A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

Zhang, Jianjun; Xie, Sicong; Gonzales, Samantha; Liu, Jianguo; Wang, Xuexia

doi:10.1002/gepi.22297

Cited by 12 publications

(36 citation statements)

References 52 publications

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“…Among these genes, 59 RA-associated (e.g., CCBL2, SLC10A4, and PLEKHA1) and 19 SLE-associated genes (e.g., INPP5B, SKP1, and TMEM80) are not implied in the original GWASs of RA and SLE (Okada et al, 2014;Bentham et al, 2015), and are likely newly candidate associated genes for each disease. These findings also confirm that our multiple-tissue eQTL weighted integrative genebased association analysis has higher power compared to the conventional single SNP analysis, as shown in many prior studies (Gusev et al, 2016;Xu et al, 2017;Guo and Wu, 2018;Wu and Pan, 2018;Xue et al, 2020;Zhang et al, 2020).…”

Section: Associated Genes Identified By Cfdrsupporting

confidence: 88%

“…Unlike prior studies which explored genetic overlap at the independent SNP level by using a pruning procedure (Lv et al, 2017;Peng et al, 2017;Hu et al, 2018a,b), we attempted to study common genetic component between RA and SLE at the gene level because gene is a more meaningful biological unit related to complex diseases compared with SNP. To do so, we performed the multiple-tissue eQTL weighted integrative analysis for a set of cis-SNPs located within a gene and produced a single P-value for the evidence of the significance of that gene (Gusev et al, 2016;Xu et al, 2017;Guo and Wu, 2018;Wu and Pan, 2018;Xue et al, 2020;Zhang et al, 2020). Specifically, for each tissue in turn and a set of predefined cis-SNPs of a gene of focus, we have:…”

Section: Association Analysis By Integrating Eqtl and Gwas Summary Stmentioning

confidence: 99%

“…We estimated λ as the ratio between the observed median of χ 2 k and the expected value of 0.456 (Devlin and Roeder, 1999;Dadd et al, 2009;Zeng et al, 2015b). Then, the P-value was easily calculated as the corrected test statistics asymptotically follow a chi-squared distribution with one degree of freedom (Gusev et al, 2016;Xu et al, 2017;Guo and Wu, 2018;Wu and Pan, 2018;Xue et al, 2020;Zhang et al, 2020). Afterwards, we obtained a set of various P-values (P k , k = 1, 2, .…”

Section: Association Analysis By Integrating Eqtl and Gwas Summary Stmentioning

confidence: 99%

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Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Zhang

Jiang

et al. 2021

Front. Genet.

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BackgroundClinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.MethodsOur analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.ResultsWe found there existed a significant positive genetic correlation (rg = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.ConclusionThis study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

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Section: Associated Genes Identified By Cfdrsupporting

confidence: 88%

Section: Association Analysis By Integrating Eqtl and Gwas Summary Stmentioning

confidence: 99%

Section: Association Analysis By Integrating Eqtl and Gwas Summary Stmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Zhang

Jiang

et al. 2021

Front. Genet.

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“…In GWAS summary statistics, the Z-scores of genetic variants in a gene are assumed to asymptotically follow a multivariate normal distribution with a correlation matrix among all genetic variants in a gene under the null hypothesis 12 , where the correlation matrix can be estimated by LD among the genetic variants in the gene 13,14 . When individual-level data are not available, LD is usually estimated using external reference panels 15,16 (i.e., 1000 Genomes Project 17 ). Due to the small sample size of reference panels used to estimate LD, statistical noise (i.e., inflated type I error rates or large numbers of false positives) often exists which needs to be accounted for 18,19 .…”

Section: Introductionmentioning

confidence: 99%

“…As pointed out by Zhang et al 16 , PrediXcan and TWAS can be viewed as a simple weighted linear combination of genetic variants with an eQTL -derived weight. In fact, the genetic architecture of complex traits is rarely known in advance and is likely to vary from one region to another across the genome and from one trait to another 16 .…”

Section: Introductionmentioning

confidence: 99%

Gene-Based Association Tests Using GWAS Summary Statistics and Incorporating eQTL

Cao¹,

Wang²,

Zhang³

et al. 2021

Preprint

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Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by the genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose two powerful and computationally efficient gene-based association tests, Overall and Copula. These two tests aggregate information from three traditional types of gene-based association tests and also incorporate expression quantitative trait locus (eQTL) data into GWAS using GWAS summary statistics. Overall utilizes the extended Simes procedure and Copula utilizes the Gaussian copula approximation-based method. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the P values of these two methods can be calculated analytically. Simulation studies show that these two tests can control type I error rate very well and have higher power than the tests that we compared. We also apply these two methods to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that these two newly developed methods can identify more significant genes than other methods we compared with.

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Gene-based association tests using GWAS summary statistics and incorporating eQTL

Cao

Wang

Zhang

et al. 2022

Sci Rep

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Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

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A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

Cited by 12 publications

References 52 publications

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Gene-Based Association Tests Using GWAS Summary Statistics and Incorporating eQTL

Gene-based association tests using GWAS summary statistics and incorporating eQTL

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