Collins Conference Room
Guy Sella (Columbia University)
This event is by invitation only.


Much of the phenotypic variation in human populations, including variation in morphological, life history and biomedical traits, is “quantitative”, in the sense that heritable variation in the trait is largely due to small contributions from many genetic variants segregating in the population. Quantitative traits have been studied since the birth of biometrics over a century ago, but only recently have technological advances made it possible to systematically dissect their genetic basis. Notably, over the past decade, human genome-wide association studies (GWAS) have begun to reveal the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait.

To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multi-dimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWAS is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWAS for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than similarly-sized GWAS for BMI, and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWAS were performed, we further find that most of the associations they identified likely involve mutations that arose during the out of Africa bottleneck at sites with selection coefficients around s=10^{-3}.

Research Collaboration
SFI Host: 
Michael Lachmann