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Optimizing genomic control in mixed model associations with binary diseases

文献类型： 外文期刊

作者： Song, Yuxin 1 ; Yang, Li'ang 2 ; Jiang, Li 3 ; Hao, Zhiyu 4 ; Yang, Runqing 3 ; Xu, Pao 1 ;

作者机构： 1.Nanjing Agr Univ, Wuxi Fisheries Coll, Wuxi 214081, Jiangsu, Peoples R China

2.Northeast Agr Univ, Coll Life Sci, Harbin, Peoples R China

3.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing 100141, Peoples R China

4.Heilongjiang Acad Agr Sci, Inst Anim Husb, Harbin, Peoples R China

关键词： binary disease; genomic control; generalized linear mixed model; joint association analysis; computational efficiency

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：13.994； 五年影响因子：12.784 ）

ISSN： 1467-5463

年卷期： 2022 年 23 卷 1 期

页码：

收录情况： SCI

摘要： Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. Further, joint association analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.

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