Statistical Methods for Modeling Heterogeneous Effects in Genetic Association Studies

Effect-size heterogeneity is a commonly observed phenomenon when aggregating studies from different ancestries to conduct trans-ethnic meta-analysis. Irrespective of the sources of heterogeneity, traditional meta-analysis approaches cannot appropriately account for the expected between-study heterogeneity. Therefore, to bridge the methodological gap, in the first two projects, I develop statistical methods for modeling the heterogeneous effects in trans-ethnic meta-analysis for genome-wide association studies (GWAS). In the third project, I extend the methods in trans-ethnic GWAS meta-analysis to a general statistical framework for modeling heterogeneity in biomedical studies. In the first project, I develop a score test for the common variant GWAS trans-ethnic meta-analysis. To account for the expected genetic effect heterogeneity across diverse populations, I adopt a modified random effects model from the kernel regression framework, and use the adaptive variance component test to achieve robust power regardless of the degree of genetic effect heterogeneity. From extensive simulation studies, I demonstrate that the proposed method has well-calibrated type I error rates at very stringent significance levels and can improve power over traditional meta-analysis methods. In the second project, I extend the common variant meta-analysis approach to the gene-based rare variant trans-ethnic meta-analysis. I develop a unified score test which is capable of incorporating different levels of heterogeneous genetic effects across multiple ancestry groups. I employ a resampling-based copula method to estimate the asymptotic distribution of the proposed test, which enables efficient estimation of p-values. I conduct simulation studies to demonstrate that the proposed approach is well-calibrated at stringent significance levels and improves power over current approaches under the existence of genetic effect heterogeneity. As a real data application, I further apply the proposed method to the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) consortia data to explore rare variant associations with several traits. In the third project, I develop a supremum score test for jointly testing the fixed and random effects in a generalized linear mixed model (GLMM). The joint testing framework has many applications in biomedical studies. One example is to use such tests for ascertaining associations under the existence of heterogeneity in GWAS meta-analysis; another example is the nonparametric test of spline curves. The supremum score test first re-parameterizes the fixed effects terms as a product of a scale parameter and a vector of nuisance parameters. With such re-parameterization, the joint test is equivalent to testing whether the scale parameter is zero. Since the nuisance parameters are unidentifiable under the null hypothesis, I propose using the supremum of score test statistics over the nuisance parameters. I employ a resampling-based copula method to approximate the asymptotic null distribution of the proposed score test statistic. I first investigate the performance of the method through simulation studies. Using the Michigan Genomics Initiative (MGI) data, I then demonstrate its application by assessing whether the genetics effects to Low Density Lipoprotein Cholesterol (LDL-C) can be modified by age.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146029/1/shijingc_1.pd

A novel random effect model for GWAS meta‐analysis and its application to trans‐ethnic meta‐analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134177/1/biom12481-sup-0001-SuppData.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134177/2/biom12481_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134177/3/biom12481.pd

Clustering Survival Outcomes using Dirichlet Process Mixture

Motivated by the national evaluation of mortality rates at kidney transplant centers in the United States, we sought to assess transplant center long- term survival outcomes by applying a methodology developed in Bayesian non-parametrics literature. We described a Dirichlet process model and a Dirichlet process mixture model with a Half-Cauchy for the estimation of the risk- adjusted effects of the transplant centers. To improve the model performance and interpretability, we centered the Dirichlet process. We also proposed strategies to increase model\u27s classification ability. Finally we derived statistical measures and created graphical tools to rate transplant centers and identify outlying centers with exceptionally good or poor performance. The proposed method was evaluated through simulation, and then applied to assess kidney transplant centers from a national organ failure registry

A Dirichlet process mixture model for survival outcome data: assessing nationwide kidney transplant centers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110837/1/sim6438.pd

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders.

Irritable bowel syndrome (IBS) results from disordered brain-gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain-gut interactions underlying IBS

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury(1-4). These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries(5). Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.Peer reviewe

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders

Funder: Kennedy Trust Rheumatology Research Prize StudentshipFunder: DFG Cluster of Excellence “Precision Medicine in Chronic In-flammation” (PMI; ID: EXC2167)Funder: EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: “Ideas” Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011199; Grant(s): 715772Funder: NWO-VIDI grant 016.178.056, the Netherlands Heart Foundation CVON grant 2018-27, and NWO Gravitation grant ExposomeNLFunder: Li Ka Shing Foundation (Li Ka Shing Foundation Limited); doi: https://doi.org/10.13039/100007421Abstract: Irritable bowel syndrome (IBS) results from disordered brain–gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain–gut interactions underlying IBS

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders

Irritable bowel syndrome (IBS) results from disordered brain-gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain-gut interactions underlying IBS