Proper Conditional Analysis in the Presence of Missing Data Identified Novel Independently Associated Low Frequency Variants in Nicotine Dependence Genes

• Activity Number: 489 - Methods Development for Mediation and Interaction in Post-GWAS Data
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Yu Jiang* and Dajiang Liu
• Companies: and Penn State College of Medicine
• Keywords: Meta-analysis; sequence data; rare variant; missing data; imputation

Abstract

Existing methods of meta-analysis of GWASs are mostly developed for datasets without missing values. In practice, genotype imputation is not always effective. Therefore, contributed summary statistics often contain missing values. Naïve methods that either replace missing summary statistics with 0 or discard studies with missing data can bias genetic effect estimates and lead to seriously inflated type-I or II errors in conditional analysis. We developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when they contain large amount of missing values. Based on this estimator, we propose a score statistic we call 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 better calibrated type-I errors and is more powerful than Gaussian imputation of summary statistics, and synthesis analysis of regression coefficients. We applied this approach to analyze the CHRNA5-CHRNB4-CHRNA3 locus identified three novel variants, independent of known association signals.