Machine Learning Department
School of Computer Science, Carnegie Mellon University


Artificial Selection Experiements for
Association in Model Organisms

Suyash Shringarpure, Eric Xing

August 2012


Keywords: Artificial selection, genetic association, sparse regression

Genetic association studies have been used to examine the genetic basis of many diseases. They have found genomic markers which contribute to risk for a number of diseases. However, genetic association studies have failed to explain the large genetic contribution to complex traits such as height.

In this report, we examine the feasiblity of using artificial selection experiments on model organisms (specifically, Drosophila melanogaster) to improve the performance of genetic association methods and understand the nature of genetic associations better. We use simulated artificial selection experiments on Drosophila melanogaster to generate genotype data and perform association using sparse regression methods. We demonstrate that this approach improves the accuracy of association methods at recovering causal polymorphisms for a range of allele frequencies and effect sizes.

24 pages

SCS Technical Report Collection
School of Computer Science