Using ancestry-informative markers to define populations and detect population stratification

Area of Science:

• Population Genetics
• Statistical Genomics
• Pharmacogenomics

Background:

• Case-control studies risk spurious associations due to population subdivision, admixture, and sampling variance.
• Ethnic differences in allele frequencies and ancestry fractions can confound genetic association studies.
• Drug response and metabolism can vary significantly across different ethnic groups, highlighting the need to address population stratification in pharmacogenomics.

Purpose of the Study:

• To address the critical issue of population stratification in genetic association studies and its implications for disease and drug response.
• To review and discuss statistical methods for detecting and correcting population stratification, including Structured Association and Genomic Control.
• To explore the optimal selection and quantity of informative marker loci for characterizing population substructure and admixture.

Main Methods:

• Utilizing genome-wide single nucleotide polymorphism (SNP) data, as provided by the International HapMap Project, to assess allele frequencies across diverse populations (Africans, Europeans, East Asians).
• Employing statistical approaches like Structured Association and Genomic Control, which use unlinked marker loci to estimate individual ancestry.
• Evaluating methods for selecting informative markers to accurately characterize population substructure and admixture levels.

Main Results:

• Population stratification can lead to false positive or false negative associations between genetic markers and phenotypes.
• Significant inter-ethnic variation exists in genetic makeup, with greatest SNP variation observed in African populations.
• Statistical methods are available to detect and adjust for population stratification, though their application in case-control studies remains limited.

Conclusions:

• Accurate assessment and correction of population stratification are crucial for reliable genetic association studies and personalized therapeutics.
• The choice and number of marker loci are critical for effectively characterizing diverse population structures and admixture.
• Further integration of population stratification testing into case-control association studies is recommended to improve the validity of genetic findings.