Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, München, Germany.
Genet Epidemiol. 2019 Jul;43(5):559-576. doi: 10.1002/gepi.22204. Epub 2019 Apr 23.
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 ~400,000 more variants with high imputation quality (mostly rare variants) compared to meta-imputation. For AMD signal detection (P < 5 × 10 ) 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.
虽然目前的全基因组关联分析通常依赖于研究特定汇总统计数据的荟萃分析，但来自多个研究的个体参与者数据 (IPD) 为建模提供了更多选择。然而，当有多研究 IPD 可用时，尚不清楚是否应在所有参与者之间（mega 导入和 mega 分析）或针对特定研究（meta 导入和 meta 分析）对这些数据进行推断和建模。在这里，我们使用来自 25 项国际年龄相关性黄斑变性 (AMD) 基因组学联盟研究的 52,189 名受试者的不同方法进行了推断和分析，包括 16,144 名 AMD 病例和 17,832 名对照用于关联分析。在经过 1,000 基因组基础导入后的 27,448,454 个遗传变异中，mega 导入产生了大约 400,000 个具有较高导入质量（主要是罕见变异）的变异，与 meta 导入相比。对于 mega 导入数据中的 AMD 信号检测（P < 5 × 10 ），大多数位置在不考虑研究成员身份的情况下通过 mega 分析检测到（40 个位置，包括 34 个已知位置）；我们认为这些位置是真实的，因为遗传效应和 P 值在分析之间是可比的。在 meta 导入数据中，我们发现了 31 个额外的信号，主要位于染色体尾部或参考面板间隙附近，在考虑全基因组扩增 (WGA) 与研究成员身份的相互作用或排除使用 WGA 参与者的研究后，这些信号消失了。对于多研究 IPD 的信号检测，我们建议使用 mega 导入和 mega 分析，meta 导入后进行 meta 分析是一种计算上吸引人的替代方法。
