Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data

Abstract

ABSTRACT: Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50–100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Keywords

• imputation
• whole-genome sequence data
• longitudinal GWAS
• milk production traits
• Holstein cattle