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A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.

ABSTRACT: There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 (ZBTB38), 6q25.3 (RGS17), 9p22.1 (HAUS6), 9p13.3 (UBAP2), 11p11.2 (RAPSN), 14q12 (AKAP6), 15q15 (KNL1) and 18q23 (ZNF236).

SUBMITTER: Ray D

PROVIDER: S-EPMC7748289 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.

Ray Debashree D Chatterjee Nilanjan N

PLoS genetics 20201208 12

There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this a ...[more]

PMID: 33290408

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Project description:AIMS:Clinical and epidemiological findings point to an association between type 2 diabetes (T2D) and osteoporosis. Genome-wide association studies (GWASs) have been fruitful in identifying some loci potentially associated with osteoporosis and T2D respectively. However, the total genetic variance for each of these two diseases and the shared genetic determination between them are largely unknown. The aim of this study was to identify novel genetic variants for osteoporosis and/or T2D. METHODS:First, using a pleiotropic conditional false discovery rate (cFDR) method, we analyzed two GWAS summary data of femoral neck bone mineral density (FN_BMD, n?=?53,236) and T2D (n?=?159,208) to identify novel shared genetic loci. FN_BMD is an important risk factor for osteoporosis. Next, to explore the potential functions of the identified potential pleiotropic genes, differential expression analysis was performed for them in monocytes and peripheral blood mononuclear cells (PBMCs) as these cells are relevant to the etiology of osteoporosis and/or T2D. Further, weighted gene co-expression analysis (WGCNA) was conducted to identify functional connections between novel pleiotropic genes and known osteoporosis/T2D susceptibility genes by using transcriptomic expression datasets in bone biopsies (E-MEXP-1618) and pancreatic islets (GSE50397). Finally, multi-trait fine mapping for the detected pleiotropic risk loci were conducted to identify the SNPs that have the highest probability of being causal for both FN_BMD and T2D. RESULTS:We identified 27 significant SNPs with cFDR<0.05 for FN_BMD and 61 SNPs for T2D respectively. Four loci, rs7068487 (PLEKHA1), rs10885421 (TCF7L2), rs944082 (GNG12-AS1 (WLS)) and rs2065929 (PIFO||PGCP1), were found to be potentially pleiotropic and shared between FN_BMD and T2D (ccFDR<0.05). PLEKHA1 was found differentially expressed in circulating monocytes between high and low BMD subjects, and PBMCs between diabetic and non-diabetic conditions. WGCNA showed that PLEKHA1 and TCF7L2 were interconnected with multiple osteoporosis and T2D associated genes in bone biopsy and pancreatic islets, such as JAG, EN1 and CPE. Fine mapping showed that rs11200594 was a potentially causal variant in the locus of PLEKHA1. rs11200594 is also an eQTL of PLEKHA1 in multiple tissue (e.g. peripheral blood cells, adipose and ovary) and is in strong LD with a number of functional variants. CONCLUSIONS:Four potential pleiotropic loci were identified for shared genetic determination of osteoporosis and T2D. Our study highlights PLEKHA1 as an important potentially pleiotropic gene. The findings may help us gain a better understanding of the shared genetic determination between these two important disorders.

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A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.

Publications

A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.

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