# Short communication: Bifidobacterium abundance in the faecal microbiota is strongly associated with milk traits in dairy cattle

L. Brulin, S. Ducrocq, G. Even, M.P. Sanchez, S. Martel, S. Merlin, C.

Audebert, P. Croiseau, J. Estellé

► To cite this version:

L. Brulin, S. Ducrocq, G. Even, M.P. Sanchez, S. Martel, et al.. Short communication: Bifidobacterium abundance in the faecal microbiota is strongly associated with milk traits in dairy cattle. Animal, 2024, 18 (8), pp.101243. 10.1016/j.animal.2024.101243. hal-04669072

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# Short communication: *Bifidobacterium* abundance in the faecal microbiota is strongly associated with milk traits in dairy cattle

<figure><img src="image_2.png" /></figure>

L. Brulin<sup>a,b,*</sup>, S. Ducrocq<sup>a,c</sup>, G. Even<sup>a,c</sup>, M.P. Sanchez<sup>b</sup>, S. Martel<sup>a,c</sup>, S. Merlin<sup>a,c</sup>, C. Audebert<sup>a,c</sup>, P. Croiseau<sup>b</sup>, J. Estellé<sup>b</sup>

## ARTICLE INFO

**Article history:**

Received 4 October 2023

Revised 27 June 2024

Accepted 2 July 2024

Available online 9 July 2024

**Keywords:**

Dairy performance

Differential abundance analysis

Diversity

Holstein cows

16S rRNA gene

## ABSTRACT

The performance of dairy cows is influenced by the microbial communities hosted within their digestive tract. While the rumen microbiota has long been associated with host phenotypes, the impact of the faecal microbiota remains elusive. In this study, we collected 697 faecal samples from commercial Holstein cows and analysed them with 16S rRNA gene analyses. For each animal, routinely recorded data, i.e., milk yield, fat yield, protein yield, fat content, protein content, and an aggregate production trait (piNEL) based on the French economic dairy index, were available to assess the links between the faecal microbiota and host production. Our findings revealed a strong and significant association between the structure of the bacterial and prokaryote community (β-diversity) and dairy production. In addition, differential abundance analyses identified 48 genera whose abundances were significantly associated with piNEL, milk, fat and protein yield. Among these genera, the increased abundance of *Bifidobacterium*, and particularly an amplicon sequence variant with a 16S rRNA V3-V4 gene region identical to *B. globosum* and *B. pseudolongum*, was found to be the most important for high-yielding animals. *Bifidobacterium* seemed to be a potential key member of the bovine faecal microbiota that should be further investigated. Conversely, the *p-1088-a5 gut group* genus was found more abundant in low-productive cows. In conclusion, this study demonstrates significant associations between the faecal microbiota and the performance of dairy cows at the whole lactation scale. A better understanding of the physiology of the gut microbiota could help to improve dairy cow production.

© 2024 The Author(s). Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

## Implications

Dairy cattle maintain a symbiotic relationship with their gastrointestinal microorganisms, which affect, among others, their overall performance. The role of faecal microbiota in humans and other domestic monogastric species is well-established, but its impact on ruminant has not been extensively researched. Using a large cohort of Holstein cows and 16S rRNA gene sequencing, we have highlighted associations between whole-lactation production phenotypes and faecal diversity. Moreover, we have identified specific associations with the *Bifidobacterium* and *p-1088-a5 gut group* genera. These results reinforce the crucial roles played by the faecal microbiota, holding potential applications for the dairy industry and consideration in probiotic trials aimed at improving dairy-related phenotypes.

## Introduction

The digestive efficiency of ruminants, directly impacting milk production, is highly dependent on the symbiotic relationship with the gastrointestinal microorganisms that they host. Microbiota analyses have reinforced the crucial role of these bacteria on host performance, functioning as real production auxiliaries (Xue et al., 2018; Fregulia et al., 2022). Most studies of the bovine microbiota have focused on the rumen. Although it contains a large diversity of microorganisms associated with performance (Xue et al., 2018; Monteiro et al., 2022; Fregulia et al., 2022), rumen sampling requires complex and intrusive manipulations. In contrast, investigations into the bovine faecal microbiota are less frequent, despite it offers both ease of collection and potentially more robust insights (Zhang et al., 2017; Monteiro et al., 2022). In this sense, major discoveries using faecal samples in humans and monogastric livestock suggest that relevant results could be obtained in faecal samples of ruminants. However, to the best of

<footnote><sup>a</sup>GD Biotech - Gènes Diffusion, Lille 59000, France</footnote>

<footnote><sup>b</sup>Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France</footnote>

<footnote><sup>c</sup>PEGASE-Biosciences, Institut Pasteur de Lille, 1 Rue du Professeur Calmette, 59019 Lille, France</footnote>

<footnote>* Corresponding author.
E-mail address: l.brulin@genesdiffusion.com (L. Brulin).</footnote>
our knowledge, the previous studies on the faecal microbiota of cows have been limited by their sample sizes, and also by the use of controlled conditions in experimental herds which might strongly differ from the commercial dairy farms' reality.

Our hypothesis is that the faecal microbiota diversity and composition have relevant relationships with the performance of dairy cows that could be exploited to optimise milk production traits. In order to test this hypothesis, in the present study, we created a large cohort of commercial animals with both microbiota and phenotypes to investigate these relationships.

## Material and methods

### Animals

Between September 2017 and December 2018, 697 non-pregnant Holstein cows, from 17 commercial herds (mean = 41 cows/herd) located in the North of France, were sampled between their 17th and 305th days of lactation. Each herd underwent two to seven sampling sessions (average = 4.7 samplings) to ensure all eligible animals (i.e., non-pregnant and at least 15 days postcalving) were sampled and to capture a better snapshot of the herd.

Since the animals were held in commercial herds, a complete and detailed description of the management practices is not available. Nevertheless, in a given farm, contemporary animals were fed with the same rations and shared the same farming conditions. Ration composition varied among farms, but all included a proportion of corn silage, between 25.4 and 73.3% (on average 51.5%), and concentrate, between 10.2 and 35.7%. Most of the rations also included minerals, beet, pressed beet pulp, fresh and ensiled grass. Some cows were also given urea or alfalfa. A summary of the diet composition is presented in Supplementary Table S1 available at https://doi.org/10.6084/m9.fishshare.26072410. Three of the herds (indicating as Herd 2, Herd 10 and Herd 15 in Supplementary Table S1 available at https://doi.org/10.6084/m9.fishshare.26072410) also provide feed additive composed of dead and alive yeasts in the diet. In the following analyses, the diet and management effects were taken into account within the herd and season effects.

### Faecal sampling, DNA extraction and 16S sequencing

Approximately 50 g of faecal samples were collected from cows by technicians at Gènes Diffusion (Douai, France) by applying a digital rectal stimulation with sterile-gloved hands. Samples were immediately stored in sterile tubes and transported at a controlled temperature of 4 °C (± 1°C) to the Gènes Diffusion research laboratory (Institut Pasteur de Lille, France) using an electric cooler. Upon arrival, samples were frozen at -20 °C and partitioned into five batches for subsequent analyses.

The DNA extraction was performed using the Nucleospin® 96 Soil kit (Macherey Nagel, Düren, Germany) following the manufacturer's recommendations. The sequencing library was based on a dual-indexed paired-end sequencing strategy and targeted the V3-V4 variable regions of the 16S rRNA gene. Briefly, two PCR were applied. To achieve this, the 5'-Tag sequences 5'-TCGTCGGCAGCGT CAGATGTGTATAAGAGACAG-3' and 5'-GTCTCGTGGGCTCGGA GATGTGTATAAGAGACAG-3' were used for the forward and reverse primers (IDT, Iowa, USA), respectively, as well as the 16S rRNA gene specific sequences 5'-CCTACGGGNGGCWGCGA-3' and 5'-GAC TACHVGGGTATCTAATCC-3' for forward and reverse primers (IDT, Iowa, USA), respectively. In addition to the Tag sequences, the primers used for PCR2 also contained barcode sequences and a locus sequence adapter suitable for the Illumina sequencing technology. After each PCR, products were purified with NucleoFast® 96 PCR

(Macherey Nagel, Düren, Germany). Following the PCRs, a Quant-iT PicoGreen ds DNA quantification (Life Technologies, Carlsbad, California, USA) was performed to produce an equimolar pool of the library of which 200 µL was purified twice by NucleomagNGS® (Macherey Nagel, Düren, Germany) and eluted in 50 µL of TE 1X pH 8.0. The purified pool library was monitored by Bioanalyzer with the High Sensitivity DNA Chips kit (Argilet, Santa Clara, California, USA) to control quality and estimate the average size of the pool. Sequencing library has been paired-end sequenced on Miseq platform (Illumina, San Diego, California, USA) with MiSeq Reagent Kit v3, which performed 600 sequencing cycles.

### Sequence analysis and quality control

A quality control of the raw fastq files was performed with FastQC, at the end of each of the sequencing runs. Sequence analyses were performed with the dada2 v.1.24-0 R package following the author's recommendations in their tutorial for big datasets. Each sequencing batch has undergone an individual quality control for which reads were truncated when Phred score was below Q20. After quality filtering, denoising pair-end merging and amplicon variant calling steps were performed on each lab batch. The DADA2 method (Callahan et al., 2016) was used to cluster the sequences with a pairwise identity threshold of 100% (amplicon sequence variants – ASV) and obtain a count of matrix of samples by ASV. After merging the table counts from the five sequencing lab batches, chimeras were removed. The taxonomic assignment, from reign to species, of the ASVs was performed using the SILVA v.138.1 library (Quast et al., 2013).

### Microbiota diversity

Diversity indexes, i.e., α- and β-diversities, were estimated on the rarefied dataset of ASV counts (number of counts = 8 000). The analyses were performed on 521 samples that had ≥ 8 000 counts. For α-diversity, we focused on observed richness and the Shannon diversity index, which were estimated using the phyloseq (v.1.40.0) and vegan (v.2.6-2) R packages, respectively. To quantify β-diversity, Jaccard and Bray-Curtis dissimilarity matrices were computed with the vegan R package.

### Phenotypes

We compared the microbiota data with a range of dairy phenotypes, including 305-days milk yield (MY), protein yield (PY) and fat yield (FY) and protein content (PC) and fat content (FC) – descriptive statistics are in Supplementary Table S2. All dairy phenotypes came from the French national dairy performance test-day recording. Besides, we used an aggregate phenotype (pINEL), which summarised both the quantity and quality traits of milk production, and which was derived from the French economic dairy index formula, as follows:

$$pINEL = 0.98 * (PY + 0.2 * FY + PC + 0.5 * FC) \quad (1)$$

Records which were out of the range mean ± 4 SD were removed from the analyses.

### Statistical analyses

Before assessing the relationship between microbiota α-diversity and host phenotypes, both of them were corrected by various factors to mitigate confounding effects:

$$y = \mu + Xb + e \quad (2)$$

with y being a vector with either the Shannon, the observed richness indices or the dairy phenotypes (i.e., MY, PY, FY, PC, FC, pINEL)
records, μ being the mean for the α-diversity index or phenotype y, X the incidence matrix and b being the vectors of the fixed effects. At the level of α-diversity indices, the lab batch, the herd, the season, the parity, and the lactation stage were considered as co-factors (Supplementary Figure S1). Dairy phenotypes were statistically precorrected for herd, parity, age at calving and month of calving of the cow. e was the vector of residual effects. Cows with parity equal to or exceeding 3 were grouped together and lactation stages in days in milk were defined as [17–29], [30–39], [40–49], [50–59], [60–69], [70–79], [80–89], [90–99], [100–119], [120–139], [140–159], [160–179], [180–219] and [220–305]. For assessing potential associations between the corrected phenotypes and the corrected α-diversity indices, a Pearson's correlation was used.

On the other hand, associations with the β-diversity indices were investigated with a permutational multivariate analysis of variance (PERMANOVA) performed with the adonis2() function (vegan R package) and 9 999 permutations, considering the same model as described above (2) except that the explained variable y was the Bray-Curtis or Jaccard dissimilarity matrix, and b the vector of fixed effects including the lab batch, herd, season, parity, lactation stage. Associations between β-diversity indices and co-factors are presented in Supplementary Figure S1. The dairy phenotype was previously corrected for the herd, parity, age at calving and month of calving of the cow.

Finally, we implemented differential abundance analyses with the ANCOM-BC2 method (Lin and Peddada, 2020) and the associated ANCOMBC R package (v.1.4.0) to evaluate whether specific taxa (ASV and genus) might be linked to dairy traits. Thus, for each phenotype, model (2) was used with y being the vector of centered log-ratio abundance for each ASV or genus as the explained variable, X the incidence matrix with the log-fold change values, and b was the vector of fixed effects including the lab batch, herd, season, parity, lactation stage and the dairy phenotype which was previously corrected for the herd, parity, age at calving and month of calving of the cow. Considering the log-fold change values, their values were obtained after a comparison with a reference level for qualitative variables and for one unit increase for continuous variable (i.e., corrected phenotype). Taxa observed in less than 30% of samples were removed as well as samples with less than 5 500 sequences, for a total number of 597 animals. The resulting P-values were adjusted using the Benjamini-Hochberg procedure and taxa were declared as differentially associated when q-value < 0.05. The R code used to perform the diversity analyses and the differential abundance analysis is available in Supplementary Material S1 available at https://doi.org/10.6084/m9.fishshare.26072410.

## Results and discussion

In terms of α-diversity, although the Observed Richness was not significantly associated with dairy traits, Shannon indexes at the ASV and genus levels exhibited negative correlations with MY, PY and pINEL (Table 1). This finding is supported by the results of Xue et al. (2018), who reported negative correlations between daily milk performances and the ruminal Shannon index. Considering β-diversity, PERMANOVA results indicated a significant association between the whole structure of the faecal microbiota and five traits (Table 1). Monteiro et al. (2022) observed similar relationships between daily milk traits and both ruminal and faecal β-diversities.

Having established the co-variation of faecal microbiota with dairy traits, we implemented differential abundance analyses with the ANCOM-BC2 method (Lin and Peddada, 2020) to evaluate potential links between specific taxa and dairy traits. While no differential abundance was observed for genera in relation to PC and

FC, 48 genera were significantly associated with pINEL, MY, PY, and FY (Fig. 1a and b). These genera are described in Supplementary Table S3. Among them, 34 were observed in half of the samples, with relative abundances (RAs) ranging between 0.009 and 8.86% (mean = 0.50%). Besides, seven genera significantly associated with the three traits were identified as part of the core microbiota (prevalence ≥ 90%). These findings underscore the importance of considering both the core microbiota and less prevalent taxa (i.e., pan microbiota), as the whole composition of the faecal microbiota appears to be associated with host performance, echoing the finding of Xue et al. (2018) on rumen microbiota. Interestingly, *Bifidobacterium* (RA = 2.68%; prevalence = 88%) and the *p-1088-a5 gut group* (RA = 0.04%; prevalence = 42%) genera consistently exhibited the strongest positive and negative relationships, respectively, with pINEL (Fig. 1c), MY, FY, and PY (Supplementary Figure S2). To our knowledge, these results are unprecedented in ruminants.

The *p-1088-a5 gut group* genus, from the Pirellulaceae family, was found to be more abundant in less productive animals. This observation mirrors findings in bovine rumen, where a negative association between *p-1088-a5 gut group* and cow productivity has been reported (Fregulia et al., 2022). In the goat abomasum, Liu et al. (2020) noted an increased abundance of *p-1088-a5 gut group* associated with enhanced calcium digestibility. While calcium is a crucial nutrient in the bovine diet, excessive consumption could be detrimental to the animal. It could be suggested that some animals with specific digestive microbiota may have an enhanced ability to digest certain nutrients, including calcium. Notably, Monteiro et al. (2022) highlighted the significant role of hindgut microbiota in metabolising certain nutrients the rumen failed to process. This could lead to overconsumption, potentially impacting animal production negatively.

On the other hand, to our knowledge, we report here for the first time an association between increased *Bifidobacterium* abundance in the faeces and the improvement of several dairy traits. These beneficial associations are consistent with *Bifidobacterium*'s well-documented role as a commensal in the mammalian gastrointestinal tract where it modulates immune responses and prevents the growth of various pathogens (Milani et al., 2016). Since healthier animals tend to be more productive, increased *Bifidobacterium* abundance could contribute to improved host performance. Furthermore, its role in carbohydrate metabolism may also explain the positive association with dairy performances. Among gut bacterial genera, the *Bifidobacterium* pan-genome contains a significant number of genes encoding for carbohydrate-active enzymes (Milani et al., 2016), including glycosyl hydrolases family 13. These enzymes are specially known for their capacity to hydrolyse a wide range of complex carbohydrates. Thus, increased *Bifidobacterium* abundance could enhance the metabolism of complex carbohydrates up to the distal portion of the digestive tract, improving both feed efficiency and production. This hypothesis is in line with Monteiro et al. (2022)'s findings. A whole metagenome shotgun analysis should be performed to confirm the association between the abundance of genes involved in carbohydrate degradation and host milk performance.

To gain more insights on specific *Bifidobacterium* strains potentially involved in beneficial effects on dairy traits, additional ANCOM-BC2 analyses were performed at the ASV rank. Interestingly, we identified a unique ASV sharing a 16S V3-V4 amplicon sequence identical to those of *B. pseudolongum* and *B. globosum*. This ASV displayed positive associations with pINEL (log-fold change = 7.38 × 10⁻⁴, *P* = 4.36 × 10⁻¹¹³), MY (log-fold change = 1.58 × 10⁻⁴, *P* = 2.61 × 10⁻³¹), PY (log-fold change = 7.23 × 10⁻⁴, *P* = 4.50 × 10⁻⁴⁰), and FY (log-fold change = 6.87 × 10⁻⁴, *P* = 1.33 × 10⁻⁹³). *B. globosum* and *B. pseudologum*, considered as subjective synonyms (Lugli et al., 2019), are commonly described
<caption>**Table 1**</caption>

Pearson's correlations and *P*-values of the association studies between α-diversity (Pearson's correlations) and β-diversity (PERMANOVA) indexes of Holstein cows' faecal microbiota samples, respectively, and the host's production phenotypes.

<table><thead><tr><th rowspan="3">Host's phenotypes</th><th colspan="4">α-diversity</th><th colspan="4">β-diversity</th></tr><tr><th colspan="2">Observed Richness</th><th colspan="2">Shannon</th><th colspan="2">Bray-Curtis</th><th colspan="2">Jaccard</th></tr><tr><th>ASV</th><th>Genus</th><th>ASV</th><th>Genus</th><th>ASV</th><th>Genus</th><th>ASV</th><th>Genus</th></tr></thead><tbody><tr><td>Milk yield (MY)</td><td>NS</td><td>NS</td><td>-0.11</td><td>-0.09</td><td>0.13</td><td>NS</td><td>0.012</td><td>NS</td></tr><tr><td>Fat yield (FY)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>0.001</td><td>&lt;0.001</td><td>0.001</td><td>&lt;0.001</td></tr><tr><td>Fat content (FC)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>0.001</td><td>&lt;0.001</td><td>0.001</td><td>&lt;0.001</td></tr><tr><td>Protein yield (PY)</td><td>NS</td><td>NS</td><td>-0.13</td><td>-0.13</td><td>0.002</td><td>0.015</td><td>0.002</td><td>0.011</td></tr><tr><td>Protein content (PC)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>pINEL<sup>1</sup> index</td><td>NS</td><td>NS</td><td>-0.11</td><td>-0.12</td><td>0.002</td><td>0.008</td><td>0.003</td><td>0.007</td></tr></tbody></table>

Abbreviations: ASV=amplicon sequence variant; NS=non-significant; PERMANOVA=permutational multivariate analysis of variance.

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Results from the differential abundance analyses performed on the faecal microbiota of Holstein cows. **A.** Venn diagram with the number of genera significantly and positively associated with milk yield (MY), protein yield (FY), fat yield (FY) and the aggregate production phenotype (pINEL=0.98 × (PY+0.2 × FY+PC+0.5 × FC)). **B.** Venn diagram with the number of genera significantly and negatively associated with MY, PY, FY and pINEL. **C.** Genera whose abundances were significantly associated with pINEL with an absolute log-fold change ≥ 0.0001 for one unit increase of pINEL. The red bars and the blue bars represent the detrimental and beneficial relationships, respectively.</figcaption></figure>

in the mammalian gut and known to metabolise a large number of carbohydrates with a specific adaptation of their glycobiome to their ecological niches (Lugli et al., 2019). More specifically, Lugli et al. (2019) identified a subcluster of *B. globosum* particularly adapted to the ruminant gut. Specific microbiological research would be necessary to determine whether the observed ASV could be a *Bifidobacterium* strain adapted to the complex carbohydrates that reach the intestines without being metabolised. Once isolated, this strain, potentially linked with a better valorisation of carbohydrates, could be tested in probiotic trials to confirm its association with improved dairy performance. In addition, faecal microbiota was also found to be influenced by host's environment (i.e., Supplementary Figure S1). Thus, it would be relevant to perform deeper analyses focusing on the rearing condition and diet and how they impact the colonisation of the dairy cattle gut by this *Bifidobacterium* strain.

In conclusion, using a large cohort of Holstein cows as a discovery population, we identified significant associations between the bacterial and prokaryote community diversity and milk-related traits, validating our initial hypothesis and suggesting an interaction between the faecal microbiota and the dairy performance during the entire lactation. Notably, we also highlighted that dairy

performance is negatively associated with *p-1088-a5 gut group* and positively associated with *Bifidobacterium*. Interestingly, an ASV highly similar to *B. globosum* or *B. pseudologum* was identified. These results represent a relevant step towards a better understanding of the relationships between the faecal microbiota and dairy performance, which may be crucial for applications in the dairy industry.

## Supplementary material

Supplementary material to this article can be found online at https://doi.org/10.1016/j.animal.2024.101243.

## Ethics approval

This study was conducted on dairy cattle reared for commercial purposes in compliance with the French regulation (Code Rural et de la Pêche Maritime) and the European Council Directive 98/58/EC. The sampling of the animals was part of the routine animal manipulations carried out by duly authorised technicians from the Gènes Diffusion breeding company (Douai, France). Thus,

<footnote n="1">¹ aggregate phenotype based on formula: $pINEL=0.98 \times (PY+0.2 \times FY+PC+0.5 \times FC)$.</footnote>
according to the French legislation, no approval from the Institu-
tional Animal Care and Use Committee or ethics committee was
necessary. Besides, the farmers involved in this study agreed to
the use of their animals' samples for research purposes.

Our study is reported in full compliance with the ARRIVE guide-
lines (https://arriveguidelines.org/).

Data and model availability statement

The data used in this study are available upon request from the
corresponding author. The raw sequences are available through
project PRJEB75421 on the EMBL-EBI's European Nucleotide
Archive (https://www.ebi.ac.uk/ena browser/view/PRJEB75421):
ENA accessions ERS19339911 to ERS19340609.

Supplementary Table S1 and Supplementary Material S1 are
available at https://doi.org/10.6084/m9 figshare.26072410.

Declaration of Generative AI and AI-assisted technologies in the
writing process

During the preparation of this work the author(s) did not use
any AI and AI-assisted technologies.

Author ORCIDs

**Louise Brulin:** https://orcid.org/0009-0006-8264-1064.
**Sébastien Ducrocq:** https://orcid.org/0009-0004-6898-156X.
**Gaël Even:** https://orcid.org/0000-0003-2930-8204.
**Marie-Pierre Sanchez:** https://orcid.org/0000-0002-1371-5342.
**Sophie Martel:** https://orcid.org/0009-0008-8859-5833.
**Sophie Merlin:** https://orcid.org/0009-0003-9380-0866.
**Christophe Audebert:** https://orcid.org/0000-0002-6509-7574.
**Pascal Croiseau:** https://orcid.org/0000-0001-6493-2892.
**Jordi Estellé:** https://orcid.org/0000-0002-6241-1732.

CRediT authorship contribution statement

**L. Brulin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Data curation. **S. Ducrocq:** Writing – review & editing, Validation, Resources, Investigation, Funding acquisition, Conceptualization. **G. Even:** Writing – review & editing, Validation, Resources, Investigation, Funding acquisition, Conceptualization. **M.P. Sanchez:** Writing – review & editing, Validation, Investigation. **S. Martel:** Writing – review & editing, Resources, Methodology, Data curation. **S. Merlin:** Writing – review & editing, Resources, Methodology, Data curation. **C. Audebert:** Writing – review & editing, Validation, Resources, Investigation, Funding acquisition, Conceptualization. **P. Croiseau:** Writing – review & editing, Validation, Investigation. **J. Estellé:** Writing – review & editing, Validation, Supervision, Methodology, Investigation.

Declaration of interest

LB, SD, GE, SMa, SMe and CA are employed by GD Biotech /
Gènes Diffusion company. The remaining authors declare that

the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential con-
flict of interest.

Acknowledgements

The authors would like to thank both the breeders and the tech-
nicians who were involved in the collection of the samples, as well
as Gérard HEUMEZ (Gènes Diffusion) for his assistance in the data
processing.

The authors also thank the INRAE MIGALE bioinformatics facil-
ity (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10.
15454/1.5572390655343293E12) for providing the computational
resources needed for our analyses.

GD Biotech would like to express its gratitude to the ANR-10-EQPX-07-01 LIGAN-PM-UMR8199 for its technical support regard-
ing the MiSeq sequencing.

Financial support statement

This work has been financially supported by the French “Asso-
ciation Nationale de la Recherche et de la Technologie” (ANRT) that
allows the PhD contract of Louise Brulin (CIFRE n°2021/0713).

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