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Genome-wide Significance Thresholds for Admixture Mapping Studies.

Kelsey E Grinde1, Lisa A Brown2, Alexander P Reiner3

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.

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|February 19, 2019
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Summary

This study introduces a new method for admixture mapping studies to accurately control for multiple testing in diverse populations. The approach provides reliable genome-wide significance thresholds, improving genetic discovery in admixed groups.

Keywords:
family-wise error rategenetic admixturegenome-wide associationmultiple testingpopulation structure

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Area of Science:

  • Genetics
  • Population Genetics
  • Statistical Genetics

Background:

  • Admixture mapping studies are increasingly common due to technological advances and efforts to diversify genetic research.
  • Challenges remain in controlling for multiple testing, especially with population structure in admixed populations.

Purpose of the Study:

  • To develop a theoretical framework and analytical approach for characterizing correlations in local ancestry and admixture mapping test statistics.
  • To establish accurate genome-wide significance thresholds for admixture mapping studies in admixed populations.

Main Methods:

  • Developed a theoretical framework to analyze local ancestry correlations in admixed populations with multiple ancestral contributions and arbitrary structure.
  • Created an analytical approach to derive genome-wide significance thresholds for admixture mapping.
  • Validated the approach using simulated traits and real genotype data from the Women's Health Initiative (WHI) SHARe study.

Main Results:

  • Derived genome-wide significant p-value thresholds for admixture mapping in African American (2.1 × 10⁻⁵) and Hispanic/Latina (4.5 × 10⁻⁶) cohorts from the WHI SHARe data.
  • Demonstrated that the proposed method controls the family-wise error rate effectively, even in structured populations.
  • Showcased the method's speed and ease of implementation via a publicly available R package.

Conclusions:

  • The developed analytical approach provides accurate and reliable genome-wide significance thresholds for admixture mapping studies.
  • Significance thresholds are sample-specific, depending on factors like ancestral population number, generations since admixture, and population structure.
  • This method offers an efficient and robust alternative for multiple testing correction in genetic studies of admixed populations.