Now that many Mendelian disease alleles have been characterized, genetics research is focusing on the small-effect variants underlying complex diseases. While traditional linkage and association studies have enjoyed some success in identifying complex disease genes, many have failed due to 1) poorly defined phenotypes, 2) inadequate marker spacing, and 3) too few samples.

The first half of this talk describes statistical methdology and MicroMerge software for facilitating association analysis of microsatellite data sets that have been genotyped by different laboratories or protocols. MicroMerge implements MCMC sampling from a Bayesian model to find the optimal alignment between data sets. The merged data helps achieve the large sample size crucial for complex disease association analysis.

Since traditional genetic analysis methods were designed for finding a single large-effect gene, complex disease genetics may benefit from systems biology methods that study multiple genes and data types simultaneously. The second part of this talk describes an integrated analysis of microarray, genetic marker and phenotype data to find genes associated with chronic fatigue syndrome. We clustered genes with similar expression patterns and related these clusters or `modules' to chronic fatigue severity. The module most associated with chronic fatigue severity was then correlated with genetic marker data to identify genes that were related to a candidate locus for chronic fatigue syndrome. Our novel gene screening strategy identified 15 genes, but FOXN1, PRDX3 and SUCLA2 were particularly promising candidates due to their known involvement in the neurological and immune systems.



Seminar Date:
April 11, 2007