Using Genome-Wide Association Analysis to Characterize Environmental Sensitivity of Milk Traits in Dairy Cattle

Abstract

Genotype-by-environment interaction (GxE) has been widely reported in dairy cattle. One way to analyze GxE is to apply reaction norm models. The first derivative of a reaction norm is the environmental sensitivity (ES). In the present study we conducted a large-scale, genome-wide association analysis to identify single-nucleotide polymorphisms (SNPs) that affect general production (GP) and ES of milk traits in the German Holstein population. Sire estimates for GP and for ES were calculated from approximately 13 million daughter records by the use of linear reaction norm models. The daughters were offspring from 2297 sires. Sires were genotyped for 54k SNPs. The environment was defined as the average milk energy yield performance of the herds at the time during which the daughter observations were recorded. The sire estimates were used as observations in a genome-wide association analysis, using 1797 sires. Significant SNPs were confirmed in an independent validation set (500 sires of the same population). To separate GxE scaling and other GxE effects, the observations were log-transformed in some analyses. Results from the reaction norm model revealed GxE effects. Numerous significant SNPs were validated for both GP and ES. Many SNPs that affect GP also affect ES. We showed that ES of milk traits is a typical quantitative trait, genetically controlled by many genes with small effects and few genes with larger effect. A log-transformation of the observation resulted in a reduced number of validated SNPs for ES, pointing to genes that not only caused scaling GxE effects. The results will have implications for breeding for robustness in dairy cattle.

Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

“…Indeed, many trait-associated SNPs were also involved in G×E. This is discussed in detail in Streit et al (2013a). …”

“…In a recent study we conducted a single-marker mixed model GWAS in Holstein dairy cattle data (Streit et al, 2013a). In brief, there were around 2300 progeny-tested bulls available, which were genotyped with the bovine 50K BeadChip (Illumina Inc., San Diego, CA).…”

“…However, this equal performance of the models does not hold for their use in GWASs. We used the Holstein dairy cattle data set mentioned above (Streit et al, 2013a) to compare the models SNP-BLUP, BayesA and BayesC in a GWAS for milk protein yield. In the BayesA and BayesC models, t distributions with 4 degrees of freedom (df) were assumed.…”

“…Hence, it seems that dominance is an important source of genetic variation for some traits, and it seems appropriate to use this additional variation if the data structure permits it (i.e., genotypes and phenotypes are collected from the same individual). For example, the data set of Streit et al (2013a) used in the previous section does not allow for dominance effect estimation because daughter yield deviations were used.…”

“…In an earlier study, we used the two-step approach described above to map G×E SNPs in German Holsteins (Streit et al, 2013a). We used milk production test-day records of around 1.3 million daughters sired by 2300 sires with 12 million first lactation test records.…”