Bayesian statistical methods in genetic association studies: Empirical examination of statistically non-significant Genome Wide Association Study (GWAS) meta-analyses in cancers: A systematic review

Jae Hyon Park, Dong Il Geum, Michael Eisenhut, Hans J. van der Vliet, Jaeil Shin

Research output: Contribution to journalArticle

Abstract

A Bayesian statistical method was developed to assess the noteworthiness of a single nucleotide polymorphism (SNP)-phenotype association that shows statistical significance in various observational studies, but it has seldom been applied to GWAS meta-analyses in cancers. Data (i.e. allelic frequency, odds ratio, 95% confidence interval, etc.) on various SNP-cancer associations were extracted from meta-analysis of GWAS and the National Human Genome Research Institute (NHGRI) Catalog of Published GWAS and were used to compute the false positive report probability (FPRP) and Bayesian false discovery probability (BFDP) to evaluate the noteworthiness of SNP-cancer associations. Independent paired t-tests showed a direct relationship between SNP-cancer P-values and both FPRP and BFDP estimates. However, a discrepancy in the number of noteworthy associations between P-value comparison and either FPRP or BFDP was found using data extracted from meta-analyses of GWAS and the GWAS Catalog. Most P-values of associations with nonsignificant P-values but with noteworthy FPRP and BFDP estimates were within the range of 10−6 to 5 × 10−8. A poorly selected genome-wide significance threshold and inclusion of a nonsignificant SNP-phenotype association into the noteworthy test can, with either noteworthy FPRP or BFDP computation, give a false impression of noteworthiness for a nonsignificant association.

Original languageEnglish
Pages (from-to)170-178
Number of pages9
JournalGene
Volume685
DOIs
Publication statusPublished - 2019 Feb 15

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Bayes Theorem
Genome-Wide Association Study
Genetic Association Studies
Meta-Analysis
Single Nucleotide Polymorphism
Neoplasms
National Human Genome Research Institute (U.S.)
Phenotype
Observational Studies
Odds Ratio
Genome
Confidence Intervals

All Science Journal Classification (ASJC) codes

  • Genetics

Cite this

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title = "Bayesian statistical methods in genetic association studies: Empirical examination of statistically non-significant Genome Wide Association Study (GWAS) meta-analyses in cancers: A systematic review",
abstract = "A Bayesian statistical method was developed to assess the noteworthiness of a single nucleotide polymorphism (SNP)-phenotype association that shows statistical significance in various observational studies, but it has seldom been applied to GWAS meta-analyses in cancers. Data (i.e. allelic frequency, odds ratio, 95{\%} confidence interval, etc.) on various SNP-cancer associations were extracted from meta-analysis of GWAS and the National Human Genome Research Institute (NHGRI) Catalog of Published GWAS and were used to compute the false positive report probability (FPRP) and Bayesian false discovery probability (BFDP) to evaluate the noteworthiness of SNP-cancer associations. Independent paired t-tests showed a direct relationship between SNP-cancer P-values and both FPRP and BFDP estimates. However, a discrepancy in the number of noteworthy associations between P-value comparison and either FPRP or BFDP was found using data extracted from meta-analyses of GWAS and the GWAS Catalog. Most P-values of associations with nonsignificant P-values but with noteworthy FPRP and BFDP estimates were within the range of 10−6 to 5 × 10−8. A poorly selected genome-wide significance threshold and inclusion of a nonsignificant SNP-phenotype association into the noteworthy test can, with either noteworthy FPRP or BFDP computation, give a false impression of noteworthiness for a nonsignificant association.",
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Bayesian statistical methods in genetic association studies : Empirical examination of statistically non-significant Genome Wide Association Study (GWAS) meta-analyses in cancers: A systematic review. / Park, Jae Hyon; Geum, Dong Il; Eisenhut, Michael; van der Vliet, Hans J.; Shin, Jaeil.

In: Gene, Vol. 685, 15.02.2019, p. 170-178.

Research output: Contribution to journalArticle

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