# Genomic evaluation for juvenile mortality including causal variants in French Holstein and Montbéliarde cattle breeds

Léa Chapard, Florian Besnard, Thierry Tribout, Sophie Aguerre, Clémentine Escouflaire, Hélène Leclerc, Sébastien Fritz, Sophie Mattalia, Aurélien Capitan, Pascal Croiseau

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Léa Chapard, Florian Besnard, Thierry Tribout, Sophie Aguerre, Clémentine Escouflaire, et al. Ge-
nomic evaluation for juvenile mortality including causal variants in French Holstein and Montbéliarde
cattle breeds. Journal of Dairy Science, In press, 10.3168/jds.2025-27072. hal-05333674

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# Genomic evaluation for juvenile mortality including causal variants in French Holstein and Montbéliarde cattle breeds

Léa Chapard,<sup>1,2*</sup> Florian Besnard,<sup>1,3</sup> Thierry Tribout,<sup>1</sup> Sophie Aguerre,<sup>1,2</sup> Clémentine Escouflaire,<sup>1,3</sup>

Hélène Leclerc,<sup>1,3</sup> Sébastien Fritz,<sup>1,3</sup> Sophie Mattalia,<sup>1,2</sup> Aurélien Capitan,<sup>1</sup> and Pascal Croiseau<sup>1*</sup>

## ABSTRACT

Juvenile mortality is a major concern for the dairy cattle industry because of its wide-ranging economic, environmental, and ethical effects. Over the last few years, an increased number of genetic defects associated with juvenile mortality has been identified, creating new opportunities and challenges for their management in selection. The implementation of a routine genomic evaluation may therefore contribute to reducing juvenile mortality. However, no existing method currently includes causal variants and incorporates their additive and dominance effects in a single-step GBLUP (ssGBLUP) model. This study was conducted on 2 French dairy breeds: Holstein (HOL) and Montbéliarde (MON), both of which are affected by known recessive genetic defects (bovine lymphocyte intestinal retention defect and cholesterol deficiency in HOL, and mitochondropathy in MON). In this study, we first estimated genetic parameters for juvenile mortality. Second, we developed ssGBLUP models that included causal variants, either by assigning higher proportions of genetic variance explained a priori by the causal variants or by estimating and incorporating causal variants' additive and dominance effects as covariates. Third, we evaluated the models' performance to select the most accurate one for genomic evaluation. To this end, 2 independent training and validation datasets were constructed. The training dataset included juvenile mortality records for 1,275,746 HOL and 476,361 MON females, of which 84,328 HOL and 29,944 MON were genotyped. Models were validated using mortality records of 689,502 HOL and 149,801 MON daughters of 262 and 254 genotyped bulls, respectively. No difference in model performance was seen at the population level. However, at the individual level, heterozygous bulls for

the causal variants were more accurately distinguished from wild type homozygous when using EBVs obtained from models that included causal variants compared with a conventional ssGBLUP. We therefore advocate the routine implementation of a genomic evaluation that accounts for causal variants to help reduce juvenile mortality. A genotyping effort for dead animals is also suggested because it would enhance model performance. Additionally, further research is needed to determine the most effective method for incorporating causal variants into ssGBLUP models and to assess whether their inclusion improves the model's prediction accuracy.

**Key words:** dairy cattle, juvenile mortality, genomic evaluation, inclusion of causal variants

## INTRODUCTION

The dairy cattle industry has undergone immense changes over the past century. Milk production has significantly increased, from approximately 3,000 L per lactation in the 1960s to 10,000 L nowadays, thanks to husbandry practices improvement and selective breeding for higher production (Stevenson and Britt, 2017). However, in response to an increased milk production and herd size resulting from the dairy industry intensification, an increase in mortality has been reported (Compton et al., 2017). Juvenile mortality is known to have a major impact on the dairy industry. It not only hampers animal welfare and reduces the number of animals available for replacement but also causes major economic losses for breeders (Gitau et al., 2010; Al-Khudhair et al., 2025). The average cost of a calf death was estimated at about $500 by Neupane et al. (2021). Furthermore, many calves die before their first lactation (Hyde et al., 2020), which has negative environmental consequences: an increase in GHG emissions from nonproductive animals. Leclerc et al. (2016) estimated a cumulative mortality rate of young animals during their first year and a half of life of 17.2% to 17.3% in the 2 main dairy cattle breeds in France: Holstein (HOL) and Montbéliarde (MON). To reduce

<footnote n="1"><sup>1</sup>Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France</footnote>

<footnote n="2"><sup>2</sup>IDLE, 75012, Paris, France</footnote>

<footnote n="3"><sup>3</sup>ELIANCE, 75012, Paris, France</footnote>
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

juvenile mortality in dairy cattle, several countries have considered the feasibility of developing a genetic evaluation for this specific trait. Heritability estimates have been reported to be low (h² = 0.60%–5.00%; Hansen et al., 2003; van Pelt et al., 2012; Pritchard et al., 2013; Leclerc et al., 2016; Gonzalez-Peña et al., 2019; Neupane et al., 2021; Axford et al., 2024). However, it was possible to distinguish “good” from “bad” bulls, which encouraged breeding societies to include juvenile mortality in their breeding programs. In France, only a genomic evaluation for perinatal mortality (stillbirths and mortality within the first 48 h) has been implemented in dairy cattle breeds so far (GenEval, 2024). However, as the animal breeding sector transitions toward greater sustainability with an increased focus on health-related traits (Sellier et al., 2019), breeding societies are placing greater emphasis on selection against juvenile mortality.

Another reason for this growing interest is that numerous previously unknown recessive genetic defects have been discovered in the past decade thanks to advances in next-generation sequencing and methodological developments (VanRaden et al., 2011b; Fritz et al., 2013, Charlier et al., 2016; Michot et al., 2016; Reynolds et al., 2021; Besnard et al., 2024). Some of these newly reported genetic defects are responsible for an increased risk of juvenile mortality in dairy cattle (Gross et al., 2016; Schütz et al., 2016; Häfliger et al., 2019; Besnard et al., 2024). For example, the recessive bovine lymphocyte intestinal retention defect (BLIRD) is caused by a point mutation in the integrin subunit β 7 (ITGB7) gene, which has been found to impair the transition of CD4 T lymphocytes from the bloodstream to intestinal tissue in HOL cattle (Besnard et al., 2024; Dutheil et al., unpublished data). Homozygous mutant animals exhibit defective intestinal immunity, stunted growth, and frequent diarrhea during the rearing phase. Most of them die within 1 to 2 yr of age or are culled during the first lactation. Cholesterol deficiency (CDH) is a co-dominantly inherited metabolic disorder caused by the insertion of a transposable element in the apolipoprotein B (APOB) gene, which encodes a protein essential for cholesterol transport in the bloodstream (Gross et al., 2016; Schütz et al., 2016; Häfliger et al., 2019). Affected HOL calves have low cholesterol levels, suffer from chronic diarrhea, and experience stunted growth. Homozygous mutants generally die within 3 wk to 6 mo of age. A frameshift insertion in nitric oxide associated 1 (NOA1) gene is responsible for a recessive genetic defect named “mitochondropathy” (MTCP) in MON cattle; it exhibits variable expressivity (Besnard et al., 2024; Grohs et al., unpublished data). This gene encodes a protein involved in the regulation of mitochondrial protein translation and respiration. Approximately half of the homozygous mutants die during embryonic development, and the remaining animals die

either perinatally due to organ malformations or between 3 mo and 1 yr of age due to impaired metabolism.

As previously stated, recent advances in sequencing technologies and bioinformatics analyses have enhanced the identification of candidate causal variants for QTL. Consequently, it is now possible to consider causal variants (or SNPs in high linkage disequilibrium with them) in genomic evaluations for specific traits. Several studies investigated the effect of considering whole-genome sequences in genomic evaluations but found small to zero improvement in genomic predictions (MacLeod et al., 2014; Pérez-Enciso et al., 2015; Erbe et al., 2016; MacLeod et al., 2016; Pérez-Enciso et al., 2017; Veerkamp et al., 2016; Wiggans et al., 2016; VanRaden et al., 2017). Conversely, including causal or candidate variants together with SNPs from a medium-density array in custom SNP arrays resulted in noticeable improvements in prediction accuracy for production traits (Brøndum et al., 2015; van den Berg et al., 2016; Fragomeni et al., 2019, Liu et al., 2020). However, it is worth noting that the gain was smaller or null for fat, mastitis, or fertility. These studies also concluded that Bayesian models were the most performant ones compared with ssGBLUP or GBLUP models. Indeed, the use of Bayesian models allows for SNP effect estimation through flexible prior specifications (Meuwissen et al., 2001; Erbe et al., 2012). This contrasts with ssGBLUP or GBLUP models, which assume that all SNPs contribute equally to genetic variance a priori. However, Mollandin et al. (2021) and Zhu et al. (2019) showed that BayesR did not provide more accurate predictions for lowly heritable traits (such as survival or fertility), nor outperform other predicting methods in contrast to moderately to highly heritable traits. Furthermore, even if the use of Bayesian models led to an improvement in accuracy by focusing on regions with major genes when the number of genotyped individuals is small (VanRaden et al., 2009; Meuwissen et al., 2016), this improvement tends toward zero as the number of genotyped individuals increases (VanRaden et al., 2011a).

Conversely, most models used in routine genomic selection are based on linear models that assume equal a priori contribution of all SNPs to genomic variance (each SNP effect follows a normal distribution with the same variance) limiting biological interpretability (VanRaden, 2008). Brøndum et al. (2015) observed that prediction reliability of GBLUP models increased when causal variants or SNPs in high linkage disequilibrium with causal variants were treated as a separate variance component, allowing these effects to have a larger variance than the other SNPs. With that in mind, Fragomeni et al. (2017) included causal variants in a single-step GBLUP (*ssGBLUP*), the preferred method for genomic evaluation thanks to its straightforward approach. They concluded
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

that predictive accuracy is optimal when realistic weights
are assigned to causal variants to construct the genomic
relationship matrix (GRM). In France, bovine genomic
evaluations are routinely performed using the HSSGB-
LUP program (Tribout et al., 2020), which implements
the hybrid single-step model proposed by Fernando et al.
(2016). This model is adapted for very large populations
because it does not need to build the GRM, nor invert it.
With this program, it is also possible to give greater im-
portance to certain genomic markers, for example, causal
variants, by assigning them a specific a priori effect vari-
ance σ<sup>2</sup><sub>a<sub>i</sub></sub>, expressed as a function of the proportion of
genetic variance explained by the <i>ith</i> SNP. A genomic
model that includes candidate SNPs with increased a
priori effect variance has been implemented for cheese-
making traits in Montbéliarde cattle because it outper-
formed conventional ssGBLUP models in prediction ac-
curacy (Sanchez et al., 2020). However, this way of in-
cluding causal variants does not consider their additive
or dominance effects, which is a gap to be addressed.
Furthermore, to our knowledge, none of the routine ge-
nomic evaluations for juvenile mortality performed in
other countries put an emphasis on variants responsible
for recessive or co-dominant genetic disorders.

Therefore, in this study, our objectives were 3-fold: (1) to estimate the genetic parameters for juvenile mortality in the 2 major French dairy cattle breeds: HOL and MON, (2) to develop ssGBLUP models for juvenile mortality including causal variants by either assigning higher a priori effect variance to these variants or by modeling their specific inheritance patterns (additive and dominance effects) and applying them to the causal variants BLIRD and CDH in HOL and MTCP in MON, and (3) to evaluate and validate the best genomic model in both French HOL and MON populations.

METHODS

No human or animal subjects were used, so this analy-
sis did not require approval by an Institutional Animal
Care and Use Committee or Institutional Review Board.

Datasets

The datasets were obtained from the French national
bovine database and contained life history information
(dates of birth and death and cause of death: slaughter
vs. natural death or euthanasia) for 1,965,248 HOL and
626,162 MON females, born between 2017 and 2022
(HOL) and 2009 and 2016 (MON), with available pedi-
gree and herd information (ID and region, i.e., geographic

location). Juvenile mortality was treated as a binary trait:
A value of 100 was assigned to animals that died between
d 3 and d 730, and a value of 0 was assigned to those that
were still alive after d 730. This way, juvenile mortality
was considered as the probability of dying between d 3
and d 730 expressed as a percentage. The studied period
in MON was before the development of the mating tool
pANO (Brochard et al., 2018), which was implemented in
2017 in this breed. This tool helps avoid at-risk matings
by estimating the potential risk of the expected progeny
being affected by one of the known genetic defects in the
population. At-risk matings were significantly reduced
after 2017 and hence juvenile mortality in MON cattle
was also reduced. Therefore, we only considered females
born before the implementation of pANO to avoid bias
in our analysis. There were at least 4 females per herd ×
birth year and region × birth month × birth year combi-
nation. In addition, the females had at least 10 paternal
half sisters born in at least 5 different herds (n = 3,998
HOL and 1,696 MON bulls). Because mortality rates fol-
low an approximately normal distribution, the daughters
of bulls with daughters' mortality rates 3 SD from the
mean mortality rate (HOL mean mortality rate = 0.170
± 0.122; MON mean mortality rate = 0.192 ± 0.119)
were removed from the analysis (n = 2,463 HOL and 717
MON daughters of 72 HOL and 35 MON bulls) to avoid
outliers carrying de novo dominant mutations (Besnard
et al., 2023).

As part of the validation study, the phenotype dataset
was divided into a training (females born in 2017–2019
[HOL] and 2009–2013 [MON]) and a validation data-
set (females born in 2020–2022 [HOL] and 2014–2016
[MON]). The training dataset, composed of 1,275,746
HOL and 476,361 MON females with juvenile mortal-
ity records, was used to perform within-breed genomic
evaluations. The validation dataset contained 689,502
HOL and 149,801 MON females from 262 HOL and 254
MON bulls. These bulls had at least ≥100 daughters in
the validation dataset, had no progeny born during the
training period, and were therefore considered as candi-
date bulls in the training dataset.

The pedigree contained the animals from the training dataset and 3 generations of their ancestors (n = 3,587,917 HOL; 1,093,469 MON). Several Illumina SNP arrays were used to genotype 84,328 HOL and 29,944 MON females and the 262 HOL and 254 MON candidate bulls at low (~7K~10~20K), medium (~50K~63K), and high (~777K) density. As described in Mesbah-Uddin et al. (2019), phasing and imputation were performed on 54,205 SNPs located on autosomes using FImpute (Sar-
golzaei et al., 2014) as part of the French routine genomic evaluation of cattle.

## Models

Several genetic models for juvenile mortality were considered: a standard one, used as a reference, that did not put an emphasis on causal variants and 3 different models that did.

The first one is adapted from the hybrid single-step model proposed by Fernando et al. (2016):

$$ \begin{bmatrix} \mathbf{y}_n \\ \mathbf{y}_g \end{bmatrix} = \mathbf{X}\beta + \begin{bmatrix} \mathbf{Z}_n & 0 \\ 0 & \mathbf{Z}_g M_g \end{bmatrix} \begin{bmatrix} u_n \\ \alpha \end{bmatrix} + \mathbf{e}, \quad [1] $$

where $\mathbf{y}$ is the vector with phenotypes; $\mathbf{Z}_n$ refers to non-genotyped animals and $\mathbf{Z}_g$ to genotyped animals; $\beta$ is the vector with the fixed effects, including herd × birth year (76,061 levels for HOL, and 37,405 for MON) and region × birth month × birth year (2,974 levels for HOL, 4,109 levels for MON); $M_g$ are centered SNP genotypes of genotyped animals (84,328 HOL and 29,944 MON animals with genotype information); $u_n \sim N(0, A_{nn}\sigma_g^2)$ corresponds to EBVs of nongenotyped animals (3,503,322 HOL and 1,063,287 MON nongenotyped animals); $\alpha \sim N(0, I\sigma_\alpha^2)$ is the vector of markers effects with

$\sigma_\alpha^2 = \frac{\sigma_g^2}{\sum_{1}^{n^{th SNP}} 2p_k(1-p_k)}$; $\mathbf{e} \sim N(0, I\sigma_e^2)$ is the vector of

residual effects; $\mathbf{X}$ and $\mathbf{Z}$ are incidence matrices for fixed effects and random animal effects, respectively, and $\mathbf{I}$ is the identity matrix.

In the second model, we assumed that $K$ causal variants (CM) explained a fraction $\chi_K$ of the total genetic variance, with $\chi_K = \sum_{j=1}^K k_j$ and $k_j$ being the fraction of genetic variance explained by the $j$th CM. As a consequence, the other variants explained a fraction of $1 - \chi_K$ of the total genetic variance. The causal variants considered were located in the ITGB7 or APOB genes, or both, for HOL cattle, and in the NOA1 gene for MON cattle, as described in the Introduction.

$$ \begin{bmatrix} \mathbf{y}_n \\ \mathbf{y}_g \end{bmatrix} = \mathbf{X}\beta + \begin{bmatrix} \mathbf{Z}_n & 0 \\ 0 & \mathbf{Z}_g M_g \end{bmatrix} \begin{bmatrix} u_n \\ \alpha \end{bmatrix} + \mathbf{e} \quad [2] $$

This model differed from model [1] in the vector of markers effects $\alpha$ with $\alpha \sim N(0, \Sigma)$, with $\Sigma$ a diagonal

matrix whose (i, i) entry is: $\sigma_{CM_j}^2 = \frac{k_j \sigma_g^2}{2p_j(1-p_j)}$ for CM

and $\sigma_{\alpha}^2 = \frac{(1-\chi_K)\sigma_g^2}{\sum_{i=\text{all SNP except CM}} 2p_i(1-p_i)}$ for all SNPs except CM, with $\sigma_{CM_j}^2$ (much) larger than $\sigma_\alpha^2$, resulting in a smaller regression toward zero for CM effects than for other SNP effects.

The third model required 2 steps: First, the CM status $\mathbf{g}_{CM_n}$ was imputed for nongenotyped animals using the formula described in Fernando et al. (2016):

$$ \mathbf{g}_{CM_n} = -(\mathbf{A}^{nn})^{-1} \mathbf{A}^{ng} \mathbf{g}_{CM_g}, $$

where $\mathbf{g}_{CM_n}$ is the vector of imputed number of allele 2 for the $j$th CM of nongenotyped animals; $\mathbf{g}_{CM_g}$ is the vector of number of allele 2 carried by genotyped animals for the $j$th CM; $\mathbf{A}^{nn}$ is the nongenotyped × nongenotyped animals block of the inverted pedigree relationship matrix; and $\mathbf{A}^{ng}$ is the genotyped × nongenotyped animals block of the inverted pedigree relationship matrix.

Second, the CM status was incorporated into model [1] as a covariate representing the additive effect of the causal mutation $a_{CM}$:

$$ \begin{bmatrix} \mathbf{y}_n \\ \mathbf{y}_g \end{bmatrix} = \mathbf{X}\beta + \begin{bmatrix} \mathbf{Z}_n & 0 \\ 0 & \mathbf{Z}_g M_g \end{bmatrix} \begin{bmatrix} u_n \\ \alpha \end{bmatrix} + \sum_{j=\text{all CM}} \mathbf{g}_{CM_j} a_{CM_j} + \mathbf{e}, \quad [3] $$

where $a_{CM_j}$ is the additive effect of the $j$th CM and $\mathbf{g}_{CM_j}$ is a vector containing the carried or imputed number of allele 2 for the $j$th CM of both genotyped and nongenotyped animals.

In model [4], a dominance effect $\delta_{CM}$ reflecting heterozygous (HT) carrier status was added to model [3] to account for the inheritance patterns of the CM:

$$ \begin{bmatrix} \mathbf{y}_n \\ \mathbf{y}_g \end{bmatrix} = \mathbf{X}\beta + \begin{bmatrix} \mathbf{Z}_n & 0 \\ 0 & \mathbf{Z}_g M_g \end{bmatrix} \begin{bmatrix} u_n \\ \alpha \end{bmatrix} + \sum_{j=\text{all CM}} \mathbf{g}_{CM_j} a_{CM_j} + \sum_{j=\text{all CM}} d_{CM_j} \delta_{CM_j} + \mathbf{e}, \quad (4) $$

where $d_{CM_j}$ reflects the HT carrier status calculated as $\left(\left|\left(\mathbf{g}_{CM_j} - 1\right)\right| - 1\right)$ and $\delta_{CM_j}$ is the dominance effect of the $j$th CM.

Genetic parameters for juvenile mortality were estimated with an animal model and the pedigree relation-
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

ship matrix using remlf90 from the BLUPF90 family programs (Misztal et al., 2014).

## Model Validation

Breeding values were estimated with the 4 models defined in the “Models” section using HSSGBLUP program (Tribout et al., 2020). The EBVs of the candidate bulls, expressed in genetic SD units, were centered in such a way that homozygous wild type (WT) had on average an EBV of 0. According to this EBV expression, the higher the EBV, the higher the expected risk of progeny mortality for the bull. As a model validation, these EBVs were compared with the observed mortality rates of their daughters by determining Pearson correlation coefficients using the cor.test() function in R (R Core Team, 2018). Furthermore, in the descriptive statistics, differences between EBVs of homozygous WT and EBVs of HT carriers of the causal variants were assessed. In addition, Spearman rank correlations between EBVs obtained with the 4 different models were estimated.

# RESULTS

## Descriptive Statistics

Table 1 shows the number of live and dead females in the genotyped and nongenotyped groups in the HOL and MON training datasets. The mortality rate of HOL females between d 3 and d 730 reached 12.96% between 2017 and 2019. They were 84,328 HOL genotyped females and 1,191,418 nongenotyped ones. Among the genotyped females, 2.05% died between d 3 and d 730. Regarding the MON training population, the juvenile mortality rate of MON females in the 2009 to 2013 period was 15.19%. A total of 29,918 MON females were genotyped compared with 446,443 nongenotyped. Only few of the genotyped ones died between d 3 and d 730 (2.40%).

Table 2 displays the genotype statuses for BLIRD and CDH genetic defects in HOL candidate bulls and genotyped females from the training set. Candidate bulls were all homozygous WT for CDH and most of them were WT for BLIRD (n = 249) except 13 HT. Among the dead females, only one was homozygous mutant for CDH, and 51 were HT. For BLIRD, 1.61% of dead females were mutants and 8.52% were HT. None of the live females were homozygous mutants for CDH, the vast majority were homozygous WT (96.80%) with 3.20% HT. Regarding BLIRD, a small proportion of live females were homozygous mutants (0.15%) or HT (9.06%).

Genotype statuses for the genetic defect MTCP of MON candidate bulls and genotyped females in the train-

<table><caption>**Table 1.** Descriptive statistics of juvenile mortality in training HOL and MON datasets</caption><thead><tr><th>Breed</th><th>Genotyped animals</th><th>Nongenotyped animals</th></tr></thead><tbody><tr><td>Holstein</td><td></td><td></td></tr><tr><td>Dead</td><td>1,731</td><td>163,561</td></tr><tr><td>Live</td><td>82,597</td><td>1,027,858</td></tr><tr><td>Montbéliarde</td><td></td><td></td></tr><tr><td>Dead</td><td>683</td><td>71,695</td></tr><tr><td>Live</td><td>29,235</td><td>374,748</td></tr></tbody></table>

ing dataset are shown in Table 3. Most of the MON candidate bulls were homozygous WT, 19 were HT. There were only 3 homozygous mutants in the training dataset: all dead animals. Around 14.20% of the dead genotyped MON females were HT, the rest were homozygous WT. A slightly lower proportion of HT was seen in the live genotyped females (12.45%).

## Genetic Parameters for Juvenile Mortality

Table 4 shows the estimated genetic parameters for juvenile mortality in HOL and MON cattle. Heritability estimates were low, ranging from 0.013 ± 0.001 (MON) to 0.015 ± 0.001 (HOL). Genetic SD were 3.27% for MON and 3.72% for HOL.

### Model Validation

On average, HT/WT candidate bulls for BLIRD and CDH, respectively, had higher EBVs than WT/WT bulls across all models (0.32–0.61 vs. 0.00; Table 5). Greater differences between HT/WT and WT/WT bulls’ EBVs, though, were observed when they were obtained from models that put an emphasis on causal variants (models [2], [3], and [4]) compared with model [1] that did not (Figure 1). Figure 2 further illustrates the genetic

<table><caption>**Table 2.** Genotype statuses of HOL animals considered in the training dataset for BLIRD and CDH genetic defects</caption><thead><tr><th rowspan="2">Status for BLIRD</th><th colspan="3">Status for CDH</th></tr><tr><th>Homozygous WT</th><th>HT</th><th>Homozygous mutant</th></tr></thead><tbody><tr><td>Candidate bulls</td><td></td><td></td><td></td></tr><tr><td>Homozygous WT</td><td>249</td><td>0</td><td>0</td></tr><tr><td>HT</td><td>13</td><td>0</td><td>0</td></tr><tr><td>Homozygous mutant</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Dead</td><td></td><td></td><td></td></tr><tr><td>Homozygous WT</td><td>1,509</td><td>48</td><td>1</td></tr><tr><td>HT</td><td>143</td><td>3</td><td>0</td></tr><tr><td>Homozygous mutant</td><td>27</td><td>0</td><td>0</td></tr><tr><td>Live</td><td></td><td></td><td></td></tr><tr><td>Homozygous WT</td><td>72,574</td><td>2,423</td><td>0</td></tr><tr><td>HT</td><td>7,260</td><td>219</td><td>0</td></tr><tr><td>Homozygous mutant</td><td>116</td><td>5</td><td>0</td></tr></tbody></table>
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<table><caption>**Table 3.** Genotype statuses for MTCP genetic defect of MON animals contained in the training dataset</caption><thead><tr><th>Status for MTCP</th><th>n</th></tr></thead><tbody><tr><td><strong>Candidate bulls</strong></td><td></td></tr><tr><td>Homozygous WT</td><td>235</td></tr><tr><td>HT</td><td>19</td></tr><tr><td>Homozygous mutant</td><td>0</td></tr><tr><td><strong>Dead</strong></td><td></td></tr><tr><td>Homozygous WT</td><td>583</td></tr><tr><td>HT</td><td>97</td></tr><tr><td>Homozygous mutant</td><td>3</td></tr><tr><td><strong>Live</strong></td><td></td></tr><tr><td>Homozygous WT</td><td>25,595</td></tr><tr><td>HT</td><td>3,640</td></tr><tr><td>Homozygous mutant</td><td>0</td></tr></tbody></table>

distribution by decile of EBVs. Under model [1], both homozygous WT and HT were evenly distributed in each decile. However, models [2], [3] and [4] showed a clear enrichment of HT bulls classified in the top EBVs deciles (ninth to tenth classes). There was no difference on average between observed daughters' mortality rates of the WT/WT bulls and those of the HT/WT (0.11 vs. 0.12). All models showed moderate correlations between EBVs and daughters' mortality rate (R = 0.41–0.43), indicating similar predictive abilities. In addition, we observed a 3.85%–5.95% difference in mean observed daughters' mortality rate (Figure 1) between “good” bulls (EBV ≤ −1 and mean observed daughters' mortality rate = 0.09) and “bad” bulls (EBV ≥ 1 and mean observed daughters' mortality rate = 0.13–0.15), depending on the model. High Spearman rank correlations (R = 0.97–1.00) were found between EBVs obtained with the different models (Figure 3) suggesting minimal reranking of candidate bulls across models.

The EBVs for juvenile mortality of MON HT bulls for MTCP were on average higher than those of MON WT regardless of the model (ranging from +0.16 to +0.66; Table 6). However, EBVs obtained from models [2], [3] and [4] showed a greater difference between homozygous WT and HT compared with model [1]. This allowed for a greater distinction between those 2 groups of bulls (Figure 4). As seen in HOL, a rather even distribution of homozygous WT and HT was seen for each decile in model [1] (Figure 5). In contrast, a large majority of HT were present in the classes with the highest EBVs (seventh–tenth classes) in models [2], [3] and [4]. The

<table><caption>**Table 4.** Genetic parameters for juvenile mortality (live = 0 and dead = 100) in French Holstein and Montbéliarde cattle</caption><thead><tr><th>Breed</th><th>Genetic variance (SE)</th><th>Residual variance (SE)</th><th>h<sup>2</sup> (SE)</th></tr></thead><tbody><tr><td>Holstein</td><td>13.81 (0.70)</td><td>885.00 (0.88)</td><td>0.015 (0.001)</td></tr><tr><td>Montbéliarde</td><td>10.72 (0.93)</td><td>812.30 (1.54)</td><td>0.013 (0.001)</td></tr></tbody></table>

<table><caption>**Table 5.** Descriptive statistics of EBVs for juvenile mortality of HOL candidate bulls regarding their genotype statuses for BLIRD and CDH genetic defects, their observed daughters' mortality rate, and the correlations between these two variables<fnref n="1" /></caption><thead><tr><th rowspan="3">Breed and model</th><th colspan="8">EBV expressed in genetic SD</th><th colspan="8">Observed daughters' mortality rate</th></tr><tr><th colspan="2">Mean</th><th colspan="2">SD</th><th colspan="2">Min</th><th colspan="2">Max</th><th colspan="2">Mean</th><th colspan="2">SD</th><th colspan="2">Min</th><th colspan="2">Max</th><th colspan="2">R</th></tr><tr><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th><th>WT/WT</th><th>HT/WT</th></tr></thead><tbody><tr><td>Holstein</td><td>0.00</td><td>0.32</td><td>0.51</td><td>0.50</td><td>-1.16</td><td>-0.70</td><td>1.47</td><td>1.31</td><td>0.11</td><td>0.12</td><td>0.02</td><td>0.01</td><td>0.05</td><td>0.10</td><td>0.18</td><td>0.15</td><td>0.41</td></tr><tr><td>Model 1</td><td>0.00</td><td>0.61</td><td>0.42</td><td>0.39</td><td>-1.01</td><td>-0.16</td><td>1.29</td><td>1.27</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.43</td></tr><tr><td>Model 2</td><td>0.00</td><td>0.56</td><td>0.51</td><td>0.50</td><td>-1.14</td><td>-0.50</td><td>1.49</td><td>1.49</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.41</td></tr><tr><td>Model 3</td><td>0.00</td><td>0.61</td><td>0.51</td><td>0.50</td><td>-1.15</td><td>-0.42</td><td>1.48</td><td>1.50</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.42</td></tr><tr><td>Model 4</td><td>0.00</td><td>0.61</td><td>0.51</td><td>0.50</td><td>-1.15</td><td>-0.42</td><td>1.48</td><td>1.50</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.42</td></tr></tbody></table>

<footnote n="1">¹WT/WT = homozygous WT for BLIRD, CDH; HT/WT = HT for BLIRD and WT for CDH, Min = minimum, Max = maximum</footnote>
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Relationships between EBVs for juvenile mortality of MON candidate bulls and their observed daughters' mortality rate per model and per genotype statuses for the genetic defects BLIRD and CDH. HT = heterozygous; WT = homozygous wild type.</figcaption></figure>

observed daughters' mortality rates of the candidate bulls
were similar on average for both homozygous WT and
HT (0.16–0.17). When looking at Pearson correlations
between EBVs for juvenile mortality of MON candidate

bulls and their observed daughters' mortality rate, they
were moderate for all models (R = 0.40–0.42). Further-
more, depending on the model, a 4.55%–4.91% differ-
ence in mean observed daughters' mortality rate (Figure
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Proportion of homozygous WT and HT HOL candidate bulls for BLIRD per quantile based on EBVs. HT = heterozygous; WT = homozygous WT.</figcaption></figure>

4) was seen between “good” bulls (EBV ≤ -1 and mean observed daughters’ mortality rate = 0.14) and “bad” bulls (EBV ≥ 1 and mean observed daughters’ mortality rate = 0.19). Little to no reranking was observed among candidate bulls regardless of the model used (R = 0.97–1.00; Figure 6).

DISCUSSION

This study focused on the development of a genomic evaluation for juvenile mortality in 2 French dairy cattle breeds, where causal variants associated with this trait have been identified. First, juvenile mortality was found to be lowly heritable in HOL and MON cattle (h² = 1.30%–1.54%), which aligns with the h² estimates observed in other dairy cattle populations (0.60%–5.00%; Hansen et al., 2003; van Pelt et al., 2012; Pritchard et
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Relationships between EBVs of HOL candidate bulls for juvenile mortality obtained with model 1 to model 4. HT = heterozygous; WT = homozygous WT.</figcaption></figure>

al., 2013; Leclerc et al., 2016; Gonzalez-Peña et al., 2019; Neupane et al., 2021; Axford et al., 2024). This confirms that juvenile mortality is a complex trait that is influenced more by a variety of environmental factors than genetics. Several studies showed that the causes of juvenile mortality vary depending on the age at death. Mortality that occurs before weaning is often due to gastrointestinal diseases, whereas the most common causes of mortality after weaning are respiratory diseases (Sivula et al., 1996; Svensson et al., 2006; Gulliksen et al., 2009; van Pelt et al., 2012; Leclerc et al., 2016). We, however, decided not to split the juvenile mortality period into 2 subperiods because the h² estimate for juvenile mortality occurring during the entire period was higher than the h² estimates for juvenile mortality occurring during the 2 subperiods (results not shown), and more importantly, homozygous mutants for the genetic defects studied usually die throughout the entire period.

The second objective of this article was to propose genomic models for juvenile mortality. The developed models using HSSGBLUP program (Tribout et al., 2020) can incorporate one or more causal variants for multiple genetic defects in 2 breeds using different strategies: assigning higher a priori effect variance to the causal variants or modeling their additive and dominance effects. In Sanchez et al. (2020), a model that attributed higher a priori effect variance to candidate SNPs was successfully used for cheese-making traits in Montbéliarde. We confirm here that our models can efficiently handle the in-

clusion of several causal variants and will be well-suited to include newly discovered causal variants in the future. This is particularly beneficial compared with Bayesian models, especially when the number of genotyped animals is as large as those of cattle populations (VanRaden et al., 2011a). Moreover, Zhu et al. (2019) showed that Bayesian models did not outperform a GBLUP model in terms of predictive abilities for lowly heritable traits such as our studied juvenile mortality. This further confirms our decision to focus on a ssGBLUP model to implement a genomic evaluation for juvenile mortality in the case of large dairy cattle populations.

The developed models for juvenile mortality showed moderate predictive abilities. This is evident through the moderate correlations between EBVs of candidate bulls for juvenile mortality and their observed daughters' mortality rate (R = 0.40–0.43). In addition, the genetic SD were also large enough (3.27%–3.72% of probability to die between d 3 and d 730) to distinguish “good” bulls from “bad” ones. Consequently, this proves the feasibility and value of implementing a genomic evaluation for the trait juvenile mortality in both French HOL and MON cattle populations. Although no distinction, in terms of predictive ability, could be made at the population level between models incorporating causal variants or not, the models showed different results at the individual level. We observed a clearer distinction between HT and homozygous WT bulls based on EBVs when the model emphasized the causal variants (models [2], [3] and [4]),
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<table><caption>**Table 6.** Descriptive statistics of EBVs for juvenile mortality of homozygous WT and HT bulls for MTCP, their observed daughters' mortality rate, and the correlations between these two variables<fnref n="1" /></caption><thead><tr><th rowspan="3">Breed and model</th><th colspan="6">EBV expressed in genetic SD</th><th colspan="6">Observed daughters' mortality rate</th></tr><tr><th colspan="2">Mean</th><th colspan="2">SD</th><th colspan="2">Min</th><th colspan="2">Max</th><th colspan="2">Mean</th><th colspan="2">SD</th><th colspan="2">Min</th><th colspan="2">Max</th></tr><tr><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th><th>WT</th><th>HT</th></tr></thead><tbody><tr><td>Montbéliarde</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.16</td><td>0.17</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Model 1</td><td>0.00</td><td>0.16</td><td>0.71</td><td>0.59</td><td>-1.71</td><td>-0.81</td><td>2.38</td><td>1.46</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.40</td></tr><tr><td>Model 2</td><td>0.00</td><td>0.66</td><td>0.64</td><td>0.59</td><td>-1.55</td><td>-0.38</td><td>2.17</td><td>1.94</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.42</td></tr><tr><td>Model 3</td><td>0.00</td><td>0.60</td><td>0.69</td><td>0.62</td><td>-1.66</td><td>-0.50</td><td>2.39</td><td>1.95</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.42</td></tr><tr><td>Model 4</td><td>0.00</td><td>0.63</td><td>0.69</td><td>0.62</td><td>-1.66</td><td>-0.48</td><td>2.39</td><td>1.98</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.42</td></tr></tbody></table>

regardless of the breed or the causal variant. No global changes in EBV ranking of bulls were observed when using one model or another as confirmed by the high correlations between EBVs obtained with all models. However, in proportion, more HT bulls were associated with higher EBVs from models [2], [3] and [4] compared with homozygous WT, making a selection between carriers and noncarriers easier. Therefore, we can agree that the use of a genomic model that puts an emphasis on causal variants responsible for genetic defects helps efficiently select against juvenile mortality. It also provides a way to distinguish carriers from noncarrier bulls, which is of interest to breeding societies and breeders. We then suggest routinely implementing a genomic evaluation for juvenile mortality to help reduce its occurrence in both HOL and MON cattle. The genomic evaluation approach, though, should not be used as a replacement for mating plans, which remains a complementary approach to avoid at-risk matings between carriers of causal variants and hence reduce causal allele frequency. Both strategies are necessary and must be jointly used to decrease juvenile mortality.

This study used large HOL and MON datasets to validate the models' performance. However, in the genotype datasets that we used, roughly 2% of the genotyped animals died between d 3 and d 730. This lack of dead genotyped animals surely affected the model predictive ability. Consequently, at the population level, none of the studied models could be considered as better than the others based on the correlation between EBVs and observed daughters' mortality rate. We therefore suggest promoting genotyping of dead animals to better capture the SNP effects, especially those of causal variants. In that way, a better distinction between model predictive ability might be possible. Furthermore, genotyping young dead animals can help detect new causal variants responsible for juvenile mortality and hence improve the genomic evaluation in the future.

CONCLUSIONS

In this study focused on juvenile mortality, h² estimates for this trait were low for both French HOL and MON cattle populations. However, the estimated genetic SD made the distinction between “good” and “bad” bulls possible. Several ssGBLUP models were developed with inclusion of causal variants by either increasing the proportions of genetic variance explained a priori by the causal variants or estimating and incorporating their additive and dominance effects. All models provided moderate predictive abilities, but none performed better than the others. However, models that put an emphasis on causal variants helped better distinguish carriers from noncarrier bulls compared with a conventional ssGBLUP.

<footnote n="1">¹WT = homozygous WT for MTCP; HT = HT for MTCP; Min = minimum, Max = maximum.</footnote>
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Relationships between EBVs for juvenile mortality of MON candidate bulls and their observed daughters' mortality rate per model and per genotype statuses for the genetic defect MTCP. HT: heterozygous; WT: homozygous wild type.</figcaption></figure>

Therefore, breeding societies should include juvenile
mortality in their breeding goals. Thus, future work will
build on these findings to implement a routine genomic
evaluation for juvenile mortality including causal vari-

ants with the aim of reducing its occurrence in French
HOL and MON cattle populations.
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Proportion of homozygous WT and HT HOL candidate bulls for BLIRD per quantile based on EBVs. HT = heterozygous; WT = homozygous WT.</figcaption></figure>

NOTES

This study was financially supported by the Welcow
project funded by APIS-GENE (Paris, France). We would
like to thank France Génétique Elevage (Paris, France),
all the technical organizations involved in the French
National Genetic Programme and VALOGENE (Paris,
France) for providing us access to the routinely col-
lected French data used for population management and
selection purposes. IDELE is grateful for the financial

support that it received from the French Ministry of Ag-
riculture (Paris, France) through the CASDAR program.
The pedigree, phenotype, and genotype information used
in this study was produced for a cattle management and
selection purposes. The data is owned by French breed-
ing organizations, who have granted INRAE permission
to use it for research purposes only. They have also
prohibited the data from being transferred to third par-
ties or public databases. Consequently, the data cannot
Chapard et al.: GENOMIC EVALUATION FOR JUVENILE MORTALITY

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Relationships between EBVs of candidate bulls for juvenile mortality obtained with model 1 to model 4 in MON cattle. HT = heterozygous; WT = homozygous WT.</figcaption></figure>

be made publicly available. However, readers can apply for a research license to access the genotyping data from VALOGENE and the pedigree and phenotypic data from France Génétique Elevage. No human or animal subjects were used, so this analysis did not require approval by an Institutional Animal Care and Use Committee or Institutional Review Board. The authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** BLIRD = bovine lymphocyte intestinal retention defect; CDH = cholesterol deficiency; CM = causal variant; GRM = genomic relationship matrix; HOL = Holstein; HT = heterozygous; Max = maximum; Min = minimum; MON = Montbéliarde; MTCP = mitochondropathy; ssGBLUP = single-step GBLUP; WT = wild type.

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ORCIDS

Léa Chapard, https://orcid.org/0000-0002-9498-9977

Thierry Tribout,, https://orcid.org/0000-0003-2500-7375

Hélène Leclerc, https://orcid.org/0000-0002-2103-2234

Sophie Mattalia, https://orcid.org/0000-0002-7672-4414

Aurélien Capitan, https://orcid.org/0000-0003-3185-0932