Dataset Information

Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics.

ABSTRACT: Methods that estimate SNP-based heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared toward analyzing quantitative traits. Here we investigate the validity of three common methods for estimating SNP-based heritability and genetic correlation between diseases. We find that the phenotype-correlation-genotype-correlation (PCGC) approach is the only method that can estimate both quantities accurately in the presence of important non-genetic risk factors, such as age and sex. We extend PCGC to work with arbitrary genetic architectures and with summary statistics that take the case-control sampling into account, and we demonstrate that our new method, PCGC-s, accurately estimates both SNP-based heritability and genetic correlations and can be applied to large datasets without requiring individual-level genotypic or phenotypic information. Finally, we use PCGC-s to estimate the genetic correlation between schizophrenia and bipolar disorder and demonstrate that previous estimates are biased, partially due to incorrect handling of sex as a strong risk factor.

SUBMITTER: Weissbrod O

PROVIDER: S-EPMC6035374 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics.

Weissbrod Omer O Flint Jonathan J Rosset Saharon S

American journal of human genetics 20180701 1

Methods that estimate SNP-based heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared toward analyzing quantitative traits. Here we investigate the validity of three common methods for estimating SNP-based heritability and genetic correl ...[more]

PMID: 29979983

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Project description:BackgroundColocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics.ResultsWe evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets.ConclusionsWe find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

Dataset Information

Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics.

Publications

Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics.

Similar Datasets

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