Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra.

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

A large population study to assess the magnitude of prenatal programming in dairy cattle.

The performance of an adult dairy cow may be influenced by events that occur before her birth. The present study investigated potential effects of 2 prenatal groups of factors, Assisted Reproductive Techniques (ART) and maternal characteristics (e.g., dam parity), on offspring performance during their first lactation, in populations of 2 dairy cow breeds: French Holstein and Montbéliarde. The different ART studied included the type of semen (conventional or X-sorted) used for Artificial Insemination (AI) and the technology of conception used (AI, embryo transfer, or in vitro fertilization). Three maternal characteristics were considered: (1) the dam age at first calving, (2) dam parity number, and (3) indicators of dam udder health during gestation (somatic cell score and events of clinical mastitis). First, we investigated whether heifer survival from 3d to 18 mo old was associated with any of the prenatal factors considered. We then estimated the associations of these prenatal factors with 8 traits of commercial interest: (1) stature, (2-4) milk, fat, and protein yields, (5) somatic cell score, (6) clinical mastitis, and (7-8) heifer and cow conception rate, all measured on genotyped cows. Linear models were used for this study with the prenatal factors as covariates in the model, and for the 8 traits, phenotypes were adjusted for their corresponding genomic estimated breeding value. The results indicated that the survival rate of heifers born from embryo transfer was significantly higher than that of heifers born from AI (probably due to preferential management practices), while the other prenatal factors did not explain differences in heifer survival. Among the Montbéliarde cows born from AI, those born from X-sorted semen showed a lightly but significantly lower milk yield than those born without X-sorting of the semen (-52 kg of milk in the first lactation). Among the Holstein cows, those born from embryo transfer presented significantly lower milk performance than cows born from AI. Regarding the maternal characteristics, none or very weak associations were found between the dam age at first calving and the offspring performance in both breeds. Dam parity, on the other hand, was associated with offspring performance for milk, fat, and protein yield in both breeds, however not in the same direction. In the Holstein breed, an increase in dam parity was favorable for offspring performance for milk, fat, and protein yield, whereas in the Montbéliarde breed, an increase in dam parity was associated with lower milk and protein yield and no association was found for fat yield. The udder health of the dam during gestation was not or only weakly associated with the traits studied in the offspring. Although some significant associations were identified due to the large sample size, the effects were modest, typically less than 1% of the phenotypic mean, and were not consistently observed across the 2 breeds.

Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows.

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle.

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Genetic relationships between weight loss in early lactation and daily milk production throughout lactation in Holstein cows.

After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from -0.26 to 0.05 in L1 and from -0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization.

Comparison of methane production, intensity, and yield throughout lactation in Holstein cows.

Genetic selection to reduce methane (CH4) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH4 and several traits of interest in dairy cows. We measured CH4, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH4 emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<-0.5) and DMI (>-0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH4 dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH4 trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.

Association between body condition genomic values and feed intake, milk production, and body weight in French Holstein cows.

Body condition score (BCS) offers a good estimate of the amount of stored fat on the body, and its variations can be used as a proxy for energy balance. Many countries have implemented a genomic evaluation of BCS, including France, where estimated breeding values are based on an individual BCS determination during the first lactation. In this article, we investigate the degree to which this genomic estimated breeding value based on a single phenotype record per cow might reflect different profiles of body reserves throughout lactation and be used to predict, and perhaps limit, their mobilization during early lactation. We also investigate whether selection on BCS affects other traits. A data set including 686 lactations of 435 Holstein cows from 3 experimental farms not used in the reference population for genomic evaluation was used to estimate the effects of the BCS direct genomic value (iBCS) on BCS, body weight, feed intake, milk production, and fat and protein contents throughout the lactation period. For each trait, the model included different iBCS regressions and an effect of the direct genomic value of the trait itself when available. It thus appeared that cows with a positive iBCS always had a higher BCS than negative iBCS cows, whatever the lactation stage, and that this difference increased during the first 6 mo to reach a difference of 0.8 point. A similar effect was seen regarding body weight, but it was the opposite for milk production, with negative iBCS cows producing slightly more milk (difference of about 3% over lactation). Feed intake increased slightly faster at the beginning of lactation for cows with positive iBCS. Therefore, iBCS is a promising tool that could help to limit intense mobilization during early lactation. Should feed efficiency be included in the breeding goal, greater attention should be paid to BCS to avoid further body mobilization in early lactation.

Detailed analysis of mortality rates in the female progeny of 1,001 Holstein bulls allows the discovery of new dominant genetic defects.

Reducing juvenile mortality in cattle is important for both economic and animal welfare reasons. Previous studies have revealed a large variability in mortality rates between breeds and sire progeny groups, with some extreme cases due to dominant mutations causing various syndromes among the descendants of mosaic bulls. The purpose of this study was to monitor sire-family calf mortality within the French and Walloon Holstein populations, and to use this information to detect genetic defects that might have been overlooked by lack of specific symptoms. In a population of heifers born from 1,001 bulls between 2017 and 2020, the average sire-family mortality rates were of 11.8% from birth to 1 year of age and of 4.2, 2.9, 3.1, and 3.2% for the perinatal, postnatal, preweaning, and postweaning subperiods, respectively. After outlining the 5 worst bulls per category, we paid particular attention to the bulls Mo and Pa, because they were half-brothers. Using a battery of approaches, including necropsies, karyotyping, genetic mapping, and whole-genome sequencing, we described 2 new independent genetic defects in their progeny and their molecular etiology. Mo was found to carry a de novo reciprocal translocation between chromosomes BTA26 and BTA29, leading to increased embryonic and juvenile mortality because of aneuploidy. Clinical examination of 2 calves that were monosomic for a large proportion of BTA29, including an orthologous segment deleted in human Jacobsen syndrome, revealed symptoms shared between species. In contrast, Pa was found to be mosaic for a dominant de novo nonsense mutation of GATA 6 binding protein (GATA6), causing severe cardiac malformations. In conclusion, our results highlight the power of monitoring juvenile mortality to identify dominant genetic defects due to de novo mutation events.

Opportunities for genomic selection of cheese-making traits in Montbéliarde cows.

As part of the From'MIR project, traits related to the composition and cheese-making properties (CMP) of milk were predicted from 6.6 million mid-infrared spectra taken from 410,622 Montbéliarde cows (19,862 with genotypes). Genome-wide association studies of imputed whole-genome sequences highlighted candidate SNPs that were then added to the EuroG10K BeadChip, which is routinely used in genomic selection. In the present study, we (1) assessed the reliability of single-step genomic BLUP breeding values (ssEBVs) for cheese yields, coagulation traits, and casein and calcium content generated from test-day records of the first 3 lactations, (2) estimated realized genetic trends for these traits over the last decade, and (3) simulated different cheese-making breeding objectives and estimated the responses for CMP as well as for other traits currently selected in the Montbéliarde breed. To estimate the reliability of ssEBVs, the available data were split into 2 independent training and validation sets that respectively contained cows with the oldest and the most recent lactation data. The training set included 155,961 cows (12,850 with genotypes) and was used to predict ssEBVs of 2,125 genotyped cows in the validation set. We first tested 4 models that included either lactation (LACT) or test-day (TD) records from the first (1) or the first 3 (3) lactations, giving equal weight to all 50K SNP effects. Mean reliabilities were 61%, 62%, 63%, and 64% for the LACT1, LACT3, TD1, and TD3 models, respectively. Using the most accurate model (TD3), we then compared the reliabilities of 3 scenarios with: SNPs from the Illumina BovineSNP50 BeadChip only, equally weighted (50K); 50K SNPs plus additional candidate SNPs, equally weighted (50K+); and 50K and candidate SNPs with additional weight given to 7 to 14 candidate SNPs, depending on the trait (CAND). The 50K+ and CAND scenarios led to similar mean reliabilities (67%) and both outperformed the 50K scenario (64%), whereas the CAND scenario generated the less biased ssEBVs. To assess genetic trends, SNP effects were estimated with a single-step GBLUP based on the TD3 model and the 50K scenario applied to the whole population (2.6 million performance records from 190,261 cows and 423,348 animals in the pedigree, of which 21,874 were genotyped) and then applied to 50K genotypes of 21,171 males and 311,761 females. We detected a positive genetic trend for all CMP during the last decade, probably due to selection for an increase in milk protein and fat content in Montbéliarde cows. Finally, we compared the selection responses to 3 different breeding objectives: the current Montbéliarde total merit index (TMI) and 2 alternative scenarios that gave a weight of 70% to TMI and the remaining 30% to either milk casein content (TMI-COMP) or a combination of 3 CMP (TMI-Cheese). The TMI-Cheese scenario yielded the best responses for all the CMP analyzed, whereas values in the TMI-COMP scenario were intermediate, with a slight effect on other traits currently included in TMI. Based on these results, a program of genomic evaluation for CMP predicted from mid-infrared spectra was designed and implemented for the Montbéliarde breed.

Influence of production, reproduction, morphology, and health traits on true and functional longevity in French Holstein cows.

In the long term, resilient animals are able to maintain their normal biological processes when confronted with environmental perturbations, reducing their risk of being culled. Therefore, longevity can be proposed as an indicator of long-term resilience. Decisions to remove a given dairy cow from the herd are mainly related to low milk production (i.e., voluntary culling) or to reasons other than production (i.e., involuntary culling). The aptitude of animals to delay any culling is defined as true longevity (TL), whereas functional longevity (FL) is the ability to avoid involuntary culling. The aim of the study was to investigate the influence of production, reproduction, morphology, and health traits on TL and FL, to identify risk factors for culling. Data included 278,217 lactations from 122,461 Holstein Friesian cows reared in 640 herds. The length of productive life, calculated as the time between first calving and culling, or censoring, was used as the measure of longevity. Survival analysis was performed using proportional hazards models assuming a piecewise Weibull distribution of the baseline hazard function, with or without adjustment for milk production to evaluate FL and TL. Insemination status, calving ease, mastitis, somatic cell count, displaced abomasum, and udder depth had significant relationships with TL and FL. Differences in estimates of relative risk between TL and FL showed that milk production often influenced culling decisions: farmers are more prone to cull animals with low production even when they had good other characteristics. The culling risk factors identified in the present study can be used to study resilience in dairy cattle and to improve genetic evaluations of functional or total longevity.

A missense mutation in the FZD7 gene is associated with dilution of the red areas of the coat in Montbéliarde cattle.

Recently, a new genetically autosomal recessive color phenotype emerged in the red pied bovine Montbéliarde breed. It is characterized by a dilution of the red areas of the coat and was denominated 'milca'. A genome-wide homozygosity scan of 106 cases followed by haplotype analysis revealed a candidate region within BTA2 between positions 89.95 and 91.63 Mb. Analysis of whole-genome sequence data generated from milca animals identified a strong candidate variant within the coding region of the Frizzled-7 gene (FZD7). This gene encodes for a G-protein coupled receptor for Wnt signaling proteins. The variant induces a glycine to alanine substitution in the second extracellular loop, p.(Gly414Ala). Cross-species amino acid alignments revealed that this glycine is conserved among orthologs and most paralogs, suggesting that it plays an important role in FZD function. In addition, genotyping data revealed that the mutant allele is restricted to the Montbéliarde breed, at a 3.7% frequency. All homozygous cows for the mutant allele exhibited the milca phenotype whereas all heterozygotes had no coat color defects. In conclusion, this study strongly suggests that, in cattle, a mutation of FZD7 alone is sufficient to cause a coat color phenotype without any strong other adverse effect.

Improved dairy cattle mating plans at herd level using genomic information.

From 2012 to 2018, 223 180 Montbéliarde females were genotyped in France and the number of newly genotyped females increased at a rate of about 33% each year. With female genotyping information, farmers have access to the genomic estimated breeding values of the females in their herd and to their carrier status for genetic defects or major genes segregating in the breed. This information, combined with genomic coancestry, can be used when planning matings in order to maximize the expected on-farm profit of future female offspring. We compared different mating allocation approaches for their capacity to maximize the expected genetic gain while limiting expected progeny inbreeding and the probability to conceive an offspring homozygous for a lethal recessive allele. Three mate allocation strategies (random mating (RAND), sequential mating (gSEQ€) and linear programing mating (gLP€)) were compared on 160 actual Montbéliarde herds using male and female genomic information. Then, we assessed the benefit of using female genomic information by comparing matings planned using only female pedigree information with the equivalent strategy using genomic information. We measured the benefit of adding genomic expected inbreeding and risk of conception of an offspring homozygous for a lethal recessive allele to Net merit in mating plans. The influence of three constraints was tested: by relaxing the constraint on availability of a particular semen type (sexed or conventional) for bulls, by adding an upper limit of 8.5% coancestry between mate pairs or by using a more stringent maximum use of a bull in a herd (5% vs 10%). The use of genomic information instead of pedigree information improved the mate allocation method in terms of progeny expected genetic merit, genetic diversity and risk to conceive an offspring homozygous for a lethal recessive allele. Optimizing mate allocation using linear programming and constraining coancestry to a maximum of 8.5% per mate pair reduced the average coancestry with a small impact on expected Net Merit. In summary, for male and female selection pathways, using genomic information is more efficient than using pedigree information to maximize genetic gain while constraining the expected inbreeding of the progeny and the risk to conceive an offspring homozygous for a lethal recessive allele. This study also underlines the key role of semen type (sexed vs conventional) and the associated constraints on the mate allocation algorithm to maximize genetic gain while maintaining genetic diversity and limiting the risk to conceive an offspring homozygous for a lethal recessive allele.

Genome-wide association study for age at puberty and resumption of cyclicity in a crossbred dairy cattle population.

Fertility is of primary economic importance in dairy cattle and the most common reason for involuntary culling. However, standard fertility traits have very low heritability that renders genetic selection slow and difficult. In this study, we explored fertility from an endocrine standpoint. A total of 1,163 crossbred Holstein-Normande females in a 3-generation familial design were studied for progesterone level measured every 10 d to determine age at puberty (PUB) and commencement of postpartum luteal activity (CPLA). Genetic parameters were estimated using REML with WOMBAT software. The heritability estimates were 0.38 ± 0.10 and 0.16 ± 0.07 for PUB and CPLA, respectively. Moreover, the 2 traits were genetically correlated (0.45 ± 0.23), suggesting a partially common determinism. Because of the family structure, a linkage disequilibrium and linkage analysis approach was preferred over standard genome-wide association study to map genomic regions associated with these traits. Ten quantitative trait loci (QTL) were detected for PUB on chromosomes 1, 3, 11, 13, 14, 21, and 29, whereas 3 QTL were associated with CPLA on chromosomes 21 and 26. Only the QTL on chromosome 21 was common to both traits. Four functional candidate genes (NCOA2, GAS2, OVOL1, and FOSL1) were identified in the detected regions. These findings will contribute to a clearer understanding of fertility determinism and enhance the value of introducing endocrinological data in fertility studies.

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies.

Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

Frequencies of milk protein variants and haplotypes estimated from genotypes of more than 1 million bulls and cows of 12 French cattle breeds.

Due to their major effects on milk composition and cheese-making properties and their putative effects on human health, there is a great deal of interest in bovine milk protein variants. The objectives of this study were to estimate frequencies of milk protein variants and haplotypes in 12 cattle breeds as well as their trends over time to assess the effect of selection on milk traits. Milk protein variants and haplotypes were identified from SNP genotype data from more than 1 million animals from 12 dairy, beef, or dual-purpose cattle breeds that had been genotyped for genomic selection. We examined a total of 15 loci in the genes that encode ß-lactoglobulin (ß-LG) and 3 caseins (αS1-CN, ß-CN, and κ-CN); genotypes were directly called from customized SNP chips (50.6%) or imputed (49.4%). Variants A and B of ß-LG were frequent in the 12 breeds. For the caseins, we found 3 variants for αS1-CN (B, C, and D), 6 for ß-CN (A1, A2, A3, B, C, and I), and 5 for κ-CN (A, B, C, D, and E). For αS1-CN, the B variant was the most frequent in all breeds except Jersey. For ß-CN, the A2 variant was the most abundant in all breeds except Tarentaise, although in Normande animals, the I variant (30.9%) was almost as common as A2 (39.7%). The C variant was very rare except in the Tarentaise sample (4.8%). The most frequent variant for κ-CN was A in 5 breeds (including Holstein), and B in the 7 other breeds. The B variant was present at a particularly high frequency in Jersey (82.6%) and Normande (85.5%) animals. The C and E variants of κ-CN appeared to be particularly frequent in the Tarentaise (12.7%) and Holstein (9%) breeds, respectively. We found 20 haplotype combinations of αS1-ß-κ CN that were present at a frequency >0.1% in at least one breed; however, only 6 to 9 haplotypes were found in any given breed, demonstrating a strong degree of linkage disequilibrium. The most frequent haplotypes were B-A1-A, B-A2-A, B-A2-B, B-I-B, C-A2-A, and C-A2-B. Some alleles were predominantly found in only one haplotype, such as the E and C variants of κ-CN and the I variant of ß-CN, which were mainly found in the B-A1-E, B-A1-C, and B-I-B haplotypes, respectively. We observed changes in the frequency of certain variants over time in several breeds, such as an increase in the frequency of variants A of ß-LG, I of ß-CN, and B of κ-CN. With these results, we update and complete frequency data that were first estimated 30 to 50 yr ago, and, for the first time in these breeds, we assess the effect of selection on milk protein variants.

Short communication: A splice site mutation in CENPU is associated with recessive embryonic lethality in Holstein cattle.

A genome scan for homozygous haplotype deficiency coupled with whole-genome sequence data analysis is a very effective method to identify embryonic lethal mutations in cattle. Among other factors, the power of the approach depends on the availability of a greater amount of genotyping and sequencing data. In the present study, we analyzed the largest known panel of Illumina BovineSNP50 (Illumina Inc., San Diego, CA) genotypes, comprising 401,896 Holstein animals, and we report the mapping of a new embryonic lethal haplotype on chromosome 27, called HH7. We fine mapped the locus in a 2.0-Mb interval using an identical-by-descent approach and analyzed genome sequence data from 4 carrier and 143 noncarrier Holstein bulls to identify the causative mutation. We detected a strong candidate variant in the gene encoding centromere protein U (CENPU), a centromere component essential for proper chromosome segregation during mitosis. The mutant allele is a deletion of 4 nucleotides located at position +3 to +6 bp after the splicing donor site of exon 11. Cross-species nucleotide alignment revealed that the nucleotide at position +3 is entirely conserved among vertebrates, suggesting that it plays an important role in the regulation of CENPU splicing. For verification, we genotyped the candidate variant in 232,775 Holstein individuals and did not observe any homozygotes, whereas 16 were expected (Poisson P-value = 1.1 × 10-7; allele frequency = 0.8%). In addition, genotyping of 250,602 animals from 19 additional breeds revealed that the mutant allele is restricted to animals of Holstein descent. Finally, we estimated the effect of the candidate variant on 2 fertility traits in at-risk mating (i.e., between carrier bulls and daughters of carrier bulls) versus non-risk mating. In agreement with a recessive lethal inheritance pattern, we observed a marked reduction in both conception rate and 56-d nonreturn rate in heifers and cows. The effect on 56-d nonreturn rate suggests that a substantial proportion of homozygous mutants die before 35 d after insemination, which is consistent with the early embryonic death previously reported in CENPU-/- mouse embryos. In conclusion, we demonstrate that with more than 400,000 genotypes, we can map very rare recessive lethal mutations segregating at a frequency below 1% in the population. We recommend performing new analyses regularly as data are accumulating.

Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows.

Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbéliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comté with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CYDM) or fresh (CYFRESH) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CYDM predictions showed coefficient of determination (R2) values up to 0.92 and 0.87, and RSy,x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CYDM [R2 = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CYFRESH (R2 = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R2 ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.

A missense mutation (p.Tyr452Cys) in the CAD gene compromises reproductive success in French Normande cattle.

We scanned the genome of 77,815 Normande cattle with different Illumina SNP chips (Illumina Inc., San Diego, CA) to map recessive embryonic lethal mutations using homozygous haplotype deficiency. We detected 2 novel haplotypes on chromosomes 11 and 24 but did not confirm 6 previously reported haplotypes. The one on chromosome 11 showed a marked reduction in conception rates and moderate decrease in nonreturn rate in at-risk versus control mating, supporting late embryonic mortality. After fine mapping and analyzing whole-genome sequences, we prioritized a missense mutation in CAD (g.72399397T>C; p.Tyr452Cys)-a gene encoding a protein (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase) essential for de novo pyrimidine biosynthesis-as a candidate causal variant. This transition mutation replaces a tyrosine residue, which is perfectly conserved among living organisms, with a cysteine residue in the carbamoyl-phosphate synthetase 2 domain of the protein. A single animal was confirmed to be homozygous for the mutation based on Sanger sequencing. However, large-scale genotyping of the candidate variant with the Illumina EuroG10k BeadChip revealed an absence of live homozygotes in a panel of 33,323 Normande animals and an absence of carriers in 348,593 animals from 19 other cattle breeds. These results support recessive embryonic lethality with nearly complete penetrance, as was previously reported in CAD mutants in several eukaryote species. The only homozygous cow had extremely poor udder conformation, suggesting a potential role of CAD in udder development, but no effect was detected when comparing daughter yield deviations of 250 heterozygous bulls with that of 2,912 homozygotes for the ancestral allele. Together, our results showed the importance of large-scale screening for homozygous haplotype deficiency with hundreds of thousands of animals, validating results with an independent data set, and considering unexpected live homozygotes, to avoid both false-positive and false-negative discoveries. These discoveries will be used primarily in mating decisions to avoid at-risk mating. In addition, we recommend including CAD in the breeding objectives of Normande cattle.

Short communication: Confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows.

In a previous study, we identified candidate causative variants located in 24 functional candidate genes for milk protein and fatty acid composition in Montbéliarde, Normande, and Holstein cows. We designed these variants on the custom part of the EuroG10K BeadChip (Illumina Inc., San Diego, CA), which is routinely used for genomic selection analyses in French dairy cattle. To validate the effects of these candidate variants on milk composition and to estimate their effects on cheesemaking properties, a genome-wide association study was performed on milk protein, fatty acid and mineral composition, as well as on 9 cheesemaking traits (3 laboratory cheese yields, 5 coagulation traits, and milk pH). All the traits were predicted from midinfrared spectra in the Montbéliarde cow population of the Franche-Comté region. A total of 194 candidate variants located in 24 genes and 17 genomic regions were imputed on 19,862 cows with phenotypes and genotyped with either the BovineSNP50 (Illumina Inc.) or the EuroG10K BeadChip. We then tested the effect of each SNP in a mixed linear model including random polygenic effects estimated with a genomic relationship matrix. We confirm here the effects of candidate causative variants located in 17 functional candidate genes on both cheesemaking properties and milk composition traits. In each candidate gene, we identified the most plausible causative variant: 4 are missense in the ALPL, SLC26A4, CSN3, and SCD genes, 7 are located in 5'UTR (AGPAT6), 3' untranslated region (GPT), or upstream (CSN1S1, CSN1S2, PAEP, DGAT1, and PICALM) regions, and 6 are located in introns of the SLC37A1, MGST1, CSN2, BRI3BP, FASN, and ANKH genes.

Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows.

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH0 PPC had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH0 PPC and CY or coagulation traits were weak (-0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.