Center for Computational Molecular Biology Seminar
Computational Biology, Foster City, California | |
Abstract: Most human variation is selectively neutral, but a large number of both rare and common allelic variants are associated with human disease. Predicting which allelic variants may be causative for disease is an open problem. Many diseases, both Mendelian and complex, have been associated with single-nucleotide changes (SNPs) that lead to an amino acid substitution in the encoded protein (nonsynonymous SNPs, or nsSNPs). Because of ascertainment bias, nsSNPs may not necessarily be the dominant cause of human disease. Nevertheless, nsSNPs provide an excellent testing ground for using evolutionary analysis to predict the functional effects of genetic variation, as computational methods for inferring selective pressure in protein-coding sequences are well-established.
We have applied models of both negative and positive selection. One signature of negative selection is that in groups of related protein sequences, many positions in the protein are "conserved"; for instance, all serine proteases must possess the catalytic serine residue. To quantify this negative selection, we developed a "substitution position-specific evolutionary conservation" (subPSEC) score. We then analyzed a large number of nsSNPs from a number of data sets: "normal" variation, Mendelian disease associated mutations and complex disease associated variation. We find that while Mendelian disease-associated nsSNPs tend to occur at highly conserved positions in proteins, complex disease nsSNPs do not. In contrast, applying a method for estimating positive selection, we show that genes involved in complex disease tend to have relatively large Ka/Ks ratios between human and mouse orthologs, suggesting that measures of recent positive selection may be useful in identifying complex disease-associated genetic variation.
In collaboration with Dr. M.R. Hayden and colleagues at UBC we have experimentally and computationally characterized amino acid substitutions in one disease-associated gene, ABCA1, to assess evolutionary prediction methods in detail. The ABCA1 transporter has been implicated in both Mendelian and complex disease. We find that evolutionary conservation is, in most cases, an excellent predictor of functional importance of an amino acid in ABCA1. However, we also find that measures of positive selection are critical for predicting some of the mutational effects.
<--- 2005 Index