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Cited by 73Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

Reduction of selection bias in genomewide studies by resampling

Sun, Lei, Bull, Shelley B.·2005·Genetic Epidemiology
DOI 10.1002/gepi.20068
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Abstract

The accuracy of gene localization, the reliability of locus‐specific effect estimates, and the ability to replicate initial claims of linkage and/or association have emerged as major methodological concerns in genomewide studies of complex diseases and quantitative traits. To address the issue of multiple comparisons inherent in genomewide studies, the use of stringent criteria for assessing statistical significance has been generally acknowledged as a strategy to control type I error. However, the application of genomewide significance criteria does not take account of the selection bias introduced into parameter estimates, e.g., estimates of locus‐specific effect size of disease/trait loci. Some have argued that reliable locus‐specific parameter estimates can only be obtained in an independent sample. In this report, we examine statistical resampling techniques, including cross‐validation and the bootstrap, applied to the initial sample to improve the estimation of locus‐specific effects. We compare them with the naïve method in which all data are used for both hypothesis testing and parameter estimation, as well as with the split‐sample approach in which part of the data are reserved for estimation. Upward bias of the naïve estimator and inadequacy of the split‐sample approach are derived analytically under a simple quantitative trait model. Simulation studies of the resampling methods are performed for both the simple model and a more realistic genomewide linkage analysis. Our results suggest that cross‐validation and bootstrap methods can substantially reduce the estimation bias, especially when the effect size is small or there is no genetic effect. Genet. Epidemiol . © 2005 Wiley‐Liss, Inc.

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  • The smaller odds ratios estimated from the replication data could be explained by the winner's curse and shrinkage to the mean.

    “Moreover, the winner's curse and shrinkage to the mean (22,25,26) could explain the smaller odds ratios that we estimated from the replication data (Table 2).”

    From A genome-wide scan for common alleles affecting risk for autism (2010) · cites this paper as [26]

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Authors
Sun, L., & Bull, S. B.
Affiliations
  • University of Toronto (CA)
  • Mount Sinai Hospital (CA)
Corresponding author
Sun, Lei
ORCID
Venue
Genetic Epidemiology
Publisher
Wiley
Published
2005-03-10
Volume
28
Issue
4
Pages
352-367
ISSN
  • 0741-0395 (ISSN-L)
  • 1098-2272
DOI
10.1002/gepi.20068
OpenAlex
W2075120043
Cited by
73
References
16
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 1 · 2021: 3 · 2019: 3 · 2018: 2 · 2017: 5 · 2016: 4 · 2015: 6 · 2014: 1 · 2013: 2 · 2012: 2
FWCI
2.69
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Genetic Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Canadian Institutes of Health Research (64871_1, 55118_1)
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
selection bias, genome-wide association study, resampling, cross-validation, bootstrapping, multiple comparisons, type I error control, estimation bias, quantitative traits, complex diseases
MeSH terms
Algorithms, Chromosome Mapping, Computer Simulation, Genetic Markers, Humans, Genetic Linkage, Models, Genetic, Genome, Human, Selection Bias, Sample Size, Siblings

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