Evolutionary triangulation: informing genetic association studies with evolutionary evidence.

Area of Science:

• Human genetics
• Population genetics
• Evolutionary biology

Background:

• Genetic studies often rely solely on statistical association, overlooking evolutionary factors shaping disease risk.
• Allele frequencies vary across populations due to migration and environmental adaptation, influencing disease prevalence.

Purpose of the Study:

• To introduce and validate Evolutionary Triangulation (ET) as a novel method for identifying disease-associated genetic variants.
• To leverage population structure and disease prevalence patterns to enhance genetic association studies.

Main Methods:

• ET compares population structures across three populations with distinct disease prevalence patterns.
• Analysis focused on phenotypes like lactase persistence, melanoma, and Type 2 diabetes mellitus.
• ET's performance was compared against the population branch statistic (PBS) method.

Main Results:

• ET successfully identified the lactase gene for lactase persistence.
• For melanoma, ET pinpointed genes related to the disease and skin pigmentation.
• ET showed promise for Type 2 diabetes mellitus, potentially revealing novel risk loci.

Conclusions:

• Evolutionary Triangulation (ET) offers a powerful approach to filter genetic association results.
• ET enhances the ability to discover disease-associated genetic loci by integrating evolutionary and population data.