Dissecting the genetics of complex traits using summary association statistics

By Bogdan Pasaniuc, Alkes L Price. Nature reviews genetics, 2016

418 citations recorded by the source corpus.

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d499c8f5f947fdf097e4ba7364b9a3ab8060d823

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Citations in the corpus

Multi-trait analysis of genome-wide association summary statistics using MTAG (2018, neutral): “As in the calculation of the GWAS-equivalent sample size, where we assume that M e is the same in every GWAS sample and in the prediction sample, the expected predictive power of a polygenic score is where h 2 is the SNP heritability of the trait 28–30 .”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, background): “Many studies provide b̂ and ŝ (see [22] for examples), and many more provide the z-scores, or data that can be used to compute the z-scores (e.”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “1–5 and Supplementary Tables 4–6).”
Polygenic scores via penalized regression on summary statistics (2016, methodology): “As we can obtain estimates of r from summary statistics databases that are publicly available for major diseases/phenotypes (see e.g. the list from Pasaniuc and Price, 2016) and LD hub ( http://ldsc.broadinstitute.org/ ), and estimates of LD ( R ) from publicly available genotype such as the 1000…”