Abstract
While current genome-wide association analyses often rely on meta-analysis of study-specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega-imputation and mega-analysis) or study-specifically ...
Abstract
While current genome-wide association analyses often rely on meta-analysis of study-specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega-imputation and mega-analysis) or study-specifically (meta-imputation and meta-analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age-related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis. From 27,448,454 genetic variants after 1,000-Genomes-based imputation, mega-imputation yielded similar to 400,000 more variants with high imputation quality (mostly rare variants) compared to meta-imputation. For AMD signal detection (P < 5 x 10(-8)) in mega-imputed data, most loci were detected with mega-analysis without adjusting for study membership (40 loci, including 34 known); we considered these loci genuine, since genetic effects and P-values were comparable across analyses. In meta-imputed data, we found 31 additional signals, mostly near chromosome tails or reference panel gaps, which disappeared after accounting for interaction of whole-genome amplification (WGA) with study membership or after excluding studies with WGA-participants. For signal detection with multistudy IPD, we recommend mega-imputation and mega-analysis, with meta-imputation followed by meta-analysis being a computationally appealing alternative.
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