Abstract Twin, adoption and family studies provide evidence for a genetic component for the susceptibility to alcoholism. The Collaborative Study on the Genetics of Alcoholism (COGA) is a comprehensive family study aimed at identifying specific genetic risk factors.
Here we focus on using clustering methods to disentangle the interacting factors that lead to the presentation of complex diseases. Relative pairs are placed in discrete subgroups, or classes, based upon their pattern of allele sharing at a sequence of markers and on concomitant risk factors. The relationship between the locus information and the affectation status of the relative pairs within each subgroup then can be assessed. Cluster analysis (CLA) and latent class analysis (LCA) were applied to sibling allele sharing data from GAW11 simulated data, and to an existing Alzheimer's disease (AD) dataset. Both methods were able to identify markers linked to all 3 disease loci in the GAW11 data. LCA and CLA also replicated regions of chromosomes identified in an analysis of the AD data using affected-sib-pair methods. These analyses indicate that classification tools may be useful for detecting susceptibility genes for complex traits.
Fifty percent of variability in HIV-1 susceptibility is attributable to host genetics. Thus identifying genetic associations is essential to understanding pathogenesis of HIV-1 and important for targeting drug development. To date, however, CCR5 remains the only gene conclusively associated with HIV acquisition. To identify novel host genetic determinants of HIV-1 acquisition, we conducted a genome-wide association study among a high-risk sample of 3,136 injection drug users (IDUs) from the Urban Health Study (UHS). In addition to being IDUs, HIV- controls were frequency-matched to cases on environmental exposures to enhance detection of genetic effects. We tested independent replication in the Women’s Interagency HIV Study (N=2,533). We also examined publicly available gene expression data to link SNPs associated with HIV acquisition to known mechanisms affecting HIV replication/infectivity. Analysis of the UHS nominated eight genetic regions for replication testing. SNP rs4878712 in FRMPD1 met multiple testing correction for independent replication (P=1.38x10-4), although the UHS-WIHS meta-analysis p-value did not reach genome-wide significance (P=4.47x10-7 vs. P<5.0x10-8) Gene expression analyses provided promising biological support for the protective G allele at rs4878712 lowering risk of HIV: (1) the G allele was associated with reduced expression of FBXO10 (r=-0.49, P=6.9x10-5); (2) FBXO10 is a component of the Skp1-Cul1-F-box protein E3 ubiquitin ligase complex that targets Bcl-2 protein for degradation; (3) lower FBXO10 expression was associated with higher BCL2 expression (r=-0.49, P=8x10-5); (4) higher basal levels of Bcl-2 are known to reduce HIV replication and infectivity in human and animal in vitro studies. These results suggest new potential biological pathways by which host genetics affect susceptibility to HIV upon exposure for follow-up in subsequent studies.
Abstract Genetic association studies have shown the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 for the risk of nicotine dependence, smoking, and lung cancer in populations of European descent. We have carried out a detailed study of this region using dense genotyping in both European-Americans and African-Americans. We genotyped 75 known single nucleotide polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American sample (N = 710) and in a European-American sample (N = 2,062). Cases were nicotine-dependent and controls were nondependent smokers. The nonsynonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2,772 subjects [P = 4.49 × 10−8; odds ratio (OR), 1.42; 95% confidence interval (CI), 1.25–1.61] as well as in African-Americans only (P = 0.015; OR, 2.04; 1.15–3.62) and in European-Americans only (P = 4.14 × 10−7; OR, 1.40; 1.23–1.59). Other SNPs that have been shown to affect the mRNA levels of CHRNA5 in European-Americans are associated with nicotine dependence in African-Americans but not in European-Americans. The CHRNA3 SNP rs578776, which has a low correlation with rs16969968, is associated with nicotine dependence in European-Americans but not in African-Americans. Less common SNPs (frequency ≤ 5%) are also associated with nicotine dependence. In summary, multiple variants in this gene cluster contribute to nicotine dependence risk, and some are also associated with functional effects on CHRNA5. The nonsynonymous SNP rs16969968, a known risk variant in populations of European-descent, is also significantly associated with risk in African-Americans. Additional SNPs contribute to risk in distinct ways in these two populations. [Cancer Res 2009;69(17):6848–56]
Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.
A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype-phenotype correlation with a priori evidence of biological relevance.We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype-phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.A comprehensive database of biological prioritization scores for all known SNPs is available at http://zork.wustl.edu/gin. This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula-no special software is necessary.Supplementary data are available at Bioinformatics online.
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