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

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).

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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

10.1101/2020.04.11.037630 ◽

2020 ◽

Author(s):

Debashree Ray ◽

Nilanjan Chatterjee

Keyword(s):

Prostate Cancer ◽

Type 2 Diabetes ◽

Null Hypothesis ◽

Null Distribution ◽

Mixture Distribution ◽

Type I ◽

Multiple Traits ◽

Test Statistic ◽

Major Power

SummaryThere 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 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 SNPs across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of SNPs 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 near ZBTB38 (3q23), RGS17 (6q25.3), HAUS6 (9p22.1), UBAP2 (9p13.3), RAPSN (11p11.2), AKAP6 (14q12), KNL1 (15q15) and ZNF236 (18q23).

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Association of genetic variants for plasma LRG1 with rapid decline in kidney function in patients with type 2 diabetes

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgab268 ◽

2021 ◽

Author(s):

Resham Lal Gurung ◽

Rajkumar Dorajoo ◽

M Yiamunaa ◽

Jian-Jun Liu ◽

Sharon Li Ting Pek ◽

...

Keyword(s):

Type 2 Diabetes ◽

Glomerular Filtration ◽

Kidney Function ◽

Genetic Variants ◽

Genome Wide Association Study ◽

Causal Effect ◽

Mendelian Randomisation ◽

Rapid Decline ◽

A Genome

Abstract Context Elevated levels of plasma Leucine Rich α-2-Glycoprotein 1 (LRG1), a component of TGF-ß signalling, are associated with development and progression of chronic kidney disease in patients with type 2 diabetes (T2D). However, whether this relationship is causal is uncertain. Objectives To identify genetic variants associated with plasma LRG1 levels and determine whether genetically predicted plasma LRG1 contributes to a rapid decline in kidney function (RDKF) in patients with T2D. Design and participants We performed a genome-wide association study (GWAS) of plasma LRG1 among 3,694 T2D individuals [1,881(983 Chinese, 420 Malay and 478 Indian) discovery from SMART2D cohort and 1,813 (Chinese) validation from DN cohort]. One- sample Mendelian randomization analysis was performed among 1,337 T2D Chinese participants with preserved glomerular filtration function (baseline estimated glomerular filtration rate (eGFR) >60ml/min/1.73m 2). RDKF was defined as an eGFR decline of 3 mL/min/1.73 m 2/year or greater. Results We identified rs4806985 variant near LRG1 locus robustly associated with plasma LRG1 levels (MetaP=6.66x10 -16). Among 1,337 participants, 344 (26%) developed RDKF and the rs4806985 variant was associated with higher odds of RDKF (meta odds ratio =1.23, P=0.030 adjusted for age and sex). Mendelian randomisation analysis provided evidence for a potential causal effect of plasma LRG1 on kidney function decline in T2D (P<0.05). Conclusion We demonstrate that genetically influenced plasma LRG1 increases the risk of RDKF in T2D patients suggesting plasma LRG1 as a potential treatment target. However, further studies are warranted to elucidate underlying pathways to provide insight into DKD prevention.

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Insulin Receptor Genetic Variants Linked to Type 2 Diabetes

Family Practice News ◽

10.1016/s0300-7073(11)70240-7 ◽

2011 ◽

Vol 41 (5) ◽

pp. 17

Author(s):

SHARON WORCESTER

Keyword(s):

Type 2 Diabetes ◽

Insulin Receptor ◽

Genetic Variants

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110-OR: Type 2 Diabetes (T2D)–Associated TCF7L2 Genetic Variants and Indices of Insulin Secretion and Sensitivity in Individuals at Risk for Type 1 Diabetes (T1D)

Diabetes ◽

10.2337/db20-110-or ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 110-OR

Author(s):

MARIA J. REDONDO ◽

MEGAN V. WARNOCK ◽

LAURA E. BOCCHINO ◽

SUSAN GEYER ◽

ALBERTO PUGLIESE ◽

...

Keyword(s):

Type 2 Diabetes ◽

At Risk ◽

Type 1 Diabetes ◽

Insulin Secretion ◽

Genetic Variants

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Established Type 2 DiabetesSusceptibility Genetic Variants in Saudi ethnicity: A Mini-Systematic Review

Journal of Biochemical and Clinical Genetics ◽

10.24911/jbcgenetics ◽

2018 ◽

pp. 57-65

Author(s):

Khalid Siddiqui ◽

Mohthash Musambil ◽

Adnan Usmani

Keyword(s):

Systematic Review ◽

Type 2 Diabetes ◽

Genetic Variants ◽

Diabetes Susceptibility

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Type 2 diabetes and the risk of mortality among patients with prostate cancer

Cancer Causes & Control ◽

10.1007/s10552-013-0334-6 ◽

2014 ◽

Vol 25 (3) ◽

pp. 329-338 ◽

Cited By ~ 35

Author(s):

Leah Bensimon ◽

Hui Yin ◽

Samy Suissa ◽

Michael N. Pollak ◽

Laurent Azoulay

Keyword(s):

Prostate Cancer ◽

Type 2 Diabetes ◽

Risk Of Mortality

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A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

International Journal of Epidemiology ◽

10.1093/ije/dyaa262 ◽

2021 ◽

Author(s):

Guanghao Qi ◽

Nilanjan Chatterjee

Keyword(s):

Type 2 Diabetes ◽

Mendelian Randomization ◽

Association Studies ◽

Real Data ◽

Causal Effects ◽

Type I ◽

Genome Wide Association Studies ◽

Simulation Studies ◽

Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

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