Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.
Genomic and evolutionary analyses of common disease in a large cohort
To use genomic approaches to identify risk factors for a set of common diseases of particular importance in development and aging. In particular, we will focus on metabolic phenotypes (disease like type 2 diabetes, and phenotypes like BMI), reproductive phenotypes (like number of children and age at menarche), and common causes of death (cancer, heart disease, and neurological disease).
While genome-wide association studies have identified thousands of genetic variants that contribute to hundreds of human diseases and traits, the mechanisms by which these variants influence traits remain unclear. Additionally, it is unknown if and how these variants influence overall survival at different ages, which in turn means we have little understanding of the evolutionary consequences of genetic variation.
We first aim to develop unbiased methods to identify genetic variants that influence multiple phenotypes.
|Lead investigator:||Professor Molly Przeworski|
|Lead institution:||Columbia University|