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

文献类型： 外文期刊

第一作者： Teng, Jun

作者： Teng, Jun;Wang, Dan;Zhao, Changheng;Zhang, Xinyi;Tang, Hui;Wang, Wenwen;Ning, Chao;Zhang, Qin;Chen, Zhi;Yang, Zhangping;Liu, Jianfeng;Sun, Dongxiao;Li, Jianbin;Mei, Cheng

作者机构：

关键词： imputation; whole-genome sequence data; longitudinal GWAS; milk production traits; Holstein cattle

期刊名称：JOURNAL OF DAIRY SCIENCE （ 影响因子：3.5； 五年影响因子：4.2 ）

ISSN： 0022-0302

年卷期： 2023 年 106 卷 4 期

页码：

收录情况： SCI

摘要： Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic val-ues 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, re-spectively. 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 candi-date 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 productiontraits, but also a general framework for longitudinal GWAS based on random regression test-day model us-ing WGS data.

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