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Addressing population-specific multiple testing burdens in genetic association studies.

Rafal S Sobota1, Daniel Shriner, Nuri Kodaman

  • 1Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire.

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|February 4, 2015
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Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) require population-specific P-value thresholds due to varying independent single nucleotide polymorphism (SNP) counts. New thresholds improve gene discovery in diverse populations, reducing false negatives.

Keywords:
GWASGenome-wide thresholdHapMapautocorrelationlinkage disequilibrium

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Population Genetics

Background:

  • Genome-wide association studies (GWAS) rely on a universal P-value threshold for statistical significance.
  • The number of independent single nucleotide polymorphisms (SNPs) varies across human populations.
  • A single threshold is inappropriate, potentially leading to inaccurate results in diverse populations.

Purpose of the Study:

  • To estimate the number of independent SNPs in different populations.
  • To develop population-specific P-value thresholds for genome-wide significance.
  • To evaluate the impact of these new thresholds on gene discovery in GWAS.

Main Methods:

  • Estimated independent SNPs using PLINK's LD-pruning function and an autocorrelation-based approach.
  • Applied these methods to Phase 3 HapMap and 1000 Genomes whole genome sequence data.
  • Calculated population-specific significance thresholds and compared them to the conventional 5 × 10(-8) cutoff.

Main Results:

  • Consistent estimates of independent SNPs were obtained across methods and datasets.
  • African populations (e.g., YRI) had the most stringent thresholds, while East Asian populations (e.g., JPT) had the least stringent.
  • Applying population-specific thresholds to existing GWAS identified additional significant genes, particularly in a Chinese breast cancer study.

Conclusions:

  • The conventional genome-wide significance threshold leads to an excess of Type 2 errors (false negatives).
  • Population-specific thresholds are crucial for accurate gene discovery in GWAS.
  • This approach enhances the identification of relevant genes, especially in populations with recent founding events.