacLean *et al.*, 2022). Population-level studies frequently reveal a complex array of gene-environment interactions. These interactions can differ significantly among various populations, across different developmental stages, and within distinct tissue types of the same species (Houle *et al.*, 2010). Investigating population-level variations often uncovers complex patterns where numerous genes interact with environmental factors to influence phenotypic outcomes (Wilson *et al.*, 2003). In insects, these complex interactions are further confounded by species reproductive behaviour and life history traits that make insect populations highly dynamic and often subject to chance demographic shifts (see Section 3: Evolutionary forces) over temporal and spatial scales, that can quickly alter the genetic background of a population (Hull *et al.*, 2024; Kaya *et al.*, 2021; Rhode *et al.*, 2020).

Taken together, complementary work on model insect species will be largely transferable and should inform future efforts. Nevertheless, particularly in a breeding context, awareness on presumably widespread highly population-specific responses as well as respec-
tive developmental and environmental interactions is
crucial. Generation of sequences and other massive
'omics' datasets is no longer the bottleneck, while com-
petences and resources for analyses and accurate phe-
notyping increasingly are.

6 Discussion

The study of genetics of insects farmed for food and
feed is a recent research discipline. While it is rapidly
expanding with dedicated societies, conferences, jour-
nals, and an increasing number of papers, there is still
much to be learned. The development of the research
area builds on well-established traditions in, on the
one hand, quantitative genetics and breeding of domes-
tic animals and, on the other hand, experimental and
fundamental genetic research on model insect species.
However, insects are an evolutionarily old and function-
ally diverse group, with major orders, such as Coleoptera
(beetles) and Diptera (flies and mosquitoes), having
several hundred million years of independent evolu-
tion. Two important consequences arise from this. First,
insects show diverse biological characteristics that dif-
fer in many aspects from traditional livestock (e.g. cows,
pigs, chicken), which often prevents direct translation
of knowledge. Second, the diversity in insect repro-
ductive systems and life-histories means that extrapo-
lation among species demands careful verification. In
this article of the BugBook, we have compiled the cur-
rent knowledge of using genetics to explore evolution-
ary histories, analyse contextual population genetic pat-
terns and functional impacts, and inform the manage-
ment and selective breeding of farmed insects. This goal
is what conceptually unites the four thematic sections
on genetic research and their differentiated approaches
and analyses outlined above.

Insect biology

Insects are small, have short generation times, pro-
duce numerous progenies, and are ectotherms, all of
which separates them from most traditional livestock.
In addition to fundamental developmental differences
between holo- and hemimetabolous insects, their var-
ious natural ecological niches imply that they have
evolved a wide range of adaptations to thrive, includ-
ing specific behaviour and cognitive abilities. Farming
management practices thus need to account for these
adaptations in a welfare context. The diverse biologi-
cal features of insects produced for food and feed come
with specific challenges. Insect researchers and breeders

should thus aim to acquire in-depth knowledge on their
physiology, metabolism, health, behaviour, and repro-
ductive biology, and methods tailored for the species in
question to enable genetic improvement through appli-
cation of molecular genetics methods. Examples of key
genetic information needed for effective insect man-
agement and breeding includes assessing population
structure and evolutionary history, unveiling G × E inter-
actions and which and how genes regulate the main
production traits, identifying key immune system com-
ponents and functions to prevent and combat infections
and diseases, and resolving sex determination with the
aim of manipulating population sex ratios. Research on
genetics and fundamental biology are thus interdepen-
dent, and basic knowledge is still scarce for most pro-
duction species, hence advancements in both domains
are essential for progress in the field of insect breeding.

Genetic basis of production traits

Although many production traits are related to Dar-
winian fitness, not all evolutionarily relevant life-history
traits are important in commercial production settings.
This means that insights into the genetic basis of insect
life-history traits obtained from fundamental research
on insect models may only be partially transferable to
production insects. Hence, the focus should be placed
more on fundamental research on genetic regulation of
commercially relevant traits. Study designs aiming to
unravel the relationship between genotypes and phe-
notypes (the genotype-to-phenotype map) are recom-
mended. Most traits of interest of farmed insects are
related to their efficient production, such as simulta-
neous maximisation of growth and minimisation of
food intake and development time. Similar to optimised
feed conversion efficiency, many production traits are
expected to be shaped by a complex interplay of fac-
tors that trigger numerous molecular pathways, and
thus to be polygenic. Investigating such composite traits
requires description of the trait components, includ-
ing functional characterisation of involved organs and
tissue, and applying quantitative genetic and genomic
approaches to identify underlying trait architecture and
regulatory networks of the involved genes. We encour-
age the community to take advantage of the many
opportunities for generating large amounts of data,
enabled by the technological advances, to make signif-
icant progress in elucidating the genetic basis of pro-
duction traits. Further, consulting extensive research on
several model insect species is advised to continuously
complement our knowledge and inform future efforts.
## Measuring phenotypes

Measuring phenotypes is a crucial aspect of farming insects and an important component of any selective breeding programme. It is not only a form of quality control or assessment of phenotypic progress but can also aid management by alerting on any potential problems in the production facilities. A hallmark of insect life cycles are the transitions between vastly different developmental stages, which require species-specific as well as stage-specific phenotyping methods. For example, flies and beetles are mostly harvested in their larval stage, whereas crickets and grasshoppers are reared through their juvenile nymphal stages until adulthood for harvest. Although measuring individual phenotypes of insects in large numbers and over time remains a challenge, we will benefit from using novel automated, sensor-based, and AI-assisted methods currently being developed to generate large and informative datasets in short time. In research or production settings, efforts should be made to obtain accurate trait measures to enable identification of genetic basis of production traits, provide insights into functional genetic correlations between traits, and ultimately inform breeding to ensure genetic progress. To improve breeding outcomes, we recommend leveraging species-wide population genetic data. Since genetic makeup influences trait expression and breeding response, focusing only on current production strains, often with unclear or comparatively narrow ancestry, may limit success. Consulting global population genetic inventories can enhance screening efficiency and reveal a broader range of valuable phenotypes.

## Environmental effects, interaction with genetics and selection trade-offs

Given the major role environment plays in insect performance, not only control of its various relevant components in a production setting is crucial, as covered in several other dedicated BugBook articles, but also a profound understanding of how parameters need to be accounted for to mitigate impairment of efficient selection. Researchers and breeders are urged to keep environmental effects constant and reproducible when setting up selection experiments and measuring phenotypes. Another challenge in the field is scale, since general solutions for translating research findings from small numbers of insects in research laboratories to industrial contexts are virtually non-existent. To bridge the gap and extrapolate breeding progress from laboratory nucleus populations to production settings, we suggest installing replicate (quasi-)industrial

facilities serving for experimental research in insect-breeding. Future approaches should also address the role and impact of environment- and scale-mediated epigenetic effects involved in plasticity responses of the various commercially farmed insects. Overlooking putatively important transgenerational non-additive genetic drivers could seriously hamper breeding progress.

Likewise, there is still a lot to be learned about the role of the environment in interaction with the genetics of insects for food and feed, which may in fact entail multiple levels of interactions, e.g. including microbiota possibly influenced by both diet and insect genetic background (Greenwood et al., 2021; Silvaraju et al., 2024). Optimising production tailored at market demands requires in-depth explorations of G × E interactions and their causal mechanisms. To ensure this, we champion the generation of in-depth knowledge on species-wide population structure, as well as on the evolutionary drivers underlying species-specific population genetic stratification. In some applied production contexts, selecting strains for maximising performance on a specific diet and otherwise constant conditions will increase economic revenue. Conversely, in settings that cannot be fully controlled or deliberately seek variable dietary substrates, generalist strains may outperform specialists, and/or allow less variable and thus more steady and predictable performance across a range of conditions. We stress that G × E interactions must be considered in breeding schemes tailored to industrial settings. Ignoring potential interactions can lead to important divergences from the expected results in the farm. In one scenario, the nucleus, centre of genetic improvement, must provide the multiplier and, ultimately, the production farm with genetically improved material. With the aim of increasing efficiency of the nucleus, the actual breeding environment is generally, although not advisably so, more favourable and/or controlled than the production site (control of abiotic conditions, diet quality, feeding regime, rearing density, pathogens and other stressors). For instance, with nucleus selection for improved conversion efficiency on concentrate feed, comparable trait gains are unlikely to be transferred to production settings using poor diets. Dedicated research is therefore needed to explore the transferability of genetic improvement to ensure selective breeding does not bypass actually decisive parameters, ultimately compromising long-term output of global farms.

Moreover, selective breeding is not an isolated process, where traits can be improved independently. Improvement in one specific trait leads to correlated
responses in others, either positive or negative, result-
ing in trade-offs. Both positive and negative correlations
can be considered neutral, favourable, or unfavourable,
depending on the context and may require adjusted
management or product processing. For instance, re-
duced adult longevity with equal reproductive output
upon increased larval biomass might be acceptable
when larval protein is the production target, making
this a negative, neutral correlation. Other trade-offs
may unfavourably affect yield and need to be identified
and carefully evaluated. A positive unfavourable corre-
lation is exemplified when higher nitrogen conversion
efficiency increases both larval growth and relative fat
contents – population level fitness will likely increase,
but aggravated lipid extraction may substantially affect
product quality. Another potentially unfavourable cor-
relation might result from selecting for increased anti-
microbial peptide production in insects, which could be
a boon for livestock feeding (Xia *et al.*, 2021). However,
resource costly insect immune responses may com-
promise growth and population fitness (Joosten *et al.*,
2020), and affect public perception on welfare aspects
(Kortsmit *et al.*, 2023).

Genetic monitoring and management of breeding
populations

To improve and optimise insect breeding programmes
and genetic management of populations, we emphasise
genetic research shall also focus on unveiling genetic
structure and thus relationships between subpopula-
tions. Many of the genetic marker techniques developed
for other organisms can readily be applied to quan-
tify and monitor genetic variation in insects produced
for food and feed. So far, much work has concentrated
on a few species and populations/stocks. Based on
improved insights into species-wide population strati-
fication, future studies should ideally include a much
wider selection of differentiated source populations to
capture the true genetic scope of each species. The
choice of methodology, sampling, and design should
be tailored towards the goal of the application, and
techniques and protocols should be developed and val-
idated for the species and the aim in question. This
calls for developing protocols for non-destructive geno-
typing of breeding candidates without compromising
performance or reproductive success. Targeted genetic
management, comparable to conventional livestock,
requires a deep understanding of the evolutionary pat-
terns, and ecological and demographic factors shaping
population genetic structure across hierarchical levels,
including wild populations and managed stocks. We

opine that exploiting the genetic potential of a species
as a whole is pivotal for a better understanding of
genome-wide signatures of insect domestication and
phenotypic consequences in a production context.

Data sharing

We encourage increasing transparency and data shar-
ing through publications to advance the field and cre-
ate long-term benefits for all breeders and the entire
insect production industry. Further, future studies on
the various non-genetic research disciplines related to
insects as food and feed are urged to report basic geno-
typing data by applying available low-tech/cost genetic
markers to help generating useful metadata. Specifi-
cally, reckoning that insect genetics has the potential
to influence product quality up to performance at the
next trophic level (see Sandrock et al., 2022), continuing
to ignore possible interactions between insect genet-
ics and production parameters as well as product pro-
cessing and/or features could turn out as a veritable
shortcoming for the sector. In addition, the field would
benefit from more openness on the research of genet-
ics of farmed insects, for example by breeding compa-
nies. Technological advances in genetic analyses provide
much opportunity for improving breeding programmes.
Sharing experiences on shaping the research question
and collecting and analysing phenotyping and genotype
data will increase the likelihood of success. We recom-
mend collaborative initiatives and coordinated interna-
tional consortium efforts that will allow more ambitious
research programmes, including larger scales (insect
numbers), and enable tackling some of the major chal-
lenges to advance the field.

7 Conclusion

Despite the challenges discussed in this article, much
progress has been achieved in the genetics of insects for
food and feed. We believe that there is great promise
for the continued development and improvement of
research in this field. However, we also need to learn
from the past in order to efficiently make progress. The
advancement of technology not only pushed the bound-
aries of what can be achieved on model and non-model
species but also changed the typical research bottleneck
from generating genetic/genomic data to their analy-
sis and providing accurate phenotypes associated with
the genetic variation. Main challenges for the future
include validating findings in small scale studies when
upscaled to mass production conditions, assessing and
controlling the effect of environmental conditions and its interaction with genetics (and identifying the impor- tant drivers), characterising and exploiting beneficial genetic variation for trait optimisation, and balancing genetic improvement with inbreeding and maintaining genetic variation and robustness of cultures. Another point of focus is the huge differences among species (in life history, population structure, and evolutionary history) that can affect the outcome of applying vari- ous techniques and approaches. Importantly, claiming that the insect sector has understood how to avoid mis- takes that were made in conventional livestock breeding deserves a responsible debate on controversial aspects like genome editing and insect welfare. Similarly, high- lighting sustainability improvement as the sector's basic motivation deserves dedicated biodiversity considera- tions, which could range from protecting wild genetic resources from excessive intra-specific introgression to monitoring threats to ecosystems through inter-specific pathogen spillover or invasion potentials. Another ver- itable challenge is that a lot of knowledge, experience, and know-how exists among the many people work- ing with insect for food and feed, however, much of this expertise is not published and thus not available for the broader research and industry communities. As a final note, we would like to mention that while the genetic improvement of farmed insects is still in its infancy, barely entering the domestication phase, genet- ics research and systematic breeding is undoubtedly the most powerful tool to further accelerate the transition to circular agriculture supported by insects. By unlocking their genetic potential, it is possible to enhance produc- tivity, sustainability, and adaptability in insect farming. This represents a key opportunity to address global chal- lenges in food security and environmental conservation, and there is an enormous amount of untapped insect diversity that may be exploited for food and feed pro- duction in the future.

**Supplementary materials**

Data is available on https://doi.org/10.1163/23524588-bja10260 under Supplementary Materials.

**Acknowledgements**

This paper is part of the BugBook project, initiated by the working group on Standardisation of meth- ods, parameters and terminology in insect research of

the EAAP insects commission. This article is based upon work from the COST Action “Improved Knowl- edge Transfer for Sustainable Insect Breeding (Insect-IMP)”, CA22140, supported by COST (European Coop- eration in Science and Technology), including open access publication. The following authors acknowledge support: CJP in part by the National Science Foun- dation through the Industry-University Cooperative Research Centers (IUCRC) NSF cooperative agreement IIP-2052565. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. JGS by grants from the Danish Council for Independent Research (DFF-1127-00081B) and the European Union's Horizon Europe 'Green ERA-Hub' project (No. 101056828) funded by the Green Development and Demonstration Pro- gramme, GUDP, under the Ministry of Food, Agriculture and Fisheries of Denmark (34009-23-2234). JO by the core financing of the Slovenian Research and Inno- vation Agency (program, P4-0133 "Sustainable agricul- ture"). KP, ES and TL by the ŶnFABRE project, funded by BPI France (grant number DOS0155720-21). LSH by grants from the Danish Council for Independent Research (DFF-0136-00171B), the Green Development and Demonstration Programme, GUDP, under the Min- istry of Food, Agriculture and Fisheries of Denmark (34009-21-1933), and DANIDA under the Ministry of Foreign Affairs, Denmark (21-09-AU). LWB by the Dutch Research Council (NWO; NWA programme, InsectFeed project, NWA.1160.18.144). WY by the German Ministry for Education and Research (BMBF) (CUBES project; grant number 031B0733A).

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