Abstract:
:We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.
journal_name
Nat Genetjournal_title
Nature geneticsauthors
Zhang Y,Qi G,Park JH,Chatterjee Ndoi
10.1038/s41588-018-0193-xsubject
Has Abstractpub_date
2018-09-01 00:00:00pages
1318-1326issue
9eissn
1061-4036issn
1546-1718pii
10.1038/s41588-018-0193-xjournal_volume
50pub_type
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