# Sequence-based GWAS reveals genes and variants associated with predicted methane emissions in French dairy cows

Solène Fresco, Marie-Pierre Sanchez, Didier Boichard, Sébastien Fritz, Pauline Martin

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Solène Fresco, Marie-Pierre Sanchez, Didier Boichard, Sébastien Fritz, Pauline Martin. Sequence-based GWAS reveals genes and variants associated with predicted methane emissions in French dairy cows. Genetics Selection Evolution, 2025, 57 (1), pp.32. 10.1186/s12711-025-00977-z. hal-05120211

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RESEARCH ARTICLE

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# Sequence-based GWAS reveals genes and variants associated with predicted methane emissions in French dairy cows

Solène Fresco¹,<fnref n="2" />* , Marie-Pierre Sanchez<fnref n="2" />, Didier Boichard<fnref n="2" />, Sébastien Fritz¹,<fnref n="2" /> and Pauline Martin<fnref n="2" />

## Abstract

**Background** Due to their contribution to global warming, methane emissions from ruminants have been the subject of considerable scientific interest. It has been proposed that such emissions might be reduced using genetic selection; proposed phenotypes differ in the measurement methods used (direct or predicted methane emissions) and in the unit under consideration (g/d, g/kg of milk, g/kg of intake, residual methane emissions). Identifying the quantitative trait loci (QTLs) and candidate genes responsible for genetic variation in methane emissions allows a better understanding of the underlying genetic architecture of these phenotypes. Therefore, the aim of this study was to identify the genomic regions associated with six methane traits predicted from milk mid-infrared (MIR) spectra (0.33 ≤ R<fnref n="2" /> ≤ 0.88) in French Holstein dairy cows using genome-wide association studies at the whole-genome-sequence level.

**Results** Six methane emission traits—in g/d, in g/kg of fat- and protein-corrected milk, and in g/kg of dry matter intake—were predicted from milk MIR spectra routinely collected by French milk recording companies. A genome-wide association study of the predicted methane emissions of 40,609 primiparous Holstein cows was conducted using imputed whole-genome-sequence data. This analysis revealed 57 genomic regions of interest; between 1 and 8 QTLs were identified on each of the autosomes except 4, 12, 21, 24 and 26. We identified multiple genomic regions that were shared by two or more predicted methane traits, illustrating their common genetic basis. Functional annotation revealed potential candidate genes, in particular FASN, DGAT1, ACSS2, and KCNIP4, which could be involved in biological pathways possibly related to methane production.

**Conclusions** The methane traits studied here, which were predicted from milk MIR spectra, appear to be highly polygenic. Several genomic regions associated with these traits contain candidate genes previously associated with milk traits. Functional annotation and comparisons with studies using direct methane measurements support some potential candidate genes involved in biological pathways related to methane production. However, the overlap with genes influencing milk traits highlights the challenge of distinguishing whether these regions genuinely influence methane emissions or reflect the use of milk MIR spectra to predict the phenotypes.

## Background

The contribution of the agricultural sector, particularly cattle breeding, to climate change has been extensively highlighted in recent years. Due to the enteric fermentation in their digestive process, cattle are the primary source of agricultural methane (CH₄) emissions [1, 2]. Various strategies have been identified to mitigate these

<footnote>*Correspondence:
Solène Fresco
solene.fresco@inrae.fr
¹ Eliance, 149 Rue de Bercy, 75595 Paris cedex 12, France</footnote>

<footnote n="2">² Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France</footnote>
emissions, including modifications in feeding strategies, feeding additives, herd management, and genetic selection [3, 4]. Among these proposals, though, only genetic selection offers a cumulative and permanent solution for reducing CH₄ emissions.

Methane production in ruminants results from the activity of microorganisms within the rumen. The fermentation of ingested feed by rumen bacteria produces hydrogen, which is primarily converted into CH₄ by archaea through methanogenesis [4]. Numerous studies have analyzed the genetic determinism of CH₄ emissions in cattle [5–9], but the ways in which this process is influenced by host genetics are still unclear and require further research.

One effective approach for investigating the genetic basis of CH₄ emissions is the use of Genome-Wide Association Studies (GWAS). GWAS identifies genomic markers associated with a phenotype of interest, and significant markers located in the same region collectively indicate Quantitative Trait Loci (QTLs). Within QTL regions, it is possible to identify candidate genes that can improve our understanding of the biological mechanisms underlying CH₄ emissions. By combining information on both genetic markers and functional annotation, likely causal variants can be proposed. This biological information can be used to design more precise and effective breeding strategies, ultimately contributing to a more sustainable agricultural sector.

Previous GWASs performed on direct CH₄ measurements identified several genomic regions that were associated with CH₄ emissions [10–14]. However, the use of direct CH₄ phenotypes, which are recorded with low-to-medium throughput methods such as Sniffer, GreenFeed, the SF₆ tracer gas method, and respiration chambers, limits the number of animals that can be included in the analyses and therefore both the detection power and resolution of the results. Some studies have used CH₄ predicted from feed intake phenotypes [15–17], though these phenotypes are similarly challenging to record at large scale. However, the development of equations to predict CH₄ emissions from milk mid-infrared (MIR) spectra [18–21] (0.38 ≤ R² ≤ 0.88) has made possible the collection of much larger datasets, enabling more accurate genetic and genomic analyses.

The present study had two primary objectives. Using a large database of French Holstein primiparous cows and milk MIR spectra, which are routinely collected by French milk recording companies, we aimed to (1) detect QTLs associated with CH₄ emissions predicted from milk MIR spectra, and (2) identify positional candidate genes within those QTLs.

# Materials and methods

## Animals and spectra data

The initial dataset consisted of 1,221,705 milk MIR spectra collected between 70 and 200 days in milk (DIM) from 359,856 primiparous Holstein cows. The following selection criteria were applied. Only lactations from the A-type recording protocol with samples from both morning and evening milkings were kept. The selected lactations started between 2015 and 2021, and each lactation had at least two records. The CH₄ phenotypes were predicted according to Fresco et al. [20], Vanlierde et al. [19], and Chilliard et al. [18] and are described in the next paragraph. All other information was extracted from the bovine national database (INRAE, CTIG Jouy-en-Josas, France).

## Methane phenotypes

Methane traits were predicted from milk MIR spectra that were standardized according to Grelet et al. [22]. Different prediction equations were applied to the milk MIR spectra to obtain six distinct CH₄-related traits:

* MeP_FA (in g/d), obtained from the equation (R²=0.88) developed by Chilliard et al. [18] based on fatty acid composition predicted from milk MIR spectra and CH₄ measurements with the SF₆ tracer gas method in a 4×4 Latin square experiment.

* MeP_RC (in g/d), obtained from the equation (R²=0.60) of Vanlierde et al. [19], which was developed using milk MIR spectra and CH₄ measurements with respiration chambers and the SF₆ tracer gas method for 299 cows from 7 breeds.

* MeP_direct (in g/d, R²=0.38), MeY (in g/kg of dry matter intake (DMI), R²=0.33), and MeI (g/kg of fat- and protein-corrected milk (FPCM), R²=0.47), obtained from the equations developed by Fresco et al. [20] from milk MIR spectra and GreenFeed (C-Lock Inc., Rapid City, SD, USA) measurements for 235 cows from 3 breeds, of which only 76 cows from 1 breed were used to calibrate the equation predicting MeY.

* MeP_indirect (in g/d, R²=0.38), obtained by multiplying the predicted MeI by the observed FPCM associated with the spectra.

Only test-day data between 70 and 200 days in milk were considered, according to Fresco et al. [20]; outside this interval, the validity of the equations predicting MeP_direct, MeI, and MeY was not guaranteed. Due to contractual restrictions, milk MIR spectra were not directly available to us. Instead, predictions were generated by ESTEL (Nancy, France), the IT company in
charge of routinely performing all milk MIR spectra predictions for French milk recording companies.

## Adjustment of phenotypes

For each trait, outlier values—defined as those falling outside three standard deviations around the mean—were excluded from the dataset. Univariate repeatability models were applied to estimate environmental effects and then to adjust the phenotypes (i.e. each prediction from the milk MIR spectra), using Genekit software [23]. The pedigree was traced back four generations and contained 903,383 animals. The linear model was the following:

$$y = X\beta + Za + Wpe + e \quad (1)$$

where y is the vector of CH₄ predictions; β is the vector including the fixed effects of herd x test-day [168,362 classes], year x month of calving [84 classes], age at calving [17 classes], and the covariate days in milk modeled by third-order Legendre polynomials; X is the corresponding incidence matrix; a ~ N(0, Aσ²ₐ) is the vector of breeding values, with A the pedigree relationship matrix among individuals and σ²ₐ the additive genetic variance; pe ~ N(0, Iσ²ₚe) is the vector of permanent environmental effects, with I the identity matrix and σ²ₚe the permanent environmental variance; Z and W are the incidence matrices; and e ~ N(0, Iσ²ₑ) is the vector of residual effects, with σ²ₑ the residual variance. Variance components were estimated from a subset of 579,753 records from 156,381 cows, already described in Fresco et al. [9]. Genetic covariance between the six CH₄ traits were estimated using bivariate analyses with Eq. (1). These components were estimated using the AI-REML algorithm, as implemented in Wombat software [24]. The corrected phenotypes were then averaged for each individual for use in the GWAS analyses.

## 50 K genotypes and imputation to whole-genome sequence

Among the cows with phenotypes, 40,609 had genotypes for the 50 K chip and were subsequently used in GWAS analyses. The 50 K genotypes were imputed to whole-genome sequences (WGS) in two steps, similar to the procedure presented by Tribout et al. [25] and Sanchez et al. [26]. Briefly, we first imputed from 50 K to high density (HD) within breed using FImpute software [27], using 776 major French Holstein bulls genotyped with the HD Illumina chip as a reference, and then from HD to WGS with Minimac software [28], using the WGS variants of a multi-breed *Bos taurus* bull population from Run9 of the 1000 Bull Genomes Project [29]. This panel included 3414 bulls from 45 breeds, including 1414

Holsteins. After quality control (MAF>0.01, Minimac imputation R²>0.20), 13,366,479 variants were retained for GWAS analyses of the 29 autosomes.

## GWAS analyses

Due to computational challenges arising from the large size of the genomic matrix, we performed the GWAS analyses by randomly splitting the population into two equal sub-populations. Single-trait association analyses were then performed across all 13,307,594 variants (MAF>0.01) for each of the six predicted CH₄ traits. All association analyses were performed using the mlma option of GCTA software (version 1.26), which applies a mixed linear model that includes the variant to be tested [30]:

$$y_c = 1\mu + xb + Zu + e \quad (2)$$

where y_c is the vector of averaged corrected phenotypes for each of the six CH₄ traits; μ is the overall mean; b is the additive fixed effect of the variant to be tested for association; x is the vector of imputed alternate allele dosages, coded from 0 to 2; u ~ N(0, Gσ²_u) is the vector of random polygenic effects, with G the genomic relationship matrix calculated using the 50 K genotypes and σ²_u the polygenic variance, estimated based on the null model (y_c = 1μ + Zu + e) and then fixed while testing for the association between each variant and the trait; and e ~ N(0, Iσ²_e) is the vector of random residual effects, with I the identity matrix and σ²_e the residual variance. Z was the incidence matrix.

We combined the two GWAS results by performing a meta-analysis (meta-GWAS) using METAL software [31] with the STDERR option, which applies the fixed effects approach. We estimated the inflation factor for each trait (1.10–1.25) and corrected the p-values accordingly. To account for multiple testing, we applied a highly conservative approach, the Bonferroni correction that considered 13,307,594 independent tests in the meta-GWAS. The 5% genome-wide threshold of significance therefore corresponded to a nominal P-value of 3.75 × 10⁻⁹ (-log10(P) = 8.4) per test. We determined QTL intervals based on a linkage disequilibrium analysis using the correlations between imputed dosages. We applied the iterative procedure described in Sanchez et al. [32]. In short, this procedure selects the variant with the highest -log10(P) per *Bos taurus* autosome (BTA), estimates its linkage disequilibrium (LD) with nearby variants within a user-defined window, and defines the QTL confidence interval based on high-LD variants. A new test statistic evaluates whether other variants’ effects are explained by LD with the top variant. Variants meeting this criterion and identified in the QTL confidence interval are removed before the next iteration.
For our study, we used the following parameters in the iterative procedure: a minimal linkage disequilibrium of 0.45, a minimal MAF of 0.01, a minimal impulation R² of 0.2, and a window size set to 4 Mbp. When we observed that the TOP1 variant was in high LD with variants at the edge of the window and that another QTL was detected just outside of the bounds of the window, we performed a second LD analysis using the same iterative procedure with the same parameters on a larger window for the concerned BTAs to determine whether we really had two neighboring QTLs or whether it was a unique QTL with LD at a greater distance. We widened the window to 8 Mbp (BTA1, 3, and 14 for MeP_RC; BTA14 for MeP_direct; BTA14 and 20 for MeI; BTA19 for MeP_FA) or 18 Mbp (BTA14 for MeY and BTA6 for MeP_RC). For each trait, the proportion of genetic variance explained by each QTL ($\sigma_{u\_QTL}^2$) was estimated using $\sigma_{u\_QTL}^2 = 100 \times (2 \times p_{ms} \times (1 - p_{ms}) \times \hat{b}_{ms}^2)/\sigma_u^2$, with $p_{ms}$ and $\hat{b}_{ms}$ the frequency and the estimated allelic substitution effect, respectively, of the variant with the most significant effect (ms) in the QTL.

## Annotation of candidate genes

Genomic regions were defined as regions containing one QTL or multiple QTLs whose confidence intervals overlapped. Within these regions, the variant with the most significant effect, i.e., ranked first in the peak, was referred to as the TOP1. The top ten most significant variants within each peak were referred to as the TOP10. QTLs were annotated using the Ensembl gene set integrating the ARS-UCD1.3 bovine reference genome [33]. Positional candidate genes were defined as those containing at least one variant from the TOP10. To further characterize these variants, the Ensembl Variant Effect Predictor (VEP, [34]) was used to provide detailed functional annotations.

## Results

### Estimation of genetic parameters using a repeatability model

Descriptive statistics for the six CH₄ traits during the first lactation, measured between 70 and 200 days in milk, are the same as in Fresco et al. [9] and are presented in Table 1. For the four CH₄ traits measured in g/d, the average emissions varied from 367 to 448 g/d, while mean MeI was 14.0 g/kg of FPCM and mean MeY was 17.8 g/kg of DMI. The genetic, permanent environmental, and residual variances for these traits are detailed in Table 2. Heritability estimates varied from 0.23 to 0.42 depending on the CH₄ trait (Table 2).

<table><caption>**Table 1** Descriptive statistics of the methane traits predicted from milk MIR spectra, used for estimation of variance components</caption><thead><tr><th>Trait</th><th>Mean</th><th>Standard deviation</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td>MeP_direct (g/d)</td><td>420</td><td>41.2</td><td>227</td><td>572</td></tr><tr><td>Mel (g/kg of FPCM)</td><td>14.0</td><td>1.82</td><td>7.4</td><td>19.7</td></tr><tr><td>MeY (g/kg of DMI)</td><td>17.8</td><td>1.40</td><td>13.4</td><td>22.9</td></tr><tr><td>MeP_ indirect (g/d)</td><td>367</td><td>80.2</td><td>95</td><td>630</td></tr><tr><td>MeP_FA (g/d)</td><td>448</td><td>43.61</td><td>295</td><td>570</td></tr><tr><td>MeP_RC (g/d)</td><td>427</td><td>41.76</td><td>292</td><td>577</td></tr></tbody></table>

<table><caption>**Table 2** Variance and heritability estimates for the six methane traits predicted from milk MIR spectra</caption><thead><tr><th>Trait</th><th>Genetic variance</th><th>Permanent environment variance</th><th>Residual variance</th><th>Heritability</th></tr></thead><tbody><tr><td>MeP_direct (g/d)</td><td>291</td><td>139</td><td>310</td><td>0.39 (0.006)</td></tr><tr><td>Mel (g/kg of FPCM)</td><td>0.36</td><td>0.14</td><td>0.56</td><td>0.34 (0.006)</td></tr><tr><td>MeY (g/kg of DMI)</td><td>0.24</td><td>0.68</td><td>0.31</td><td>0.35 (0.006)</td></tr><tr><td>MeP_ indirect (g/d)</td><td>690</td><td>1204</td><td>1050</td><td>0.23 (0.007)</td></tr><tr><td>MeP_FA (g/d)</td><td>274</td><td>160</td><td>459</td><td>0.31 (0.006)</td></tr><tr><td>MeP_RC (g/d)</td><td>364</td><td>133</td><td>368</td><td>0.42 (0.006)</td></tr></tbody></table>

### Genome-wide association studies for the six predicted CH₄ traits

The results of the meta-GWAS combining the single-trait GWAS analyses of the two randomly divided groups of individuals are presented in Fig. 1. The number of variants with a genome-wide significant effect (−log10(P) ≥ 8.4) varied from 157 to 9758 depending on the CH₄ trait in question (Table 3). There were between 1 and 3422 significant variants per QTL, with an average of 313 variants (standard deviation = 618). Notably, 86.5% (90 of 104) and 92.0% (897 of 975) of the TOP1 and TOP10 variants were imputed, respectively. Only 4.9% of the TOP1 and 5.1% of the TOP10 variants presented a MAF between 0.01 and 0.02. The maximum −log10(P) value observed was 181, for a variant on BTA14 associated with MeY.

We detected 2 to 28 QTLs per CH₄ trait, for a total of 104 QTLs across all six CH₄ traits (Table 4a, b, c, d, and e). These QTLs were distributed across 24 of the 29 autosomes, with 1 to 15 QTLs per BTA. The confidence intervals of the QTLs varied from 2 to 11,980,490 bp, and 3 QTL were defined by a single variant. When neighboring QTLs had overlapping confidence intervals, we combined them into genomic regions; this resulted in
<figure><img src="image_3.png" /><figcaption>**Fig. 1** -log10(P) values plotted against the position of variants on *Bos taurus* autosomes for the meta-analyses of methane traits predicted from milk mid-infrared spectra. MeP_direct = prediction of methane production in g/d [20], MeI = prediction of methane intensity in g/kg of fat- and protein-corrected milk [20], MeY = prediction of methane yield in g/kg of dry matter intake [20], MeP_indirect = predicted MeI multiplied by the observed fat- and protein-corrected milk [20], MeP_FA = prediction of methane production in g/d [18], and MeP_RC = prediction of methane production in g/d [19]</figcaption></figure>
<table><caption>**Table 3** Number of significant variants, number of QTLs, and percentage of genetic variance explained</caption><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Number of significant variants</th><th rowspan="2">Number of QTLs</th><th rowspan="2">SE of QTL effect</th><th colspan="3">% of genetic variance explained by the QTL</th></tr><tr><th>Minimum</th><th>Maximum</th><th>Total</th></tr></thead><tbody><tr><td>MeP_direct (g/d)</td><td>4649</td><td>17</td><td>0.01–0.74</td><td>0.12</td><td>4.0</td><td>12.0</td></tr><tr><td>MeI (g/kg of FPCM)</td><td>9758</td><td>25</td><td>0.01–0.07</td><td>0.16</td><td>7.1</td><td>24.7</td></tr><tr><td>MeY (g/kg of DMI)</td><td>7922</td><td>17</td><td>0.01–0.02</td><td>0.14</td><td>12.5</td><td>20.7</td></tr><tr><td>MeP_indirect (g/d)</td><td>157</td><td>2</td><td>0.45–0.57</td><td>0.63</td><td>0.9</td><td>1.5</td></tr><tr><td>MeP_FA (g/d)</td><td>2911</td><td>15</td><td>0.15–0.42</td><td>0.12</td><td>3.1</td><td>11.7</td></tr><tr><td>MeP_RC (g/d)</td><td>7186</td><td>28</td><td>0.15–0.90</td><td>0.14</td><td>6.4</td><td>23.0</td></tr></tbody></table>

the identification of 57 distinct genomic regions, each containing between 1 and 8 QTLs. Of these, 22 genomic regions contained at least 2 QTLs affecting one or multiple CH₄ traits. Four regions were of particular interest: one located on BTA14 at ~64 Mbp (region 28), which was associated with all six CH₄ traits; one located on BTA14 at ~0.5 Mbp (region 26), which was associated with five of the six CH₄ traits; and two regions located on BTA1 at ~142 Mbp (region 2) and BTA15 at ~65 Mbp (region 32), which were associated with four of the six CH₄ traits.

The CH₄ traits with the highest number of genomic regions in common were MeP_direct and MeP_RC (8 genomic regions), followed by MeP_RC and MeY (7), MeI and MeY (7), MeP_direct and MeP_FA (6), and MeP_RC and MeI (6) (Table 5).

The percentage of genetic variance explained for each trait varied from 1.5% to 24.7% (Table 3). The variants explaining the highest percentage of genetic variance were found in QTL 51 (4.0%) for MeP_direct; QTL 80 (3.1%) for MeP_FA; QTL 52 (6.4%) for MeP_RC; QTL 42 (0.9%) for MeP_indirect; QTL 44 (7.1%) for MeI; and QTL 50 (12.5%) for MeY. Overall, 30 QTLs contained a TOP1 variant with a -log10(P) value exceeding 20 that we defined as highly significant (Table 4). These included 6 highly significant QTLs for MeP_RC, 5 for MeP_FA, 5 for MeP_direct, none for MeP_indirect, 10 for MeI, and 4 for MeY. Several of these highly significant QTLs were located in the same genomic regions: region 2 on BTA1 at ~142 Mbp for MeP_RC, MeP_direct, MeI, and MeY; region 26 on BTA14 at ~0.5 Mbp for MeP_FA and MeI; region 32 on BTA15 at ~65 Mbp for MeP_direct, MeP_FA, and MeY; and region 51 on BTA25 at ~26 Mbp for MeP_RC and MeP_direct.

## Functional annotation of GWAS peaks

The total number of genes within the confidence intervals of at least one QTL was 937, with an average of 9 genes per QTL and a range from 0 to 59; in 16 QTLs, no genes were highlighted. Positional candidate genes were defined as those containing at least one of the TOP10 variants for

each QTL. We identified 35, 35, 26, 28, 37, and 5 positional candidate genes for MeI, MeY, MeP_direct, MeP FA, MeP_RC, and MeP_indirect, respectively.

The genomic region associated with all six CH₄ traits contained the *VPS13B* gene, while the genomic regions associated with four or five of the CH₄ traits contained the *DGAT1*, *SLC37A1*, and *EHF/PDHX* genes. The QTLs explaining the highest percentage of genetic variance were located in the genomic regions containing the *VPS13B*, *DGAT1*, and *FASN* and *CCDC57* genes.

The TOP10 variants for each QTL were annotated using VEP, and some had multiple annotations (Table 6). Between 0% (MeP_indirect) and 18.4% (MeI) of the TOP10 variants were located in intergenic regions, while 47.3% (MeP_direct) to 72.1% (MeP_indirect) were located in intronic regions. Among the TOP10 variants located in coding regions, 1.2% to 2.5% were synonymous variants, and between 0.6% and 5.5% corresponded to missense mutations. Among these missense mutations, some were classified as deleterious based on their SIFT score: two transcripts of the same variant of the *VPS13B* gene for MeI, MeY, MeP_RC, and MeP_direct; two transcripts of two variants of the gene *ENSBTAG00000050361* for MeP_direct; and one transcript of a variant of the *PSCA* gene for MeI. Additionally, we identified a frameshift variant in the *PLD6* gene within QTL 75 on BTA19 at ~34 Mbp for MeP_FA.

## Discussion

To the best of our knowledge, this study is the first GWAS of imputed whole-genome sequences to use such a large population of dairy cattle to investigate CH₄ emissions predicted from routinely collected milk MIR spectra.

## Genomic regions shared between CH₄ traits

We found many genomic regions in common among the six CH₄ traits. Out of the 57 genomic regions identified in our study, 21 were shared by at least two CH₄ traits, corresponding to 67 QTLs of the 104 identified. The CH₄ traits that shared the highest number of regions (8)
<table><caption>**Table 4** QTLs identified for predicted methane traits</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Trait</th><th rowspan="2">BTA</th><th rowspan="2">QTL</th><th rowspan="2">Confidence interval of the QTL</th><th colspan="7">Variant with the most significant effect (all had an imputation R<sup>2</sup> of 1)</th><th rowspan="2">Name of genes TOP10</th></tr><tr><th>Start (bp)</th><th>End (bp)</th><th>N variants</th><th>N genes</th><th>Position (bp)</th><th>-log<sub>10</sub>P</th><th>b</th><th>SE</th><th>Name of gene TOP1</th></tr></thead><tbody><tr><td>1</td><td>Mel</td><td>1</td><td>57,130,469</td><td>57,632,402</td><td>9</td><td>4</td><td>57,631,653</td><td>8.5</td><td>0.06</td><td>0.01</td><td>ENSBTAG000000057099</td><td>CD200, ENSBTAG00000060686, ENSBTAG00000057099, ENSBTAG000000001235</td></tr><tr><td>2</td><td>MeP_RC</td><td>1</td><td>139,923,727</td><td>146,447,185</td><td>2355</td><td>47</td><td>142,814,697</td><td>111.2</td><td>-5.80</td><td>0.22</td><td>SLC37A1</td><td>SLC37A1</td></tr><tr><td>2</td><td>MeP_direct</td><td>1</td><td>141,872,915</td><td>144,306,817</td><td>1183</td><td>13</td><td>142,814,697</td><td>44.1</td><td>-3.04</td><td>0.18</td><td>SLC37A1</td><td>SLC37A1</td></tr><tr><td>2</td><td>MeY</td><td>1</td><td>141,915,045</td><td>144,306,817</td><td>1177</td><td>12</td><td>142,806,542</td><td>37.5</td><td>-0.08</td><td>0.01</td><td>SLC37A1</td><td>SLC37A1</td></tr><tr><td>2</td><td>Mel</td><td>1</td><td>142,591,987</td><td>142,595,307</td><td>7</td><td>0</td><td>142,594,565</td><td>13.2</td><td>0.06</td><td>0.01</td><td></td><td></td></tr><tr><td>2</td><td>Mel</td><td>1</td><td>142,604,730</td><td>142,991,203</td><td>615</td><td>6</td><td>142,879,906</td><td>24.0</td><td>0.09</td><td>0.01</td><td></td><td>SLC37A1</td></tr><tr><td>3</td><td>MeP_FA</td><td>2</td><td>131,103,444</td><td>131,359,941</td><td>354</td><td>7</td><td>131,245,133</td><td>11.7</td><td>-2.44</td><td>0.30</td><td></td><td>ENSBTAG00000068711</td></tr><tr><td>4</td><td>MeP_RC</td><td>3</td><td>13,177,910</td><td>17,220,237</td><td>65</td><td>5</td><td>17,171,600</td><td>12.2</td><td>-4.91</td><td>0.59</td><td></td><td>ENSBTAG00000065185</td></tr><tr><td>4</td><td>MeP_RC</td><td>3</td><td>13,672,338</td><td>17,166,665</td><td>149</td><td>28</td><td>15,470,670</td><td>20.8</td><td>3.28</td><td>0.30</td><td>EFNA1</td><td>SLC50A1, EFNA1, ENSBTAG00000061595</td></tr><tr><td>4</td><td>MeY</td><td>3</td><td>15,504,899</td><td>15,602,932</td><td>40</td><td>7</td><td>15,555,371</td><td>8.8</td><td>0.06</td><td>0.01</td><td>DCST1</td><td>ADAM15, DCST1, DCST2, ZBTB7B</td></tr><tr><td>5</td><td>Mel</td><td>5</td><td>92,843,362</td><td>94,221,469</td><td>16</td><td>1</td><td>93,375,037</td><td>10.0</td><td>0.09</td><td>0.01</td><td></td><td>EPS8</td></tr><tr><td>5</td><td>Mel</td><td>5</td><td>93,391,179</td><td>93,749,199</td><td>205</td><td>2</td><td>93,516,066</td><td>26.6</td><td>-0.10</td><td>0.01</td><td>MGST1</td><td>MGST1</td></tr><tr><td>5</td><td>MeP FA</td><td>5</td><td>93,512,771</td><td>93,751,239</td><td>124</td><td>2</td><td>93,516,319</td><td>22.7</td><td>-2.42</td><td>0.21</td><td>MGST1</td><td>MGST1</td></tr><tr><td>6</td><td>MeP_RC</td><td>6</td><td>23,596,912</td><td>23,604,273</td><td>3</td><td>1</td><td>23,597,653</td><td>8.9</td><td>5.49</td><td>0.78</td><td>PP3CA</td><td>PP3CA</td></tr><tr><td>7</td><td>MeP_RC</td><td>6</td><td>33,528,107</td><td>35,173,221</td><td>31</td><td>3</td><td>34,288,754</td><td>24.1</td><td>-9.36</td><td>0.78</td><td>CCSER1</td><td>CCSER1, ENSBTAG00000065318, ENSBTAG00000065966</td></tr><tr><td>8</td><td>MeP_RC</td><td>6</td><td>35,571,947</td><td>36,587,776</td><td>5</td><td>3</td><td>35,594,567</td><td>11.7</td><td>-4.82</td><td>0.59</td><td>ENSBTAG00000062289</td><td>GPRIN3, ENSBTAG00000062289, ABCG2</td></tr><tr><td>9</td><td>MeP_RC</td><td>6</td><td>36,955,669</td><td>37,003,208</td><td>17</td><td>2</td><td>36,997,477</td><td>11.6</td><td>4.40</td><td>0.54</td><td>ENSBTAG00000065096</td><td>ENSBTAG00000065096</td></tr><tr><td>10</td><td>MeP_RC</td><td>6</td><td>38,148,560</td><td>38,485,730</td><td>34</td><td>0</td><td>38,454,876</td><td>16.5</td><td>-4.28</td><td>0.44</td><td></td><td>KCNP4</td></tr><tr><td>11</td><td>MeP_RC</td><td>6</td><td>38,485,998</td><td>40,748,806</td><td>7</td><td>1</td><td>38,494,188</td><td>24.8</td><td>10.00</td><td>0.83</td><td></td><td></td></tr><tr><td>12</td><td>Mel</td><td>6</td><td>44,250,735</td><td>44,262,727</td><td>3</td><td>0</td><td>44,262,048</td><td>8.6</td><td>-0.05</td><td>0.01</td><td></td><td></td></tr><tr><td>13</td><td>Mel</td><td>6</td><td>44,283,395</td><td>44,308,333</td><td>8</td><td>0</td><td>44,299,242</td><td>14.7</td><td>-0.06</td><td>0.07</td><td></td><td></td></tr><tr><td>14</td><td>Mel</td><td>6</td><td>44,329,162</td><td>46,642,860</td><td>856</td><td>19</td><td>44,747,933</td><td>37.1</td><td>0.11</td><td>0.01</td><td>ENSBTAG00000064660</td><td>ENSBTAG00000064660</td></tr><tr><td>14</td><td>MeY</td><td>6</td><td>44,674,698</td><td>45,967,046</td><td>48</td><td>3</td><td>45,060,087</td><td>13.3</td><td>0.06</td><td>0.01</td><td>ENSBTAG00000064660</td><td>ENSBTAG00000064660</td></tr><tr><td>15</td><td>Mel</td><td>6</td><td>82,582,052</td><td>82,605,868</td><td>18</td><td>0</td><td>82,593,766</td><td>8.5</td><td>0.05</td><td>0.01</td><td></td><td></td></tr><tr><td>16</td><td>Mel</td><td>6</td><td>83,640,723</td><td>85,702,894</td><td>369</td><td>17</td><td>85,503,742</td><td>20.0</td><td>-0.10</td><td>0.01</td><td>ENSBTAG00000060764</td><td>ENSBTAG00000060764, CSN1S2</td></tr><tr><td>16</td><td>MeY</td><td>6</td><td>85,424,759</td><td>86,448,746</td><td>68</td><td>7</td><td>86,221,365</td><td>9.6</td><td>0.07</td><td>0.01</td><td>GRSF1</td><td>CSN1S1, GRSF1</td></tr><tr><td>16</td><td>Mel</td><td>6</td><td>85,703,928</td><td>85,703,928</td><td>1</td><td>0</td><td>85,703,928</td><td>9.0</td><td>-0.06</td><td>0.01</td><td></td><td>CA8S1</td></tr><tr><td>16</td><td>Mel</td><td>6</td><td>85,736,321</td><td>86,158,733</td><td>35</td><td>4</td><td>85,736,621</td><td>10.8</td><td>-0.10</td><td>0.01</td><td>CA8S1</td><td>CA8S1</td></tr><tr><td>17</td><td>Mel</td><td>7</td><td>68,579,274</td><td>68,592,765</td><td>58</td><td>2</td><td>68,579,369</td><td>9.3</td><td>0.09</td><td>0.01</td><td>ENSBTAG00000057983</td><td>ENSBTAG00000057983, ENSBTAG00000059254</td></tr><tr><td>18</td><td>MeP_RC</td><td>8</td><td>41,366,883</td><td>42,068,650</td><td>11</td><td>0</td><td>41,366,883</td><td>9.1</td><td>1.94</td><td>0.27</td><td></td><td></td></tr><tr><td>19</td><td>MeP_RC</td><td>9</td><td>100,858,075</td><td>102,458,517</td><td>450</td><td>13</td><td>101,345,396</td><td>15.0</td><td>-2.11</td><td>0.23</td><td>ENSBTAG00000059544</td><td>ENSBTAG00000059544</td></tr><tr><td>20</td><td>Mel</td><td>10</td><td>1,341,660</td><td>2,214,558</td><td>119</td><td>2</td><td>2,187,345</td><td>12.3</td><td>0.04</td><td>0.01</td><td>APC, ENSBTAG00000058588</td><td>APC, ENSBTAG00000058588</td></tr><tr><td>20</td><td>MeP_RC</td><td>10</td><td>2,137,692</td><td>2,214,558</td><td>116</td><td>0</td><td>2,191,110</td><td>11.9</td><td>1.22</td><td>0.15</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 4 (continued)</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Trait</th><th rowspan="2">BTA</th><th rowspan="2">QTL</th><th rowspan="2">Confidence interval of the QTL</th><th colspan="4">Variant with the most significant effect (all had an imputation R<sup>2</sup> of 1)</th><th colspan="2">Name of genes TOP10</th></tr><tr><th>Start (bp)</th><th>End (bp)</th><th>N variants</th><th>N genes</th><th>Position (bp)</th><th>-log<sub>10</sub>P</th><th>b</th><th>SE</th><th>Name of gene TOP1</th></tr></thead><tbody><tr><td>21</td><td>MeY</td><td>10</td><td>34</td><td>46,048,106</td><td>46,548,638</td><td>438</td><td>7</td><td>46,051,704</td><td>13.2</td><td>-0.08</td><td>0.01</td><td>DAPK2</td><td>DAPK2, ENSBTAG000000060901, USP3</td></tr><tr><td>21</td><td>MeI</td><td>10</td><td>35</td><td>46,088,320</td><td>46,548,638</td><td>172</td><td>5</td><td>46,541,616</td><td>9.9</td><td>-0.06</td><td>0.01</td><td>ENSBTAG000000057172</td><td>USP3, ENSBTAG000000057172</td></tr><tr><td>22</td><td>MeI</td><td>11</td><td>36</td><td>14,229,710</td><td>14,642,556</td><td>128</td><td>2</td><td>14,229,992</td><td>10.6</td><td>-0.05</td><td>0.01</td><td>MEMO1</td><td>MEMO1</td></tr><tr><td>23</td><td>MeI</td><td>11</td><td>37</td><td>103,226,637</td><td>103,286,957</td><td>374</td><td>4</td><td>103,254,495</td><td>27.7</td><td>-0.12</td><td>0.01</td><td>ENSBTAG00000014678</td><td>ENSBTAG00000014678</td></tr><tr><td>24</td><td>MeY</td><td>13</td><td>38</td><td>45,882,199</td><td>47,070,574</td><td>194</td><td>6</td><td>46,366,318</td><td>11.3</td><td>0.05</td><td>0.01</td><td>GTPBP4</td><td>GTPBP4</td></tr><tr><td>25</td><td>MeP_FA</td><td>13</td><td>39</td><td>64,053,550</td><td>64,262,227</td><td>52</td><td>3</td><td>64,147,879</td><td>11.7</td><td>-1.86</td><td>0.23</td><td>NCOA6</td><td>NCOA6, ACSS2, GSS</td></tr><tr><td>26</td><td>MeI</td><td>14</td><td>46</td><td>180,920</td><td>3,104,982</td><td>220</td><td>21</td><td>1,704,582</td><td>26.7</td><td>-0.09</td><td>0.01</td><td>PSCA</td><td>PSCA, ENSBTAG000000026340, ABDRB1, TSNARE1</td></tr><tr><td>26</td><td>MeI</td><td>14</td><td>44</td><td>233,681</td><td>993,201</td><td>522</td><td>37</td><td>608,230</td><td>73.7</td><td>-0.26</td><td>0.01</td><td>DGAT1</td><td>CPSF1, ENSBTAG00000000857, SCRT1, DGAT1</td></tr><tr><td>26</td><td>MeP_FA</td><td>14</td><td>41</td><td>264,797</td><td>993,201</td><td>324</td><td>33</td><td>550,784</td><td>30.3</td><td>-4.57</td><td>0.35</td><td>CPSF1</td><td>CPSF1, ADCK5, DGAT1</td></tr><tr><td>26</td><td>MeP_direct</td><td>14</td><td>43</td><td>384,242</td><td>901,826</td><td>111</td><td>17</td><td>568,472</td><td>11.2</td><td>3.05</td><td>0.37</td><td>ADCK5</td><td>ADCK5, DGAT1, HSF1</td></tr><tr><td>26</td><td>MeP_indirect</td><td>14</td><td>42</td><td>384,242</td><td>707,908</td><td>128</td><td>16</td><td>565,009</td><td>12.5</td><td>-4.11</td><td>0.45</td><td>ADCK5</td><td>ADCK5, DGAT1, HSF1</td></tr><tr><td>26</td><td>MeP_RC</td><td>14</td><td>40</td><td>497,507</td><td>955,774</td><td>73</td><td>11</td><td>500,872</td><td>9.2</td><td>-3.24</td><td>0.45</td><td>TONSL</td><td>TONSL, CPSF1, ENSBTAG00000000857, SCRT1, DGAT1, HSF1</td></tr><tr><td>27</td><td>MeP_FA</td><td>14</td><td>45</td><td>995,464</td><td>1,915,755</td><td>136</td><td>12</td><td>1,028,657</td><td>13.5</td><td>-1.70</td><td>0.20</td><td>IQANK1</td><td>IQANK1, FAM83H, ZC3H3, ADGRB1</td></tr><tr><td>28</td><td>MeY</td><td>14</td><td>50</td><td>59,747,181</td><td>71,727,670</td><td>3422</td><td>59</td><td>64,752,983</td><td>181.3</td><td>0.42</td><td>0.01</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>28</td><td>MeI</td><td>14</td><td>47</td><td>61,219,666</td><td>68,426,997</td><td>2910</td><td>48</td><td>64,454,721</td><td>83.2</td><td>0.23</td><td>0.01</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>28</td><td>MeP_direct</td><td>14</td><td>51</td><td>61,457,257</td><td>65,916,772</td><td>1760</td><td>31</td><td>64,779,893</td><td>59.7</td><td>-8.29</td><td>0.42</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>28</td><td>MeP_RC</td><td>14</td><td>52</td><td>61,527,782</td><td>65,872,284</td><td>1411</td><td>31</td><td>64,926,343</td><td>54.5</td><td>-13.86</td><td>0.76</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>28</td><td>MeP_FA</td><td>14</td><td>49</td><td>63,173,429</td><td>65,641,447</td><td>320</td><td>22</td><td>64,672,856</td><td>20.1</td><td>-4.45</td><td>0.42</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>28</td><td>MeP_indirect</td><td>14</td><td>48</td><td>64,043,186</td><td>65,402,152</td><td>29</td><td>5</td><td>64,672,856</td><td>11.3</td><td>4.96</td><td>0.57</td><td>VPS13B</td><td>VPS13B, ENSBTAG000000055757</td></tr><tr><td>29</td><td>MeY</td><td>15</td><td>53</td><td>27,315,640</td><td>28,691,118</td><td>212</td><td>17</td><td>27,646,671</td><td>11.4</td><td>0.05</td><td>0.01</td><td>SIK3</td><td>ENSBTAG00000067623, SIK3</td></tr><tr><td>30</td><td>MeY</td><td>15</td><td>54</td><td>53,137,808</td><td>53,767,920</td><td>373</td><td>8</td><td>53,359,144</td><td>11.1</td><td>0.07</td><td>0.01</td><td>PAAF1</td><td>MRLPL48, COA4, PAAF1, DNAUB13</td></tr><tr><td>31</td><td>MeP_direct</td><td>15</td><td>55</td><td>63,019,643</td><td>63,055,774</td><td>71</td><td>1</td><td>63,055,774</td><td>9.9</td><td>1.02</td><td>0.13</td><td>ENSBTAG000000052796</td><td>ENSBTAG000000052796</td></tr><tr><td>32</td><td>MeP_direct</td><td>15</td><td>61</td><td>63,736,668</td><td>65,705,004</td><td>252</td><td>10</td><td>65,333,701</td><td>38.5</td><td>-2.46</td><td>0.16</td><td>EHF</td><td>EHF</td></tr><tr><td>32</td><td>MeP_FA</td><td>15</td><td>58</td><td>63,804,912</td><td>65,346,043</td><td>200</td><td>5</td><td>65,312,559</td><td>26.0</td><td>1.89</td><td>0.15</td><td>EHF</td><td>EHF</td></tr><tr><td>32</td><td>MeY</td><td>15</td><td>56</td><td>64,794,022</td><td>66,090,611</td><td>67</td><td>3</td><td>64,794,049</td><td>11.9</td><td>0.04</td><td>0.01</td><td>ABTB2</td><td>ABTB2</td></tr><tr><td>32</td><td>MeY</td><td>15</td><td>59</td><td>64,918,334</td><td>65,678,067</td><td>251</td><td>9</td><td>65,323,234</td><td>25.9</td><td>-0.05</td><td>0.01</td><td>ENSBTAG000000059416, ENSBTAG000000063234, EHF</td><td>ENSBTAG000000059416, ENSBTAG000000063234, EHF</td></tr><tr><td>32</td><td>MeP_RC</td><td>15</td><td>57</td><td>65,227,767</td><td>65,239,009</td><td>3</td><td>0</td><td>65,239,009</td><td>8.6</td><td>1.23</td><td>0.18</td><td></td><td></td></tr><tr><td>32</td><td>MeP_RC</td><td>15</td><td>60</td><td>65,242,698</td><td>65,338,417</td><td>32</td><td>0</td><td>65,324,226</td><td>14.3</td><td>1.68</td><td>0.19</td><td></td><td></td></tr><tr><td>32</td><td>MeP_FA</td><td>15</td><td>62</td><td>65,355,672</td><td>65,515,606</td><td>13</td><td>3</td><td>65,355,672</td><td>9.1</td><td>-2.01</td><td>0.29</td><td>PDHX</td><td>PDHX</td></tr><tr><td>32</td><td>MeP_direct</td><td>15</td><td>63</td><td>66,074,954</td><td>66,096,784</td><td>12</td><td>0</td><td>66,089,302</td><td>10.6</td><td>-1.17</td><td>0.15</td><td></td><td></td></tr><tr><td>33</td><td>MeP_FA</td><td>16</td><td>65</td><td>1,756,944</td><td>1,907,186</td><td>19</td><td>1</td><td>1,786,309</td><td>13.3</td><td>-2.87</td><td>0.33</td><td>SOX13</td><td>SOX13</td></tr><tr><td>33</td><td>MeP_direct</td><td>16</td><td>64</td><td>1,784,784</td><td>1,791,556</td><td>8</td><td>0</td><td>1,786,309</td><td>10.1</td><td>2.56</td><td>0.33</td><td></td><td></td></tr><tr><td>34</td><td>MeP_FA</td><td>16</td><td>66</td><td>24,251,952</td><td>25,510,748</td><td>148</td><td>2</td><td>24,280,507</td><td>16.5</td><td>-1.60</td><td>0.17</td><td>MTARC1</td><td>MTARC1</td></tr></tbody></table>
<table><caption>**Table 4 (continued)**</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Trait</th><th rowspan="2">BTA</th><th rowspan="2">QTL</th><th rowspan="2">Confidence interval of the QTL</th><th colspan="5">Variant with the most significant effect (all had an imputation R<sup>2</sup> of 1)</th><th rowspan="2">Name of genes TOP10</th></tr><tr><th>Start (bp)</th><th>End (bp)</th><th>N variants</th><th>N genes</th><th>Position (bp)</th><th>-log<sub>10</sub>P</th><th>b</th><th>SE</th><th>Name of gene TOP1</th></tr></thead><tbody><tr><td>35</td><td>Mel</td><td>17</td><td>67</td><td>51,586,460</td><td>51,586,460</td><td>1</td><td>0</td><td>51,586,460</td><td>8.5</td><td>0.04</td><td>0.01</td><td></td></tr><tr><td>36</td><td>Mel</td><td>17</td><td>68</td><td>53,714,812</td><td>54,541,221</td><td>51</td><td>3</td><td>53,794,372</td><td>9.2</td><td>0.08</td><td>0.01</td><td>KDM2B, RNF34</td></tr><tr><td>37</td><td>MeP_RC</td><td>18</td><td>69</td><td>8,826,358</td><td>10,690,398</td><td>10</td><td>2</td><td>8,828,778</td><td>9.6</td><td>6.42</td><td>0.88</td><td>ENS8BTAG000000069108, ENS8BTAG00000057902</td></tr><tr><td>38</td><td>MeP_RC</td><td>18</td><td>71</td><td>15,689,862</td><td>16,420,403</td><td>8</td><td>2</td><td>16,155,339</td><td>10.1</td><td>6.80</td><td>0.90</td><td>ITFG1, PHKB</td></tr><tr><td>38</td><td>MeP_direct</td><td>18</td><td>70</td><td>16,152,862</td><td>16,155,339</td><td>2</td><td>0</td><td>16,155,339</td><td>8.8</td><td>5.32</td><td>0.74</td><td></td></tr><tr><td>39</td><td>MeP_RC</td><td>19</td><td>72</td><td>10,283,388</td><td>10,294,221</td><td>5</td><td>0</td><td>10,294,221</td><td>9.3</td><td>1.17</td><td>0.16</td><td></td></tr><tr><td>40</td><td>MeP_FA</td><td>19</td><td>75</td><td>32,335,650</td><td>34,947,083</td><td>4</td><td>2</td><td>34,947,083</td><td>8.6</td><td>1.57</td><td>0.23</td><td>PLD6</td></tr><tr><td>40</td><td>MeP_FA</td><td>19</td><td>73</td><td>33,521,281</td><td>33,536,010</td><td>30</td><td>1</td><td>33,527,729</td><td>8.7</td><td>-2.25</td><td>0.33</td><td>PECC1</td></tr><tr><td>40</td><td>MeP_FA</td><td>19</td><td>74</td><td>33,666,818</td><td>34,856,338</td><td>28</td><td>12</td><td>34,729,508</td><td>12.7</td><td>2.17</td><td>0.26</td><td>RAI1</td></tr><tr><td>41</td><td>MeY</td><td>19</td><td>76</td><td>42,238,476</td><td>42,626,158</td><td>688</td><td>14</td><td>42,265,893</td><td>10.9</td><td>0.07</td><td>0.01</td><td>RAB5C</td></tr><tr><td>41</td><td>MeP_RC</td><td>19</td><td>77</td><td>42,238,476</td><td>42,626,158</td><td>689</td><td>14</td><td>42,281,725</td><td>11.6</td><td>-2.81</td><td>0.35</td><td></td></tr><tr><td>42</td><td>MeP_FA</td><td>19</td><td>80</td><td>47,997,400</td><td>54,407,498</td><td>1102</td><td>58</td><td>50,775,172</td><td>91.5</td><td>-4.29</td><td>0.18</td><td>CCDC57, FASN</td></tr><tr><td>42</td><td>MeP_direct</td><td>19</td><td>78</td><td>49,996,881</td><td>50,198,903</td><td>17</td><td>2</td><td>50,198,788</td><td>10.0</td><td>1.73</td><td>0.23</td><td>UTS2R</td></tr><tr><td>42</td><td>MeP_direct</td><td>19</td><td>79</td><td>50,207,005</td><td>50,780,248</td><td>97</td><td>4</td><td>50,721,397</td><td>12.2</td><td>-1.66</td><td>0.19</td><td>CCDC57</td></tr><tr><td>43</td><td>MeP_direct</td><td>19</td><td>81</td><td>60,404,660</td><td>61,078,226</td><td>264</td><td>2</td><td>60,441,152</td><td>9.7</td><td>-1.26</td><td>0.17</td><td></td></tr><tr><td>44</td><td>MeY</td><td>20</td><td>82</td><td>31,462,828</td><td>32,320,669</td><td>13</td><td>3</td><td>31,888,449</td><td>25.2</td><td>0.13</td><td>0.01</td><td>GHR</td></tr><tr><td>44</td><td>Mel</td><td>20</td><td>83</td><td>32,018,037</td><td>32,103,408</td><td>3</td><td>2</td><td>32,018,037</td><td>9.1</td><td>-0.09</td><td>0.01</td><td>GHR</td></tr><tr><td>45</td><td>Mel</td><td>20</td><td>85</td><td>55,290,311</td><td>60,131,394</td><td>2933</td><td>16</td><td>58,195,251</td><td>31.9</td><td>0.12</td><td>0.01</td><td>ANKH</td></tr><tr><td>45</td><td>MeP_RC</td><td>20</td><td>84</td><td>55,514,835</td><td>57,379,190</td><td>770</td><td>10</td><td>56,489,500</td><td>13.0</td><td>2.86</td><td>0.33</td><td>MYO10</td></tr><tr><td>45</td><td>MeY</td><td>20</td><td>86</td><td>58,191,503</td><td>59,878,202</td><td>830</td><td>3</td><td>58,377,254</td><td>14.6</td><td>0.06</td><td>0.01</td><td>ANKH</td></tr><tr><td>46</td><td>MeP_RC</td><td>22</td><td>87</td><td>53,951,400</td><td>54,565,136</td><td>138</td><td>3</td><td>54,475,210</td><td>14.2</td><td>-3.07</td><td>0.34</td><td>ATP282</td></tr><tr><td>47</td><td>MeP_RC</td><td>22</td><td>88</td><td>54,611,746</td><td>54,677,719</td><td>42</td><td>1</td><td>54,629,523</td><td>15.5</td><td>1.44</td><td>0.15</td><td>ATP282</td></tr><tr><td>48</td><td>MeY</td><td>23</td><td>89</td><td>16,740,006</td><td>17,219,907</td><td>2</td><td>1</td><td>16,740,006</td><td>9.4</td><td>0.05</td><td>0.01</td><td>PTK7</td></tr><tr><td>49</td><td>MeY</td><td>23</td><td>90</td><td>45,223,069</td><td>45,229,851</td><td>6</td><td>1</td><td>45,223,636</td><td>8.6</td><td>0.04</td><td>0.01</td><td>ELOVL2</td></tr><tr><td>50</td><td>MeP_direct</td><td>25</td><td>91</td><td>25,899,872</td><td>25,899,872</td><td>1</td><td>0</td><td>25,899,872</td><td>8.9</td><td>1.18</td><td>0.16</td><td></td></tr><tr><td>51</td><td>MeP_direct</td><td>25</td><td>93</td><td>25,904,431</td><td>27,723,625</td><td>621</td><td>50</td><td>26,061,709</td><td>26.0</td><td>-2.29</td><td>0.18</td><td>IL27</td></tr><tr><td>51</td><td>MeP_RC</td><td>25</td><td>92</td><td>25,906,818</td><td>27,473,500</td><td>495</td><td>45</td><td>26,048,468</td><td>23.7</td><td>-2.60</td><td>0.22</td><td>CLN3</td></tr><tr><td>52</td><td>MeP_RC</td><td>27</td><td>95</td><td>36,353,153</td><td>36,617,333</td><td>109</td><td>10</td><td>36,522,458</td><td>18.4</td><td>2.42</td><td>0.23</td><td>GPAT4</td></tr><tr><td>52</td><td>MeP_direct</td><td>27</td><td>94</td><td>36,378,091</td><td>36,558,128</td><td>32</td><td>4</td><td>36,522,458</td><td>13.2</td><td>1.69</td><td>0.19</td><td>GINS4, ENS8BTAG00000058864, GPAT4, NKX6-3</td></tr><tr><td>53</td><td>MeP_direct</td><td>27</td><td>96</td><td>40,911,676</td><td>41,646,056</td><td>158</td><td>2</td><td>41,391,206</td><td>22.1</td><td>-1.92</td><td>0.16</td><td>ENS8BTAG00000056465</td></tr><tr><td>53</td><td>MeP_RC</td><td>27</td><td>97</td><td>41,375,472</td><td>41,627,439</td><td>141</td><td>2</td><td>41,391,206</td><td>16.1</td><td>-1.94</td><td>0.20</td><td>ENS8BTAG00000056465</td></tr><tr><td>53</td><td>MeP_FA</td><td>27</td><td>98</td><td>41,379,852</td><td>41,601,820</td><td>57</td><td>2</td><td>41,392,181</td><td>10.7</td><td>1.25</td><td>0.16</td><td>ENS8BTAG00000056465</td></tr></tbody></table>
<table><caption>**Table 4 (continued)**</caption><thead><tr><th rowspan="2">Region</th><th rowspan="2">Trait</th><th rowspan="2">BTA</th><th rowspan="2">QTL</th><th rowspan="2">Confidence interval of the QTL</th><th colspan="4">Variant with the most significant effect (all had an imputation R<sup>2</sup> of 1)</th><th colspan="2">Name of genes TOP10</th></tr><tr><th>Start (bp)</th><th>End (bp)</th><th>N variants</th><th>N genes</th><th>Position (bp)</th><th>-log<sub>10</sub>P</th><th>b</th><th>SE</th><th>Name of gene TOP1</th></tr></thead><tbody><tr><td>54</td><td>MeP_direct</td><td>27</td><td>99</td><td>41,737,999</td><td>41,971,860</td><td>5</td><td>3</td><td>41,971,860</td><td>8.9</td><td>-1.68</td><td>0.23</td><td>ENSBTAG00000064242</td><td>THR8, ENSBTAG00000065779, ENS-BTAG00000064242</td></tr><tr><td>55</td><td>MeI</td><td>28</td><td>101</td><td>6,192,309</td><td>7,165,451</td><td>125</td><td>2</td><td>6,458,572</td><td>21.4</td><td>0.06</td><td>0.01</td><td>KCNK1</td><td>KCNK1</td></tr><tr><td>55</td><td>MeP_RC</td><td>28</td><td>100</td><td>6,457,299</td><td>6,529,103</td><td>8</td><td>1</td><td>6,457,425</td><td>9.2</td><td>3.33</td><td>0.46</td><td>KCNK1</td><td>KCNK1</td></tr><tr><td>56</td><td>MeP_RC</td><td>29</td><td>102</td><td>9,486,040</td><td>9,545,883</td><td>9</td><td>1</td><td>9,502,594</td><td>13.0</td><td>1.76</td><td>0.20</td><td></td><td>PICALM</td></tr><tr><td>57</td><td>MeY</td><td>29</td><td>104</td><td>40,666,856</td><td>42,245,387</td><td>93</td><td>12</td><td>41,325,720</td><td>15.2</td><td>0.17</td><td>0.02</td><td></td><td>SLC22A8, ENSBTAG00000037570</td></tr><tr><td>57</td><td>MeP_direct</td><td>29</td><td>103</td><td>41,149,123</td><td>42,009,447</td><td>55</td><td>7</td><td>41,319,432</td><td>10.4</td><td>-3.83</td><td>0.49</td><td>SLC22A8</td><td>SLC22A6, SLC22A8</td></tr></tbody></table>

The genotyped TOP1 variants are indicated in italics
<table><caption>**Table 5** Genetic correlations and number of genomic regions shared between pairs of predicted methane traits</caption><thead><tr><th>Trait</th><th>MeP_direct</th><th>Mel</th><th>MeY</th><th>MeP_indirect</th><th>MeP_FA</th><th>MeP_RC</th></tr></thead><tbody><tr><td>MeP_direct (g/j)</td><td></td><td>0.25</td><td>0.81</td><td>0.36</td><td>0.21</td><td>0.62</td></tr><tr><td>Mel (g/kg of FPCM)</td><td>3</td><td></td><td>0.38</td><td>0.52</td><td>-0.15</td><td>0.18</td></tr><tr><td>MeY (g/kg of DMI)</td><td>4</td><td>7</td><td></td><td>0.36</td><td>0.13</td><td>0.50</td></tr><tr><td>MeP_indirect (g/d)</td><td>2</td><td>2</td><td>1</td><td></td><td>0.03</td><td>0.20</td></tr><tr><td>MeP_FA (g/d)</td><td>6</td><td>3</td><td>2</td><td>2</td><td></td><td>0.24</td></tr><tr><td>MeP_RC (g/d)</td><td>8</td><td>6</td><td>7</td><td>2</td><td>4</td><td></td></tr></tbody></table>

were MeP_RC and MeP_direct, which is consistent with their methodological similarity: they both represent CH₄ emissions in g/d and their prediction equations were calibrated using the same methodology [19, 20]. The number of shared genomic regions was not related to the genetic correlations between the CH₄ traits. Indeed, MeP_RC and MeP_direct [genetic correlation of 0.62 (Table 5)] shared 8 genomic regions, while MeP_direct and MeY [genetic correlation of 0.81 (Table 5)] shared 4, and MeP_FA and MeP_direct [genetic correlation of 0.21 (Table 5)] shared 6. Interestingly, the results obtained for MeP_indirect were notably different from those of the other CH₄ traits. In this study, MeP_indirect was associated with only two QTLs; these included the genes *VPS13B* and *DGAT1* which were linked with multiple CH₄ traits in this study.

In contrast to our results, which revealed multiple genomic regions in common among the CH₄ traits investigated, most previously published GWAS analyses of multiple CH₄ traits (expressed in different units or predicted using different equations) have reported only a few shared genomic regions; such studies have examined MeP and MeY predicted from milk MIR spectra [35] as well as direct measurements of MeP, MeI, and MeY [10, 11].

### Genomic regions shared with different previously published GWAS studies of CH₄ traits

The genetic architecture of CH₄ emissions has previously been studied using direct CH₄ measurements [10–13, 35] or CH₄ predicted from traits related to feed intake [15–17], the concentration of volatile fatty acids in the rumen [14], milk fat composition [35], and milk MIR spectra [35] in both dairy and beef breeds. Given the diversity of these approaches and the scarcity of shared regions among previous studies, we anticipated that there would be little overlap between the genomic regions highlighted in this study and those reported in the literature. Indeed, only three of the genomic regions identified here were among those described in the literature. Genomic region 42 on BTA19 at ~50 Mbp, which in the present study was associated with MeP_direct and MeP_FA and

for which FASN and CCDC57 were the most probable candidate genes, was also highlighted in the studies of van Engelen [35] and Manzanilla-Pech et al. [11], which examined CH₄ emissions in g/kg of DMI and g/d, respectively. Genomic region 26 on BTA14 at ~0.5 Mbp, which contained the *DGAT1* gene, was associated with all predicted CH₄ traits except MeY in our study; similarly, this region was reported to be associated with CH₄ emissions in both g/d and g/kg of DMI by van Engelen [35]. Finally, genomic region 28 on BTA14 at ~64 Mbp was associated with all six predicted CH₄ traits analyzed here, with *VPS13B* being the most likely candidate gene; this region was also identified in the study of Calderón-Chagoya et al. [13], which used CH₄ emissions expressed in mg/L.

The small number of genomic regions shared among studies might be the result of the different methods used for measuring CH₄ emissions, which are known to have correlations of less than one [36, 37]. Another explanation could be that previous GWAS analyses were based on smaller sample sizes (ranging from 150 to 1962 animals), which limited their detection power. Most of these studies used 50 K genotypes, whereas sequence data, like those used in our study, increase the resolution of QTL detection [38]. Additionally, differences in breed composition among studies (Holstein cows [10–12, 14, 15], beef breeds [10, 16, 17], combination of purebred, crossbred, and Zebu cattle [13]) likely affect the results as well, as different QTLs may segregate in different breeds. This hypothesis is supported by the results of Manzanilla-Pech et al. [10], who highlighted that different genes are involved in the expression of CH₄ traits in Angus and Holstein breeds.

### CH₄ traits are associated with genes related to milk production and composition

Among the positional candidate genes identified in our study, many have been previously confirmed to be associated with milk traits [25, 39], traits predicted from milk MIR spectra [26, 40], and specific wavelengths of milk MIR spectra [41]. Among these, we notably found *VPS13B*, *SLC37A1*, *DGAT1*, *FASN*, *CCDC57*, *ANKH*, *PICALM*, *MTARC1*, *GPAT4*, *DNAJB13*, *KCNK1*, *MGST1*,
<table><caption>**Table 6** Functional annotation of the 10 most significant variants of each QTL</caption><thead><tr><th rowspan="2">Trait</th><th colspan="2">MeP_direct (g/d)</th><th colspan="2">Mel (g/kg of FPCM)</th><th colspan="2">MeY (g/kg of DMI)</th><th colspan="2">MeP_indirect (g/d)</th><th colspan="2">MeP_FA (g/d)</th><th colspan="2">MeP_RC (g/d)</th></tr><tr><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th></tr></thead><tbody><tr><td>Variants</td><td>134</td><td></td><td>194</td><td></td><td>142</td><td></td><td>18</td><td></td><td>134</td><td></td><td>226</td><td></td></tr><tr><td>Annotations</td><td>383</td><td></td><td>441</td><td></td><td>394</td><td></td><td>68</td><td></td><td>422</td><td></td><td>626</td><td></td></tr><tr><td>Intergenic</td><td>45</td><td>11.7</td><td>81</td><td>18.4</td><td>21</td><td>5.3</td><td></td><td></td><td>27</td><td>6.4</td><td>81</td><td>13.0</td></tr><tr><td>Intron</td><td>181</td><td>47.3</td><td>233</td><td>52.8</td><td>282</td><td>71.6</td><td>62</td><td>72.1</td><td>240</td><td>56.9</td><td>414</td><td>66.2</td></tr><tr><td>Downstream_gene</td><td>78</td><td>20.4</td><td>53</td><td>12.0</td><td>24</td><td>6.1</td><td>16</td><td>18.6</td><td>58</td><td>13.8</td><td>46</td><td>7.4</td></tr><tr><td>Upstream_gene</td><td>42</td><td>11.0</td><td>30</td><td>6.8</td><td>27</td><td>6.8</td><td>6</td><td>6.9</td><td>57</td><td>13.5</td><td>39</td><td>6.2</td></tr><tr><td>5_prime_UTR</td><td></td><td></td><td>11</td><td>2.5</td><td>14</td><td>3.5</td><td></td><td></td><td></td><td></td><td>11</td><td>1.7</td></tr><tr><td>3_prime_UTR</td><td>3</td><td>0.8</td><td>6</td><td>1.4</td><td>3</td><td>0.8</td><td></td><td></td><td>14</td><td>3.3</td><td>7</td><td>1.1</td></tr><tr><td>Regulatory region</td><td>4</td><td>1.0</td><td>5</td><td>1.1</td><td>7</td><td>1.8</td><td></td><td></td><td>7</td><td>1.7</td><td>9</td><td>1.4</td></tr><tr><td>Non_coding_transcript_exon</td><td>1</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.2</td><td>3</td><td>0.5</td></tr><tr><td>Synonymous</td><td>8</td><td>2.1</td><td>11</td><td>2.5</td><td>9</td><td>2.3</td><td>1</td><td>1.2</td><td>8</td><td>1.9</td><td>12</td><td>1.9</td></tr><tr><td>Missense</td><td>21</td><td>5.5</td><td>11</td><td>2.5</td><td>5</td><td>1.3</td><td></td><td></td><td>9</td><td>2.1</td><td>4</td><td>0.6</td></tr><tr><td>Frameshift</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.2</td><td></td><td></td></tr></tbody></table>

USP3, GHR, and THRB genes. These genes were frequently present in the genomic regions containing the most significant variants (-log10(P)≥20), the region that explained the highest percentage of genetic variance for the six CH₄ traits, or those that were shared by multiple CH₄ traits. For instance, genomic region 28 on BTA14 at ~64 Mbp, which was shared by all CH₄ traits, included the candidate gene *VPS13B*; genomic region 26 on BTA14 at ~0.5 Mbp, which was associated with five CH₄ traits, included the candidate gene *DGAT1*; and genomic region 2 on BTA1 at ~142 Mbp, which was associated with four CH₄ traits, included the candidate gene *SLC37A1*. Additionally, TOP10 variants from the *VPS13B* gene were missense mutations classified as deleterious by the SIFT score for the traits MeI, MeY, MeP_RC, and MeP_direct.

Independent studies using CH₄ emissions collected with Sniffers corroborate two of the genes identified in our genomic regions. Manzanilla-Pech et al. [11] identified a SNP significantly associated with MeP within our genomic region 42 (BTA 19~50 Mbp) for which FASN is the most probable candidate gene, and both Pszczola et al. [12] and Calderón-Chagoya et al. [13] identified SNPs associated with MeP less than 1 Mbp before the region. Calderón-Chagoya et al. [13] also identified a SNP within the genomic region 28 (BTA 14~64 Mbp) for which the candidate gene is *VPS13B*. Additionally, using CH₄ emissions predicted from milk FA or milk MIR spectra, van Engelen [35] found MeP and MeY to be associated with the *DGAT1* gene.

Of these genes, FASN and DGAT1 could be potential functional candidate genes because they are involved in biological relationships underlying the prediction

of CH₄ from milk MIR spectra. The volatile fatty acids (VFA) produced by the rumen bacteria during fermentation are correlated with hydrogen production, and thus CH₄ production. VFAs are absorbed by the ruminal epithelium and transferred to the mammary gland, where acetate and butyrate are used as substrates for the de novo synthesis of short-chain fatty acids (SCFA) in milk [42, 43]. The FASN gene encodes the fatty acid synthase protein, which is involved in the production of SCFAs in the mammary gland from acetyl-CoA and butyryl-CoA derived from acetate and butyrate [44]. The *DGAT1* gene encodes the diacylglycerol O-acyltransferase 1 protein which produces triacylglycerol from glycerol and SCFAs [44, 45]. Additionally, here we identified an interesting QTL on BTA13, associated with MeP_FA, in which the candidate gene was ACSS2, whose protein is involved in the production of acetyl-CoA from acetate in the mammary gland [44].

## Candidate genes for CH₄ emissions related to rumen microbiota

The rumen microbiota is known to differ between low and high CH₄-emitters [46, 47] and to be influenced by host genetics [46, 48, 49]. Although one study found that the effect of host genetics on CH₄ emissions was almost independent of the effect of host genetics on the microbiota [50], others have proposed developing genetic selection on rumen microbiota to reduce CH₄ emissions [51–53]. Multiple studies have been conducted on the relationships between a host's genes and its microbiota, including GWASs on beef cattle [48, 54], dairy cattle [46, 55], and sheep [56], but there has been little overlap in the QTLs identified in different studies. We
looked for overlapping genomic regions between our GWAS analyses of CH₄ predicted from milk MIR spectra and the GWAS analyses in the literature to identify possible candidate genes based on biological interpretation. In particular, some of the loci highlighted by Wang et al. [56] are located in the genomic regions identified in our study, which include the candidate genes *EPS8*, *PPP3CA*, *CCSER1*, *KCNIP4*, *NCOA6*, *ABTB2*, *ITFG1*, *PTK7*, and *KCNK1*.

KCNIP4 may be of particular interest as it was a candidate gene in a highly significant QTL for MeP_RC. Moreover, the corresponding genomic region was also found in another study to be associated with members of the bacterial genera *Prevotella*, *Ruminococcus*, and *Eubacterium* [56]. These bacteria can influence VFA and hydrogen production in the rumen, in the case of *Prevotella* by producing propionate—an alternative to hydrogen production [57]—when digesting starch [58, 59], or, in the case of *Ruminococcus* and *Eubacterium*, by producing acetate and butyrate—and thus hydrogen—when digesting cellulose [58, 59]. Through their influence on VFA and hydrogen production, these bacteria may be able to influence CH₄ production. Interestingly, they have been found to be overrepresented in the rumen of either high-emitting animals (*Ruminococcus* and *Eubacterium* [51, 60, 61]) or low-emitting animals (*Prevotella* [47, 60, 61]). Other candidate genes in our study were also identified in regions associated with these bacterial genera, such as the gene *CCSER1* (*Prevotella*) and the genes *EPS8*, *PPP3CA*, *NCOA6*, and *ABTB2* (*Eubacterium*) [56].

## Quality of the data

In this study, we conducted GWAS using WGS data from a population of over 40,000 cows, which provided high-resolution detection power for detecting QTLs. It is particularly noteworthy that most TOP10 variants we identified were imputed ones (92.0%), which highlights the potential of imputed WGS data for identifying new candidate variants for causal mutations. Additionally, relatively few of these TOP10 variants were annotated as intergenic variants (from 0 to 18.4%), meaning that we mostly identified either the genes or their known regulatory regions. Our results confirm the utility of using WGS data for QTL detection, although in this case our efforts were also supported by the precision of the imputation (average R² of 0.84 for TOP10 variants), which was permitted by the large reference population of 1414 Holstein bulls in Run9 of the 1000 Bull Genomes Project [29].

## Limitations

The phenotypes used in this study are CH₄ emissions predicted from milk MIR spectra, which reflect milk

composition. The first consequence is that their genetic parameters may be biased compared with those of directly measured traits. Although the heritability values estimated in this study for predicted MeI and MeY are consistent with those estimated from direct measurements (0.30 and 0.33 for MeI [8, 62], 0.38 for MeY [8]), our heritability estimates for predicted MeP (0.23–0.42) are generally higher than the ones reported in the literature (0.12–0.36 [8, 11, 62–65]). This can be partly explained by the inherent heritability of milk MIR spectra. Several studies have estimated the heritability of different wavelengths in the regions of milk MIR spectra used for prediction [66–68], reaching 0.42, 0.27, and 0.30, respectively. Therefore, the traits predicted from milk MIR spectra are heritable because they are a linear combination of heritable wavelength absorbances.

The second consequence is that it was not surprising to find QTLs associated with genes that are already known to be associated with either milk composition or milk MIR spectra. This makes it difficult to verify if these QTLs are truly associated with CH₄ production or only with milk production and composition. There can be confounding effects due to the genetic correlations between the predicted CH₄ traits and the milk traits. We estimated genetic correlations between the six predicted CH₄ traits and milk traits using bivariate analyses with Eq. (1), that suggest moderate confounding effects with milk production and composition. Except for MeP_indirect, genetic correlations with milk yield ranged from –0.25 to 0.23, with protein yield from –0.18 to 0.04, and with fat yield from –0.39 to 0.42. MeP_indirect showed stronger genetic correlations with these traits, of 0.84, 0.76, and 0.47, respectively, but presented only 2 QTLs, which contain the *DGAT1* and the *VPS13B* genes. The genetic correlations with the fat yield raises concerns about the causality of the association between the predicted CH₄ traits and the identified genes. Further research is required to validate or invalidate these potential candidate genes. However, this would necessitate large populations with direct CH₄ measurements to prevent the confounding effects of milk production and composition traits that are inherent to prediction from milk MIR spectra.

## Conclusions

By studying CH₄ emissions predicted from milk MIR spectra, we were able to obtain a large amount of data, which, when combined with imputed WGS data in GWAS analyses, gave us a high detection power and a high resolution of the results. For the six CH₄ traits examined here, we were able to identify a total of more than 100 QTLs; of these, several were shared among multiple traits, thus highlighting their common genetic basis.
We identified three potential candidate genes that are involved in the production of milk FAs from VFAs (FASN on BTA19, DGAT1 on BTA14, and ACSS2 on BTA13) along with one gene that is associated with bacterial genera (Prevotella, Ruminococcus, and Eubacterium) with known effects on VFA and hydrogen production (KCNIP4 on BTA6). Further research is necessary to determine if these genes are associated with predicted CH₄ emissions or if the association is inherent to the nature of the prediction from milk MIR spectra.

### Acknowledgements

The authors thank the French milk recording compagnies for providing milk MIR spectra, and Jean-Pierre Allard and Guillaume Courtois (ESTEL, France) for calculating the CH₄ predictions. SF acknowledges Sébastien Taussat (Eliance, France) and Louise Brulin (GD Biotech—Gènes Diffusion, France) for methodological support and help in interpreting the results.

### Author contributions

PM, DB, and SeF conceived the study. SF performed the statistical analyses and drafted the manuscript. SF, MPS, DB, SeF, and PM participated in interpreting and discussing the results. MPS, DB, SeF, and PM revised and proofread the manuscript. All authors read and approved the final manuscript.

### Funding

Data were collected as part of the METHABREED project funded by APIS-GENE (Paris, France). Solène Fresco is the recipient of a CIFRE PhD grant from Eliance, with the financial support of the Association Nationale de la Recherche et de la Technologie (grant number CIFRE n°2021/1241) and APIS-GENE (grant number AP-2021-045).

### Availability of data and materials

Raw genotype and phenotype data belong to French farmers and have commercial value. Restrictions apply to their availability, and they are not publicly available. The authors can be contacted for a reasonable request.

### Declarations

#### Ethics approval and consent to participate

This study was based on data routinely collected on dairy farms during milk recording. We did not perform any experiment on animals; therefore, no ethical approval was required.

#### Consent for publication

Not applicable.

#### Competing interests

The authors declare that they have no competing interests.

Received: 2 December 2024 Accepted: 22 May 2025

Published online: 17 June 2025

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