全基因组关联研究
遗传相关
生物
遗传关联
相关性
特质
单核苷酸多态性
学历
样本量测定
遗传学
神经性厌食
心理学
基因型
遗传变异
临床心理学
统计
饮食失调
计算机科学
基因
经济增长
数学
经济
程序设计语言
几何学
作者
Brendan Bulik-Sullivan,Hilary Finucane,Verneri Anttila,Alexander Gusev,Felix R. Day,Po−Ru Loh,Laramie Duncan,John R. B. Perry,Nick Patterson,Elise Robinson,Mark J. Daly,Alkes L. Price,Benjamin M. Neale
出处
期刊:Nature Genetics
[Springer Nature]
日期:2015-09-28
卷期号:47 (11): 1236-1241
被引量:3093
摘要
Brendan Bulik-Sullivan, Benjamin Neale, Hilary Finucane, Alkes Price and colleagues introduce a new technique for estimating genetic correlation that requires only genome-wide association summary statistics and that is not biased by sample overlap. Using this method, they find genetic correlations between anorexia nervosa and schizophrenia, and between educational attainment and autism spectrum disorder. Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
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