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PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

ABSTRACT: Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

SUBMITTER: Lee CH

PROVIDER: S-EPMC7820744 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Lee Cue Hyunkyu CH Shi Huwenbo H Pasaniuc Bogdan B Eskin Eleazar E Han Buhm B

American journal of human genetics 20201221 1

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We p ...[more]

PMID: 33352115

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Project description:BackgroundRecent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.MethodsWe analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.ResultsWe confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.ConclusionsOur study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Project description:MotivationFine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.ResultsSimulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).Availability and implementationThe HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Dataset Information

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Publications

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

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