TY - JOUR

T1 - sumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics

AU - Belonogova, Nadezhda M.

AU - Svishcheva, Gulnara R.

AU - Kirichenko, Anatoly V.

AU - Zorkoltseva, Irina V.

AU - Tsepilov, Yakov A.

AU - Axenovich, Tatiana I.

N1 - Funding Information: NMB GRS AVK IVZ YAT TIA received the funding from a budget project of the Institute of Cytology and Genetics (project number FWNR-2022-0020). NMB GRS AVK IVZ TIA received funding from the Russian Foundation for Basic Research (20-04-00464, https://www.rfbr.ru) YAT received the funding from the program "5-100 Best Universities" of the Ministry of Science and Higher Education of the Russian Federation (https://www. 5top100.ru/) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The study was conducted using the UK Biobank resource under application #59345. Publisher Copyright: Copyright: © 2022 Belonogova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2022/6

Y1 - 2022/6

N2 - Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.

AB - Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.

KW - Genetic Association Studies

KW - Genome-Wide Association Study/methods

KW - Phenotype

KW - Polymorphism, Single Nucleotide/genetics

KW - Quantitative Trait Loci

UR - http://www.scopus.com/inward/record.url?scp=85131817301&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/4b339d0d-4c0d-3ab2-a606-6fe9b5a9dc14/

U2 - 10.1371/journal.pcbi.1010172

DO - 10.1371/journal.pcbi.1010172

M3 - Article

C2 - 35653402

AN - SCOPUS:85131817301

VL - 18

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 6

M1 - e1010172

ER -