Powerful, Scalable and Resource-Efficient Rare Variant Meta-Analysis of Whole-Genome Sequencing Studies Using Summary Statistics and Functional Annotations

• Activity Number: 230 - Recent Advances in Statistical Methods for Omics Data
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Xihao Li* and Jerome I. Rotter and Pradeep Natarajan and Gina M. Peloso and Zilin Li and Xihong Lin
• Companies: Harvard T.H. Chan School of Public Health and The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center and Massachusetts General Hospital and Boston University School of Public Health and Harvard T.H. Chan School of Public Health and Harvard University
• Keywords: Whole-Genome Sequencing; Whole-Exome Sequencing; Rare Variant; Meta-Analysis; Summary Statistics; Functional Annotation

Abstract

Large-scale whole-genome/exome sequencing (WGS/WES) studies have enabled the analysis of rare variants (RVs) associated with complex human traits and diseases. Existing RV meta-analysis approaches are not scalable when applied to WGS/WES data. We propose MetaSTAAR, a powerful and resource-efficient RV meta-analysis framework, for large-scale WGS association studies. MetaSTAAR accounts for population structure and relatedness for both continuous and dichotomous traits. By storing LD information of RVs in a new sparse matrix format, the proposed framework is highly storage efficient and computationally scalable for analyzing large-scale WGS/WES data without information loss. Furthermore, MetaSTAAR dynamically incorporates multiple functional annotations to empower RV association analysis, and enables conditional analyses to identify RV-set signals independent of nearby common variants. We applied MetaSTAAR to identify RV-sets associated with four quantitative lipid traits in 30,138 related samples from the NHLBI TOPMed Program Freeze 5 data, consisting of 14 ancestrally diverse studies and 255 million variants in total, as well as the UK Biobank WES data of ~200,000 related samples.