Abstract
BackgroundPseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.ResultsWe propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.ConclusionsOur rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.
Highlights
Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usu‐ ally used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle
In genome-wide associate studies (GWAS) of milk production traits in dairy cattle, multiple phenotypic measurements for each individual are transformed into a single measure, such as, 305-day yield, an estimated breeding value (EBV) or a deregressed proof (DRP)
GWAS using raw phenotypes are considered as the “gold standard” [5], and pseudo-phenotypes can result in reduced power or even inflate false positive rates (FPR) for the detection of quantitative trait nucleotides (QTN) [6]
Summary
Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usu‐ ally used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. In genome-wide associate studies (GWAS) of milk production traits in dairy cattle, multiple phenotypic measurements for each individual are transformed into a single measure, such as, 305-day yield, an estimated breeding value (EBV) or a deregressed proof (DRP). These pseudo-phenotypes are used as response variables in the GWAS statistical model [2,3,4].
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