# Genetic parameters and genome-wide association studies of milk infrared spectra from Lacaune dairy ewes

C Marie-Etancelin, R Rupp Genphyse

► To cite this version:

C Marie-Etancelin, R Rupp Genphyse. Genetic parameters and genome-wide association studies of milk infrared spectra from Lacaune dairy ewes. Journal of Dairy Science, 2025, 108 (8), pp.8648-8665. 10.3168/jds.2025-26259. hal-05186416

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# Genetic parameters and genome-wide association studies of milk infrared spectra from Lacaune dairy ewes

C. Marie-Etancelin* and R. Rupp

GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France

## ABSTRACT

Fourier-transform infrared spectroscopy is a widely used analytical tool in dairy production—either in cattle or small ruminant—for predicting milk composition and quality. Nevertheless, not all the information contained in the spectrum is used for its predictions. This study examined the genetic parameters and GWAS of all wavelengths of infrared milk spectra in Lacaune dairy ewes to explore their genetic determinism and compare it with that of milk composition traits predicted by the spectra. Data from 1,063 ewes were collected during their first lactation over 9 yr (2014–2022) at 5 milk recording controls, including infrared spectra with 1,060 wavelengths and predicted milk composition (fatty acids and proteins). The ewes were genotyped with a 54K SNP chip. Heritability estimates across the infrared spectrum ranged from 0.26 to 0.69, with higher values observed in the late lactation stages. Genome-wide association study revealed significant QTL for both spectra wavelengths and predicted milk traits, with some loci being specific to milk IR spectra. Key regions on *Ovis aries* (OAR) chromosome 3 (OAR3, around *Socs2*) and OAR11 were associated with both milk composition and spectral traits. Additionally, novel QTL for milk urea (OAR6) and certain fatty acids (OAR17) were identified, as well as QTL within spectra on 4 chromosomes (OAR1, OAR10, OAR13, and OAR20). This study demonstrates that direct GWAS on infrared spectra provides complementary insights to traditional approaches on predicted milk composition traits, uncovering new genetic regions of interest and advancing our understanding of milk composition’s genetic architecture. These findings highlight the potential of milk spectra as a tool for enhancing genetic selection in dairy sheep.

**Key words:** GWAS, MIR spectra, milk composition, Lacaune dairy ewes

## INTRODUCTION

Fourier-transform infrared (IR) spectroscopy applied to milk has become a cornerstone analytical technique in dairy production, offering a robust method for predicting the concentration of various milk components, including fat, protein, lactose, and fatty acids (Soyeurt et al., 2006). Widely used in cattle as well in small ruminants such as sheep and goat, this technology has been instrumental for genetic purposes, to improve milk quality. Beyond traditional milk composition traits, the application of IR spectroscopy has expanded to encompass predictions of broader animal phenotypes, including energy balance (Rovere et al., 2024) and methane emissions (Vanlierde et al., 2015; Fresco et al., 2024). Recognizing the growing significance of IR spectra as predictors of various traits, the livestock industry has begun to systematically preserve individual spectra obtained during dairy selection testing. However, the intrinsic properties of the milk IR spectrum, particularly its constituent wavelengths, merit detailed investigation. Indeed, each wavelength corresponds to interactions with specific chemical bonds (e.g., C=O, N–H, C–OH, or C–H), providing nuanced information about milk composition that extends beyond traditional predictive models. Recognizing the growing utility of IR spectra in generating multitrait datasets, including both traditional milk composition traits and wavelength-specific traits, the livestock industry has begun to systematically preserve individual spectra obtained during dairy selection testing.

Genetic studies have demonstrated that individual wavelengths within the IR spectrum of cow and goat milk are heritable (for cow: Soyeurt et al., 2010; Bittante and Cecchinato, 2013; Wang et al., 2016; Wang and Bovenhuis, 2018; Zaalberg et al., 2019; Du et al., 2020; and for goat: Dagnachew et al., 2013), with recent investigations extending this knowledge to ewe milk (Congiu et al., 2024; Machefert et al., 2024). Therefore, the genetic variability contained in IR spectra paves the way for GWAS. In cattle, QTL mapping for specific wavelengths or for the complete IR spectrum has been conducted since 2018 (Wang and Bovenhuis, 2018; Benedet et al., 2019;

<footnote>Received January 2, 2025.</footnote>

<footnote>Accepted April 24, 2025.</footnote>

<footnote>*Corresponding author: christel.marie-etancelin@inrae.fr</footnote>
Zaalberg et al., 2020; Tiplady et al., 2021), but equiva-
lent GWAS in goats and sheep remains limited. More-
over, in the context of dairy sheep, milk composition
exhibits substantial variability throughout the lactation
stage, with marked increases in fat and protein content
during late lactation (Fraysse et al., 1996). Consequently,
the IR spectrum will also be significantly influenced by
lactation stage, underscoring the importance of genetic
analysis tailored to these dynamics.

The aim of this study is to carry out a GWAS on each of
the wavelengths of the IR spectra of milk from Lacaune
dairy ewes, considering the effects of lactation stage, and
to compare the QTL detected with those identified for
the dairy traits predicted from the IR spectra of the milk:
the common QTL were visualized in 3 dimensions. The
objective is to gain a better understanding of the genetic
determinism of wavelengths in the IR spectrum of milk,
and to access new QTL than those derived from predicted
traits, where appropriate.

MATERIALS AND METHODS

Animals

The present study was conducted on data collected
on 1,063 Lacaune dairy sheep at the INRAE Experi-
mental Unit La Fage (UE 321 agreement A312031,
Roquefort, France, https://doi.org/10.15454/1
.548325523466425e12). Genetic monitoring of all ewes
using blood genotyping has been approved (ethics com-
mittee approval 2019110713292766_version 6 #27486).

**Husbandry.** The study focused on Lacaune dairy ewes during their first lactation. In short, the renewal ewes in our experimental flock were bred in July each year and lambed from the end of December to mid-February. After 3 wk of mixed suckling and milking phase, the lambs were separated from the dams and exclusive milking began. Milking recording controls (MRC) were planned each 3 wk during these exclusive milking phases, at morning and evening milking. In 9 yr, from 2014 to 2022, 1,063 ewes in first lactation (between 59 and 146 ewes per year) were studied from mid-March to the beginning of June during 5 MRC, except in 2020 due to COVID, where only the 2 MRC in March and June were done. Lactation stage was on average 57 d for the first MRC. In all, 68% of ewes lambed after animal insemination (average difference between lambing dates of 10.2 d), and 32% of ewes lambed on the first or second natural mating (average difference between lambing dates of 39.3 d). The 1,063 ewes came from 210 rams (between 1 and 21 daughters per sire), half from each of the 2 Lacaune breeding organizations, ensuring that our flock is well connected to the commercial Lacaune population.

<table><caption>**Table 1.** Lactation stage (in days from lambing) for each of the 5 milk recording controls (MRC)</caption>
   <thead>
    <tr>
     <td>
      Item
     </td>
     <td>
      N
      <sup>
       1
      </sup>
     </td>
     <td>
      Mean
     </td>
     <td>
      SD
     </td>
     <td>
      Minimum
     </td>
     <td>
      Maximum
     </td>
    </tr>
   </thead>
   <tbody>
    <tr>
     <td>
      MRC1
     </td>
     <td>
      1,020
     </td>
     <td>
      57.6
     </td>
     <td>
      9.8
     </td>
     <td>
      12
     </td>
     <td>
      76
     </td>
    </tr>
    <tr>
     <td>
      MRC2
     </td>
     <td>
      909
     </td>
     <td>
      79.2
     </td>
     <td>
      9.9
     </td>
     <td>
      33
     </td>
     <td>
      97
     </td>
    </tr>
    <tr>
     <td>
      MRC3
     </td>
     <td>
      901
     </td>
     <td>
      101.6
     </td>
     <td>
      10.3
     </td>
     <td>
      54
     </td>
     <td>
      118
     </td>
    </tr>
    <tr>
     <td>
      MRC4
     </td>
     <td>
      1,046
     </td>
     <td>
      120.4
     </td>
     <td>
      9.8
     </td>
     <td>
      74
     </td>
     <td>
      138
     </td>
    </tr>
    <tr>
     <td>
      MRC5
     </td>
     <td>
      1,035
     </td>
     <td>
      142.3
     </td>
     <td>
      10.0
     </td>
     <td>
      97
     </td>
     <td>
      161
     </td>
    </tr>
   </tbody>
  </table>

**Phenotyping Protocol.** In dairy sheep, an official MRC was composed by 2 consecutive milkings (evening and next morning), from which 2 milk quantities (MY) were registered and 2 milk samples were collected and sent for analysis at the Interprofessional Milk Analysis Laboratory (AGROLAB's Aurillac, France). As described by Machefert et al. (2024), spectra were standardized at the Regional Association of Breeders' Organizations (ARSOE Soual, France) according to Grelet et al. (2015) methodology to reduce spectral variability between instruments and over time. Then individual milk IR (MIR) spectra with 1,060 wavelength numbers were recovered for the 1,063 × 2 samples: the first 798 wavelength numbers corresponded to mid IR sensus stricto, whereas the wavelengths numbers 799 to 1,060 concerned the near IR.

From these spectra, we predicted the fine composi-
tion of milk, that is, the main milk proteins (Ferrand et
al., 2012) and fatty acid contents (Ferrand-Calmels et
al., 2014) expressed in gram per 100 mL: (1) αS₁-CN,
αS₂-CN, β-CN, κ-CN, α-LA, and β-LG with an R² in
validation dataset ranging from 0.73 to 0.93 for CN and
from 0.21 to 0.77 for the 2 whey proteins (Ferrand et al.,
2012), and (2) butyric acid (C4:0), caproic acid (C6:0),
caprylic acid (C8:0), capric acid (C10:0), lauric acid
(C12:0), myristic acid (C14:0), palmitic acid (C16:0),
oleic acid (cis-9 C18:1), rumenic acid (cis-9,trans-11
C18:2), and α-linolenic acid (C18:3n-3), with an R² in
validation dataset of 0.74 for α-linolenic acid and rang-
ing from 0.93 to 0.97 for all other fatty acids (Ferrand-
Calmels et al., 2014).

Unfortunately, for the first 3 yr (2014–2016), the
spectra of some MRC were missing because these
were not systematically kept by laboratory (details of
the number of MIR spectra in Appendix Table A1).
In addition, direct measurements were obtained from
AGROLAB’s for the 1,063 × 2 samples. Milk SCC was
quantified with a Fossomatic cell counter (Foss, Nan-
terre, France) and transformed in a milk SCS as SCS
= 3 + log₂(SCC/100,000). Since 2017, milk urea (MU)
was quantified from a bovine prediction using mid IR
spectra. Milk fat content (FC) and protein content (PC)
were estimated with a Milko-Scan FT6000 instrument

<footnote n="1">¹N = number of ewes per MRC.</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Mean values of milk infrared spectra along the 1,060 wavenumbers according to the milk recording control (MRC). The gray shading indicates the average values of the 5 MRC ± 2 SD.</figcaption></figure>

(Foss, Nanterre, France), and the milk content ratio (RC) was computed as the ratio between FC and PC.

All traits and spectra previously obtained for each morning and evening milkings are then averaged per day. For the 1,063 ewes, the final dataset included 4,911 daily official milk recording traits (MY, FC, PC, RC, and SCS), 2,927 daily MU, 3,989 daily MIR spectra, and predictions of milk fatty acids (C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, cis-9 C18:1, cis-9,trans-11 C18:2, and C18:3 n-3) and proteins ($\alpha_{S1}$-CN, $\alpha_{S2}$-CN, $\beta$-CN, $\kappa$-CN, $\alpha$-LA, and $\beta$-LG) in milk. Data breakdown by MRC is detailed in Appendix Table A1.

**Genotyping.** The DNA extraction from blood samples and genotyping using a medium-density SNP chip (Illunima Ovine SNP50_B BeadChip v.2.0.2: 54 241 SNPs) were performed for the 1,063 ewes, either at the Laboratoire d'Analyses Génétiques pour les Espèces Animales (Jouy-en-Josas, France) for 634 ewes, or at Aveyron-Labo (Rodez, France) for 429 ewes. Quality control was applied to genotypes: samples with a genotyping call rate lower than 95% were discarded; SNP with a call frequency lower than 90% and a minor allele frequency lower than 5% were removed. Markers were mapped on

the 26 autosomes of the *Ovies aries* genome assembly Oar_v3.1 (OAR-v3-1, 2022; International Sheep Genome Consortium, 2012). Genotyping of the *Socs2* mutation was performed by allele-specific amplification using the KASPar SNP genotyping system, followed by fluorescence detection on an ABI7900HT (for more details, see Rupp et al., 2015). Then the *Socs2* genotype was added to the SNP50 chip dataset and included in the map of *Ovis aries* (OAR) chromosome 3 (OAR3) at 129,722,200 bp position. The final dataset included 1,063 genotyped individuals and 43,628 autosomal SNPs.

## Statistical Analysis

Hereafter, what we mean by “trait” will be the 6 official milk production traits (MY, FC, PC, RC, SCS, MU), the predictions of fine milk composition (10 predicted fatty acids and 6 predicted proteins), and each of the wavenumbers of the MIR spectrum. These traits will be considered separately for each of the 5 MRC to study the effect of lactation stage.

**Variance Component Estimation.** The variance components and genomic breeding value of each trait were
<table><caption>**Table 2.** Average dairy traits (mean ± SD) for each of the 5 milk recording controls (MRC) in first lactation Lacaune ewes</caption><thead><tr><th>Item</th><th>Unit</th><th>MRC1</th><th>MRC2</th><th>MRC3</th><th>MRC4</th><th>MRC5</th></tr></thead><tbody><tr><td>O-MR<sup>1</sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MY</td><td>mL</td><td>1182 ± 265</td><td>1066 ± 246</td><td>926 ± 207</td><td>761 ± 171</td><td>567 ± 127</td></tr><tr><td>FC</td><td>g/L</td><td>61.9 ± 7.9</td><td>67.4 ± 8.6</td><td>74.6 ± 10.4</td><td>81.5 ± 10.2</td><td>87.4 ± 11.7</td></tr><tr><td>PC</td><td>g/L</td><td>49.3 ± 3.4</td><td>51.6 ± 3.7</td><td>54.6 ± 4.3</td><td>58.2 ± 5.4</td><td>57.9 ± 5.6</td></tr><tr><td>RC</td><td>—</td><td>1.26 ± 0.17</td><td>1.31 ± 0.17</td><td>1.37 ± 0.18</td><td>1.40 ± 0.15</td><td>1.51 ± 0.15</td></tr><tr><td>SCS</td><td>—</td><td>3.08 ± 1.66</td><td>3.06 ± 1.70</td><td>3.43 ± 1.73</td><td>3.21 ± 1.76</td><td>3.38 ± 1.64</td></tr><tr><td>MU</td><td>mg/L</td><td>468 ± 65</td><td>445 ± 70.8</td><td>392 ± 87</td><td>386 ± 122</td><td>377 ± 108</td></tr><tr><td>MIR prediction<sup>2</sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C4:0</td><td>g/L</td><td>2.41 ± 0.29</td><td>2.51 ± 0.31</td><td>2.58 ± 0.41</td><td>2.66 ± 0.31</td><td>2.71 ± 0.31</td></tr><tr><td>C6:0</td><td>g/L</td><td>1.84 ± 0.23</td><td>1.93 ± 0.24</td><td>2.01 ± 0.27</td><td>2.01 ± 0.25</td><td>1.93 ± 0.30</td></tr><tr><td>C8:0</td><td>g/L</td><td>1.57 ± 0.35</td><td>1.74 ± 0.34</td><td>1.72 ± 0.34</td><td>1.80 ± 0.35</td><td>1.65 ± 0.38</td></tr><tr><td>C10:0</td><td>g/L</td><td>5.67 ± 0.91</td><td>5.98 ± 1.02</td><td>5.86 ± 1.28</td><td>5.90 ± 1.36</td><td>5.89 ± 1.61</td></tr><tr><td>C12:0</td><td>g/L</td><td>3.77 ± 0.60</td><td>3.98 ± 0.74</td><td>3.81 ± 0.99</td><td>3.96 ± 1.07</td><td>4.09 ± 1.13</td></tr><tr><td>C14:0</td><td>g/L</td><td>9.22 ± 1.11</td><td>10.10 ± 1.79</td><td>9.56 ± 1.54</td><td>9.96 ± 1.92</td><td>10.84 ± 1.76</td></tr><tr><td>C16:0</td><td>g/L</td><td>16.91 ± 2.14</td><td>17.91 ± 2.35</td><td>19.34 ± 2.64</td><td>20.42 ± 2.42</td><td>21.64 ± 2.98</td></tr><tr><td>C18:1</td><td>g/L</td><td>11.44 ± 2.57</td><td>12.84 ± 4.17</td><td>17.99 ± 4.15</td><td>19.33 ± 4.53</td><td>21.24 ± 3.58</td></tr><tr><td>C18:2</td><td>g/L</td><td>1.48 ± 0.25</td><td>1.60 ± 0.48</td><td>1.89 ± 1.23</td><td>2.37 ± 0.37</td><td>2.66 ± 0.38</td></tr><tr><td>C18:3 n-3</td><td>g/L</td><td>0.36 ± 0.08</td><td>0.41 ± 0.11</td><td>0.48 ± 0.24</td><td>0.62 ± 0.11</td><td>0.71 ± 0.11</td></tr><tr><td>α-LA</td><td>g/L</td><td>1.44 ± 0.11</td><td>1.52 ± 0.14</td><td>1.50 ± 0.22</td><td>1.55 ± 0.20</td><td>1.57 ± 0.21</td></tr><tr><td>β-LG</td><td>g/L</td><td>4.13 ± 0.38</td><td>4.36 ± 0.44</td><td>5.24 ± 1.01</td><td>5.13 ± 0.54</td><td>5.15 ± 0.61</td></tr><tr><td>α<sub>S1</sub>-CN</td><td>g/L</td><td>12.15 ± 0.92</td><td>12.53 ± 1.01</td><td>13.71 ± 1.38</td><td>14.33 ± 1.50</td><td>14.45 ± 1.64</td></tr><tr><td>α<sub>S2</sub>-CN</td><td>g/L</td><td>4.26 ± 1.11</td><td>5.13 ± 1.78</td><td>5.00 ± 1.78</td><td>5.32 ± 1.40</td><td>5.93 ± 1.35</td></tr><tr><td>β-CN</td><td>g/L</td><td>17.75 ± 1.53</td><td>19.41 ± 1.90</td><td>19.80 ± 2.83</td><td>21.57 ± 2.50</td><td>21.96 ± 2.80</td></tr><tr><td>κ-CN</td><td>g/L</td><td>4.09 ± 0.34</td><td>4.08 ± 0.32</td><td>4.42 ± 0.46</td><td>4.45 ± 0.46</td><td>4.43 ± 0.51</td></tr></tbody></table>

estimated using the BLUPF90+ software with the AI-REML method (Misztal et al., 2002). Due to test-day model convergence issues, the single trait animal model used was the following:

$$y = Xb + Wg + e, \quad [1]$$

where y is the vector of observations, b is the vector of fixed environmental effects, that is, the year (9 levels: from 2014 to 2022) and the batch of lambing (2 levels: after animal insemination or after natural mating); g is the vector of additive genetic effects; and e is the vector of residual effects. The X and W are incidence matrices for b and g, respectively.

The assumptions of the model are $g \sim N(0, H\sigma_g^2)$, where H is a matrix that combines pedigree- and genome-based relationships (Legarra et al., 2009; Misztal et al., 2009) and $\sigma_g^2$ is the additive genetic variance; and $e \sim N(0, I\sigma_e^2)$, where I is an identity matrix and $\sigma_e^2$ is the residual variance. An inverse of H is as follows (Aguilar et al., 2010):

$$H^{-1} = A^{-1} + \begin{pmatrix} 0 & 0 \\ 0 & G^{-1} - A_{22}^{-1} \end{pmatrix}, \quad [2]$$

where A is the relationship matrix based on pedigree for all animals, A<sub>22</sub> is the relationship matrix for genotyped animals, and G is the weighted genomic relationship matrix as

$$G = (1 - \alpha) (c + dG_b)_+ \alpha A_{22}, \quad [3]$$

where α is a small value generally between 0 and 0.05 used to blend G<sub>b</sub> and A<sub>22</sub>, c and d are used to put A and G in the same scale, and G<sub>b</sub> = $\begin{pmatrix} ZZ' \\ 2\sum p_i(1-p_i) \end{pmatrix}$ as proposed by VanRaden (2008), where Z is a centered matrix for SNP content, p<sub>i</sub> is the minor allele frequency of the ith SNP. The entire pedigree included 5,971 individuals obtained by tracing back 5 generations of ancestors of the 1,063 genotyped ewes.

Notice that traits with very low estimates, that is, $\sigma_g^2$ or $\sigma_e^2$ lower than $5.10^{-6}$, were removed from h<sup>2</sup> figures and from the following analyses. For IR spectra, comparison of the average genetic and residual variance estimates according to MRC was done with a variance analysis, using aov3 function of sasLM package (version 0.9.11; Bae, 2023) in R software (version 4.2.1; R Core Team, 2022).

<footnote n="1">¹MY = milk yield; FC = fat content; PC = protein content; RC = ratio between FC and PC; MU = milk urea.</footnote>

<footnote n="2">²C4:0 = butyric acid; C6:0 = caproic acid; C8:0 = caprylic acid; C10:0 = capric acid; C12:0 = lauric acid; C14:0 = myristic acid; C16:0 = palmitic acid; cis-9 C18:1 = oleic acid; cis-9,trans-11 C18:2 = rumenic acid; C18:3 n-3 = α-linolenic acid.</footnote>
**GWAS.** Genome-wide association studies of each of the 6 dairy traits, the 16 predicted milk composition traits, and the wavenumbers were performed using a single-step approach (Aguilar et al., 2019): SNP effects (ŷ) were estimated by back-solving the estimated breeding (ĝ) values as follows:

$$ \hat{u}|\hat{g} = (1-\alpha) \ d \mathbf{Z}' \frac{1}{2\sum p_i(1-p_i)} \mathbf{G}^{-1}\hat{g}_2 \quad [4] $$

$$ \operatorname{Var}(\hat{u}_i) = \frac{1}{2\sum p_i(1-p_i)}(1-\alpha) \ d z_i' \mathbf{G}^{-1} (\mathbf{G}\sigma_g^2 - \mathbf{C}^{g_2g_2}) \times \mathbf{G}^{-1} z_i(1-\alpha) \frac{1}{2\sum p_i(1-p_i)}, \quad [5] $$

where $\mathbf{C}^{g_2g_2}$ is the submatrix that corresponds to the genotyped animals, and containing the prediction error variances of their EBV $\hat{g}_2$.

Then P-values were estimated for each SNP as

$$ P\text{-value}_i = 2 \left[ 1 - \Phi \left( \frac{\hat{u}_i}{sd(\hat{u}_i)} \right) \right], \quad [6] $$

where $\hat{u}_i$ is the estimate for each SNPi.

The SNP effects and P-values were computed using BLUPF90+ and POSTGSF90 (Misztal et al., 2002). Multiple testing was corrected using the method proposed by Gao et al. (2008): a principal component analysis (PCA) with R-FactoMineR (version 2.6; Lê et al., 2008) package was applied to the 2 matrices (SNP matrix and traits matrix) to estimate the number of independent tests. To capture 99% of the variability of each of the matrices, the PCA showed that 1,259 eigenvalues were necessary for the 42,489 SNP, and 64 eigenvalues for the 1,082 traits matrix (1,060 wavelengths and 22 zootechnical traits). Per MRC, we estimated the number of independent tests to be 1,259 × 64 = 80,576, and we defined a significant threshold at 5% (as −log₁₀[0.05/(80,576)] = 6.21) to control error rates due to multiple testing. The QTL visualization was carried out in 3 dimensions for spectra and in 2 dimensions for predicted trait with the R-plotly (version 4.10.4; Sievert, 2020) and R-ggplot2 (version 3.5.1; Wickham, 2016) packages, respectively.

Finally, when colocalized QTL were observed between spectra and predicted or official milk traits, a close linkage versus pleiotropism (CLIP) test (David et al., 2013) was performed to distinguish between pleiotropy or close linkage: a window of 11 markers around the SNP with the maximum −log₁₀(P-value) was used to perform the test.

# RESULTS

## Descriptive Statistics

Milk recording controls were carried out every 3 wk: the ewes studied have an average lactation stage of 57 d at the first MRC (MRC1) and 142 d at the fifth and last MRC (MRC5; Table 1). The range of lactation stage varied from 12 to 161 d, with a mean of 64 d. Note that the lower number of animals at the third milk recording control (MRC3) and at the fourth milk recording control (MRC4) is due to the COVID lockdown period in 2020. For more information on headcount by year, MRC, and traits, see the Appendix Table A1.

In terms of spectrum, the 1,060 wavelength (WL) absorbances varied from 0.21 to 2.01 (Figure 1). Near IR zone was very stable, whereas mid IR fluctuated a lot. Whatever the IR zone, the spectra profiles of the 5 MRC were very similar, with little variation (mean CV 3.8%), except for the zones between WL182–195 and WL560–710, where the CV is greater than 7% up to 55% (Figure 1).

Between the first and fifth MRC, average MY per milking decreased by 1.182 l to 0.567 l, whereas milk composition became richer: +25.5 g/L and +8.6 g/L of FC and PC, respectively (Table 2). As shown by the increase of the RC ratio, this enrichment is faster for fat than protein. These increased concentrations were found in all its components (milk fatty acids and milk proteins), in particular with the doubling of the C18:1 concentration. At the same time, SCC in milk remained stable over the course of the 5 MRC, whereas urea decreased in value (from 468 to 377 mg/L) with an increased SD (65 mg/L to 108 mg/L).

## Genetic Determinism of Infrared Spectra

**Heritabilities Along Wavenumbers According to Lactation Stages.** As observed on the raw values of the spectra, the wavenumbers from 182 to 195 and from 560 to 710 have very low and erratic heritabilities (Figure 2). These 165 wavelengths have been excluded from subsequent analyses. The heritabilities of the rest of the spectrum for the 5 MRC ranged from 0.09 to 0.69, with an average SE of 0.05 (ranging from 2 × 10⁻⁸ to 0.093). On either side of the removed wavelengths, we define 3 zones. The first one (Z1; wavelengths 1 to 181), showed a high variability between wavelengths of h² values, ranging from 0.26 to 0.69. The second zone (Z2; wavelengths 196 to 559), showed heritabilities in the same range as Z1 but was characterized by a first area with stable h² estimates, then by an area where heritabilities could not be estimated due to too low variances, and finally, by a part where the spectrum had very variable h² estimates.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Heritabilities of milk infrared spectra along the 1,060 wavenumbers according to the milk recording control (MRC) in first lactation Lacaune ewes. Three zones are defined (Z1, Z2, and Z3), separated by 2 areas with very low and erratic heritability values. Average SE of heritabilities (mSE h²) were computed for each of the 3 Z areas.</figcaption></figure>

The third zone (Z3; wavelengths 711 to 1,060), showed very stable estimates of h².

Following the same 3 zones pattern, there were significant differences between MRC in genetic variance estimates, and partly in residual variance estimates (Table 3). Estimates of genetic variance increased significantly with lactation stage in all 3 zones of the spectrum, with higher values for the latter part of lactation (MRC4 and MRC5) compared with the early part (MRC1 and MRC2). At the same time, estimates of residual variances in spectrum zones 1 and 2 appeared to be indistinguishable between MRC, whereas in zone 3, these estimates varied according to controls, with significantly higher residual values in MRC3 and MRC5. The comparison of genetic variances between the 3 zones of the spectrum revealed that zone 2 exhibited the larger genetic and residual variances compared with zones 1 and 3. But whereas zone 3 showed lower average genetic variances than zone 1, it conversely had higher residual variances as early as MRC3. Variations in h² between zones and MRC (Figure 2) are therefore not due to the same evolution of the 2 variances. For more details on variations in variance estimates along the spectrum, see Appendix Figure A1.

**QTL for the Wavenumbers of Spectra.** Significant QTL for IR spectra were reported for each of the 3 zone Z

areas in Table 4. No QTL was revealed at the 2 first MRC, whereas the number of significant QTL increased with the lactation stage as well as the *P*-value of the most significant QTL: 4 QTL at MRC3 (maximum −log₁₀[*P*-value] of 6.92), 5 QTL at MRC4 (maximum −log₁₀[*P*-value] of 11.95), and 9 QTL at MRC5 (maximum −log₁₀[*P*-value] of 12.02). Moreover, the QTL were mostly found in the first 2 areas (Z1 and Z2): for Z3, only a QTL on OAR11 was significant. Several QTL appeared only in 1 MRC, which was the case for QTL on OAR1 and OAR10 at MRC5 and QTL on OAR20 at MRC3. On OAR13, the same QTL was revealed at MRC3 and MRC4 in Z1 with the same 5 wavelengths.

Finally, QTL on OAR3 and OAR11 that were identified at the 3 MRC and in at least 2 of the 3 zones of the spectra reached very high −log₁₀(*P*-value): 12.02 for OAR3 at SNP 11673 and MRC5, and 9.11 for OAR11 at SNP 26803 and MRC4. For chromosome 3, the SNP with the lowest −log₁₀(*P*-value) was SNP 11671 at MRC4 and SNP 11673 at MRC5: only 1 SNP and 73,586 pb separated these 2 maxima. For chromosome 11, the lowest *P*-values according to the zones of the spectra were at SNP 26803 or SNP 26814 for MRC4 and SNP 26821 or SNP 26832 for MRC5. Figures 3 detail the estimated *P*-values along the spectra wavelengths for MRC4 and the 2 chromosomes 3 and 11, respectively.
<table><caption>**Table 3.** ANOVA of genetic ($\sigma_g^2$) and residual ($\sigma_e^2$) variances estimates for spectra according to MRC for each of the 3 zones</caption><thead><tr><th rowspan="2">Item</th><th rowspan="2">Zone</th><th rowspan="2">P-value</th><th colspan="5">Lsmeans<sup>1</sup></th></tr><tr><th>MRC1</th><th>MRC2</th><th>MRC3</th><th>MRC4</th><th>MRC5</th></tr></thead><tbody><tr><td rowspan="3">($\sigma_g^2$)</td><td>Z1</td><td>***</td><td>1.22 ± 2.30<sup>a</sup></td><td>1.16 ± 1.77<sup>a</sup></td><td>1.51 ± 2.43<sup>ab</sup></td><td>1.89 ± 2.99<sup>b</sup></td><td>2.60 ± 4.74<sup>c</sup></td></tr><tr><td>Z2</td><td>***</td><td>1.53 ± 2.69<sup>a</sup></td><td>1.45 ± 2.59<sup>a</sup></td><td>2.13 ± 4.50<sup>a</sup></td><td>3.40 ± 7.74<sup>b</sup></td><td>3.88 ± 1.02<sup>b</sup></td></tr><tr><td>Z3</td><td>***</td><td>0.75 ± 0.66<sup>a</sup></td><td>1.11 ± 0.81<sup>b</sup></td><td>1.26 ± 0.78<sup>b</sup></td><td>1.83 ± 2.33<sup>c</sup></td><td>1.87 ± 3.47<sup>c</sup></td></tr><tr><td rowspan="3">($\sigma_e^2$)</td><td>Z1</td><td>NS</td><td>1.83 ± 4.71</td><td>2.40 ± 6.48</td><td>2.53 ± 6.59</td><td>2.25 ± 4.57</td><td>2.33 ± 4.66</td></tr><tr><td>Z2</td><td>NS</td><td>2.74 ± 5.81</td><td>4.31 ± 10.71</td><td>3.65 ± 8.97</td><td>3.31 ± 8.76</td><td>4.17 ± 13.85</td></tr><tr><td>Z3</td><td>***</td><td>1.06 ± 1.73<sup>a</sup></td><td>2.59 ± 2.82<sup>b</sup></td><td>3.36 ± 4.19<sup>c</sup></td><td>2.65 ± 4.28<sup>b</sup></td><td>3.61 ± 7.60<sup>c</sup></td></tr></tbody></table>

For OAR3 (Figure 3a), we observed 3 main peaks located in Z1 and Z2 and dome-shaped -log₁₀(P-value). For QTL on OAR11 (Figure 3b), the profiles showed a large significant area in Z3 with over 336 wavenumbers beyond the threshold, and in Z1 only 16 wavenumbers.

## Genetic Determinism of Dairy Traits and Milk Predictions

**Heritabilities of Dairy Traits and Milk Predictions According to Lactation Stages.** Heritability estimates for the traits ranged from 0.05 ± 0.06 for rumenic acid (cis-9 trans-11 C18:2) at MRC3 to 0.83 ± 0.09 for urea in milk at MRC2 (Figure 4). Within official milk recording (O-MR) traits, h² estimates ranged from 0.12 ± 0.06 to 0.25 ± 0.07 for SCS, 0.16 ± 0.07 to 0.31 ± 0.07 for MY, and from 0.62 ± 0.11 to 0.83 ± 0.09 for MU. The FC and PC have more variable h² estimates between MRC, ranging from 0.37 ± 0.07 to 0.63 ± 0.06 and from 0.40 ± 0.08 to 0.66 ± 0.07, respectively, whereas the RC, between them, was more stable with h² values ranging from 0.38 ± 0.07 to 0.46 ± 0.07.

In terms of fatty acid predictions, the saturated ones (from C4:0 to C16:0) showed the same pattern of h² values according to MRC (Figure 2), ranging from 0.30 ± 0.07 to 0.64 ± 0.07, with the first 3 MRC always having lower h² values (0.30 ± 0.07 to 0.49 ± 0.09) than the last 2 MRC (0.47 ± 0.08 to 0.64 ± 0.07). These patterns between MRC were also comparable to the FC one. The h² values for the 3 UFA were always below 0.40, with a wide range of variation between MRC for rumenic and α-linolenic acids. In fact, these 2 UFA have particularly low h² values at MRC3 (~0.05 ± 0.06), due to a strong increase of the residual variances (Appendix Figure A2). In terms of proteins predictions, h² estimates ranged from 0.12 ± 0.06 to 0.58 ± 0.05, with systematically the lowest values observed at MRC3. Serum proteins (α-LA and β-LG), in particular, have very low estimates at this control (0.12 ± 0.06 and 0.18 ± 0.07,

respectively), which was due to an increase in residual variances (Appendix Figure A2), as for the rumenic and α-linolenic acids. Heritability estimates for the 4 caseins were more clustered between MRC, with largest variations for αS₂-CN ranging from 0.28 ± 0.08 at MRC3 to 0.56 ± 0.09 at MRC1.

**QTL for Dairy Traits and Milk Predictions.** For all dairy traits and the 5 MRC, 34 QTL were detected on 6 different chromosomes (Table 5): OAR3 with 14 QTL, OAR6 and OAR17 with 8 QTL each, OAR12 with 2 QTL, and OAR11 and OAR20 with a single 1 each. The number of QTL increased with the lactation stage: 3 QTL at MRC1, 4 QTL at MRC2, 6 QTL at MRC3 and MRC5, and the maximum numbers at MRC4 with 15 QTL. The most significant QTL with a -log₁₀(P-value) exceeding 10 were on OAR6 for MU at MRC5 with -log₁₀(P-value) of 12.6, on OAR17 for milk butyric acid at MRC3 with -log₁₀(P-value) of 11.7, and on OAR3 for fat content at MRC4 and MRC5 with -log₁₀(P-value) of 10.7 in both cases.

On OAR6, QTL for MU and αS₂-CN were detected repeatedly in 4 of 5 MRC: the position of the maximum was not always the same, but for the 2 traits, the strongest QTL appears at the last MRC with a maximum -log₁₀(P-value) of 12.6 and 8.3 for MU and αS₂-CN, respectively (Figure 5). On OAR17, we observed for butyric and caproic acids 2 QTL at the same position with significant signals at the first 3 MRC. Two fleetling QTL, appearing in only 1 MRC were observed on OAR11 for a ratio between FC and PC at MRC4 and OAR20 for α-linolenic acid at MRC3.

Finally, 14 QTL for different dairy traits on OAR3 were only significant at the 2 last MRC: 11 signals at MRC4 and 3 at MRC5. According to traits and MRC, 2 SNPs carried the maximum P-value on OAR3: SNP number 11673 (SOCS mutation) and SNP number 11671, 73,586 bp before SOCS. Associated with SNP 11673, we found a QTL for SCS as expected, with a -log₁₀(P-value) of 6.4. At this location, we also observed QTL for FC,

<footnote>a-cVariance values with different letters significantly differ (P-value < 0.05) between MRC.</footnote>

<footnote n="1">¹Lsmeans variances need to be multiplied by 10⁻⁴.</footnote>

<footnote>***P < 0.001. NS = nonsignificant.</footnote>
<table><caption>Table 4. Significant QTL obtained for each of the 3 zones of wavenumbers according to the milk recording control (MRC)<fnref n="1" /></caption><thead><tr><th rowspan="2">OAR</th><th rowspan="2">SNP</th><th rowspan="2">SNP name</th><th rowspan="2">Position (bp)</th><th rowspan="2">QTL size<sup>2</sup></th><th colspan="3">Z1</th><th colspan="3">Z2</th><th colspan="3">Z3</th></tr><tr><th>MRC3</th><th>MRC4</th><th>MRC5</th><th>MRC3</th><th>MRC4</th><th>MRC5</th><th>MRC3</th><th>MRC4</th><th>MRC5</th></tr></thead><tbody><tr><td rowspan="2">1</td><td rowspan="2">734</td><td rowspan="2">rs398691646</td><td rowspan="2">41,492,360</td><td>nLW</td><td>—</td><td>—</td><td>4w</td><td>—</td><td>—</td><td>11w</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>6.27</td><td>—</td><td>—</td><td>6.76</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">3</td><td rowspan="2">11671</td><td rowspan="2">rs417727436</td><td rowspan="2">129,648,614</td><td>nLW</td><td>—</td><td>65w</td><td>—</td><td>—</td><td>226w</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>11.95</td><td>—</td><td>—</td><td>11.64</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">11673</td><td rowspan="2">OAR3socs2</td><td rowspan="2">129,722,200</td><td rowspan="2">129,722,200</td><td>nLW</td><td>—</td><td>—</td><td>31w</td><td>—</td><td>—</td><td>59w</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>9.15</td><td>—</td><td>—</td><td>12.02</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">11683</td><td rowspan="2">rs416702772</td><td rowspan="2">130,365,171</td><td rowspan="2">130,365,171</td><td>nLW</td><td>5w</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>6.54</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">10</td><td rowspan="2">25110</td><td rowspan="2">rs430003617</td><td rowspan="2">18,707,689</td><td>nLW</td><td>—</td><td>—</td><td>3w</td><td>—</td><td>—</td><td>11w</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>6.47</td><td>—</td><td>—</td><td>6.82</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">11</td><td rowspan="2">26797</td><td rowspan="2">rs402411249</td><td rowspan="2">33,379,161</td><td>nLW</td><td>4w</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>6.33</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">26803</td><td rowspan="2">rs401296484</td><td rowspan="2">33,733,902</td><td rowspan="2">33,733,902</td><td>nLW</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>336w</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>9.11</td><td>—</td><td>—</td></tr><tr><td rowspan="2">26814</td><td rowspan="2">rs424294429</td><td rowspan="2">34,239,169</td><td rowspan="2">34,239,169</td><td>nLW</td><td>—</td><td>16w</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>8.70</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">26821</td><td rowspan="2">rs425606615</td><td rowspan="2">34,726,544</td><td rowspan="2">34,726,544</td><td>nLW</td><td>—</td><td>—</td><td>5w</td><td>—</td><td>—</td><td>6w</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>6.23</td><td>—</td><td>—</td><td>7.58</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">26832</td><td rowspan="2">rs421956413</td><td rowspan="2">35,221,856</td><td rowspan="2">35,221,856</td><td>nLW</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>366w</td><td>—</td></tr><tr><td>Max</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>9.00</td></tr><tr><td rowspan="2">13</td><td rowspan="2">29868</td><td rowspan="2">rs415596912</td><td rowspan="2">74,644,297</td><td>nLW</td><td>5w</td><td>5w</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>6.92</td><td>6.84</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="2">20</td><td rowspan="2">37501</td><td rowspan="2">rs424743329</td><td rowspan="2">32,621,630</td><td>nLW</td><td>5w</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Max</td><td>6.38</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

MY, and β-CN with decreasing −log₁₀(P-value) from 10.7 to 6.6. At SNP 11671, we also revealed strong QTL for FC and β-CN with comparable −log₁₀(P-value) for a given trait but at the complementary control: QTL for FC was at SNP 11671 in MRC4 and at SNP 11673 in MRC5, whereas QTL for β-CN was at SNP 11673 in MRC4 and at SNP 11671 in MRC5. Additional numerous QTL peaking at SNP 11671 were also revealed at MRC4 for SFA (caproic, caprylic, capric, lauric, and myristic acids) and for proteins (αS₁-CN and β-LG).

## QTL Colocalized Between Dairy Traits and Wavenumbers

Only 2 chromosomes (OAR3 and OAR11) carried QTL colocalized for the spectra and dairy traits with 2 areas: one around SNP 11671-SNP 11673 on OAR3 and one around SNP 26814 on OAR11. Then CLIP tests of colocalized QTL between a trait and a wavenumber were performed to estimate if there are 2 distinct QTL (Table 6).

A total of 3,823 tests were done: on OAR3 at MRC5 for 90 wavenumbers and 3 traits (β-CN, MY, and FC); still on OAR3 at MRC4 for 291 wavenumbers and 11 traits (FC, αS₁-CN, β-LG, C6:0, C8:0, C10:0, C12:0,

C14:0, MY, SCS, and β-CN); and on OAR11 at MRC4 for 352 wavenumbers and RC trait; most of these tests did not reject pleiotropy between a trait and a wavenumber that have colocalized QTL. Thus all tests performed on OAR11 at MRC4 and OAR3 at MRC5 were inconclusive because the pleiotropy hypothesis was not rejected. This is also the case on OAR3 at MRC4 for all the traits, except for SCS, where among the 291 wavelengths with QTL tested, 176 wavelengths (44 in Z1 zone and 132 in Z2 zones) were considered with 2 distinct QTL but not a pleiotropic QTL.

## DISCUSSION

The aim of the study was to explore genetic architecture within the wavelengths of milk IR spectra and compare them to the QTL of dairy traits, most of which are predicted from these IR spectra. So, first, h<fnref n="2" /> estimations along the spectra and for all predicted traits were computed, and the lactation stage effect was investigated because there are huge modifications of milk composition as lactation progresses. Then QTL obtained for wavelengths and predicted traits were compared and discussed.

<footnote n="1">¹For each MRC and spectrum zone, number of wavenumbers above the threshold and maximum significance (−log₁₀[P-value]) are reported, as well as the SNP number and position for the lowest minus −log₁₀(P-value). The lowercase “w” after a number indicates the number of wavelengths affected by the QTL.</footnote>

<footnote n="2">²For each MRC and spectrum zone, number of wavenumbers above the threshold (nLW) and maximum significance (−log₁₀(P-value);Max) are reported, as well as the SNP number and position for the lowest −log₁₀(P-value).</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** QTL at MRC4 on (a) OAR3 and (b) OAR11, according to wavelengths. The plane shows the threshold.</figcaption></figure>

## Genetic Determinism Pattern Along the Complete IR Spectrum

The h² patterns along the IR spectrum of our ewes' milk (Figure 2) were very similar to those observed in dairy cattle for a complete spectrum (from 925 to 5,010 cm⁻¹; Soyeurt et al., 2010; Bittante and Cecchinato, 2013; Wang et al., 2016; Zaalberg et al., 2019; Du et al., 2020), and for a selection of mid IR wavelengths (below 3,000 cm⁻¹) in Norwegian goat (Dagnachew et al., 2013) and in Lacaune sheep (Machefert et al., 2024). Indeed, the h² profile of the spectrum began with significant but

variable h² values (between 925 and 1,600 cm⁻¹ or wavelengths 1–181), then a noisy spectral region appeared (between 1,600 and 1,680 cm⁻¹ or wavelengths 182–195) related to water absorption (Wang and Bovenhuis, 2018). The second part of the profile (between 1,680 and 3,080 cm⁻¹ or wavelengths 196–559) was characterized by a plateau of h² values, followed by a gradual decrease in value, and finally by higher but more variable heritabilities. In articles where the complete spectrum was studied, a second noisy spectral region due to water absorption appeared between 3,080 and 3,660 cm⁻¹ (i.e., wavelengths 560–710; Wang and Bovenhuis, 2018), followed by more stable but lower h² values (from 3,660 to 5,010 cm⁻¹ or wavelengths 711–1,060). Although different in composition, the h² profiles of the spectra of bovine, ovine, and caprine milk appear to be very similar, contrary to the results recently published of Sardinian ewes (Congiu et al., 2024).

For the complete spectrum, the range of h² values we obtained varied from 0.26 to 0.69 along the spectrum (excluding erratic areas known as “water region”): these estimates were in the same range of h² as Wang et al. (2016) but a little bit higher than the other ones published. Except Du et al. (2020), who reported a low h² ranging from 0.05 to 0.11, most studies estimated heritabilities of mid IR spectra between 0.20 and 0.40 (Soyeurt et al., 2010; Dagnachew et al., 2013; Machefert et al., 2024) or between 0.10 and 0.30 (Bittante and Cecchinato, 2013; Zaalberg et al., 2019; Congiu et al., 2024). Our higher h² values could be explained by the fact that our data come from a single herd—unlike other studies, which combine data from several farms—, which reduces residual variances and therefore increases heritabilities.

## Effect of Lactation Stage on the Genetic Architecture of Spectra and Predicted Traits

Thus, with regard to the spectrum, our study revealed significant shifts in h² values depending on the ewe's stage of lactation (in particular, +0.2 to +0.3 h² points between wavenumbers 222 and 358) but with significant differences depending on the areas of the spectrum concerned. In Z1 and 2, there was a progressive increase in genetic variance as lactation progressed, whereas the residual variance is stable, which led to a higher h² in the last 2 MRC. Machefert et al. (2024), on an independent Lacaune dataset, also outlined higher h² values (+0.1 on average) for late lactation ewes (5–7 mo) compared with early lactation ewes (2–3 mo) between wavelengths 2,276 to 1,800 cm⁻¹ (i.e., wavenumbers 230–350). In a Chinese Holstein population, Du et al. (2020) also demonstrated a lactation stage effect in midspectra on specific wavenumbers related to lactose, protein, and fat regions. In last part of the spectrum, both residual and
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Heritabilities and SE of official dairy traits obtained by lab (O-MR traits) and fatty acids and proteins predicted from milk infrared spectra according to the milk recording control (MRC). MY = milk yield; FC = fat content; PC = protein content; RC = ratio between FC and PC; MU = milk urea; C4:0 = butyric acid; C6:0 = caproic acid; C8:0 = caprylic acid; C10:0 = capric acid; C12:0 = lauric acid; C14:0 = myristic acid; C16:0 = palmitic acid; *cis*-9 C18:1 = oleic acid; *cis*-9,*trans*-11 C18:2 = rumenic acid; C18:3 n-3 = α-linolenic acid.</figcaption></figure>

genetic variances increased with lactation stage, which
made it impossible to define a clear trend in h² evolu-
tion with the stage of lactation. A literature comparison
of lactation stage effect in the near IR area was difficult
because, to our knowledge, no studies have been carried
out in this part of the spectrum.

Regarding predicted dairy traits, heritabilities were
similarly clearly higher for the later lactation stage
(fourth and fifth controls), particularly for SFA (Fig-
ure 4) and FC and PC, where genetic variance doubled
compared with the first 3 MRC. We could, therefore,
see a fairly parallel evolution of the heritabilities
between these traits and the Z1 and 2 zones of the
spectrum because, according to Du et al. (2020), the
protein region is in between 1,240 to 1,600 cm⁻¹ (i.e.,
wavenumbers 83–176) and fat regions in between
1,680 to 1,770 cm⁻¹ and 2,800 to 3,015 cm⁻¹ (i.e.,
wavenumbers 197–220 and 487–543, respectively).
It should be emphasized that the third control was
characterized by low heritabilities particularly for the
poly-UFA (rumenic and α-linolenic acids) and soluble
protein (α-LA and β-LG) due to a huge increase of
residual variance. This emphasis in residual variance
on particular milk components—and also some spectra
wavelengths—was probably linked to the beginning of
grassing of the flock: at the end of March or beginning
of April—simultaneously to the third MRC—the ewes
went out to pasture for the first time in the winter. This
sudden change in diet disrupted the milk composition,

modifying the UFA in milk (Khanal et al., 2008) and
also soluble proteins, which makes the milk difficult to
process into cheese (Pellegrini et al., 1997).

The same trend was observed for QTL as for genetic
variance with lactation stage: more signals were observed
in late lactation stages (particularly fourth and fifth con-
trols) in the IR spectra or predicted traits, whereas no
QTL were detected in the IR spectra and few in dairy
traits in the 2 first controls.

QTL Detection—of Wavelengths and Predicted
Traits—Nominating Candidate Genes

Common QTL in Spectrum and Predicted Traits.

The QTL detections implemented enabled us to identify
2 common areas of the ovine genome with a major ef-
fect both in the IR spectrum and for the predicted traits:
on OAR11 around 34 Mb with -log₁₀(P-values) reach-
ing 7.82 and 9.11 for predicted traits and wavelengths,
respectively, and on OAR3 at 129 to 130 Mb reaching
10.70 and 12.02 for predicted traits and wavelengths,
respectively. In the same spirit as Benedet et al. (2019),
who combined single wavelength GWAS and partial
least squares regression analyses to attempt to distin-
guish between a QTL with many SNP or 2 or more QTL
in linkage disequilibrium, we performed the CLIP test
between dairy traits and wavelengths where colocalized
QTL were observed (that is to say on OAR3 ~130 Mb
and on OAR11 ~34.2 Mb).
<table><caption>**Table 5.** Significant QTL obtained for official dairy traits (O-MR traits) and fatty acids and proteins predicted from milk infrared spectra according to the milk recording control (MRC)<fnref n="1" /></caption><thead><tr><th rowspan="2">OAR</th><th rowspan="2">Trait<sup>2</sup></th><th rowspan="2">SNP number</th><th rowspan="2">SNP name</th><th rowspan="2">SNP position</th><th colspan="5">-log<sub>10</sub>(P-value)</th></tr><tr><th>MRC1</th><th>MRC2</th><th>MRC3</th><th>MRC4</th><th>MRC5</th></tr></thead><tbody><tr><td rowspan="12">3</td><td>FC</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>10.70</td><td>—</td></tr><tr><td>α<sub>S1</sub>-CN</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>7.76</td><td>—</td></tr><tr><td>β-CN</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>β-LG</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>7.02</td><td>—</td></tr><tr><td>C6:0</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>7.15</td><td>—</td></tr><tr><td>C8:0</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>8.16</td><td>—</td></tr><tr><td>C10:0</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>6.58</td><td>—</td></tr><tr><td>C12:0</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>6.63</td><td>—</td></tr><tr><td>C14:0</td><td>11671</td><td>rs417727436</td><td>129,648,614</td><td>—</td><td>—</td><td>—</td><td>6.86</td><td>—</td></tr><tr><td>MY</td><td>11673</td><td>OAR3_socs2</td><td>129,722,200</td><td>—</td><td>—</td><td>—</td><td>6.60</td><td>8.48</td></tr><tr><td>FC</td><td>11673</td><td>OAR3_socs2</td><td>129,722,200</td><td>—</td><td>—</td><td>—</td><td>—</td><td>10.69</td></tr><tr><td>SCS</td><td>11673</td><td>OAR3_socs2</td><td>129,722,200</td><td>—</td><td>—</td><td>—</td><td>6.40</td><td>—</td></tr><tr><td>β-CN</td><td>11673</td><td>OAR3_socs2</td><td>129,722,200</td><td>—</td><td>—</td><td>—</td><td>7.50</td><td>—</td></tr><tr><td rowspan="6">6</td><td>MU</td><td>18826</td><td>rs402382941</td><td>74,188,622</td><td>—</td><td>—</td><td>—</td><td>8.40</td><td>—</td></tr><tr><td rowspan="5">α<sub>S2</sub>-CN</td><td>18998</td><td>rs430618559</td><td>85,122,265</td><td>—</td><td>6.30</td><td>8.15</td><td>—</td><td>12.57</td></tr><tr><td>18900</td><td>rs422804797</td><td>78,620,736</td><td>7.14</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>18987</td><td>rs415389528</td><td>84,128,221</td><td>—</td><td>—</td><td>—</td><td>7.39</td><td>—</td></tr><tr><td>18991</td><td>rs430830129</td><td>84,749,548</td><td>—</td><td>7.94</td><td>—</td><td>—</td><td>—</td></tr><tr><td>18992</td><td>rs410141696</td><td>84,825,559</td><td>—</td><td>—</td><td>—</td><td>—</td><td>8.35</td></tr><tr><td>11</td><td>RC</td><td>26814</td><td>rs424294429</td><td>34,239,169</td><td>—</td><td>—</td><td>—</td><td>7.82</td><td>—</td></tr><tr><td rowspan="2">12</td><td>cis-9,trans-11 C18:2</td><td>27868</td><td>rs407260292</td><td>34,654,736</td><td>—</td><td>—</td><td>6.22</td><td>—</td><td>—</td></tr><tr><td>β-LG</td><td>27992</td><td>rs426312256</td><td>41,162,045</td><td>—</td><td>—</td><td>6.53</td><td>—</td><td>—</td></tr><tr><td rowspan="3">17</td><td>C4:0</td><td>34410</td><td>rs425644563</td><td>50,544,649</td><td>—</td><td>—</td><td>—</td><td>6.60</td><td>—</td></tr><tr><td rowspan="2">C6:0</td><td>34406</td><td>rs430413343</td><td>50,418,326</td><td>7.47</td><td>6.39</td><td>11.73</td><td>—</td><td>8.06</td></tr><tr><td>34406</td><td>rs430413343</td><td>50,418,326</td><td>6.57</td><td>6.57</td><td>7.09</td><td>—</td><td>—</td></tr><tr><td>20</td><td>C18:3 n-3</td><td>36979</td><td>rs424908643</td><td>3,453,242</td><td>—</td><td>—</td><td>6.62</td><td>—</td><td>—</td></tr></tbody></table>

The CLIP test result showed 2 different but close QTL only on OAR3 for SCS and 176 wavelengths. For all other traits (fatty acids or proteins with QTL at this location), the test could not reject a pleiotropic QTL affecting the spectrum wavelengths. However, this region ~130 Mb was well known in Lacaune dairy ewes, as it contains the *Socs2* gene which, when mutated, increases susceptibility to inflammation of the mammary gland (Rupp et al., 2009, 2015). Here we confirmed the QTL for SCC with the maximum -log₁₀(P-value) at *Socs2* genotype (at 129,722,200 bp), but other dairy traits also showed association with the region. This was particularly the case of the FC (with -log₁₀ (P-value) >10), the MY, the SFA (from C6:0-C14:0), and some proteins (β-CN, α<sub>S1</sub>-CN, and β-LG). Whereas the pleiotropic effects of *Socs2* on milk production and FC (as well as on BW and stature) have already been demonstrated by Rupp et al. (2015), we have highlighted a new effect of *Socs2* on fat composition and, more original, on certain milk proteins. Moreover, QTL discovered in some wavelengths in Z1

and 2 allowed us to postulate that the *Socs2* gene, clearly responsible for mammary inflammation and SCS variation, might not be the gene that affects the FC and PC in milk but that there was a second, very close QTL in this region modulating the milk composition in sheep.

On OAR11 at ~34 Mb, the pleiotropy between RC (between FC and PC) and the wavelengths (mainly in Z3) were not rejected. Although considered for the “cheesability” of ewe’s milk, the fat/protein ratio was rarely studied in genetic terms, hence, the difficulty in finding elements of comparison in literature with our QTL on RC detected on OAR11 at ~34.2 Mb. In dairy ruminants, QTL for fatty acids—particularly long chain ones—were reported on OAR11 and *Bos taurus* chromosome 19 (BTA19; homologous to OAR11; Carta et al., 2008; García-Fernández et al., 2010; Boichard et al., 2014; Bouwman et al., 2012; Knutsen et al., 2018) but mainly at ~50 Mb in the area of the FASN gene, which was considered as the more suitable candidate (Bouwman et al., 2014; Knutsen et al., 2018). But recently,

<footnote n="1">¹For each MRC, the maximum -log10(P-value) of the QTL are reported, as the SNP number, name and position on OAR.</footnote>

<footnote n="2">²MY = milk yield; FC = fat content; RC = ratio between fat content and protein content; MU = milk urea; C4:0 = butyric acid; C6:0 = caproic acid; C8:0 = caprylic acid; C10:0 = capric acid; C12:0 = lauric acid; C14:0 = myristic acid; cis-9,trans-11 C18:2 = rumenic acid; C18:3 n-3 = α-linolenic acid.</footnote>
<figure><img src="image_7.png" /><figcaption>**Figure 5.** QTL on OAR6 at MRC5 for (a) milk urea and (b) casAs2.</figcaption></figure>

Atashi et al. (2023) identified on BTA19 a region between 34.3 and 36.4 Mb associated with C16:0, SFA, PUFA, and long-chain fatty acids and highlighted 3 candidate genes: SREBF1, RAI1, and PEMT. In *O. aries* map, the 3 genes were located on OAR11 between 34.3 and 34.6 Mb, which made them also pertinent candidates for our QTL on RC. Moreover, the QTL for wavelengths on OAR11 concerned mainly the Z3 region (near IR

wavelengths and not mid ones), which has been little explored by authors: in fact, this part of the spectrum is highly variable depending on the IR machines (Grelet et al., 2021) and is not used for prediction purposes. As our data came from a single analysis laboratory and a single spectro-photometer, we were able to use it and discovered on OAR11 a large QTL (−log₁₀(*P*-value) >9) on more than 300 consecutive wavelengths.
<table><caption>**Table 6.** CLIP test of co-localized QTL between dairy traits and milk infrared spectra</caption><thead><tr><th>OAR</th><th>SNP range</th><th>MRC</th><th>Spectra</th><th>Trait</th><th>CLIP test</th></tr></thead><tbody><tr><td rowspan="4">3</td><td>SNP11667 to SNP11677</td><td>4</td><td>65 WL in Z1 and 226 WL in Z2</td><td>FC, MY, α<sub>S1</sub>-CN, β-CN, β-LG, C6:0, C8:0, C10:0, C12:0, C14:0</td><td>Pleiotropy not rejected for all 10 traits and all 291 wavelengths</td></tr><tr><td>SNP11667 to SNP11677</td><td>4</td><td>65 WL in Z1 and 226 WL in Z2</td><td>SCS</td><td>Pleiotropy not rejected for 115 wavelengths</td></tr><tr><td>SNP11667 to SNP11677</td><td>4</td><td>65 WL in Z1 and 226 WL in Z2</td><td>SCS</td><td><b>Pleiotropy rejected for 44 WL in Z1 and for 132 WL in Z2<sup>1</sup></b></td></tr><tr><td>SNP11667 to SNP11677</td><td>5</td><td>31 in Z1 and 59 in Z2</td><td>β-CN, MY, FC</td><td>Pleiotropy not rejected for all 3 traits and all 90 wavelengths</td></tr><tr><td>11</td><td>SNP26809 to SNP26819</td><td>4</td><td>16 in Z1 and 336 in Z3</td><td>RC</td><td>Pleiotropy not rejected for the trait and all 352 wavelengths</td></tr></tbody></table>

**QTL in the Spectrum Only.** The direct detection of QTL in the IR spectrum enabled us to identify 4 new areas of the ovine genome with no corresponding QTL on predicted traits: more precisely, QTL on OAR1, OAR10, OAR13, and OAR20 were revealed. Other authors have used the same approach to search for QTL in the spectrum, either on a selection of wavelengths in bovine (Wang and Bovenhuis, 2018) or on the whole spectrum of milk of ewe (Congiu et al., 2024) or of cow (Benedet et al., 2019; Zaalberg et al., 2020; Tiplady et al., 2021). None of our QTL in the spectrum were identified by these authors. In dairy sheep, this was to be expected given that the h² profile in the spectrum published by Congiu et al. (2024) was so different from our own. In cattle, as there was already little overlap between our QTL for predicted traits and those in the literature, it was consistent not to have overlap between our QTL from the spectrum and the ones discovered by Wang and Bovenhuis (2018), Benedet et al. (2019), Zaalberg et al. (2020), and Tiplady et al. (2021). The exploration of the genes underlying QTL within the spectrum were not efficient to identify candidate genes, because of too numerous genes under QTL on OAR10, OAR13, and OAR20. For OAR1, the maximum -log₁₀(P-value) of the QTL was at 41.5 Mb, close to the leptin gene (41.1–41.2 Mb), which variants are known to affect the milk composition in SFA of cows (Haruna et al., 2021).

**QTL for Predicted Traits Only.** Among dairy traits, our most original QTL (i.e., not already published in dairy) was observed on OAR6 for MU at 85.1 Mb. First of all, our estimates showed a very high h² of MU (greater than 0.60) whatever the stage of lactation, much higher than h² estimates in cattle, which vary between 0.19 to 0.38 (Stoop et al., 2007; Beatson et al., 2019; Ariyarathne et al., 2021). Moreover, on the homologous bovine chromosome (BTA6), several studies have also identified a QTL for MU: first, Bouwman et al. (2010) found it in Dutch

Holstein-Friesian cows with a low-density SNP chip at a telomeric position, then Ariyarathne et al. (2021) in dairy cows discovered 4 QTL for MU on this chromosome (at 4–5, 73–74, 87–88, and 84–85 Mb), whereas van den Berg et al. (2022) with a metaanalysis revealed a large QTL signal in a New Zealand population (between 85.3 and 86.5 Mb). In ewes, not on milk but on blood plasma, a QTL on OAR6 for urea was detected (de Souza et al., 2021) at 77 to 78 Mb. All of this literature confirmed the QTL we observed for MU in OAR6. However, for the underlying genes, bibliography published was less consistent. Using the NCBI genome data viewer of *O. aries*, the genes found by Cecchinato et al. (2014), Ariyarathne et al. (2021), and de Souza et al. (2021) to be in association with MU were not on OAR6 or were too far away from our QTL, respectively. By exploring the 84.3 to 84.9 Mb zone, we identified 8 consecutive “transmembrane serine protease 11D” loci (LOC114115323, TMPRSS11D, LOC101121013, LOC101121267, LOC101121519, LOC101115964, LOC101116231, LOC101121777) involved in the proteolysis process, which could explain the MU QTL we identified at 85.1 Mb.

On the same OAR6 area, a QTL for α<sub>S2</sub>-CN was detected, with a maximum position varying from 78.6 to 84.8 Mb according to the MRC, which confirmed the finding of Martinez Boggio et al. (2022) on the same flock. Anyway, Boichard et al. (2014) have already detected in the Lacaune population on OAR6 (at 86 Mb) this QTL affecting all caseins but particularly α<sub>S2</sub>-CN, in line with Diez-Tascón et al. (2001) in Churra, who published a QTL affecting the PC in the same area. Although the QTL zone did not strictly overlap, the close region between 86.2 and 86.4 Mb was well known to contain a cluster of casein genes.

As for OAR6-QTL, the QTL we found on OAR17 for C4:0 and C6:0 at 50.4 to 50.5 Mb were consistent with previously published results. In cattle, a QTL for short-

<footnote n="1">¹Bold text denotes the rejection of pleiotropy hypothesis.</footnote>
chain fatty acids was detected on BTA17 (homologous to OAR17) by Boichard et al. (2014) and Bouwman et al. (2012) and confirmed by Duchemin et al. (2014), who considered the region between 29.0 and 34.0 Mb as the most consistent but without finding a promising gene. In sheep, Carta et al. (2008), for C6:0, C8:0, and C10:0 in a back-cross Sardinian-Lacaune population, and Martinez Boggio et al. (2022), for C6:0 and C8:0 in a Lacaune purebred population, detected also a QTL on OAR17 around 51.2 Mb. Duchemin et al. (2014) also highlighted a decreasing genetic variance explained by the QTL from C6:0 to C14:0 and hypothesized a possible elongation process or an early termination of de novo synthesized. This hypothesis was reinforced by Knutsen et al. (2018) findings, who identified at 53 Mb on BTA17 a QTL for C4:0 close to the *AACS* gene. In ovine, this AACS gene was located between 50,762 and 50,816 kb, which is also very close to the QTL we found for C4:0 and C6:0.

## CONCLUSIONS

This GWAS analyzing all 1,060 wavelengths of the IR spectrum from ewes' milk allowed us to uncover significant common QTL with predicted traits. Notably, in the near IR zone, we identified a major QTL on OAR11, likely associated with the fat-to-protein ratio. In the mid IR zone, a QTL was detected in the *Socs2* gene region on OAR3, which was linked to numerous traits related to the fine composition of milk. Our approach has also enabled us to detect QTL in IR spectra unobserved from predicted traits. Additionally, QTL detection based on predicted traits made it possible to validate known QTL (milk and cell counts on OAR3, caseins on OAR6, and short fatty acids on OAR17) but also to identify new QTL for dairy ewes, in particular, MU on OAR6. For all QTL detections, we have shown the preponderance of lactation stage (too early a stage means little or no QTL) but also the effect of a change in diet (switch to pasture) on soluble milk proteins and C18:2-C18:3.

## NOTES

Financial support was provided by INRAE (Toulouse, France) and the CRB-Anim infrastructure project (grant no. ANR-11-INBS-0003) for the genotyping. We thank the technical staff of the INRAE experimental Unit of La Fage (Saint-Jean-et-Saint-Paul, France; https://doi.org/10.15454/1.548325523466425E12) for their care of the ewes and for collecting the IR spectra of the milk and the animals' pedigrees. Genetic monitoring of all ewes using blood genotyping has been approved (ethics committee approval 2019110713292766_version 6 #27486). The authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** CLIP = close linkage versus pleiotropism; FC = fat content; IR = infrared; MIR = milk IR; MRC = milking recording controls; mSE = average SE of heritabilities; Max = maximum significance; MU = milk urea; MY = milk quantities; nLW = number of wavenumbers above the threshold; OAR = *Ovis aries*; O-MR = official milk recording; PC = protein content; PCA = principal component analysis; RC = milk content ratio; WL = wavelengths; Z1 = zone 1; Z2 = zone 2; Z3 = zone 3.

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## ORCIDS

C. Marie-Etancelin https://orcid.org/0000-0001-8950-0420

R. Rupp https://orcid.org/0000-0003-3375-5816

## APPENDIX

<table><caption>**Table A1.** Detailed number of animals by year of phenotyping, milk recoding controls and types of data (official milk recording, milk urea, milk infrared predictions)<fnref n="1" /></caption><thead><tr><th rowspan="2">Item</th><th rowspan="2">Type</th><th colspan="10">Year of phenotyping</th><th rowspan="2">Total</th></tr><tr><th>2014</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td rowspan="3">MRC1</td><td>O-MR</td><td>122</td><td>110</td><td>126</td><td>142</td><td>60</td><td>91</td><td>146</td><td>140</td><td>83</td><td>1,020</td></tr><tr><td>MU</td><td>0</td><td>0</td><td>0</td><td>142</td><td>60</td><td>91</td><td>146</td><td>140</td><td>83</td><td>662</td></tr><tr><td>MIR</td><td>122</td><td>0</td><td>0</td><td>142</td><td>59</td><td>90</td><td>145</td><td>140</td><td>82</td><td>780</td></tr><tr><td rowspan="3">MRC2</td><td>O-MR</td><td>126</td><td>115</td><td>130</td><td>146</td><td>61</td><td>97</td><td>0</td><td>149</td><td>85</td><td>909</td></tr><tr><td>MU</td><td>0</td><td>0</td><td>0</td><td>146</td><td>61</td><td>97</td><td>0</td><td>149</td><td>84</td><td>537</td></tr><tr><td>MIR</td><td>124</td><td>115</td><td>0</td><td>145</td><td>59</td><td>94</td><td>0</td><td>148</td><td>84</td><td>769</td></tr><tr><td rowspan="3">MRC3</td><td>O-MR</td><td>128</td><td>116</td><td>130</td><td>142</td><td>58</td><td>94</td><td>0</td><td>148</td><td>85</td><td>901</td></tr><tr><td>MU</td><td>0</td><td>0</td><td>0</td><td>142</td><td>58</td><td>94</td><td>0</td><td>148</td><td>85</td><td>527</td></tr><tr><td>MIR</td><td>125</td><td>0</td><td>128</td><td>136</td><td>58</td><td>94</td><td>0</td><td>146</td><td>85</td><td>772</td></tr><tr><td rowspan="3">MRC4</td><td>O-MR</td><td>128</td><td>116</td><td>130</td><td>141</td><td>58</td><td>96</td><td>146</td><td>147</td><td>84</td><td>1,046</td></tr><tr><td>MU</td><td>0</td><td>0</td><td>0</td><td>141</td><td>58</td><td>96</td><td>146</td><td>147</td><td>84</td><td>672</td></tr><tr><td>MIR</td><td>0</td><td>115</td><td>130</td><td>141</td><td>58</td><td>95</td><td>0</td><td>146</td><td>84</td><td>769</td></tr><tr><td rowspan="3">MRC5</td><td>O-MR</td><td>127</td><td>116</td><td>130</td><td>139</td><td>57</td><td>96</td><td>143</td><td>145</td><td>82</td><td>1,035</td></tr><tr><td>MU</td><td>0</td><td>0</td><td>0</td><td>137</td><td>56</td><td>93</td><td>141</td><td>12</td><td>82</td><td>521</td></tr><tr><td>MIR</td><td>0</td><td>116</td><td>130</td><td>137</td><td>56</td><td>93</td><td>141</td><td>144</td><td>82</td><td>899</td></tr><tr><td rowspan="2">Total</td><td>O-MR</td><td>631</td><td>573</td><td>646</td><td>710</td><td>294</td><td>474</td><td>435</td><td>729</td><td>419</td><td>4,911</td></tr><tr><td>MIR</td><td>371</td><td>346</td><td>388</td><td>701</td><td>290</td><td>466</td><td>286</td><td>724</td><td>417</td><td>3,989</td></tr></tbody></table>

<footnote n="1">¹O-MR = official milk recoding data (milk quantity, milk fat, milk protein, SCC); MU = milk urea data; MIR = milk infrared data (spectra and milk compositions' predictions); MRC1 to MRC5 = first to fifth milk recording control, respectively.</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure A1.** Detailed genetic variances (solid line) and residual variances (dotted line) for the 3 zones of the IR spectra of each of the 5 milk recording controls (MRC1 to MRC5) according to wavelengths numbers and variance of absorbance values.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure A2.** Detailed genetic and residual variances (and SE estimates) and $h^2$ for all traits at the 5 milk recording controls (MRC1 to MRC5).</figcaption></figure>