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Design and analysis of admixture mapping studies.

C J Hoggart1, M D Shriver, R A Kittles

  • 1Noncommunicable Disease Epidemiology Unit, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom. clive.hoggart@lshtm.ac.uk

American Journal of Human Genetics
|April 17, 2004
PubMed
Summary

Population admixture studies effectively identify disease susceptibility loci by analyzing genetic differences between continental groups. Focusing on affected individuals offers high statistical power for detecting rare disease risk alleles and improving genetic mapping resolution.

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Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Genomic Medicine

Background:

  • Intercontinental population admixture provides a powerful tool for mapping disease susceptibility loci.
  • Differential distribution of risk alleles between ancestral populations can be leveraged for genetic discovery.

Purpose of the Study:

  • To evaluate the statistical power and mapping resolution of admixture mapping for disease susceptibility loci.
  • To assess the efficiency of different study designs, particularly affected-only studies, in admixed populations.

Main Methods:

  • Examined statistical power and mapping resolution under ideal conditions (no uncertainty in admixture and ancestry.
  • Simulated studies in a typical African American population to estimate power and resolution for detecting risk loci.

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  • Utilized Bayesian methods (ADMIXMAP) to infer locus ancestry from marker data and developed strategies for estimating ancestry-specific allele frequencies.

    • Main Results:

    • An affected-only study design is most efficient for rare diseases, with 800 individuals providing 90% power to detect loci with a risk ratio of 2.
    • Expected mapping resolution is approximately 4 cM in a typical African American population.
    • Combining data from unadmixed and admixed populations improves estimation of ancestry-specific allele frequencies.
    • Moderate marker density for initial genome search followed by saturation improves information extraction.

    Conclusions:

    • Admixture mapping is a powerful approach for identifying disease susceptibility loci in admixed populations.
    • Affected-only designs are statistically efficient for rare diseases.
    • Practical implementation requires robust methods for inferring locus ancestry and estimating allele frequencies, with strategies for optimizing marker selection.