Machine Learning Department
School of Computer Science, Carnegie Mellon University
Artificial Selection Experiements for
Suyash Shringarpure, Eric Xing
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.
||SCS Technical Report Collection
School of Computer Science