Sequence-based GWAS in 180 000 German Holstein cattle reveals new candidate genes for milk production traits

Abstract

BackgroundThe use of genome-wide association studies (GWAS) has led to the identification of numerous quantitative trait loci and candidate genes in dairy cattle. To obtain sufficient power of GWAS and to identify quantitative trait nucleotides, whole-genome sequence data is required. Sequence data facilitates the identification of potential causal variants; however, sequencing of whole genomes is still expensive for a large number of animals. Imputation is a quick and efficient way of obtaining sequence data from large datasets. Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causative variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 252,285 cattle were imputed to the sequence level and large-scale GWAS was carried out to identify new association signals.ResultsWe confirmed many known and identified 30 previously unreported candidate genes for milk, fat, and protein yield. While all of the genes were functionally associated with the traits, some showed pleiotropic effects as well. Specifically, association with mammary gland development, fatty acid synthesis, metabolism of lipids, or milk production QTLs in other farm animals has been reported. Variants associated with these genes explained a large percentage of genetic variance, compared to random ones.ConclusionsOur findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. Although milk production traits in cattle are comprehensively researched, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown in our study. With constantly growing sample sizes, we expect more insights into the genetic architecture of production traits in the future.

Mendelian randomization analysis of 34,497 German Holstein cows to infer causal associations between milk production and health traits

“…For all 34,497 cows, 50K chip and imputed WGS data were available. The 50K chip data were provided by the VIT and the imputation is described in Križanac et al [ 27 ]. After filtering out the SNPs located on the sex chromosomes and those with a minor allele frequency (MAF) lower than 0.01, 44,126 SNPs of the 50K chip remained for the analysis.…”

“…Moreover, the dosage R-squared parameter (DR2) that measures the squared correlation between the true and the estimated allele dosage [ 28 ] was applied to assess the quality of the imputed WGS data. Variants were filtered out if the DR2 was lower than 0.75 [ 27 ] and, in total, 16,882,734 imputed WGS variants remained for further analysis.…”

Genome Wide Analysis for Milk, Fat and Protein Yield, and Milking Speedin Turkish Holstein Dairy Cattle

“…In dairy cattle breeding programs, GWAS analysis has played an important role in enhancing productivity and facilitating more informed and accurate decision-making processes. Since 2010, GWAS analysis performed for production traits in Holstein cattle (Jiang et Križanac et al 2023). Identifying the gene regions associated with speci c traits is instrumental in comprehending the underlying mechanisms responsible for these traits.…”