多效性
生物
表型
虚假关系
计算生物学
全基因组关联研究
遗传关联
数量性状位点
遗传学
进化生物学
基因
单核苷酸多态性
基因型
计算机科学
机器学习
作者
Nadia Solovieff,Chris Cotsapas,Phil H. Lee,Shaun M. Purcell,Jordan W. Smoller
摘要
Modern genomic studies are revealing widespread associations between single genetic variants and multiple distinct traits, including diseases. This Review discusses the biological underpinnings of such pleiotropy and the available bioinformatic tools for the detection and characterization of these effects, as well as the implications for understanding human disease. Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
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