The effect of minor allele frequency on the likelihood of obtaining false positives

Determining the most promising single-nucleotide polymorphisms (SNPs) presents a challenge in genome-wide association studies, when hundreds of thousands of association tests are conducted. The power to detect genetic effects is dependent on minor allele frequency (MAF), and genome-wide association studies SNP arrays include SNPs with a wide distribution of MAFs. Therefore, it is critical to understand MAF's effect on the false positive rate

GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example

Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype

Disease risk prediction with rare and common variants

A number of studies have been conducted to investigate the predictive value of common genetic variants for complex diseases. To date, these studies have generally shown that common variants have no appreciable added predictive value over classical risk factors. New sequencing technology has enhanced the ability to identify rare variants that may have larger functional effects than common variants. One would expect rare variants to improve the discrimination power for disease risk by permitting more detailed quantification of genetic risk. Using the Genetic Analysis Workshop 17 simulated data sets for unrelated individuals, we evaluate the predictive value of rare variants by comparing prediction models built using the support vector machine algorithm with or without rare variants. Empirical results suggest that rare variants have appreciable effects on disease risk prediction

PoolHap: Inferring Haplotype Frequencies from Pooled Samples by Next Generation Sequencing

With the advance of next-generation sequencing (NGS) technologies, increasingly ambitious applications are becoming feasible. A particularly powerful one is the sequencing of polymorphic, pooled samples. The pool can be naturally occurring, as in the case of multiple pathogen strains in a blood sample, multiple types of cells in a cancerous tissue sample, or multiple isoforms of mRNA in a cell. In these cases, it's difficult or impossible to partition the subtypes experimentally before sequencing, and those subtype frequencies must hence be inferred. In addition, investigators may occasionally want to artificially pool the sample of a large number of individuals for reasons of cost-efficiency, e. g., when carrying out genetic mapping using bulked segregant analysis. Here we describe PoolHap, a computational tool for inferring haplotype frequencies from pooled samples when haplotypes are known. The key insight into why PoolHap works is that the large number of SNPs that come with genome-wide coverage can compensate for the uneven coverage across the genome. The performance of PoolHap is illustrated and discussed using simulated and real data. We show that PoolHap is able to accurately estimate the proportions of haplotypes with less than 2% error for 34-strain mixtures with 2X total coverage Arabidopsis thaliana whole genome polymorphism data. This method should facilitate greater biological insight into heterogeneous samples that are difficult or impossible to isolate experimentally. Software and users manual are freely available at http://arabidopsis.gmi.oeaw.ac.at/quan/poolhap/

Prioritizing genes associated with prostate cancer development

<p>Abstract</p> <p>Background</p> <p>The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.</p> <p>Methods</p> <p>A <it>Z </it>score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.</p> <p>Results</p> <p>Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--<it>CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4</it>, and <it>AURKA</it>--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.</p> <p>Conclusions</p> <p>By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.</p

Effect of BRCA2 sequence variants predicted to disrupt exonic splice enhancers on BRCA2 transcripts

Background: Genetic screening of breast cancer patients and their families have identified a number of variants of unknown clinical significance in the breast cancer susceptibility genes, BRCA1 and BRCA2. Evaluation of such unclassified variants may be assisted by web-based bioinformatic prediction tools, although accurate prediction of aberrant splicing by unclassified variants affecting exonic splice enhancers (ESEs) remains a challenge

Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies

There is strong evidence that rare variants are involved in complex disease etiology. The first step in implicating rare variants in disease etiology is their identification through sequencing in both randomly ascertained samples (e.g., the 1,000 Genomes Project) and samples ascertained according to disease status. We investigated to what extent rare variants will be observed across the genome and in candidate genes in randomly ascertained samples, the magnitude of variant enrichment in diseased individuals, and biases that can occur due to how variants are discovered. Although sequencing cases can enrich for casual variants, when a gene or genes are not involved in disease etiology, limiting variant discovery to cases can lead to association studies with dramatically inflated false positive rates