文献类型： 外文期刊
作者： Zhang, Ying 1 ; Song, Yuxin 2 ; Gao, Jin 2 ; Zhang, Hengyu 3 ; Yang, Ning 4 ; Yang, Runqing 5 ;
作者机构： 1.Heilongjiang Bayi Agr Univ, Coll Anim Sci & Vet Med, Daqing, Peoples R China
2.Nanjing Agr Univ, Wuxi Fisheries Coll, Nanjing, Peoples R China
3.Heilongjiang Bayi Agr Univ, Dept Informat & Comp Sci, Daqing, Peoples R China
4.China Agr Univ, Coll Anim Sci & Technol, Beijing 100094, Peoples R China
5.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing 100141, Peoples R China
关键词： longitudinal data; genome wide association analysis; random regression model; hierarchical mixed model; computing efficiency
期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：11.622； 五年影响因子：10.288 ）
ISSN： 1467-5463
年卷期： 2021 年 22 卷 5 期
页码：
收录情况： SCI
摘要： A hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.
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