A Fast and Accurate Method for Genome-Wide Scale Phenome-Wide G × E Analysis and Its Application to UK Biobank

Details

• Personal Author:
  Bi W ; Dey R ; Fritsche LG ; Lee, Stephen A. ; Mukherjee B ; Zhao Z
• Description:
  The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios. [Description provided by NIOSH]
• Subjects:
  Environment Genetics Occupational Health Safety
• Keywords:
  Author Keywords: UK Biobank Biobank Data Analysis Diseases Environmental Factors Etiology Gene-environment Interaction Genes Genomics Models Saddlepoint Approximation Sampling Unbalanced Case-controlratios

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• ISSN:
  0002-9297
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Massachusetts ; Michigan ; OSHA Region 1 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  105
• Issue:
  6
• NIOSHTIC Number:
  nn:20059158
• Citation:
  Am J Hum Genet 2019 Dec; 105(6):1182-1192
• Contact Point Address:
  Seunggeun Lee, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109
• Email:
  leeshawn@umich.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  American Journal of Human Genetics
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d2626441d08868f76a6036fb3b791c4933f18e03bd154a1eeb8316f5311e08a20fd35a52e3733a5b6aca60cf15d92909d79387ad8094edd360707c89c7ead025
• Download URL:
  https://stacks.cdc.gov/view/cdc/223461/cdc_223461_DS1.pdf
• File Type:
  [PDF - 1.34 MB ]