scholarly journals Power Comparison of Admixture Mapping and Direct Association Analysis in Genome-Wide Association Studies

2012 ◽  
Vol 36 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Huaizhen Qin ◽  
Xiaofeng Zhu
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Admixture Mapping ◽  
Genome Wide Association Studies ◽  
Power Comparison ◽  
Genome Wide ◽  
Direct Association

scholarly journals Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

Genetics ◽  
2019 ◽  
Vol 213 (4) ◽  
pp. 1225-1236 ◽  
Author(s):  
Weimiao Wu ◽  
Zhong Wang ◽  
Ke Xu ◽  
Xinyu Zhang ◽  
Amei Amei ◽  
...  
Keyword(s):  
Association Analysis ◽  
Binary Data ◽  
Association Studies ◽  
Association Test ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cocaine Use ◽  
Genome Wide ◽  
A Genome ◽  
Time Varying Covariates

Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

scholarly journals SNP Set Association Analysis for Genome-Wide Association Studies

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e62495 ◽  
Author(s):  
Min Cai ◽  
Hui Dai ◽  
Yongyong Qiu ◽  
Yang Zhao ◽  
Ruyang Zhang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Lina Cai ◽  
Eleanor Wheeler ◽  
Nicola D. Kerrison ◽  
Jian’an Luan ◽  
Panos Deloukas ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Association Analysis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Global Public Health ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
A Genome

AbstractType 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct project, centred in 8 countries in the European Prospective Investigations into Cancer and Nutrition study, is one of the largest prospective studies of T2D. Established as a nested case-cohort study to investigate the interplay between genetic and lifestyle behavioural factors on the risk of T2D, a total of 12,403 individuals were identified as incident T2D cases, and a representative sub-cohort of 16,154 individuals was selected from a larger cohort of 340,234 participants with a follow-up time of 3.99 million person-years. We describe the results from a genome-wide association analysis between more than 8.9 million SNPs and T2D risk among 22,326 individuals (9,978 cases and 12,348 non-cases) from the EPIC-InterAct study. The summary statistics to be shared provide a valuable resource to facilitate further investigations into the genetics of T2D.

scholarly journals Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

2019 ◽  
Author(s):  
Weimiao Wu ◽  
Zhong Wang ◽  
Ke Xu ◽  
Xinyu Zhang ◽  
Amei Amei ◽  
...  
Keyword(s):  
Association Analysis ◽  
Complex Traits ◽  
Binary Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cocaine Use ◽  
Genome Wide ◽  
A Genome ◽  
Time Varying Covariates

SUMMARYLongitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for the phenotype distribution due to ascertainment. Here, we propose L-BRAT, a retrospective, generalized estimating equations-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

scholarly journals A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry

2020 ◽  
Vol 4 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Zhaohui Du ◽  
Niels Weinhold ◽  
Gregory Chi Song ◽  
Kristin A. Rand ◽  
David J. Van Den Berg ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Association Studies ◽  
Meta Analysis ◽  
African Ancestry ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Admixture Mapping ◽  
Genome Wide Association Studies ◽  
Risk Alleles ◽  
Genome Wide

Abstract Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10−6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10−12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

scholarly journals Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Xiaobo Guo ◽  
Zhifa Liu ◽  
Xueqin Wang ◽  
Heping Zhang
Keyword(s):  
Association Analysis ◽  
Animal Studies ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Complex Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.

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