人类疾病与性状的遗传相关性图谱。
An atlas of genetic correlations across human diseases and traits.
作者信息
Bulik-Sullivan Brendan, Finucane Hilary K, Anttila Verneri, Gusev Alexander, Day Felix R, Loh Po-Ru, Duncan Laramie, Perry John R B, Patterson Nick, Robinson Elise B, Daly Mark J, Price Alkes L, Neale Benjamin M
机构信息
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
出版信息
Nat Genet. 2015 Nov;47(11):1236-41. doi: 10.1038/ng.3406. Epub 2015 Sep 28.
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.
识别复杂性状与疾病之间的遗传相关性可以提供有用的病因学见解，并有助于确定可能的因果关系的优先级。目前的方法在从全基因组关联研究（GWAS）数据估计遗传相关性时面临的主要挑战是缺乏个体水平的基因型数据，以及荟萃分析中广泛存在的样本重叠。我们通过引入一种技术——跨性状LD评分回归来规避这些困难，该技术用于估计遗传相关性，只需要GWAS汇总统计数据，且不受样本重叠的影响。我们使用这种方法来估计24个性状之间的276种遗传相关性。结果包括神经性厌食症与精神分裂症、厌食症与肥胖症以及受教育程度与几种疾病之间的遗传相关性。这些结果凸显了全基因组分析的强大作用，因为目前神经性厌食症没有显著相关的单核苷酸多态性（SNP），受教育程度也只有三个。
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