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

Abstract

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.

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows

“…In addition to the three well-known QTL regions described above, we also found evidence that other genomic regions have highly significant effects on the traits analyzed; specifically, our analysis highlighted the SLC37A1 , ALPL , MGST1 , SEL1L3 , FASN , ANKH , BRI3BP , SCD , and GPAT4 genes, which we had also previously detected in a sequence-based GWAS on milk protein and fatty acid composition [45, 46]. We confirm here their effects on milk composition and note their effects on CMP.…”

Imputation for sequencing variants preselected to a customized low-density chip

“…The result was consistent with the previous studies. In Montbéliarde cows 17 , a similar two-step imputation strategy was used to impute preselected sequencing SNPs when using a customized low-density SNP chip as a reference panel. In other dairy cattle populations, a multiple-step imputation strategy was superior to a one-step imputation strategy when imputing from 3 K and 6 K chips to the HD chip where animals were firstly imputed to the 54 K chip and then to the HD chip 31 , or from the 54 K chip to WGS where animals were firstly imputed to the HD chip and then to WGS 32 .…”

“…Instead of using the WGS data as a reference panel, using a customized low-density chip could achieve a much larger reference population but with lower linkage disequilibrium between preselected sequencing SNPs and the other SNPs in the chip. By using a customized low-density SNP chip as a reference panel, Sanchez et al 17 imputed 194 candidate variants for milk protein and fatty acid composition in Montbéliarde cows and showed that the mean squared correlation between imputed and true genotypes was 91.6% for SNPs with MAF higher than 1%.…”

Analyse génétique de la « fromageabilité » du lait de vache prédite par spectrométrie dans le moyen infrarouge en race Montbéliarde

“…Grâce à l'imputation réalisée sur les vaches From'MIR (Sanchez et al, 2018b), nous avions à disposition les génotypes de la puce 50K augmentée des SNP de la partie recherche de la puce EuroG10K. Parmi les SNP de la partie recherche, figuraient certains variants candidats (prédictifs ou causaux) dont les effets sur la composition du lait ont été mis en évidence dans le projet PhénoFinlait (Boichard et al, 2014 ;Sanchez et al, 2017) et confirmés dans le projet From'MIR (Sanchez et al, 2018b). Dixhuit de ces variants (SNP), représentant chacun une région QTL avec de gros effets sur la composition et/ou la « fromageabilité » du lait, ont ainsi été sélectionnés.…”