Association Between Episodic Memory and Genetic Risk Factors for Alzheimer’s Disease in South Asians from the Longitudinal Aging Study in India–Diagnostic Assessment of Dementia (LASI‐DAD)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156473/3/jgs16735-sup-0001-supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156473/2/jgs16735_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156473/1/jgs16735.pd

An Empirical Comparison of Meta‐analysis and Mega‐analysis of Individual Participant Data for Identifying Gene‐Environment Interactions

For analysis of the main effects of SNPs, meta‐analysis of summary results from individual studies has been shown to provide comparable results as “mega‐analysis” that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene‐environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene‐environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P ‐values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega‐analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta‐analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta‐versus mega‐analyses for interactions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106866/1/gepi21800.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106866/2/gepi21800-sup-0001-SuppMat.pd

A meta-analysis of genome-wide linkage scans for hypertension: The National Heart, Lung and Blood Institute Family Blood Pressure Program

Background: Four multicenter Networks (GenNet, GENOA, HyperGEN, SAPPHIRe) form the National Heart, Lung and Blood Institute Family Blood Pressure Program (FBPP), to search for hypertension/blood pressure (BP) genes. The networks used different family designs and targeted multiple ethnic groups, using standardized protocols and definitions. Linkage genome scans were done on samples within each network (N = 6245 relatives). Methods: The evidence was synthesized using meta-analysis. Results: Combining ethnic groups, no region reached LOD \u3e2, but several small peaks were identified, including chromosome 2p where two other recent reports find hypertension linkage. Conclusions: No regions show uniformly large effects on BP/hypertension in all populations. © 2003 American Journal of Hypertension, Ltd

The impact of data quality on the identification of complex disease genes: Experience from the Family Blood Pressure Program

The application of genome-wide linkage scans to uncover susceptibility loci for complex diseases offers great promise for the risk assessment, treatment, and understanding of these diseases. However, for most published studies, linkage signals are typically modest and vary considerably from one study to another. The multicenter Family Blood Pressure Program has analyzed genome-wide linkage scans of over 12 000 individuals. Based on this experience, we developed a protocol for large linkage studies that reduces two sources of data error: pedigree structure and marker genotyping errors. We then used the linkage signals, before and after data cleaning, to illustrate the impact of missing and erroneous data. A comprehensive error-checking protocol is an important part of complex disease linkage studies and enhances gene mapping. The lack of significant and reproducible linkage findings across studies is, in part, due to data quality. © 2006 Nature Publishing Group All rights reserved

Testing cross‐phenotype effects of rare variants in longitudinal studies of complex traits

Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next‐generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross‐phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare‐variant approaches exist for testing cross‐phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross‐phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome‐wide scale due to the use of a closed‐form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144294/1/gepi22121_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144294/2/gepi22121.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144294/3/gepi22121-sup-0001-SuppMat.pd

Copy Number Variations Associated With Obesity‐Related Traits in African Americans: A Joint Analysis Between GENOA and HyperGEN

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95398/1/oby.2012.162.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/95398/2/oby_2790_sm_oby2012162_coi.pd

Insulin Resistance Exacerbates Genetic Predisposition to Nonalcoholic Fatty Liver Disease in Individuals Without Diabetes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/1/hep41353.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/2/hep41353_am.pd

Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP, taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value < 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations