Optimizing genomic control in mixed model associations with binary diseases

文献类型： 外文期刊

第一作者： Song, Yuxin

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

作者机构：

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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序