Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab426
Keywords
binary disease; genomic control; generalized linear mixed model; joint association analysis; computational efficiency
Funding
- National Key R&D Program of China [2018YFD0900201]
- Special Scientific Research Funds for Central Non-profit Institutes, Chinese Academy of Fishery Sciences [2019ZY09]
Ask authors/readers for more resources
In this study, we extended GRAMMAR to handle binary diseases and reduced polygenic effects by regulating genomic heritability, which simplified GLMM-based association analysis in large-scale data and improved the statistical power to detect QTNs.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available