Identifying genetic variants that affect viability in large cohorts.

Area of Science:

• Human Evolutionary Genetics
• Population Genetics
• Genomic Epidemiology

Background:

• Direct measurement of evolutionary fitness is crucial for understanding human evolutionary genetics.
• Existing methods lack the ability to directly assess the influence of genetic variants on contemporary viability.
• Investigating genetic variants' effects on age-specific mortality and lifespan can reveal ongoing selection pressures.

Purpose of the Study:

• To develop and apply a novel method for assessing whether genetic variants currently influence individual viability.
• To identify specific genetic variants and sets of variants associated with age-specific mortality and lifespan.
• To explore the relationship between genetic variants influencing various traits and human longevity.

Main Methods:

• Developed a method to test allele frequency variation across ages, controlling for ancestry.
• Applied the method to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and UK Biobank participants.
• Analyzed associations between genetic variants affecting puberty timing, age at first birth, cholesterol, coronary artery disease (CAD) risk, body mass index (BMI), and asthma with lifespan.

Main Results:

• Identified a few common variants with significant effects on age-specific mortality, including APOE ε4 and variants near CHRNA3.
• Found that variants delaying puberty timing and age at first birth are associated with longer parental lifespan.
• Detected significant signals for variants influencing cholesterol, CAD risk, BMI, and asthma, with consistent effects across cohorts and ancestries, and notable sex differences.

Conclusions:

• The developed method provides a powerful tool for studying selection in contemporary human populations.
• Genetic variants influencing traits like puberty timing, reproduction, and disease risk are associated with lifespan, suggesting ongoing evolutionary selection.
• The findings highlight the potential of large-scale biomedical datasets for advancing our understanding of human evolution and genetic influences on health and aging.