Genome-wide Association Study Of Type Research Articles

Heterogeneous computing architecture for fast detection of SNP-SNP interactions.

BackgroundThe extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested.ResultsWe have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort.ConclusionsGeneral purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.

Adjustment of familial relatedness in association test for rare variants.

High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.

Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension.

Genome wide association studies (GWAS) have been used to search for associations between genetic variants and a phenotypic trait of interest. New technologies, such as next-generation sequencing, hold the potential to revolutionize GWAS. However, millions of polymorphisms are identified with next-generation sequencing technology. Consequently, researchers must be careful when performing such a large number of statistical tests, and corrections are typically made to account for multiple testing. Additionally, for typical GWAS, the p value cutoff is set quite low (approximately <10−8). As a result of this p value stringency, it is likely that there are many true associations that do not meet this threshold. To account for this we have incorporated a priori biological knowledge to help identify true associations that may not have reached statistical significance. We propose the application of a pipelined series of statistical and bioinformatic methods, to enable the assessment of the association of genetic polymorphisms with a disease phenotype--here, hypertension--as well as the identification of statistically significant pathways of genes that may play a role in the disease process.

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals.Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study.Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/.Contact: samuli.ripatti@helsinki.fi; samuel.kaski@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.

PANOGA: a web server for identification of SNP-targeted pathways from genome-wide association study data

Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.

Genome-wide association study of endometrial cancer in E2C2

Endometrial cancer (EC), a neoplasm of the uterine epithelial lining, is the most common gynecological malignancy in developed countries and the fourth most common cancer among US women. Women with a family history of EC have an increased risk for the disease, suggesting that inherited genetic factors play a role. We conducted a two-stage genome-wide association study of Type I EC. Stage 1 included 5,472 women (2,695 cases and 2,777 controls) of European ancestry from seven studies. We selected independent single-nucleotide polymorphisms (SNPs) that displayed the most significant associations with EC in Stage 1 for replication among 17,948 women (4,382 cases and 13,566 controls) in a multiethnic population (African America, Asian, Latina, Hawaiian and European ancestry), from nine studies. Although no novel variants reached genome-wide significance, we replicated previously identified associations with genetic markers near the HNF1B locus. Our findings suggest that larger studies with specific tumor classification are necessary to identify novel genetic polymorphisms associated with EC susceptibility.Electronic supplementary materialThe online version of this article (doi:10.1007/s00439-013-1369-1) contains supplementary material, which is available to authorized users.

Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Privacy-Preserving Data Exploration in Genome-Wide Association Studies.

Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

A Drosophila functional evaluation of candidates from human genome-wide association studies of type 2 diabetes and related metabolic traits identifies tissue-specific roles for dHHEX

BackgroundGenome-wide association studies (GWAS) identify regions of the genome that are associated with particular traits, but do not typically identify specific causative genetic elements. For example, while a large number of single nucleotide polymorphisms associated with type 2 diabetes (T2D) and related traits have been identified by human GWAS, only a few genes have functional evidence to support or to rule out a role in cellular metabolism or dietary interactions. Here, we use a recently developed Drosophila model in which high-sucrose feeding induces phenotypes similar to T2D to assess orthologs of human GWAS-identified candidate genes for risk of T2D and related traits.ResultsDisrupting orthologs of certain T2D candidate genes (HHEX, THADA, PPARG, KCNJ11) led to sucrose-dependent toxicity. Tissue-specific knockdown of the HHEX ortholog dHHEX (CG7056) directed metabolic defects and enhanced lethality; for example, fat-body-specific loss of dHHEX led to increased hemolymph glucose and reduced insulin sensitivity.ConclusionCandidate genes identified in human genetic studies of metabolic traits can be prioritized and functionally characterized using a simple Drosophila approach. To our knowledge, this is the first large-scale effort to study the functional interaction between GWAS-identified candidate genes and an environmental risk factor such as diet in a model organism system.

A Generalized Kruskal–Wallis Test Incorporating Group Uncertainty with Application to Genetic Association Studies

Motivated by genetic association studies of SNPs with genotype uncertainty, we propose a generalization of the Kruskal-Wallis test that incorporates group uncertainty when comparing k samples. The extended test statistic is based on probability-weighted rank-sums and follows an asymptotic chi-square distribution with k - 1 degrees of freedom under the null hypothesis. Simulation studies confirm the validity and robustness of the proposed test in finite samples. Application to a genome-wide association study of type 1 diabetic complications further demonstrates the utilities of this generalized Kruskal-Wallis test for studies with group uncertainty. The method has been implemented as an open-resource R program, GKW.

Genome-wide Studies in Pharmacogenomics: Harnessing the Power of Extreme Phenotypes

Baldwin et al. on paclitaxel-induced sensory peripheral neuropathy [5], and Brown et al. on temozolomideassociated cytotoxicity [6] have advanced our understanding of DNA sequence variations and drug response, and several pharmaco genomic tests are on their way to clinical practice [7]. The studies on toxic drug reactions in particular have extra power compared with typical GWAS. For example, the study by Daly et al. on flucloxacillin-induced liver injury, an extreme and rare drug response phenotype, included only 51 affected individuals and 282 controls, yet it reported the unprecedented odds risk ratio of 80.6 (p = 8.7 × 10 for carriers of HLA-B*5701) [4], while typical GWAS odds risk ratios are below 2. We propose that the extreme phenotypes approach should be prioritized for genomewide pharmacogenomic studies, including those exploring genomic biomarkers for treatment efficacy as an alternative to enrolling very large random patient cohorts. This is based on both the likelihood of an enrichment for genomic signal due to the ‘Mendelian’ nature of the pheno type, the favorable study costs and reduced ‘noise’ arising from inaccurate pheno typing. Taken together, it seems far more effective to compare genome-wide data between ‘excellent drug responders’ versus nonresponders, compared with analyzing such data from huge patient cohorts representing a large spectrum of intermediate responders. The superior power of the extreme phenotype approach is exemplified by the recent study of Edmond et al., who identified DCTN4 as a modifier of chronic infection in cystic fibrosis among 91 individuals whose exomes were sequenced [8]. As a further example, expression levels of CHL1 were recently reported as Four years ago this journal featured a theme issue on genome-wide association studies (GWAS) in pharmacogenomics. In our accompanying editorial we noted that “relatively few GWAS on drug response were so far published compared with the large numbers of disease risk GWAS” and called upon the pharmacogenomics research community to apply this powerful, hypothesis-free research approach for improving knowledge on drug safety and efficacy [1]. Our call seems to have been timely, as the past few years have witnessed an increase in pharmacogenomics-oriented genome-wide studies. Among PubMed-listed pharmacogenomics-related manuscripts, the fraction of those mentioning both ‘pharmacogenomics’ and ‘genome-wide’ as keywords have risen fourfold over the past few years, from 2.1% in 2006 to nearly 9% of total manuscripts published between 2009 and 2012 (Table 1). However, as in past years, only a tiny fraction (under 2%) of published genome-wide studies (in all areas) are concerned with pharmacogenomics (Table 1), a disappointingly low figure considering the huge societal burden of adverse drug events and ineffective therapeutics [2].

Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies

Genome-wide association studies (GWASs) have discovered thousands of variants that are associated with human health and disease. Whilst early GWASs have primarily focused on genetically homogeneous populations of European, East Asian and South Asian ancestries, the next-generation genome-wide surveys are starting to pool studies from ethnically diverse populations within a single meta-analysis. However, classical epidemiological strategies for meta-analyses that assume fixed- or random-effects may not be the most suitable approaches to combine GWAS findings as these either confer low statistical power or identify mostly loci where the variants carry homogeneous effect sizes that are present in most of the studies. In a trans-ethnic meta-analysis, it is likely that some genetic loci will exhibit heterogeneous effect sizes across the populations. This may be due to differences in study designs, differences arising from the interactions with other genetic variants, or genuine biological differences attributed to environmental, dietary or lifestyle factors that modulate the influence of the genes. Here we compare different strategies for meta-analyzing GWAS across genetically diverse populations, where we intentionally vary the effect sizes present across the different populations. We subsequently applied the methods that yielded the highest statistical power to a trans-ethnic meta-analysis of seven GWAS in type 2 diabetes, and showed that these methods identified bona fide associations that would otherwise have been missed by the classical strategies.

LRP6 Enhances Glucose Metabolism by Promoting TCF7L2-Dependent Insulin Receptor Expression and IGF Receptor Stabilization in Humans

Common genetic variations in Wnt signaling genes have been associated with metabolic syndrome and diabetes by mechanisms that are poorly understood. A rare nonconservative mutation in Wnt coreceptor LRP6 (LRP6(R611C)) has been shown to underlie autosomal dominant early onset coronary artery disease, type 2 diabetes, and metabolic syndrome. We examined the interplay between Wnt and insulin signaling pathways in skeletal muscle and skin fibroblasts of healthy nondiabetic LRP6(R611C) mutation carriers. LRP6 mutation carriers exhibited hyperinsulinemia and reduced insulin sensitivity compared to noncarrier relatives in response to oral glucose ingestion, which correlated with a significant decline in tissue expression of the insulin receptor and insulin signaling activity. Further investigations showed that the LRP6(R611C) mutation diminishes TCF7L2-dependent transcription of the IR while it increases the stability of IGFR and enhances mTORC1 activity. These findings identify the Wnt/LRP6/TCF7L2 axis as a regulator of glucose metabolism and a potential therapeutic target for insulin resistance.

Emerging function of fat mass and obesity-associated protein (fto).

Emerging function of fat mass and obesity-associated protein (fto).

Evaluating coverage of exons by HapMap SNPs

Genome-wide association (GWA) studies are currently one of the most powerful tools in identifying disease-associated genes or variants. In typical GWA studies, single-nucleotide polymorphisms (SNPs) are often used as genetic makers. Therefore, it is critical to estimate the percentage of genetic variations which can be covered by SNPs through linkage disequilibrium (LD). In this study, we use the concept of haplotype blocks to evaluate the coverage of five SNP sets including the HapMap and four commercial arrays, for every exon in the human genome. We show that although some Chips can reach similar coverage as the HapMap, only about 50% of exons are completely covered by haplotype blocks of HapMap SNPs. We suggest further high-resolution genotyping methods are required, to provide adequate genome-wide power for identifying variants.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

Presence of Multiple Independent Effects in Risk Loci of Common Complex Human Diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.

Replication of genome‐wide association signals of type 2 diabetes in Han Chinese in a prospective cohort

A recent genome-wide association study for type 2 diabetes in Han Chinese identified several novel genetic variants. We investigated their associations with quantitative measures to explore the mechanism by which these variants influence glucose homoeostasis. We also examined whether these variants predict progression to diabetes in a large prospective family based Chinese cohort. Five single nucleotide polymorphisms (SNPs) near the protein tyrosine phosphatase, receptor type, D (PTPRD), SRR, MAF/WWOX, and KCNQ1 genes were genotyped in 1138 subjects of Chinese origin from the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance study. At baseline, the risk-conferring rs7192960 C allele near the MAF/WWOX genes was associated with lower homoeostasis model assessment of β-cell (HOMA-β) (P = 0·01) and second-phase insulin response in oral glucose tolerance test (OGTT) (P = 0·04). The risk-conferring rs2237897 C alleles in the KCNQ1 gene were associated with higher fasting glucose (P = 0·009), lower HOMA-β (P = 0·03), and lower first-phase insulin response in OGTT (P = 0·03). Over an average follow-up period of 5·43 years, participants with the risk-conferring rs17584499 TT genotype in the PTPRD gene were more likely to progress from nondiabetes to diabetes than were noncarriers (hazard ratio: 8·82, P = 4 × 10(-5) ). The risk-conferring T allele in the PTPRD gene was associated with greater increase in homoeostasis model assessment of insulin resistance (HOMA-IR) (P = 0·04) over time. PTPRD gene expression in human adipose tissues was negatively associated with fasting insulin levels and HOMA-IR. Genetic variants near the KCNQ1 and MAF/WWOX genes are associated with reduced insulin secretion. The PTPRD genetic variant appears to be associated with progression to diabetes in Han Chinese, most likely through increased insulin resistance.

What have we learnt from genetic studies of type 2 diabetes in Chinese?

What have we learnt from genetic studies of type 2 diabetes in Chinese?

Bayesian variable selection regression for genome-wide association studies and other large-scale problems

We consider applying Bayesian Variable Selection Regression, or BVSR, to genome-wide association studies and similar large-scale regression problems. Currently, typical genome-wide association studies measure hundreds of thousands, or millions, of genetic variants (SNPs), in thousands or tens of thousands of individuals, and attempt to identify regions harboring SNPs that affect some phenotype or outcome of interest. This goal can naturally be cast as a variable selection regression problem, with the SNPs as the covariates in the regression. Characteristic features of genome-wide association studies include the following: (i) a focus primarily on identifying relevant variables, rather than on prediction; and (ii) many relevant covariates may have tiny effects, making it effectively impossible to confidently identify the complete "correct" subset of variables. Taken together, these factors put a premium on having interpretable measures of confidence for individual covariates being included in the model, which we argue is a strength of BVSR compared with alternatives such as penalized regression methods. Here we focus primarily on analysis of quantitative phenotypes, and on appropriate prior specification for BVSR in this setting, emphasizing the idea of considering what the priors imply about the total proportion of variance in outcome explained by relevant covariates. We also emphasize the potential for BVSR to estimate this proportion of variance explained, and hence shed light on the issue of "missing heritability" in genome-wide association studies.