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A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.

Authors
  • Jiang, Lai1
  • Xu, Shujing1
  • Mancuso, Nicholas1, 2, 3
  • Newcombe, Paul J4
  • Conti, David V1, 2, 3
  • 1 Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.
  • 2 Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California.
  • 3 Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California.
  • 4 MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom. , (United Kingdom)
Type
Published Article
Journal
American journal of epidemiology
Publication Date
Jan 06, 2021
Identifiers
DOI: 10.1093/aje/kwaa287
PMID: 33404048
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of estimates from association analyses of single nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a two-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. We propose to extend our previous approach for the joint analysis of marginal summary statistics to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate estimates as prior information yields an analysis similar to Mendelian Randomization (MR) and TWAS approaches. hJAM is applicable to multiple correlated SNPs and intermediates to yield conditional estimates for the intermediates on the outcome, thus providing advantages over alternative approaches. We investigate the performance of hJAM in comparison to existing MR and TWAS approaches and demonstrate that hJAM yields an unbiased estimate, maintains correct type-I error and has increased power across extensive simulations. We apply hJAM to two examples: estimating the causal effects of body mass index (GIANT consortium) and type 2 diabetes (DIAGRAM, GERA, and UKB) on myocardial infarction (UK Biobank) and estimating the causal effects of the expressions of gene NUCKS1 and PM20D1 on the risk of prostate cancer (PRACTICAL and GTEx). © The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected]

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