A current mystery impacting genome-wide association (GWAS) studies is the problem of “missing heritability”, where the total variation explained by all of the candidate loci identified in GWAS analyses to date can explain only a small percentage of the heritability of complex phenotypes. There are many possible explanations for why heritability is “missing” (see the following review). One possibility is that there are a large number of genetic loci that contribute to trait heritability, each of which has an effect that is too small to be easily detected with typical GWAS studies. We are taking an experimental approach to assess this hypothesis and to develop methodologies for detecting candidate loci with effects small enough that they are “invisible” to a typical GWAS study.

The intuitive motivation behind this work is that loci affect a phenotype by altering a pathway or network. Loci may therefore have (relatively) large effects on aspects of critical pathways, while having a small effect on a downstream phenotype. We may therefore be able to discover such loci using a GWAS approach, if we analyze the right component of the pathway, i.e. if we analyze a trait affected by the locus, which is important for how the pathway produces variation in the downstream phenotype.

The model system we are using for this work is the wing of Drosophila melanogaster. While the causes of quantitative variation in wings are largely uncharacterized, there are well-studied pathways where experimental alteration has been demonstrated to alter the wings, without producing a dramatic mutant phenotype. We are using such pathways as a guide for our analyses. The entire project involves a GWAS analysis of both the wing and gene expression in wing pathways using lines of the Drosophila Genetic Reference Panel (DGRP) and the application of network modeling tools to establish connections between genetic loci, pathways, and the wing phenotype. This work therefore makes use of the methods we are developing for GWAS analysis, network discovery, and for medical genomics, providing an integrative foundation for our research.

Output of the automated system that we use to measure wing size (and cell number), developed by Dr. David Houle at Florida State University.