Wednesday, December 31, 2008
Tuesday, December 30, 2008
Patient Rule-Induction Method (PRIM)
Dyson et al. have a new version of their PRIM method that accounts for non-additive interactions. This paper will appear soon in Genetic Epidemiology.
Dyson G, Frikke-Schmidt R, Nordestgaard BG, Tybjærg-Hansen A, Sing CF. Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease. Genetic Epidemiology, in press (2009).
This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.
Monday, December 22, 2008
Dr. David Reif
Wednesday, December 17, 2008
Dr. Brett McKinney
Saturday, December 13, 2008
Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis
Here is an interesting new paper on epistasis.
Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis. Shao H, Burrage LC, Sinasac DS, Hill AE, Ernest SR, O'Brien W, Courtland HW, Jepsen KJ, Kirby A, Kulbokas EJ, Daly MJ, Broman KW, Lander ES, Nadeau JH. Proc Natl Acad Sci U S A. 2008 Dec 9. [Epub ahead of print] [PubMed]
The genetic architecture of complex traits underlying physiology and disease in most organisms remains elusive. We still know little about the number of genes that underlie these traits, the magnitude of their effects, or the extent to which they interact. Chromosome substitution strains (CSSs) enable statistically powerful studies based on testing engineered inbred strains that have single, unique, and nonoverlapping genetic differences, thereby providing measures of phenotypic effects that are attributable to individual chromosomes. Here, we report a study of phenotypic effects and gene interactions for 90 blood, bone, and metabolic traits in a mouse CSS panel and 54 traits in a rat CSS panel. Two key observations emerge about the genetic architecture of these traits. First, the traits tend to be highly polygenic: across the genome, many individual chromosome substitutions each had significant phenotypic effects and, within each of the chromosomes studied, multiple distinct loci were found. Second, strong epistasis was found among the individual chromosomes. Specifically, individual chromosome substitutions often conferred surprisingly large effects (often a substantial fraction of the entire phenotypic difference between the parental strains), with the result that the sum of these individual effects often dramatically exceeded the difference between the parental strains. We suggest that strong, pervasive epistasis may reflect the presence of several phenotypically-buffered physiological states. These results have implications for identification of complex trait genes, developmental and physiological studies of phenotypic variation, and opportunities to engineer phenotypic outcomes in complex biological systems.
Saturday, December 06, 2008
Pathway-Based Analysis of GWAS Data
Our paper using pathways to analyze and interpret GWAS data has been published by Human Genetics and is now available online. Dr. Kathleen Askland from Brown University used our EVA software to carry out this analysis and made some interesting discoveries.
Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Askland K, Read C, Moore JH. Hum Genet. in press (2009). [PubMed]
Despite known heritability, the complex genetic architecture of bipolar disorder (likely including trait, locus and allelic heterogeneity, as well as genetic interactions) has confounded genetic discovery for many years. Even modern day whole genome association studies (WGAS) using over half a million common SNPs have implicated only a handful of genes at the genomewide level. Temporally coincident with this series of WGAS, a host of pathways-based analyses (PBAs) have emerged as novel computational approaches in the examination of large-scale datasets, but thus far rarely have been applied to WGAS data in psychiatric disorders. Here, we report a series of PBAs conducted using exploratory visual analysis, an analytic and visualization software tool for examining genomic data, to examine results from the National Institutes of Mental Health and Wellcome-Trust Case Control Consortium WGAS in bipolar disorder. Consistent with a host of prior linkage findings, some candidate gene association studies, and recent WGAS, our strongest findings suggest involvement of ion channel structural and regulatory genes, including voltage-gated ion channels and the broader ion channel group that comprises both voltage- and ligand-gated channels. Moreover, we found only modest overlap in the particular genes driving the significance of these gene sets across the analyses. This observation strongly suggests that variation in ion channel genes, as a class of genes, may contribute to the susceptibility of bipolar disorder and that heterogeneity may figure prominently in the genetic architecture of this susceptibility.