Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes
Year: 2018
Type: review
Abstract: Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.
Source: PLoS Genetics
Authors: Yu Jiang, Sai Chen, Daniel McGuire, Fang Chen, Mengzhen Liu, William G. Iacono, John K. Hewitt, John E. Hokanson, Kenneth Krauter, Markku Laakso, Kevin W. Li, Sharon M. Lutz, Matthew McGue, Anita Pandit, Gregory J. M. Zajac, Michael Boehnke, Goncalo R. Abecasis, Scott I. Vrieze, Xiaowei Zhan, Bibo Jiang, Dajiang J. Liu
Institutions: Pennsylvania State University, University of Michigan, University of Minnesota, University of Colorado Boulder, Colorado School of Public Health, University of Colorado Anschutz, University of Eastern Finland, Kuopio University Hospital, The University of Texas Southwestern Medical Center
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: Pfizer, AstraZeneca, GlaxoSmithKline, COPD Foundation, Sunovion, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases
Awards: Method to Enhance the Rare Variant Genetic Architecture Analysis in Meta-Analyses, G1356451498, Tools for integrative genomics and disease association study for the X chromosome, G2260741178, G2417563097, Genetic Association Meta-Analyses of Smoking and Drinking for the Sequencing Age, Genetic Determinants of COPD in Diverse Ethnic Groups, Center on Antisocial Drug Dependence: The Genetics of HIV Risk Behaviors, G4606287987, Common Genetic Pathways Underlying CVD and COPD, Design and Analysis of Human Gene Mapping Studies, G5258357286, Methods to Unveil the Genetic Architecture for Nicotine Dependence via NGS data, (1 of 2) Genetic Epidemiology of COPD, (2 of 2) Genetic Epidemiology of COPD, (1 of 2) Genetic Epidemiology of COPD, Twin Family Study of Vulnerability to Substance Abuse, Adult consequences of youth substance use: Twin study enriched for SUD risk, Twin Family Study of Vulnerability to Substance Abuse, G820522361, G8354759825, G8643874822, G8819318481, University of Michigan Training Program in Genomic Science, Michigan Diabetes Research Center (MDRC), G993448119
OpenAlex ID: W2884743956