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# Genetic and transcriptomic analysis of a Saccharina latissima segregating F2 family detects groups of individuals with different responses to temperature stress

Zofia Nehr, Komlan Avia, Olivier Godfroy, Bertrand Jacquemin, Stephane Mauger, Perla Farhat, Emilie Gouhier, Jerome Coudret, Antoine Fort, Ronan Sulpice, et al.

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Zofia Nehr, Komlan Avia, Olivier Godfroy, Bertrand Jacquemin, Stephane Mauger, et al.. Genetic and transcriptomic analysis of a Saccharina latissima segregating F2 family detects groups of individuals with different responses to temperature stress. Algal Research - Biomass, Biofuels and Bioproducts, 2025, 91, pp.104203. 10.1016/j.algal.2025.104203. hal-05187351

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https://cnrs.hal.science/hal-05187351v1

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# Genetic and transcriptomic analysis of a *Saccharina latissima* segregating F2 family detects groups of individuals with different responses to temperature stress

Zofia Nehr<sup>a,1</sup>, Komlan Avia<sup>a,b,c,1</sup>, Olivier Godfroy<sup>a</sup>, Bertrand Jacquemin<sup>b</sup>, Stephane Mauger<sup>b,2</sup>, Perla Farhat<sup>a</sup>, Emilie Gouhier<sup>b</sup>, Jerome Coudret<sup>b</sup>, Antoine Fort<sup>d</sup>, Ronan Sulpice<sup>d</sup>, Corinne Cruaud<sup>e</sup>, Lucie Jaugeon<sup>b</sup>, Philippe Potin<sup>a</sup>, Myriam Valero<sup>b</sup>, J. Mark Cock<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Genetic map

Quantitative trait locus

*Saccharina latissima*

Seaweed

Temperature stress

Transcriptomics

## ABSTRACT

*Saccharina latissima* is an important species for the emerging seaweed aquaculture industry in the northern Atlantic. In this study, genotype and phenotype data for a segregating F2 family was used to generate a high-density genetic map for *S. latissima* and to identify temperature-stress-related quantitative trait loci (QTLs). A temperature stress experiment detected distinct phenotypic classes with different stress responses within the F2 family and these phenotypic classes were shown to exhibit different transcriptomic responses to heat stress. The existence of heat tolerant, resilient and sensitive individuals within this segregating family indicates that it should be possible to breed temperature tolerant strains of *S. latissima*. This conclusion was supported by the identification of three QTLs that influenced recovery after temperature stress. The results of this study and the resources generated, in particular the high-density genetic map, represent an important foundation for future *S. latissima* breeding programs.

## 1. Introduction

Seaweed cultivation is attracting increasing interest as a means to efficiently and sustainably produce biomass for human consumption and other applications [1-3]. Over recent decades, the most spectacular developments of seaweed farming have occurred in Asia [3-5] but there are emerging seaweed cultivation industries in other parts of the world, particularly Europe and in both North and South America [3,6,7].

In the northern Atlantic, the most commonly farmed species [2] is *Saccharina latissima* (Linnaeus) C.E. Lane, C. Mayes, Druehl & G.W. Saunders [8] or sugar kelp. This brown seaweed is a perennial, principally subtidal, rocky shore species that occurs as widespread natural populations in the northern Atlantic, northern Pacific and Arctic oceans

[9-11]. Its life cycle involves an alternation between a large sporophyte stage that can grow up to 4 m in length and a microscopic, dioecious (i.e. separate haploid male and female thalli) gametophyte stage [10,12]. *S. latissima* grows rapidly in open sea farms, where it is principally produced for human consumption, but this seaweed also represents a source of alginates and has additional uses for cosmetics, animal feed or fertiliser [2,13].

Existing *S. latissima* farms rely almost exclusively on seeding material obtained from wild sugar kelp populations [14,15]. This approach not only implies a reliance on natural populations, which are increasingly impacted by the effects of global heating due to climate change [16-21], but it also means that cultivation uses wild strains that have not been domesticated and adapted for farming [22]. This is in contrast to the

<footnote><sup>a</sup> Sorbonne Université, CNRS, Integrative Biology of Marine Models Laboratory, Station Biologique de Roscoff, Roscoff, France</footnote>

<footnote><sup>b</sup> IRL 3614, UMR 7144, DISEEM, CNRS, Sorbonne Université, Station Biologique de Roscoff, Roscoff 29688, France</footnote>

<footnote><sup>c</sup> INRAE, Université de Strasbourg, UMR SVQV, 68000 Colmar, France</footnote>

<footnote><sup>d</sup> Plant Systems Biology Lab, Ryan Institute and Marei Centre for Marine, Climate and Energy, School of Biological & Chemical Sciences, University of Galway, Galway, Ireland</footnote>

<footnote><sup>e</sup> Genoscope, Institute François Jacob, CEA, Université Paris-Saclay, Evry, 91057, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: cock@sb-roscoff.fr (J.M. Cock).</footnote>

<footnote n="1">¹ Joint first authors</footnote>

<footnote n="2">² Current address: Littoral Environnement et Sociétés, UMR 7266, CNRS, La Rochelle Université, France.</footnote>
*Saccharina japonica* industry in Asia, which has benefitted from several decades of breeding to improve cultivated strains [23–33]. However, in recent years programs have been initiated in both Europe and North America with the objective of breeding improved strains for the *S. latissima* cultivation industry [34–38]. One important consideration for these programs is the structured nature of *S. latissima* populations in both Europe and North America [9,11,39–44], which necessitates breeding and domestication strategies that avoid contamination of local wild populations with introduced, non-native germplasm [45].

Improved strains can be bred by directly selecting for phenotypic improvements but the breeding process is more efficient if it also integrates genotype information. To implement such a combined approach, it is necessary to establish genetic and genomic resources for the species of interest, resources that would ideally include a genome sequence, information about genetic variants, a genetic map and methodologies to implement genetic approaches such as the detection of quantitative trait loci (QTLs), for example. A good quality, annotated genome assembly has recently been made available for *S. latissima* [46]. In this study, we generated a segregating F2 family for *S. latissima* and have used this resource to construct a high-density, SNP-based genetic map and to develop methodology for QTL detection. The specific objective of the study was to evaluate the effects of genetic background on growth and stress tolerance in *S. latissima*. We report the detection of three QTLs related to recovery from temperature stress. A transcriptomic analysis of F2 individuals that exhibited markedly different responses to temperature stress allowed the identification of differentially expressed genes (DEGs) associated with tolerance and resilience to this stress. The information obtained by this study represents an important resource for future breeding programs aimed at developing improved *S. latissima* cultivars, particularly lines with improved growth and/or temperature tolerance characteristics.

## 2. Materials and methods

### 2.1. Establishment of a segregating family of F2 sporophytes

The segregating F2 family was derived from two *S. latissima* sporophytes (Fig. 1) collected at Perharidy near Roscoff (north Finistère, Brittany, France; latitude 48.73087, longitude -4.004526; January 2016; strain SLPER139) and Saint-Guénolé (south Finistère, Brittany, France; latitude 47.820255, longitude -4.366666; October 2015, strain SLSTG055). The two sporophytes were from regions with different surface sea temperature ranges [47] and, based on genotyping of samples from several sites around the coast of Brittany and Normandy [42], were from genetically distinct *S. latissima* populations (Fig. 1). The two field-collected sporophytes were induced to sporulate in the laboratory by simulating low tide as follows: 1 cm² explants were excised from the fertile parts of the blades (sori), wiped clean, rinsed in autoclaved seawater and then wiped dry. The explants were placed at 13 °C in the dark for 6–8 h to induce spore release, then in 50 mL Falcon tubes filled with natural sea water and containing a glass slide, and agitated overnight in the dark at 13 °C. Spore release was verified by examining the glass slide under a light microscope (Olympus, Lyon, France) and slides that had fixed spores were placed in filtered, sterilised [48] Provasoli-enriched [49] natural seawater (PES) in a 90 mm diameter Petri dish. The gametophytes derived from these spores were grown under continuous red light (10 µmol m⁻² s⁻¹) at 15 °C, conditions that allow the gametophytes to grow without inducing fertility [50]. The red light was from Osram 18 W/840 fluorescent “daylight” neon tubes (Osram GmbH, Germany) filtered through primary red lighting gelatine sheet (reference 106, Lee Filters, United Kingdom). Irradiances were measured using a LI-180 LI-COR spectrometer (LI-COR Biosciences GmbH, Germany).

Mixes of male and female gametophytes from the two strains were combined and inoculated onto lengths of cotton string. For this, three lengths of 100% cotton string (2 m in length, 3 mm diameter, knotted at each end) were prepared by wrapping them around 10 cm of PVC tubing

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Origin of the *S. latissima* strains. Sampling sites for the two parental strains (indicated by red arrows) shown in relation to seawater temperature fluctuations and the structure of *S. latissima* populations along the coast of Brittany and Normandy, France (population sampling sites are indicated with yellow stars). The colours of the coastal regions, which correspond to the scale bar, indicate mean annual sea surface temperature based on data for the period 1990 to 2012, estimated from [47]. Numbers in brackets indicate temperature fluctuations in each region over the study period [47]. The panel on the right shows discriminant analysis of principal components (DAPC) posterior probability of membership for sporophytes of the five *S. latissima* populations based on 4069 single nucleotide polymorphism markers (modified from [43]), showing that the five populations are genetically distinct. PER, Perharidy (4.004526 W, 48.73087 N); STG, Saint-Guénolé (4.401469 W, 47.820255 N); LOC, Locmariaquer (2.953724 W, 47.491326 N); LAN, Lanildut (4.792607 W, 48.479023 N); LEZ, Lézardrieux (3.016897 W, 48.889614 N); FER, Fermanville (1.486632 W, 49.726228 N). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(16 mm diameter Lucoflex; Dyka, France) that had been notched at each end to receive the knots. The assembled coils were then sterilised overnight under ultraviolet light before being placed in filtered seawater in a 50 mL Falcon tube at 13 °C overnight. The gametophytes were fragmented to facilitate pipetting and attachment to the strings. About half of the seawater was then removed from the Falcon tubes containing the cotton string coils so that 10 mL of the mixed gametophyte suspension could be added and the tube was topped up with about 30 mL of PES (date: 08/03/2016). The Falcon tubes were incubated on their side for 24 h at 13 °C with gentle agitation and the cotton string coils were then removed and allowed to dry for 10 to 20 min before being placed upright in the cover of a 55 mm Petri dish in a 15 L glass aquarium (date: 09/03/2016) supplied with running filtered seawater from an upstream reservoir containing 160 L of natural seawater (filtered at 1 µm) mixed with 1.6 L of concentrated Provasoli supplement and cooled to 11 °C. The inoculated coils were cultured in an aquarium system at 11 °C with 16 h:8 h day:night illumination (approx. 25 µmol m⁻² s⁻¹ provided by four “daylight” Osram 18 W/840 placed above the aquarium) to simulate winter conditions and induce gametogenesis. After 36 days young 3 to 4 mm sporophytes were visible and the cotton string coils were transferred to an aquarium tank (225 × 51 × 15 cm) with 20 L/h circulation of PES, illuminated by six fluorescent tubes. After 30 days under these conditions, the cotton strings were wound around anchored cultivation ropes (200 m of 18 mm diameter rope) starting at about 1.5 m from the end buoy in an open sea culture system located at the Biocean farm in Morlaix Bay, Brittany, France (latitude 48.709483, longitude -3.953283). After six months (April to October 2016), the sporophytes were transferred to a large seawater reservoir (1300 m³) oxygenated with bubbled air under natural light and at ambient seawater temperature (about 13 °C) at the Roscoff Biological Station and maintained for about two months while they became fertile (appearance of sori). A single F1 hybrid sporophyte (designated F1.27.2) was selected by genotyping with microsatellite markers (Table S1; [51]) that detected alleles characteristic of both the north and south Finistère populations and therefore enabled the detection of hybrid progeny. The hybrid F1 individual F1.27.2 sporulated to produce a mix of male and female gametophytes.

To create an F2 sporophyte family, a second bulk gametophyte cross was carried out as described above for the production of the F1 sporophyte generation. The mixture of male and female gametophytes produced by strain F1.27.2 were grown under continuous red light (10 µmol m⁻² s⁻¹) at 15 °C for about 20 days before being seeded onto cotton string and transferred to white light to induce fertility. Lengths of string bearing young sporophytes were then transferred to open sea ropes for cultivation at the Biocean farm in Morlaix Bay, Brittany, France (latitude 48.709483, longitude -3.953283). At the end of the growing period (from the 29th of May to the 18th of September 2017), 129 individuals were harvested for phenotyping and genotyping. The harvested sporophytes were stored overnight in a tank of running natural seawater before the phenotyping and freezing of explants the following day. The F2 sporophytes were photographed directly after harvesting (Fig. S1, Dataset S1). An additional 60 sporophytes derived from this cross were grown in the laboratory in PES in air-bubbled, 10 L carboys (Nalgene, USA) at 13 °C with an illumination of about 25 µmol m⁻² s⁻¹. These 60 individuals were genotyped but were not phenotyped. The F2 sporophyte family was designated F2.27.2 (for F2 generation derived from the F1 sporophyte F1.27.2).

## 2.2. Genotyping using ddRAD-seq

### 2.2.1. DNA extractions

For each extraction, a 17 mm diameter disc of tissue from the meristematic region of the sporophyte blade (Fig. S2) was dried in silica gel for storage. The tissue was disrupted using a TissueLyzer II (Qiagen, Hilden, Germany) (four pulses of 45 s) and DNA was extracted using the NucleoSpin® 96 Plant II kit (Macherey-Nagel GmbH & Co. KG,

Germany). The lysis step was carried out for 15 min at 60 °C with strong agitation to resuspend all the disrupted tissue. Extraction was carried out according to manufacturer's instructions except that we carried out one additional wash with buffer PW1 and one additional wash with buffer PW2 to more effectively remove PCR inhibitors and polysaccharides. DNA was eluted twice with 100 µL of PE Buffer pre-heated to 70 °C. The genomic DNA extracts were purified using the NucleoSpin gDNA Clean-up XS, Micro kit for DNA clean up and concentration (Macherey-Nagel GmbH & Co. KG, Germany). The purifications were performed according to the manufacturer's instructions with elution into 30 µL (2 × 15 µL) of the supplied elution buffer.

### 2.2.2. Construction of ddRAD libraries

The protocol for preparation of double digest restriction-site associated DNA (ddRAD) libraries [52] was adapted from Peterson et al. [53]. The integrity of genomic DNA was assessed by electrophoresis on an agarose gel. DNA concentration was measured using PicoGreen® (Fisher Scientific, Waltham, USA) and adjusted to 2.5 ng·µL⁻¹. The genomic DNA was then digested with PstI and MseI (New England Biolabs, https://www.neb.com, Ipswich, USA), which had been chosen based on in silico digestion simulations carried out with the R [54] package SimRAD [55]. Samples of 100 ng of DNA were digested overnight at 37 °C with 10 U MseI and 10 U PstI in 1× CutSmart buffer (total volume of 50 µL). Digested DNA was purified with AMPure XP beads (Beckman Coulter Genomics, Danvers, USA). Adapters were added using T4 DNA ligase (New England Biolabs, https://www.neb.com/, Ipswich, USA). Illumina flowcell annealing sequences, multiplexing indices and sequencing primer annealing regions were added to all fragments bearing adapters by PCR amplification using the Q5® Hot Start High-Fidelity DNA polymerase kit (New England Biolabs, Ipswich, USA) so that each individual library could be identified with a unique combination of barcode and index. Aliquots (5 µL) of each library were pooled and the pools precipitated with ethanol (2.5 vol. ethanol, 0.1 vol. 3 M sodium acetate pH 5.2). Fragments of between 480 and 930 bp were then selected with a Pippin-Prep kit (Sage Science, Beverly, USA). Both an Agilent® 2100 Bioanalyzer® (Agilent Technologies, Santa Clara, USA) and qPCR were used to quantify the pooled library. The pooled ddRAD library was sequenced (125 bp paired-end reads) on a total of five lanes of an Illumina HiSeq 2500 (chemistry V4) platform to produce 966 million raw reads. Adapters were removed, and the reads were trimmed to 112 bp using Trimmomatic [56]. After demultiplexing, filtering low quality and ambiguous barcodes and trimming adapter contaminations, 660 million reads were retained giving an average of 3.5 million reads per sample and a mean coverage per sample of 16× (Table S2).

## 2.3. Genetic map construction

Genome Analysis Toolkit (GATK) version 4.5.0.0 [57] was used to map paired-end and singleton ddRAD-seq reads generated for the 189 F2 progeny population and their F1 hybrid sporophyte parent onto the S. latissima v1 genome assembly (ENA assembly accession number GCA_964200175) and to identify polymorphic genetic markers. The variant call format (VCF) file produced by GATK was then filtered with bcftools version 1.19 [58] to remove SNPs that were potentially derived from multicopy genes (mean sequence coverage depth per site >4 standard deviations) and loci or samples with >20 % missing data. Loci showing non-Mendelian segregation (χ² test at *p* < 0.05) were also removed. The genetic map was generated with Lep-MAP3 [59] from the final VCF file. Briefly, the ParentCall2 module of Lep-MAP3 was used to call parental genotypes, the SeparateChromosomes2 module was used to split the markers into linkage groups, and the OrderMarkers2 module was used to order the markers within each linkage group using 30 iterations per group, and to then compute the genetic distances. The phased output data were converted into R/qlt format (4way-cross) for QTL mapping. The total genetic length of the genome (Ge) was
calculated by adding twice the average spacing between markers to the
length of each linkage group (LG) to account for chromosome ends
beyond the terminal markers of each LG [60]. The map coverage was
then estimated as the ratio between the observed map length and Ge.

## 2.4. Phenotyping

### 2.4.1. Morphometry

The 129 F2 individuals that were grown in open sea conditions were individually photographed and the following morphometric measurements recorded: stipe length, total length and width at widest point of the harvested blade. Statistical analyses were conducted with R 4.4.1 [54]. The normality of the data distribution was tested using the Shapiro-Wilk normality test. When data were not normally distributed, statistical differences were assessed using the non-parametric Kruskal-Wallis test (the kruskal.test function of the stats package) followed by Dunn's post-hoc pairwise comparisons in the R package FSA [61]. When data were distributed normally, statistical differences were assessed using one-way ANOVA and post-hoc pairwise comparisons carried out using Tukey's Honestly Significant Difference (HSD) test.

### 2.4.2. Epiphyte cover

Epiphyte cover was estimated semi-quantitatively by measuring the lengths of the regions of the blade with either sparse or dense growth of epiphytes.

### 2.4.3. Fresh and dry weight

Ratios of fresh weight (FW) to dry weight (DW) were obtained by weighing a 10 to 15 cm long section taken from the middle part of the harvested blade (about 15–20 cm from the meristem) after wiping it dry (FW) and again after lyophilisation (DW) in a lyophiliser (Cryo Rivoire S.A.R.L, France) for 36 to 48 h.

### 2.4.4. Metabolic compounds

One 17 mm blade disc for each of the F2 individuals (Fig. S2) was lyophilised (Cryo Rivoire, La Tour, France) and ground to a powder. Duplicate assays (technical duplicates) were carried out for glucose, fructose, sucrose, rhamnose, nitrate, nitrite, ammonium, amino acids and protein on about 5 mg of powder [62–65]. Soluble metabolites were extracted with sequential hot ethanolic extraction (100 %, 80 % and 50 % ethanol with pooling of the three different extracts in the same tube) and the insoluble pellet was resuspended in 0.1 M sodium hydroxide [62]. The redissolved insoluble pellet was used to determine protein content [66]. The other assays used the pooled hot ethanolic extracts. No nitrite nor soluble sugars (glucose, fructose, sucrose and rhamnose) were detected in the samples, probably due to the overnight storage of harvested material in the dark, and these parameters were not considered further. Mannitol was not measured.

## 2.5. Stress tolerance

A temperature stress tolerance experiment (Fig. 2) was carried out that incorporated all of the F2 sporophytes whose blades were sufficiently large to allow excision of the required number of blade discs (100 individuals). Eighteen 17 mm discs were excised from the blade of each F2 individual (Fig. S2) and nine discs per individual were acclimatised for 4 days in 120 mL of PES in 140 mm diameter Petri dishes at 13 °C under 25 µmol m⁻² s⁻¹ white light (12 h:12 h day:night, no agitation, no aeration). The medium was changed after 24 h but no further medium changes were carried out for the rest of the experiment. The discs were then transferred to 90 mm diameter Petri dishes containing 35 mL of PES and either incubated at 13 °C for 7 days (3 discs per individual, control treatment) or heat stressed at 21 °C for 7 days (6 discs per individual, stress treatment). The heat-stressed discs were either harvested immediately after the heat stress (3 discs per individual, 21 °C stress) or allowed to recover at 13 °C for 16 days before harvesting (3 discs per

individual, 21 °C stress plus recovery). The 'control' discs were harvested after seven days at 13 °C. The maximum quantum yield of PSII (dark adapted) was measured as Fv/Fm using a Diving PAM II (Heinz Walz GmbH, Germany) after heat stress and after the recovery period to evaluate the stress state of the algal tissues. After 5 min of dark adaptation, samples were illuminated with modulated excitation light, provided by a combination of photodiodes emitting at 470, 520, 645 and 665 nm, at a frequency of 25 Hz, in order to define the basal fluorescence level (F0). After signal stabilization, a light pulse was triggered by a red light emitting photodiode array (3500 µmol photon/m²/s intensity for 200 ms at 655 nm), allowing the maximal fluorescence level (Fm) to be measured. The emitted fluorescence was detected at a wavelength range above 710 nm. PSII quantum yield was then calculated as Fv/Fm = (Fm–F0)/Fm. Based on the results of the PSII quantum yield measurements, individuals were assigned to five phenotypic classes: Tolerant individuals, for which Fv/Fm was stable above 0.55 after stress and after recovery, Resilient individuals whose Fv/Fm increased during recovery, Delayed-sensitive individuals whose Fv/Fm was above 0.55 after stress but declined during recovery, Sensitive individuals whose Fv/Fm was below 0.55 and continued to decrease during recovery and Highly-sensitive individuals whose Fv/Fm was below 0.55 after stress and below 0.2 after recovery. Statistical tests were carried out to compare the responses of the five phenotypic classes. The normality of the data distribution was tested using the Shapiro-Wilk normality test. Statistical differences were assessed using the Kruskal-Wallis test (the kruskal.test function of the stats package) followed by Dunn's post-hoc pairwise comparisons in the R package FSA [61]. Hierarchical clustering was performed using the Euclidean distance metric calculated based of Fv/Fm mean values for each group after stress and after recovery using the R package hclust [67]. Following the PSII quantum yield measurements blade discs were frozen in liquid nitrogen and stored at -80.

## 2.6. Detection of QTLs

QTLs were identified using the R package R/qtl [68]. The scanone function, with the Haley-Knott regression method, which is known for its robustness to non-normal phenotype distributions [69], was used to perform interval mapping in R/qtl. One thousand permutations were carried out to obtain *p*-values for the logarithm of odds (LOD) scores. Bayes credible intervals were used to define 95 % confidence intervals for QTLs (Supplementary file S1). The Python module svgwrite 1.4.3 was used to illustrate the linkage map and highlight markers that were located within the QTL confidence intervals.

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Design of the stress experiment. The experiment was carried out with nine tissue discs per individual, providing triplicate measurements per individual for each of the three conditions: control, stress and recovery. The experiment was carried out with tissue samples from 100 individuals.</figcaption></figure>
## 2.7. RNA extraction and generation of RNA-seq data

RNA was extracted from blade tissue discs from three randomly-selected individuals each from the phenotypic classes Tolerant, Resilient and Sensitive (i.e. nine individuals in total) that had been harvested during the stress experiment described above (section 2.5). For each individual, three extractions were carried out, corresponding to control, stress and recovery conditions. As there were three individuals per phenotypic class, this resulted in three biological replicates per condition (in total: three phenotypic classes x three individuals per class x three conditions per individual). One of these extractions failed and consequently the total number of RNA samples extracted was 26 (Supplementary table S2). For each sample, a single blade disc was extracted with the Macherey-Nagel NucleoSpin RNA plus kit. The manufacturer's instructions were followed except that columns were washed thrice with washing buffer WB2 (600 µL, 600 µL, then 250 µL) and the RNA was then eluted twice with 30 µL of RNase-free water. RNA purity was estimated using a NanoDrop One (ThermoFisher Scientific, Waltham, USA), RNA concentration was estimated using a Qubit (ThermoFisher Scientific, Waltham, USA) with the Qubit RNA High Sensitivity assay kit and the integrity of the RNA was accessed using a 2100 Bioanalyzer Instrument (Agilent Technologies, Santa Clara, USA) and the RNA 6000 nano kit (Agilent Technologies, Santa Clara, USA) following the Plant RNA nano protocol. Note that blade discs for individuals of the Highly sensitive class did not yield sufficiently good quality RNA for transcriptomic analysis, presumably due to the poor physiological state of the tissue after stress.

The Stranded mRNA Prep Ligation Kit (Illumina, San Diego, CA, USA) was used to prepare RNA-seq libraries according to the manufacturer's protocol, starting with 100 ng of total RNA. The libraries were sequenced in paired-end mode with an Illumina NovaSeq 6000 instrument (Illumina, San Diego, CA, USA) producing 150 base pair reads. Raw sequence data was cleaned with Trimmomatic (version 0.39, parameters: ILLUMINACLIP to remove Illumina adapters, AVGQUAL:24, TRAILING:22, LEADING:22, MINLEN:35) and then mapped to a chromosome-scale assembly of the *S. latissima* genome (*Saccharina latissima* v2 assembly available at https://phaeoexplorer.sb-roscoff.fr/) using HISAT2 version 2.2.1 [70]. The reads mapped to each gene were counted with featureCounts [71] and counts converted to Transcripts per million (TPM) using an in-house Python script.

## 2.8. Analysis of gene expression

Differentially expressed genes (adjusted *p*-value <0.05, log2 fold-change >1 and TPM > 1) were detected using DESeq2 version 1.38.3 [72] in R version 4.2.3. Principal component analysis (PCA) was carried out with the prcomp function from the stats core package and plotted using ggplot2 version 3.5.1 [73]. Spearman correlation was calculated using the get_dist function in the factoextra package version 1.0.7 (https://github.com/kassambara/factoextra) on normalized counts after variance stabilizing transformation, generated by DESeq2. The distance map was generated using the factoextra fvr_dist function and hierarchical clustering was carried out with hclust [67] using Ward's minimum variance method ("ward.D2" parameter). Jaccard indices were calculated and plotted using vegan version 2.6.8 (https://cran.r-project.org/web/packages/vegan/index.html).

The gene ontology (GO) terms associated with each protein were extracted from the *Saccharina latissima* v2.0 annotation (https://phaeoexplorer.sb-roscoff.fr/) and GO ontology classes and term definitions were retrieved using the GO.db package version 3.19.1 (https://bioconductor.org/packages/release/data/annotation/html/GO.db.html) in R version 4.4.1. Over-representation analysis was carried out using the enricher function from clusterProfiler version 4.12.6 [74]. Significantly enriched GO terms (adjusted *p*-value ≤ 0.01 and count ≥ 10) were plotted using ggplot2 version 3.5.1 [73].

# 3. Results

## 3.1. Establishment of a segregating F2 family and construction of a ddRAD-seq-based genetic map

A segregating family of 189 F2 sporophyte individuals was derived from two parental sporophytes isolated from two different sites with genetically differentiated populations, Roscoff in northern Brittany and Saint-Guénolé in southern Brittany (Fig. 1), via the production of a single F1 hybrid. The *S. latissima* family was genotyped using ddRAD-seq, generating a final total of 11,788 variant markers that were mapped to 42 linkage groups (LGs), with a minimum logarithm of the odds (LOD) score of six. The genetic map (Fig. 3, Tables S3, S4) had a total length of 2941.2 centimorgans (cM), corresponding to 99.15 % of the estimated genome (Ge) length (Ge = 2966 cM). On average two adjacent markers were 0.3 cM apart, with the largest gap being 7.2 cM. The 42 LGs ranged in size from 12.2 cM (39 markers) to 131.5 cM (665 markers). Based on an assembled genome size of 531 Mbp genome size [46], the global recombination rate was estimated to be 5.59 cM/Mbp, which is lower than an estimate of 9.60 cM/Mbp for *S. japonica* (based on [75] and the genome size estimate in [76]) and is about half the value (12.28 cM/Mbp) that has been estimated for *Ectocarpus* species 7 [77].

## 3.2. Phenotyping of the segregating F2 family

A panel of parameters was measured to study phenotypic variance across the *S. latissima* F2 family (Table S5). These included morphometric measurements, fresh to dry weight ratios, evaluation of the levels of four metabolic compounds and measurements of responses to temperature stress. The morphometric parameters measured were stipe length, total length and width at the widest point of the blade and measurements were obtained for the following metabolic compounds: nitrate, ammonium, amino acids and protein. To evaluate responses to temperature stress, excised, acclimatised blade discs were incubated under either 13 °C (control) or 21 °C (stress) conditions for seven days. Capacity to recover from stress was evaluated in the same experiment by incubating excised, acclimatised blade discs at 21 °C for seven days followed by 16 days at 13 °C (recovery). In vivo measurements of chlorophyll fluorescence Fv/Fm were used as a proxy to monitor thermal stress intensity and recovery.

Plots of the distributions of the phenotyping data indicated that there was substantial variance across individuals of the F2 family for all the parameters measured, including morphological and biochemical parameters and Fv/Fm measurements from the stress experiment (Fig. S3).

Analyses of correlations between Fv/Fm measurements made under control conditions, after stress and after recovery showed that 16.4 % of the variance after stress could be explained by the physiological state under control conditions (*p*-value = 2.289e-05) and 16.9 % of the variance after recovery could be explained by the physiological state directly following stress (*p*-value = 1.435e-05).

Morphometric parameters were also analysed to assess their relationship with the phenotypic classes in terms of response to stress. Analysis of variance (ANOVA) revealed no significant differences in total length or stipe length across phenotypic classes (*p*-values >0.1 for all comparisons). However, blade width varied significantly between classes (ANOVA, *p*-value = 0.023). Subsequent post-hoc analysis using Tukey's Honest Significant Difference test indicated that highly-sensitive individuals had significantly narrower blades than delayed sensitive individuals (*p*-value = 0.039). Blade width was also correlated with Fv/Fm after recovery. This correlation was weak (R² = 0.141) but significant (*p*-value = 8.902e-05).

Epiphyte cover was estimated semi-quantitatively after harvest of the 129 F2 sporophytes (Table S5). The epiphyte cover was quite low (on average about 19 % of the blade surface but with the cover being highly variable across the family) considering that the individuals had been cultivated on the open sea culture system over the summer months from
<figure><img src="image_7.png" /><figcaption>B</figcaption></figure>

QTL1

LG1 position (cM)

<figure><img src="image_8.png" /></figure>

SL_06 position

QTL2

LG8 position (cM)

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

SL_13 position

genome chromosomes

SL_13

other contigs

SL_13 predicted genes

in QTL-3 region

not in QTL-3 region

QTL3

LG31 position (cM)

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

SL_12 position

genome chromosomes

SL_12

other contigs

SL_12 predicted genes

in QTL-2 region

not in QTL-2 region

(caption on next page)
<figure><img src="image_13.png" /><figcaption>**Fig. 4.** Response of *S. latissima* F2 progeny to temperature stress. A. Mean Fv/Fm values for five different phenotypic classes during the temperature stress experiment. Error bars indicate standard error. The numbers in brackets indicate the number of individuals assigned to each phenotypic category. B-E. Principal Component Analysis (PCA) plots showing multivariate relationships between samples for all the phenotypic classes (B.) and for only the Tolerant (C.), Sensitive (D.) or Resilient (E.) phenotypic classes. Sample names (e.g. C042T) indicate the growth condition (control, stress or recovery; C, S, R) followed by the sample number and then the phenotypic class (Tolerant, Resilient or Sensitive; T, R, S). See Table S5 for the phenotype data and assignment to the phenotypic classes.</figcaption></figure>
April to October. The relatively low epiphyte cover may have been due to the age of the individuals, which were pre-fertile juveniles.

### 3.3. Identification of QTLs

Combined analysis of the genotype and phenotype data identified three quantitative trait loci (QTLs) related to recovery after temperature stress: on LG1 (LOD-score = 4.99, *p*-value = 0.048), on LG8 (LOD-score = 5.18, *p*-value = 0.036) and on LG31 (LOD-score = 6.02, *p*-value = 0.008). The genes located in the genetic intervals corresponding to the QTLs are indicated in Table S6.

Analysis of the effect of the QTLs on the quantum yield of photosystem II after recovery revealed epistatic interactions between all of the identified loci. For each QTL pair, one specific allelic combination was linked with a reduced tolerance to stress as measured after recovery (Supplementary file S1). We therefore propose a QTL model in which the loci are allowed to interact, in addition to their individual effect. This model explains 34.8 % (*p*-value <0.001) of the variance.

### 3.4. Transcriptomic analysis of the *S. latissima* temperature stress response

The F2 individuals exhibited varied responses to the high temperature treatment during the temperature stress experiment. Based on these responses, they could be grouped into five classes: Tolerant, Resilient, Delayed sensitive, Sensitive and Highly sensitive (Figs. 4A and S4, Table S5). To analyse the responses of the F2 progeny to temperature stress at the level of gene expression, three representative individuals were selected from each of the Tolerant, Sensitive and Resilient classes and gene expression was analysed by extracting RNA from blade disks that had received 'control', 'stress' or 'recovery' treatments (see previous section; Fig. 2, Table S2).

In a PCA of this data (Fig. 4B-E), the first principal component (PC1) correlated approximately with the transcriptomic responses to the stress treatment in as far as, for all individuals, stress treatment samples were consistently located to the right and control and recovery samples to the left. Moreover, the control, stress and recovery samples for the Tolerant class were consistently located further to the left than the equivalent samples for the Sensitive class, when each sample type (control, stress or recovery) was considered separately, again consistent with component PC1 corresponding to the level of stress. This difference suggests that the stress response of the Sensitive class individuals was stronger than that of the Tolerant individuals and, moreover, it suggests that the Sensitive individual samples may have been experiencing a basal level of stress even under control or recovery conditions. Note that excision of blade discs and incubation without aeration is likely to have induced a basal level of stress, even when the discs were maintained under the control temperature, and these factors may have affected some genotypes more than others. Under this interpretation of the data, one Sensitive class individual (F2.27.2-54) appears to have had a particularly high level of stress response under control and recovery conditions with the level of stress being even greater under stress conditions. Datapoints for the Resilient individual samples exhibited a similar pattern to those of the Tolerant and Sensitive individuals with respect to the PC1 component but were located towards the centre of the distribution (Fig. 4B,D) suggesting that these individuals had a higher level of 'basal stress' under control and recovery conditions than the Tolerant individuals but did not experience as strong a stress response as the Sensitive individuals under the stress conditions.

Overall, hierarchical clustering and evaluation of the distances between samples based on Spearman correlation supported the conclusions drawn from the PCA (Fig. S5). Recovery samples for the Tolerant and Resilient classes of individuals grouped together with Tolerant control samples, indicating that they had returned to a non-stressed state. On the other hand, the recovery samples for the Sensitive class of individuals grouped with stress samples for both the Resilient and

Sensitive classes supporting the conclusion that the Sensitive individuals did not recover from the stress treatment. Finally, stress samples for the Tolerant class clustered with most of the control condition samples for Resilient and Sensitive class individuals, indicating that the former were not markedly stressed by the heat treatment.

### 3.5. Differential gene expression

DESeq2 was used to identify genes that were differentially expressed in response to temperature stress (comparison of control and stress samples), during recovery from temperature stress (comparison of stress and recovery samples) or to determine whether recovery samples had returned to their pre-stress state (comparison of control and recovery samples). These analyses were carried out separately for each of the three different phenotypic classes: Tolerant, Resilient and Sensitive (Fig. 5 top and left, Tables S6, S7). In terms of the overall numbers, the greatest number of differentially expressed genes (DEGs) was detected for the Tolerant class, followed by the Resilient and then the Sensitive individuals. This trend suggested that resistance to temperature stress may depend on an individual's capacity to induce a strong transcriptomic response to the stress. Comparisons of the transcriptomes of the Tolerant, Resilient and Sensitive classes of individual under control conditions indicated that the Tolerant class transcriptome differed from those of the Resilient and Sensitive classes, even under control conditions (Fig. 5 bottom right, Tables S6, S7).

Together, the multiple differential expression analyses identified a total of 8130 genes that were differentially regulated in at least one comparison (Tables S6, S7). These genes represent 44.2 % of the predicted proteome, indicating that the response to temperature stress in *S. latissima* involves a large proportion of the genome. Comparison of the DEG sets for Tolerant, Resilient and Sensitive classes identified few genes that were consistently differentially expressed across all three phenotypic classes (Fig. 5), indicating that the three classes responded differently at all stages of the temperature stress experiment. A broader comparison of the DEG sets using the Jaccard index (Fig. S6) also detected very little overlap between sets of DEGs, supporting this conclusion. In the Jaccard analysis, some overlap was however detected between genes that were upregulated during stress in the Tolerant class and downregulated during recovery in the Tolerant and Resilient classes (and vice versa), consistent with reversal of the stress response during recovery (Fig. S6). Interestingly, there was also overlap between the set of genes upregulated in Sensitive compared to Tolerant individuals under control conditions and the set of genes upregulated in Tolerant individuals following stress treatment (and vice versa for downregulated genes; Fig. S6), consistent with the suggestion (above) that the Sensitive individuals were in a stressed state even under control conditions.

The set of genes that was differentially expressed following stress treatment of Tolerant class individuals is of particular interest as these genes may play an important role in stress tolerance. The expression patterns of these genes were analysed across the different phenotypic classes and across treatments (Fig. 6). In the Tolerant individuals, upregulated genes were systematically downregulated after recovery but not all the genes downregulated during stress regained their previous expression levels after recovery. The pattern of expression in Resilient individuals was similar to that observed for Tolerant individuals but less marked, particularly at the transition from control to stress conditions. These genes did not show a clear pattern of expression in Sensitive class individuals, with most genes that were upregulated or downregulated in Tolerant individuals failing to be induced or to be downregulated, respectively, in the Sensitive individuals (Fig. 6).

A screen for significantly enriched GO terms associated with the different sets of DEG (Fig. 7) indicated that the temperature stress treatment led to a significant downregulation of genes predicted to be involved in photosynthesis in all three phenotypic classes. However, the fold changes were greater for the Resilient and Sensitive classes than for the Tolerant class. In parallel, genes implicated in other functions were
<figure><img src="image_14.png" /><figcaption>**Fig. 5.** Transcriptomic analysis of the response of *S. latissima* F2 progeny to temperature stress. Top and left. Venn comparisons of the DEG sets for the Tolerant, Sensitive and Resilient phenotypic classes across three different comparisons: control versus stress (top left), stress versus recovery (bottom left) and control versus recovery (top right). Bottom right. Venn comparison of DEG sets corresponding to pairwise comparisons of the Tolerant, Sensitive and Resilient phenotypic classes under control conditions. The label for each comparison (e.g. CRvSR) indicates the growth condition (control, stress or recovery; C, S, R) followed by phenotypic class (Tolerant, Resilient or Sensitive; T, R, S) for each of the two datasets separated by v for versus.</figcaption></figure>

upregulated, particularly in the Resilient class, where the upregulated genes were implicated in processes related to the redox state of the cell. In response to temperature stress, therefore, the Resilient individuals appeared to be downregulating homeostatic cellular processes, notably photosynthesis, and upregulating systems necessary to counteract the cellular effects induced by the stress treatment, notably redox reactions. The Sensitive individuals also strongly downregulated photosynthesis genes but did not exhibit a significant induction of redox-related genes suggesting that they were less capable of mounting an effective stress response. The Tolerant individuals did not exhibit significant induction of redox-related genes either but, the weak downregulation of homeostasis-related genes (photosynthesis and translation) in this phenotypic class suggests that they were less impacted by the temperature treatment and this could explain why they did not significantly increase the expression of stress-related genes. The predominance of photosynthesis- and redox-related genes among the genes differentially expressed following temperature stress is consistent with earlier work [78] and has also been observed in response to hyposaline stress [79,80].

During the recovery phase of the experiment, transcripts of genes involved in photosynthesis and translation increased in abundance in the Resilient and Tolerant classes but not in the Sensitive class (Fig. 7), indicating that the former were able to reinitiate stalled homeostatic processes when returned to the non-stress temperature but that the latter were not. In the Resilient individuals, the stress-related functions induced by the temperature treatment were downregulated during the

recovery phase. As expected, the Tolerant and Sensitive classes did not exhibit this characteristic, presumably because neither had induced these genes in response to the stress treatment.

Taken together the differential gene expression analyses indicated that the Sensitive class was susceptible to stress because it was unable to mount an effective stress response, that the Resilient class was able to resist the stress treatment by mounting an effective stress response and that the Tolerant class was able to function in an approximately normal fashion under the stress conditions applied here. For the Tolerant class, the extensive transcriptome modifications observed in response to stress presumably allowed this class to acclimatisate to the stress conditions in a manner that avoided toxic consequences of the stress treatment such as redox imbalance for example (as suggested by the absence of a significant upregulation of redox pathway genes).

### 3.6. Stress-related expression of genes located within the QTL intervals

The three QTL intervals contained 92 genes (Fig. 8, Table S6). About a third of these genes (33 genes) were detected in at least one of the differential expression analyses and two of the 33 genes were also in the group of 500 genes that contributed most to the PCA variance (Fig. 8, Table S6). The QTL intervals were not enriched in differentially expressed genes (which represented 44.5 % of the genome overall, see above) but this information may be helpful for the identification of the causal loci within the QTLs in the future, especially as several of the differentially expressed genes have interesting predicted functions,
<figure><img src="image_15.png" /><figcaption>**Fig. 6.** Expression patterns of DEGs from the comparison of Tolerant blade discs under control and stress conditions (Tables S6, S7) across the entire set of samples from the stress experiment. Up-regulated genes from this comparison are shown on the left and down-regulated genes on the right. The upper panels show the distribution of the mean expression values for each gene across replicates for each condition (control, stress and recovery) and for each phenotypic class (Tolerant, Resilient and Sensitive), expressed as Z scores. The lower panels indicate the expression of each gene under each of the three conditions for each individual as a Z score.</figcaption></figure>

related to the stress response (e.g. a 'cold-shock' DNA-binding protein) or to signalling (e.g. a zinc finger DNA-binding protein and a plant homeodomain and bromodomain protein). Heat maps showing the expression patterns of the genes within the QTL intervals across all the samples of the stress experiment are shown in Fig. S7.

## 4. Discussion

The segregating F2 family analysed in this study exhibited considerable phenotypic variation, despite the geographical proximity of the sampling sites for the two parental strains, which are both in western Brittany and separated by about 200 km of coastline (Fig. 1). Assuming that a significant proportion of this variation has a genetic basis, this is

an important observation for future programs aimed at using locally-resourced strains in breeding strategies in order to avoid the introduction of foreign germplasm through wide crosses, as it suggests that sufficient genetic variability may be available within local populations.

Analyses of the transcriptomes of the F2 individuals under control, temperature stress and recovery conditions indicated that the response to heat stress involved a large number of genes. Overall, 44.5 % of the predicted genes were differentially expressed in at least one of the comparisons that were carried out in the study. This result is consistent with an earlier study [78] in which 32 % of *S. latissima* genes exhibited differential expression in response to heat stress. *Ectocarpus* species 7 responses to hyposaline, hypersaline and oxidative stress have also been shown to involve large numbers of genes [81].
<figure><img src="image_16.png" /><figcaption>Fig. 7. Enriched GO terms (biological processes) in sets of upregulated (DEG-up) and downregulated (DEG-down) DEGs for two comparisons (control versus stress and stress versus recovery conditions) calculated separately for the Resilient, Sensitive and Tolerant phenotypic classes.</figcaption></figure>

The individuals of the F2 family exhibited various different responses in a heat stress experiment allowing them to be divided into several distinct phenotypic classes. The phenotypic classes were determined arbitrarily based on measurements of the maximum quantum yield of PSII and deeper sampling would probably have revealed that the classes represent intermediates on a continuum. However, the phenotypic classes proved to be useful and relevant, revealing differential responses within the F2 family to temperature stress both at the level of PSII quantum yield and gene expression.

Analysis of the stress experiment underlined the importance of assessing tolerance directly after stress treatment and after a period of recovery as sensitivity to stress can be delayed and tolerance can be due to an ability to recover from a stress treatment over time. The groups of genes that were differentially expressed in response to heat stress and recovery varied across the phenotypic classes and the expression patterns were consistent with the phenotypic responses of the individuals, i. e. whether they were tolerant, resilient or sensitive to heat stress. In general, heat stress led to a downregulation of genes involved in general metabolic processes, particularly photosynthesis, and an upregulation of stress-related pathways, particularly redox-related genes.

ddRAD sequencing identified >11,000 SNPs across the genome and this genotype data was used to construct a high-density genetic map for *S. latissima*. To date, genetic maps have been generated for very few macroalgae [24,75,82–88] so the *S. latissima* map represents an important new resource, especially given the interest in this species for aquaculture. In addition, the large number of SNPs identified using the ddRAD sequence data represent a useful resource for genetic analysis and breeding.

The ddRAD data and genetic data were also used to search for QTLs that explained the phenotypic variability within the F2 family. These analyses did not detect any QTLs associated with morphometric or metabolic parameters but this does not necessarily mean that there is no significant genetic variance for these parameters. The power to detect QTLs may have been affected by several features of the analysis including the size of the F2 segregating family, the amount of variance in the segregating family and the signal to noise ratio in the measurements. In contrast, three QTLs were found to be associated with recovery from temperature stress, together explaining 34.8 % of the variance. It was not surprising that QTLs were detected for the recovery phase of the experiment as this was the phase where the greatest variance between
<figure><img src="image_17.png" /><figcaption>**Fig. 8.** Genes within the stress recovery QTL intervals that were significantly differentially expressed in the transcriptomic analysis. Gene functions were manually assigned based on BLASTp comparisons and domain composition. DEGs, genes that were differentially expressed in at least one of the DESeq2 comparisons (Fig. 5, Tables S6, S7); PCA, the 500 genes that contribute most strongly to the PCA variance (Table S6).</figcaption></figure>

phenotypic classes was detected (based on Fv/Fm measurements). These QTLs indicate a significant genetic variance for tolerance to temperature stress, indicating that it should be possible to breed strains with improved resistance or resilience to waves of elevated temperature (heatwaves). This resistance or resilience could be manifested as either a capacity to maintain growth during a heatwave or as a capacity to recover rapidly after the stress period. Strains with increased temperature tolerance would not only be of interest in the context of global climate change but would also potentially allow energy savings during the hatchery phase of production or within breeding programs by removing the need for strict temperature controls.

The three QTL intervals were too broad to identify causal genes but detailed analysis of the genes within the intervals, including analyses of gene expression (stress DEGs) and predicted gene function, identified several interesting candidate genes that could potentially be targets for further analyses.

In an earlier study, Guzinski et al. [42] identified seven outlier SNPs that were significantly associated with several environmental variables (including sea surface temperature) and therefore putatively involved in adaptation to environmental variations, including temperature. Three of these SNPs (SNP1269, SNP1558, SNP1865) were correlated with sea surface temperatures (SST) and carbon dissolved organic matter (CDOM) and showed allele frequency patterns that were likely driven by historical events that shaped *S. latissima* population structure during the cycles of expansion and contraction of the species range following the last glacial maximum [38]. Three others (SNP629, SNP1197 and SNP3660) were described as being putatively associated with local selective processes such as the survival of sporophytes that experience high levels of variation in SST, CDOM and salinity [42]. None of the seven SNPs correspond with the intervals of the QTLs reported in the current study but they may indicate additional loci of interest with respect to tolerance to temperature stress.

## 5. Conclusion

This study provides a number of resources and observations that will

be relevant to future attempts to breed improved strains of *S. latissima* for aquaculture. The genetic map, together with a large collection of SNP markers, is an important resource for future genotype-based breeding. In addition, the temperature-stress-related QTLs and DEGs represent interesting starting points for future attempts to breed strains with increased tolerance to temperature stress. And finally, the observation that Fv/Fm measurements correlated well with stress-related gene expression patterns indicates that the former would be a facile approach to select for temperature tolerant genotypes. Taken together, the work presented here represents a significant step forward in efforts to develop *S. latissima* breeding programs.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.algal.2025.104203.

## CRediT authorship contribution statement

**Zofia Nehr:** Writing – original draft, Supervision, Investigation, Formal analysis, Data curation. **Komlan Avia:** Writing – review & editing, Investigation, Formal analysis. **Olivier Godfroy:** Writing – review & editing, Visualization, Formal analysis, Data curation. **Bertrand Jacquemin:** Writing – review & editing, Investigation. **Stephane Mauger:** Writing – review & editing, Investigation. **Perla Farhat:** Writing – review & editing, Resources. **Emilie Gouhier:** Writing – review & editing, Investigation. **Jerome Coudret:** Writing – review & editing, Investigation. **Antoine Fort:** Writing – review & editing, Investigation. **Ronan Sulpice:** Writing – review & editing, Supervision. **Corinne Cruaud:** Writing – review & editing, Resources. **Lucie Jaugeon:** Writing – review & editing, Investigation. **Philippe Potin:** Writing – review & editing, Supervision, Project administration. **Myriam Valero:** Writing – review & editing, Supervision, Project administration, Conceptualization. **J. Mark Cock:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Investigation, Data curation, Conceptualization.
## Funding

This work was supported by the Investissements d'Avenir project Idealg (ANR-10-BTR-04-01), the European BG-01 BlueGrowth H2020 project Genialg (727892), the France Génomique National infrastructure project Phaeoexplorer (ANR-10-INBS-09), the Horizon Europe Innovation Action project SeaMark (101060379), the CNRS international research network DABMA (00022), the ANR project Epicycle (ANR-19-CE20-0028-01), the CNRS and Sorbonne University.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank Marion Ballenghien for help with the phenotyping of the FI sporophytes and biometric phenotyping, Stéphane Loisel for help with open sea cultivation, Christophe Destombe for help with biometric phenotyping and Jean-Charles Leclerc for help with the construction of Fig. 1. We are grateful to the Roscoff Bioinformatics platform ABiMS (http://abims.sb-roscoff.fr), which is part of the Institut Français de Bioinformatique (ANR-11-INBS-0013) and BioGenouest network, for providing both help and computing and storage resources.

## Data availability

All the sequence data used for this study have been deposited in the European Bioinformatics Institute/European Nucleotide Archive (EBI/ENA) database under the project accessions PRJEB81797 for the ddRAD-seq data (this study) and PRJEB72149 for the RNA-seq data [46]. See Table S2 for details.

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