# Whole genome short read data from 567 bulls of 14 breeds provides insight into genetic diversity of French cattle

Mekki Boussaha, Camille Eché, Christophe Klopp, Cécile Grohs, Marine Milhes, Amandine Suin, Tabatha Bulach, Rachel Fourdin, Thomas Faraut, Claire Kuchly, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mekki Boussaha, Camille Eché, Christophe Klopp, Cécile Grohs, Marine Milhes, et al. Whole genome short read data from 567 bulls of 14 breeds provides insight into genetic diversity of French cattle. Data in Brief, 2025, 62, pp.112049. 10.1016/j.dib.2025.112049. hal-05268512

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Submitted on 19 Sep 2025

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

# Whole genome short read data from 567 bulls of 14 breeds provides insight into genetic diversity of French cattle

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

Mekki Boussaha<sup>a</sup>, Camille Eché<sup>b</sup>, Christophe Klopp<sup>c</sup>, Cécile Grohs<sup>a</sup>, Marine Milhes<sup>b</sup>, Amandine Suin<sup>b</sup>, Tabatha Bulach<sup>b</sup>, Rachel Fourdin<sup>b</sup>, Thomas Faraut<sup>d</sup>, Claire Kuchly<sup>b</sup>, Sébastien Fritz<sup>a,e</sup>, Caroline Vernette<sup>b</sup>, Maulana Naji<sup>a</sup>, Valentin Sorin<sup>a</sup>, Aurélien Capitan<sup>a</sup>, Christine Gaspin<sup>c</sup>, Denis Milan<sup>b,d</sup>, Didier Boichard<sup>a</sup>, Carole Iampietro<sup>b,*</sup>, Cécile Donnadieu<sup>b</sup>

ARTICLE INFO

Article history:
Received 2 May 2025
Revised 22 August 2025
Accepted 3 September 2025
Available online 9 September 2025

Dataset link: Whole-genome sequencing of 567 bulls (Original data)

Dataset link: Genetic variants from 567 French cattle samples (Original data)

ABSTRACT

Technological developments in high-throughput sequencing and advances in bioinformatic analysis allowed to sequence and study a very large number of genomes from a single species (cattle). Analyzing this data set enabled to generate the corresponding genomic variant database, especially for single nucleotide polymorphisms (SNPs) and small insertion or deletion (Indels) variations. These variants and genotypes allowed to better characterize the genetic diversity of these breeds.

In this work, we sequenced 567 bulls from 14 differ-
ent breeds (Holstein, Montbéliarde, Normande, Brown
Swiss, Simmental, Abondance, Tarentaise, Vosgienne,
Blonde d'Aquitaine, Charolaise, Limousine, Aubrac, Fla-
mande, Parthenaise). Each sample was sequenced at an

Data Article

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

<footnote><sup>b</sup>INRAE, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France</footnote>

<footnote><sup>c</sup>Plateforme Bio-informatique Genotoul, Mathématiques et Informatique Appliquées de Toulouse, INRAE, Castanet-Tolosan, France</footnote>

<footnote><sup>d</sup>GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Castanet-Tolosan Cedex, F-31326, France</footnote>

<footnote><sup>e</sup>ELIANCE, 75012 Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: carole.iampietro@inrae.fr (C. Iampietro).</footnote>
Keywords:
Sequencing
Variants
Bovine
Pangenome
Mutations
SNP
Polymorphism

approximately 15x depth on the Illumina Novaseq6000 platform. We detected 34,252,080 variants, 25,115,987 of which were already known in the Ensembl variation database version 110 and 9,136,093 were absent and were considered as novel variants. This data set represents a useful resource for the community to better identify SNPs or indels such as mutation anticipation and provides new insights into bovine genetic diversity.

© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

<table><caption>Specifications Table</caption><tr><td>Subject</td><td>Animal Science</td></tr><tr><td>Specific subject area</td><td>Investigating the genetic diversity of French cattle by sequencing the genomes of animals from multiple breeds and providing a broader range of SNPs.</td></tr><tr><td>Type of data</td><td>Raw sequencing reads (fastq files), variant (vcf file)</td></tr><tr><td>Data collection</td><td>The animals have been selected to cover the whole country of France and to belong to the 2 breeding types (dairy and suckling). All genomic DNAs were isolated from frozen semen samples using an automated DNA purification system. Whole genome short reads were produced using NovaSeq6000 (Illumina).</td></tr><tr><td>Data source location</td><td>Frozen semen samples were provided from various French breeding companies. Extracted ADN was stored at INRAE, Jouy-en-Josas, France.</td></tr><tr><td>Data accessibility</td><td>Repository name: European Nucleotide Archive (ENA) and https://entrepot.recherche.data.gouv.fr/ Data identification number: PRJEB64023 (ENA) Direct URL to data: https://doi.org/10.57745/BIFKZF https://www.ebi.ac.uk/ena/browser/view/PRJEB64023</td></tr><tr><td>Related research article</td><td>None</td></tr></table>

## 1. Value of the Data

* This data set is valuable, as it includes young and founder bulls from 14 bovine dairy and suckling breeds, well representing the current French cattle population.

* All these sequencing data are available and can be used by other researchers, for example to anticipate mutations that cause genetic diseases and to allow a systematic diagnosis of all sequenced bulls.

* They are also useful for the detection and analysis of zootechnical variants in cattle breeding, whether for dairy or suckling breeds.

* What's more, this exhaustive catalog of bovine genomic polymorphisms provides us with a reference base for imputing variants in animals genotyped by low pass sequencing.

## 2. Background

France has the largest cattle population in Europe: 17 million heads. Behind this figure lies a large diversity of breeds, with around fifty breeds adapted to different environmental conditions and farming systems [1].

Today, almost half of the cattle breeds have a selection system identifying bulls whose genetic value is based on the performance of their daughters. In each generation, bulls with optimal genetic value are selected for artificial insemination (AI), facilitating the spread of genetic improvement. Therefore, the ancestral bulls of a breed (also called founders) represent a significant portion of its genetic diversity. As a result, their genomes are likely to carry genetic variants
that explain the phenotypic variability within the breed, as well as deleterious recessive variants that will be spread until they become homozygous.

Since each breed corresponds to a small number of founders, sequencing the genome of these founder sires allows us to better characterize the extent of the genetic diversity within and between breeds.

In the present study we sequenced the genome of 567 AI bulls corresponding to the 14 most important French cattle breeds for which a selection program is currently underway.

### 3. Data Description

All read sets were sequenced with Illumina Novaseq6000 technology (Table 1).

The 567 samples belong to 14 breeds selected for this dataset (Table 2). Among these, five breeds (Holstein, Montébéliarde, Normande, Brown Swiss also named Brune and Simmental), already have a lot of founders data from sequencing efforts in France or in the 1000GB project international consortium. For these breeds, it was decided to sequence the genomes of 316 animals corresponding to the most common young sires recently marketed for AI.

For the other nine breeds (Abondance, Tarentaise, Vosgienne, Blonde d'Aquitaine, Charolaise, Limousine, Aubrac, Rouge Flamande, and Parthenaise), we sequenced the genome of important founder bulls. These were in fact "old" bulls that had been used extensively in AI in France and were considered as strong contributors to the breed genetics, and for which semen doses were available as biological samples.

Proportion of read passing quality filters for all breeds was above 95 % (Fig. 1), and per breed mapping rate was above 90 % (Fig. 2). Average coverage for all breeds is at 15X (Fig. 3)

This sequence dataset allowed a search for small genome variations using the GATK-Unified Genotyper software. This resulted in the identification of 34,252,080 variants. Approximately 32,214,102 (94 %) were bi-allelic variants out of which 28,931,312 corresponded to SNPs,

<caption>**Table 1**</caption>

Read set general description.

<table><caption>**Table 2**</caption><tr><td>Number of breeds</td><td>14</td></tr><tr><td>Number of samples</td><td>567</td></tr><tr><td>Number of files</td><td>2802</td></tr><tr><td>Total number of reads</td><td>257,526,959,592</td></tr><tr><td>Total number of nucleotides</td><td>38,552,948,856,468</td></tr></table>

Selected breeds and number of animals.

<table><thead><tr><td>Breed</td><td>Short name</td><td>Number of animals</td></tr></thead><tbody><tr><td>Abondance</td><td>ABO</td><td>13</td></tr><tr><td>Aubrac</td><td>AUB</td><td>40</td></tr><tr><td>Blonde d'Aquitaine</td><td>BAQ</td><td>30</td></tr><tr><td>Brune</td><td>BSW</td><td>9</td></tr><tr><td>Charolaise</td><td>CHA</td><td>60</td></tr><tr><td>Holstein</td><td>HOL</td><td>145</td></tr><tr><td>Limousine</td><td>LIM</td><td>88</td></tr><tr><td>Montébéliarde</td><td>MON</td><td>91</td></tr><tr><td>Normande</td><td>NMD</td><td>63</td></tr><tr><td>Parthenaise</td><td>PAR</td><td>4</td></tr><tr><td>Rouge Flamande</td><td>RDC</td><td>5</td></tr><tr><td>Simmental</td><td>SIM</td><td>5</td></tr><tr><td>Tarentaise</td><td>TAR</td><td>10</td></tr><tr><td>Vosgienne</td><td>VOS</td><td>4</td></tr><tr><td>Total</td><td></td><td>567</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Distributions of read passing quality filters for each breed. The x axis presents the short names of different breeds (presented in Table 2) and the y axis the percentages of non-quality filtered reads. Each colored box plot presents the distribution of the percentage of non-quality filtered reads of the sampled bulls for a breed.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Mapping rate distribution for each breed. The x axis presents the short names of different breeds (presented in Table 2) and the y axis the percentage of reads mapped on the reference. Each colored box plot presents the distribution of the alignment rates of the bulls sampled for a breed.</figcaption></figure>

1,836,784 were deletions and 1,446,006 insertions. For 12,516 insertions and 18,149 deletions, their size was higher than 50 nucleotides and can be categorized as structural variants. The largest deletions and insertions identified were respectively 308 and 289 bp long. Altogether, the variants covered the entire genome and on average we observed one polymorphism every 94 nucleotides (Table 3).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Read coverage for each breed. The x axis presents the reference genome coverage of each read bull read set. The y axis corresponds to the number of samples showing this coverage. Each horizontal block and color corresponds to a breed.</figcaption></figure>

Overall, 25,115,987 (73.3 %) of the identified variants were known in the Ensembl variation 110 database (build 143). The remaining variants (~27 %) were considered as novel variants and should help to better highlight the genetic variability in cattle. Out of these novel variants, 8,072,484 were found to be bi-allelic variants and corresponded to 6,083,529 SNPs, 764,692 small deletions, 15,670 large deletions (SVs), 1,198,685 small insertions and 9908 large insertions (SVs)

To evaluate the quality of our sequencing data-derived genotypes, we performed two different analyses.

First, we used the ratio of transitions over transversions (Ts/Tv) as a diagnostic measure to assess the quality of our sequencing data. The average Ts/Tv ratio observed in our whole-genome sequencing data was 2.26 and ranged from 2.24 to 2.28 (Table 4). This average rate is slightly higher than the 2.15 rate which we reported earlier [2]. In fact, it has been previously reported that the Ts/Tv ratio should be around 2.1 for whole-genome sequencing and that lower ratios may indicate that the sequencing data includes false positives caused by random sequencing errors [3]. Therefore, the Ts/Tv ratio estimated in our study is indicative of good sequencing data quality.

Note: Only SNPs are used for this statistic. This Ts/Tv ratio is a 'raw' ratio (ratio of observed events).

Second, we compared our sequencing data-derived genotypes to SNP array-derived genotypes using the Bovine 50 K SNP BeadChip (BovineSNP50 BeadChip version 1 or 2, Illumina, San Diego, CA). Overall, both sequence-derived and SNP assay produced genotyping data sources were available for 557 samples. The average genotype concordance rate was around 99.51 % and ranged from 94.63 to 99.85 % (see additional file "concordance_rate").

In order to validate the correct assignment of animals to their breed, we conducted PCA analysis. After applying all filters (described in the Materials and Methods section), 664,950 variants were retained and used for principal component analysis. Our PCA approach allowed us to describe the genetic structure of the 14 breeds and showed that all animals were successfully assigned to their corresponding breed and provided a good description of the breed structure (Fig. 4). Our results are of particular interest as they could be considered as a global statistical validation step to evaluate the quality of sequence-derived genotypes.
<caption>**Table 3**</caption>

Chromosomal distribution of variants. The columns correspond respectively to the chromosome Id, its length, the identified number of variants and the inter-variants distance which corresponds to the average distance on the reference genome between two variants (unit pb).

<table><caption>**Table 4**</caption><thead><tr><th>Chromosomes</th><th>Length</th><th>Variants</th><th>Distance between 2 variants on the reference genome</th></tr></thead><tbody><tr><td>1</td><td>158,534,110</td><td>2,119,337</td><td>75</td></tr><tr><td>2</td><td>136,231,102</td><td>1,674,722</td><td>81</td></tr><tr><td>3</td><td>121,005,158</td><td>1,506,303</td><td>80</td></tr><tr><td>4</td><td>120,000,601</td><td>1,670,293</td><td>72</td></tr><tr><td>5</td><td>120,089,316</td><td>1,582,472</td><td>76</td></tr><tr><td>6</td><td>117,806,340</td><td>1,547,483</td><td>76</td></tr><tr><td>7</td><td>110,682,743</td><td>1,382,195</td><td>80</td></tr><tr><td>8</td><td>113,319,770</td><td>1,395,018</td><td>81</td></tr><tr><td>9</td><td>105,454,467</td><td>1,301,147</td><td>81</td></tr><tr><td>10</td><td>103,308,737</td><td>1,364,105</td><td>76</td></tr><tr><td>11</td><td>106,982,474</td><td>1,307,516</td><td>82</td></tr><tr><td>12</td><td>87,216,183</td><td>1,363,627</td><td>64</td></tr><tr><td>13</td><td>83,472,345</td><td>1,030,393</td><td>81</td></tr><tr><td>14</td><td>82,403,003</td><td>1,011,664</td><td>81</td></tr><tr><td>15</td><td>85,007,780</td><td>1,256,958</td><td>68</td></tr><tr><td>16</td><td>81,013,979</td><td>1,071,535</td><td>76</td></tr><tr><td>17</td><td>73,167,244</td><td>1,025,514</td><td>71</td></tr><tr><td>18</td><td>65,820,629</td><td>922,253</td><td>71</td></tr><tr><td>19</td><td>63,449,741</td><td>796,878</td><td>80</td></tr><tr><td>20</td><td>71,974,595</td><td>940,616</td><td>77</td></tr><tr><td>21</td><td>69,862,954</td><td>904,394</td><td>77</td></tr><tr><td>22</td><td>60,773,035</td><td>741,570</td><td>82</td></tr><tr><td>23</td><td>52,498,615</td><td>1,052,655</td><td>50</td></tr><tr><td>24</td><td>62,317,253</td><td>825,580</td><td>75</td></tr><tr><td>25</td><td>42,350,435</td><td>592,067</td><td>72</td></tr><tr><td>26</td><td>51,992,305</td><td>663,034</td><td>78</td></tr><tr><td>27</td><td>45,612,108</td><td>653,030</td><td>70</td></tr><tr><td>28</td><td>45,940,150</td><td>635,508</td><td>72</td></tr><tr><td>29</td><td>51,098,607</td><td>776,586</td><td>66</td></tr><tr><td>X</td><td>139,009,144</td><td>1,066,765</td><td>130</td></tr><tr><td>Y</td><td>43,300,181</td><td>70,862</td><td>611</td></tr><tr><td>Total</td><td>2,628,394,924</td><td>34,252,080</td><td>94</td></tr></tbody></table>

Distribution of Ts/Tv (transitions / transversions).

<table><tr><td>Transitions</td><td>3,546,644,621</td></tr><tr><td>Transversions</td><td>1,569,468,314</td></tr><tr><td>Ts/Tv ratio</td><td>2.26</td></tr></table>

# 4. Experimental Design, Materials and Methods

## 4.1. Sample collection & technical validation

Semen samples were collected from ejaculates of selection program AI bulls by cattle breeding companies, and stored with solvent-free animal product. The Qiasymphony extraction kit was used to extract DNA from these samples. DNA was stored at -20 °C. Genomic DNA concentrations were measured using the Qubit fluorimetry system (Life Technologies) with the High Sensitivity (HS) kit for detection of double-stranded DNA (Thermo Fisher, Part #Q32854). Fragment size distributions were assessed using the Fragment Analyser Kit (Agilent). Purity was measured using a Nanodrop system (Thermofisher). All samples were purified with AMPureXP beads (Beckman Coulter, A63882) to obtain correct ratios i.e. 260/280: 1.8-2 and 260/230:2-2.2.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Results of PCA analysis. PCA (PC1/PC2) results were shown for the 14 breeds used in the study.</figcaption></figure>

## 4.2. Sequencing

All sequencing processes were performed at the GeT-PlaGe core facility at INRAE Toulouse, France (DOI: 10.17180/nvxj-5333) ([http://get.genotoul.fr](http://get.genotoul.fr)).

DNA-seq libraries were prepared according to the Illumina protocol TruSeq NanoDNA. DNA was fragmented by sonication on Pixul, size selection was performed using Sample Purification Beads of the library kit (ratio 1/1 beads water) and adaptors IDT for Illumina - TruSeq DNA UD Indexes (96 Indexes, 96 Samples, 20,022,370) were ligated before sequencing. Library quality was assessed using a Fragment Analyzer (Agilent) with High Sensitivity NGS Kit (DNF-474-0500). Sizes of 800 bp were obtained. They were then quantified by qPCR using the KAPA Library Quantification Kit (Roche, 07,960,140,001) and sequenced by pools of ~60. Pools were sequenced on 34 S4 lanes and 4 SP lanes NovaSeq 6000 using a paired-end read length of 2 x 150 bp with the Illumina Reagent kit (300 cycles) which corresponds to 19X coverage.

To evaluate the quality of our sequencing data-derived genotypes, we compared, for each sample, our sequencing data-derived genotypes to SNP array-derived genotypes using the Illumina 50 K Bovine SNP assay (BovineSNP50 BeadChip version 1 or 2, Illumina, San Diego, CA).

## 4.3. Whole-genome sequence alignment to the reference and variant calling

Raw paired read sequences were first trimmed of adapters, low quality bases (qscore <20) at start and end. Reads with mean qscore less than 20 or length less than 35 bp were then filtered out using the Trimmomatic software version 0.38 [4,5].

Sequence alignments were carried out using the Burrows-Wheeler Alignment tool (BWA 0.7.17 [6] with the mem option with default parameters for mapping reads to the ARS-UCD1.2 bovine reference genome. Potential PCR duplicates, which can adversely affect the variant calls, were removed using the MarkDuplicates tools from the Picard package version v2.18.2 [7]. Base quality recalibration was subsequently performed according to GATK best practices guidelines
[8]. Only properly paired reads with a mapping quality of at least 30 (−q = 30) were retained. The resulting BAM files were then used to produce a GVCF file for each sample. Finally, variant calling was performed on all GVCF files using the GATK GenotypeGVCFs options.

4.4. Population Structure Analysis

We conducted PCA analysis using version 2.00a4 of the PLINK software [9], and used the gg-
plot2 library in R for visualizing the results [10]. First, we performed SNP quality control, defin-
ing our criteria as follows: only SNPs were used in the analysis, only autosomes were selected
for further analysis, only call rates of more than 95 % were kept, and only minor allele frequency
of more than 0.05 was accepted. Moreover, to avoid biases caused by linkage disequilibrium, we
also pruned SNPs for LD using the command 'indep (50 10 0.1)'. Finally, we calculated PCA (–pca
566) and visualized it by the ggplot2 package in R (V4.3.3).

Limitations

Not applicable.

Ethics Statement

The experiments reported in this paper comply with the ethical guidelines of the French National Research Institute for Agriculture, Food and Environment (INRAE) and its French research partners. No animal was intentionally bred for this study, and no invasive sampling was performed. Therefore, no ethical approval was required for this study. Breeding companies, as partners in the project, provided commercial straws. These samples were not produced for the project, but are prepared and distributed for AI as part of cattle selection programs.

Credit Author Statement

**M Boussaha:** Formal analysis, Visualization, Validation, Writing - Original Draft, Writing - Review & Editing. **C Eché:** Writing - Original Draft, Investigation. **C Grohs:** Resources, Investigation, Writing - Original Draft, Writing - Review & Editing. **M Milhes:** Investigation. **A Suin:** Investigation. **T Bulach:** Investigation. **R Fourdin:** Investigation. **C Kuchly:** Data Curation. **C Köpp:** Formal analysis, Visualization, Validation, Writing - Original Draft, Writing - Review & Editing. **C Vernette:** Data Curation. **T Faraut:** Writing - Original Draft. **S Fritz:** Resources, Conceptualization. **M Naji:** Formal analysis, Visualization, Review & Editing. **V Sorin:** Formal analysis. **A Capitan:** Writing - Original Draft, Writing - Review & Editing. **D Boichard:** Funding acquisition, Conceptualization. **C Gaspin:** Funding acquisition, Conceptualization. **D Milan:** Funding acquisition, Conceptualization: Funding acquisition, Conceptualization. **C Iampietro:** Project administration, Supervision, Writing - Original Draft, Writing - Review & Editing. **C Donnadieu:** Funding acquisition, Conceptualization, Supervision.

Funding

This work was supported by "La Région Occitanie" and European Union (Operational Program FEDER-FSE MIDI-PYRENEES ET GARONNE 2014-2020, call for projects "Regional Platforms of Research and Innovation" of the Occitanie region). APIS-GENE and also industrial organisations of the ruminant sector CNIEL, Interbev, Eliance, CNE who contributed to the financing of these
analyses. GeT-PlaGe facility was supported by France Génomique National infrastructure, funded
as part of “Investissement d'avenir” program managed by Agence Nationale pour la Recherche
 contract ANR-10-INBS-09).

Data Availability

Whole-genome sequencing of 567 bulls (Original data) (Dataverse)

Genetic variants from 567 French cattle samples (Original data) (Dataverse)

Acknowledgements

We thank members of SeqOccIn Consortium for their feedback before and during this
study. The SIGENAE team carried out part of the analysis. We are greatful to the geno-
toul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.
5572369328961167E12) for providing computing and storage resources.

Declaration of Competing Interest

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

**Supplementary Materials**

Supplementary data associated with this article can be found, in the online version, at doi:
10.1016/j.dib.2025.112049.

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