Translational Psychiatry has accepted for publication our paper "Genetic Risk Factors in two Utah Pedigrees at High Risk for Suicide." First author Hilary Coon, Ph.D. will be presenting this paper as a poster at ASHG 2013 in Boston.
We used unique population-based data resources to identify 22 high-risk extended pedigrees that show clustering of suicide twice to three times that expected from age-sex-cohort adjusted incidence rates that also incorporate pedigree structure. We have studied genetic risk factors in 5 of these large pedigrees each with 17-51 related suicide decedents, 5-9 of which have previously-collected DNA. These decedents were genotyped with the Illumina HumanExome BeadChip. Genotypes were analyzed using the Variant Annotation, Analysis, and Search (VAAST) program package that computes likelihoods of risk variants using the functional impact of the DNA variation, aggregative scoring of multiple variants across each gene, and pedigree structure. We prioritized variants that were: 1) shared across pedigree members, 2) rare in publicly-available sequence data from 1,358 controls, and 3) screened against 258 other Utah suicides not in the pedigrees to eliminate potential false positives. Sequence variants were prioritized statistically and then implicated genes were screened for previous disease associations and functional relevance. Findings included membrane protein genes and several genes with neuronal involvement and/or known associations with psychiatric conditions. Genes implicated in particular pedigrees may be associated with significant co-morbid psychiatric or medical conditions and/or demographic attributes unique to that pedigree. While the study is limited to variants included on the HumanExome BeadChip, these findings warrant further exploration, and demonstrate the utility of this high-risk pedigree resource.
We recently had a paper accepted for publication by and presentation at the Pacific Symposium on Biocomputing, as hosted by Stanford University. The accepted paper and resulting talk is entitled "Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data," and here is a copy of the abstract:
The two-hit model of carcinogenesis provides a valuable framework for understanding the role of DNA repair and tumor suppressor genes in cancer development and progression. Under this model, tumor development can initiate from a single somatic mutation in individuals that inherit an inactivating germline variant. Although the two-hit model can be an overgeneralization, the tendency for the pattern of somatic mutations to differ in cancer patients that inherit predisposition alleles is a signal that can be used to identify and validate germline susceptibility variants. Here, we present the Somatic-Germline Interaction (SGI) tool, which is designed to identify statistical interaction between germline variants and somatic mutational events from next-generation sequence data. SGI interfaces with rare-variant association tests and variant classifiers to identify candidate germline susceptibility variants from case-control sequencing data. SGI then analyzes tumor-normal pair next-generation sequence data to evaluate evidence for somatic-germline interaction in each gene or pathway using two tests: the Allelic Imbalance Rank Sum (AIRS) test and the Somatic Mutation Interaction Test (SMIT). AIRS tests for preferential allelic imbalance to evaluate whether somatic mutational events tend to amplify candidate germline variants. SMIT evaluates whether somatic point mutations and small indels occur more or less frequently than expected in the presence of candidate germline variants. Both AIRS and SMIT control for heterogeneity in the mutational process resulting from regional variation in mutation rates and inter-sample variation in background mutation rates. The SGI test combines AIRS and SMIT to provide a single, unified measure of statistical interaction between somatic mutational events and germline variation. We show that the tests implemented in SGI have high power with relatively modest sample sizes in a wide variety of scenarios. We demonstrate the utility of SGI to increase the power of rare variant association studies in cancer and to validate the potential role in cancer causation of germline susceptibility variants.
Genetic Epidemiology recently published our new paper focusing on recent improvements made to the VAAST software.
VAAST 2.0: Improved Variant Classification and Disease-Gene Identification Using a Conservation-Controlled Amino Acid Substitution Matrix
The need for improved algorithmic support for variant prioritization and disease-gene identification in personal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation-controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST's variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen-2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case-control datasets for Crohn disease (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population-attributable disease risks, and allelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease-gene finders with a simple ranking-by-statistical-significance protocol, and the performance compares very favorably to state-of-art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size.
More information and a downloadable PDF can be found here.