双变量因果混合模型量化了复杂特征之间的多基因重叠，超出了遗传相关性。
Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation.
机构信息
NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424, Oslo, Norway.
Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA, 92037, USA.
出版信息
Nat Commun. 2019 Jun 3;10(1):2417. doi: 10.1038/s41467-019-10310-0.
Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.
越来越多的全基因组关联研究（GWAS）证据表明，复杂人类特征和疾病（如精神障碍）之间存在大量共同的遗传影响。在这里，我们介绍了一种统计工具 MiXeR，它使用 GWAS 汇总统计数据来量化多基因重叠，而不受遗传相关性的影响。MiXeR 的结果以 Venn 图的形式呈现，显示了不同特征之间独特和共享的多基因成分。在每个表型解释 SNP 遗传力的 90%时，MiXeR 估计有 8300 个变体可导致精神分裂症，6400 个变体可导致双相情感障碍。在这些变体中，6200 个在这两种疾病之间共享，它们具有很高的遗传相关性。此外，MiXeR 揭示了精神分裂症和教育程度之间的多基因重叠。尽管遗传相关性接近零，但这两种表型共享 8300 个因果变体，而另外 2500 个变体仅影响教育程度。通过考虑复杂表型的多基因性、可发现性和遗传性，MiXeR 分析可能会提高我们对跨表型遗传结构的理解。
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