Power Of Genome-wide Association Studies Research Articles

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.

Building a Biomedical Cyberinfrastructure for Collaborative Research

Abstract For the potential power of genome-wide association studies (GWAS) and translational medicine to be realized, the biomedical research community must adopt standard measures, vocabularies, and systems to establish an extensible biomedical cyberinfrastructure. Incorporating standard measures will greatly facilitate combining and comparing studies via meta-analysis. Incorporating consensus-based and well-established measures into various studies should reduce the variability across studies due to attributes of measurement, making findings across studies more comparable. This article describes two well-established consensus-based approaches to identifying standard measures and systems: PhenX (consensus measures for phenotypes and eXposures), and the Open Geospatial Consortium (OGC). NIH support for these efforts has produced the PhenX Toolkit, an assembled catalog of standard measures for use in GWAS and other large-scale genomic research efforts, and the RTI Spatial Impact Factor Database (SIFD), a comprehensive repository of geo-referenced variables and extensive meta-data that conforms to OGC standards. The need for coordinated development of cyberinfrastructure to support measures and systems that enhance collaboration and data interoperability is clear; this paper includes a discussion of standard protocols for ensuring data compatibility and interoperability. Adopting a cyberinfrastructure that includes standard measures and vocabularies, and open-source systems architecture, such as the two well-established systems discussed here, will enhance the potential of future biomedical and translational research. Establishing and maintaining the cyberinfrastructure will require a fundamental change in the way researchers think about study design, collaboration, and data storage and analysis.

Impact of Phenotype Definition on Genome-Wide Association Signals: Empirical Evaluation in Human Immunodeficiency Virus Type 1 Infection

Discussion on improving the power of genome-wide association studies to identify candidate variants and genes is generally centered on issues of maximizing sample size; less attention is given to the role of phenotype definition and ascertainment. The authors used genome-wide data from patients infected with human immunodeficiency virus type 1 (HIV-1) to assess whether differences in type of population (622 seroconverters vs. 636 seroprevalent subjects) or the number of measurements available for defining the phenotype resulted in differences in the effect sizes of associations between single nucleotide polymorphisms and the phenotype, HIV-1 viral load at set point. The effect estimate for the top 100 single nucleotide polymorphisms was 0.092 (95% confidence interval: 0.074, 0.110) log(10) viral load (log(10) copies of HIV-1 per mL of blood) greater in seroconverters than in seroprevalent subjects. The difference was even larger when the authors focused on chromosome 6 variants (0.153 log(10) viral load) or on variants that achieved genome-wide significance (0.232 log(10) viral load). The estimates of the genetic effects tended to be slightly larger when more viral load measurements were available, particularly among seroconverters and for variants that achieved genome-wide significance. Differences in phenotype definition and ascertainment may affect the estimated magnitude of genetic effects and should be considered in optimizing power for discovering new associations.

Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.

Variance Components Models for Physical Activity With Age as Modifier: A Comparative Twin Study in Seven Countries

Physical activity is influenced by genetic factors whose expression may change with age. We employed an extension to the classical twin model that allows a modifier variable, age, to interact with the effects of the latent genetic and environmental factors. The model was applied to self-reported data from twins aged 19 to 50 from seven countries that collaborated in the GenomEUtwin project: Australia, Denmark, Finland, Norway, Netherlands, Sweden and United Kingdom. Results confirmed the importance of genetic influences on physical activity in all countries and showed an age-related decrease in heritability for 4 countries. In the other three countries age did not interact with heritability but those samples were smaller or had a more restricted age range. Effects of shared environment were absent, except in older Swedish participants. The study confirms the importance of taking age effects into account when exploring the genetic and environmental contribution to physical activity. It also suggests that the power of genome-wide association studies to identify the genetic variants contributing to physical activity may be larger in young adult cohorts.

Linkage to the 8p21.1 Region Including the CLU Gene in Age at Onset Stratified Alzheimer's Disease Families

Two powerful genome-wide association studies have recently reported significant association between sporadic late-onset Alzheimer's disease (AD) and markers at the CLU locus in chromosome 8p. In this study, we have stratified our previously analyzed 109 Swedish AD families according to range in age at onset and performed whole-genome linkage analysis and subsequent fine-mapping in 8p21. The subgroup analyzed in the fine-mapping consisted of 28 families with AD, having a within-family onset-range not exceeding 8 years and an age at onset between 49 ≤ 70 years. A maximum non-parametric linkage peak (LOD = 3.5) was found between markers D8S1809 and 236c6-1. Intriguingly this linked 9.5cM region contains clusterin (CLU), which is one of the two top susceptibility genes for AD. Our finding may be a reflection of linkage to the CLU susceptibility gene, in the same way as familial AD has previously been linked to the APOE locus.

Acceleration Genotype Imputation for Large Dataset on GPU

Genotype imputation is an important approach for improving the power of genome-wide association studies. We try to use GPU to accelerate its calculation. The results show that the GPU implementation can reach a 10x speedup for small sample size, and its speedup ability gradually increases with the size of data set, therefore is especially useful for huge data (i.e. GWAS).

A knowledge-based weighting framework to boost the power of genome-wide association studies.

BackgroundWe are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances.Methodology/Principal FindingsWe present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance.Conclusions/SignificanceWith user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases.

Multi-locus Test Conditional on Confirmed Effects Leads to Increased Power in Genome-wide Association Studies

Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.

Novel genetic loci associated with prostate cancer in the Japanese population

Takata et al.1 recently reported in Nature Genetics that they have identified five novel genetic loci that are associated with prostate cancer in the Japanese population. Using most updated Illumina Quad BeadChip to genotype 3001 prostate cancer patients and 5415 control subjects, they identified 263 single-nucleotide polymorphisms (SNPs) showing significant association with prostate cancer in Japan. Further analysis indicated that 80 SNPs reside in the previously known regions, and five of them are novel susceptibility loci associated with the prostate cancer. These five SNPs apparently are not associated with Gleason score (Gleason score <7 compared with ≥7) or cancer stage (non-advanced cancer compared with advanced cancer) in the affected patients. Neither is there any correlation with age. An attempt to identify the genes associated with these loci uncovered three candidate genes that might be associated with Japanese prostate cancer patients: GPRC6A (G protein-coupled receptor, family C, group 6, member A), C2orf43 (open reading frame 43 of chromosome 2) and FOXP4 (forkhead box P4). Interestingly, GPRC6A is known to be highly expressed in the Leydig cells of the testis, and mice deficient in Gprc6a show male feminization and a metabolic manifestation of higher circulating estradiol and reduced levels of testosterone, two hormones that are critical for initiation and progression of prostate cancer. Prostate cancer is the leading cause of male death in the Western world. It has also become more and more prevalent in Asian countries. It is widely accepted that genes are the fundamental determinants in almost all human cancers, so the genetic analysis of prostate cancer can provide a mechanistic understanding of the process of carcinogenesis, thus identifying potential targets for therapeutic intervention. We are also in a very exciting new era when such studies are aided by the availability of the complete sequence of the human genome, as well as increasing collections of SNPs that can be used to associate allelic information of the genes in the whole genome with a disease state in a statistically significant manner, hence the genome-wide association studies (GWAS). Previously, such studies had to be done in high-risk individuals to screen the genome for locations of potential cancer-related genes. Now the high-resolution SNP chips make it possible to conduct a population study of prostate cancer patients to detect possible underlying genetic basis for prostate cancer, and generally more patient genomes are scanned, more new susceptibility genes will be identified, even those with a lower penetrance in and a milder impact on the general population. The accomplishments by these researchers need to be viewed in the context of prior publications describing GWAS in prostate cancer in European populations.2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Nearly 30 different SNPs have been identified as associated with prostate cancer; some of these are indeed replicated in the current study. The saturation of GWAS on prostate cancer likely suggests a dwindling possibility of finding new prostate cancer susceptibility genes, yet remarkably this study identified five new loci associated with prostate cancer, which suggests that there are complex and diverse etiological processes leading to prostate cancer among different ethnic backgrounds. Quite possibly diet, cultural and behavioral differences might also influence prostate cancer formation. A corollary of their study will be that some of the known SNPs associated with European prostate cancer patients likely do not have the same degree of association in the Japanese population, an outcome confirmed by the current study. It also suggests that the power of GWAS can be enhanced by examining different ethnic populations to uncover gene variants that are responsible for causing human diseases. Furthermore, these GWAS with different genetic backgrounds highlight the most fundamental genetic causes that are common among all ethnic backgrounds while revealing potentially race-specific genetic predispositions to prostate cancer. This information will be helpful in designing or testing pharmaceutical interventions applicable to all peoples or specific to certain ethnic groups. The rapid discovery of new susceptibility loci associated with various human diseases, in this case, prostate cancer, provides a valuable source of new markers with diagnostic and prognostic value. However, translation of these findings into effective therapies requires new experimental models and human clinical trials. At the moment, the exact nature of these susceptibility loci is unknown, as are their potential molecular mechanisms in the development of prostate cancer. Experiments in all disciplines, including molecular studies, animal models and human tissue examinations, will be necessary to translate the knowledge of GWAS into useful tools to help prostate cancer patients, as well as to prevent new cancers. We are in the beginning of an exciting new era. This paper, while discovering new genetic loci potentially associated with prostate cancer, also provides researchers opportunities to uncover novel molecular mechanisms of prostate carcinogenesis.

Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging

The combining of genome-wide association (GWA) data across populations represents a major challenge for massive global meta-analyses. Genotype imputation using densely genotyped reference samples facilitates the combination of data across different genotyping platforms. HapMap data is typically used as a reference for single nucleotide polymorphism (SNP) imputation and tagging copy number polymorphisms (CNPs). However, the advantage of having population-specific reference panels for founder populations has not been evaluated. We looked at the properties and impact of adding 81 individuals from a founder population to HapMap3 reference data on imputation quality, CNP tagging, and power to detect association in simulations and in an independent cohort of 2138 individuals. The gain in SNP imputation accuracy was highest among low-frequency markers (minor allele frequency [MAF] < 5%), for which adding the population-specific samples to the reference set increased the median R(2) between imputed and genotyped SNPs from 0.90 to 0.94. Accuracy also increased in regions with high recombination rates. Similarly, a reference set with population-specific extension facilitated the identification of better tag-SNPs for a subset of CNPs; for 4% of CNPs the R(2) between SNP genotypes and CNP intensity in the independent population cohort was at least twice as high as without the extension. We conclude that even a relatively small population-specific reference set yields considerable benefits in SNP imputation, CNP tagging accuracy, and the power to detect associations in founder populations and population isolates in particular.

Methods for testing association between uncertain genotypes and quantitative traits

Interpretability and power of genome-wide association studies can be increased by imputing unobserved genotypes, using a reference panel of individuals genotyped at higher marker density. For many markers, genotypes cannot be imputed with complete certainty, and the uncertainty needs to be taken into account when testing for association with a given phenotype. In this paper, we compare currently available methods for testing association between uncertain genotypes and quantitative traits. We show that some previously described methods offer poor control of the false-positive rate (FPR), and that satisfactory performance of these methods is obtained only by using ad hoc filtering rules or by using a harsh transformation of the trait under study. We propose new methods that are based on exact maximum likelihood estimation and use a mixture model to accommodate nonnormal trait distributions when necessary. The new methods adequately control the FPR and also have equal or better power compared to all previously described methods. We provide a fast software implementation of all the methods studied here; our new method requires computation time of less than one computer-day for a typical genome-wide scan, with 2.5 M single nucleotide polymorphisms and 5000 individuals.

ParaHaplo 2.0: a program package for haplotype-estimation and haplotype-based whole-genome association study using parallel computing

BackgroundThe use of haplotype-based association tests can improve the power of genome-wide association studies. Since the observed genotypes are unordered pairs of alleles, haplotype phase must be inferred. However, estimating haplotype phase is time consuming. When millions of single-nucleotide polymorphisms (SNPs) are analyzed in genome-wide association study, faster methods for haplotype estimation are required.MethodsWe developed a program package for parallel computation of haplotype estimation. Our program package, ParaHaplo 2.0, is intended for use in workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on both Japanese in Tokyo, Japan and Han Chinese in Beijing, China of the HapMap dataset.ResultsParallel version of ParaHaplo 2.0 can estimate haplotypes 100 times faster than a non-parallel version of the ParaHaplo.ConclusionParaHaplo 2.0 is an invaluable tool for conducting haplotype-based genome-wide association studies (GWAS). The need for fast haplotype estimation using parallel computing will become increasingly important as the data sizes of such projects continue to increase. The executable binaries and program sources of ParaHaplo are available at the following address: http://en.sourceforge.jp/projects/parallelgwas/releases/

Optimizing the power of genome‐wide association studies by using publicly available reference samples to expand the control group

Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as “genetically matched controls” for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify “axes” of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study. Genet. Epidemiol. 34: 319–326, 2010. © 2010 Wiley-Liss, Inc.

Magnitude of Stratification in Human Populations and Impacts on Genome Wide Association Studies

Genome-wide association studies (GWAS) may be biased by population stratification (PS). We conducted empirical quantification of the magnitude of PS among human populations and its impact on GWAS. Liver tissues were collected from 979, 59 and 49 Caucasian Americans (CA), African Americans (AA) and Hispanic Americans (HA), respectively, and genotyped using Illumina650Y (Ilmn650Y) arrays. RNA was also isolated and hybridized to Agilent whole-genome gene expression arrays. We propose a new method (i.e., hgdp-eigen) for detecting PS by projecting genotype vectors for each sample to the eigenvector space defined by the Human Genetic Diversity Panel (HGDP). Further, we conducted GWAS to map expression quantitative trait loci (eQTL) for the ∼40,000 liver gene expression traits monitored by the Agilent arrays. HGDP-eigen performed similarly to the conventional self-eigen methods in capturing PS. However, leveraging the HGDP offered a significant advantage in revealing the origins, directions and magnitude of PS. Adjusting for eigenvectors had minor impacts on eQTL detection rates in CA. In contrast, for AA and HA, adjustment dramatically reduced association findings. At an FDR = 10%, we identified 65 eQTLs in AA with the unadjusted analysis, but only 18 eQTLs after the eigenvector adjustment. Strikingly, 55 out of the 65 unadjusted AA eQTLs were validated in CA, indicating that the adjustment procedure significantly reduced GWAS power. A number of the 55 AA eQTLs validated in CA overlapped with published disease associated SNPs. For example, rs646776 and rs10903129 have previously been associated with lipid levels and coronary heart disease risk, however, the rs10903129 eQTL was missed in the eigenvector adjusted analysis.

Evaluation of single-nucleotide polymorphism imputation using random forests

Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs.

Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results

The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies.

Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests

For genome-wide association studies in family-based designs, a new, universally applicable approach is proposed. Using a modified Liptak's method, we combine the p-value of the family-based association test (FBAT) statistic with the p-value for the Van Steen-statistic. The Van Steen-statistic is independent of the FBAT-statistic and utilizes information that is ignored by traditional FBAT-approaches. The new test statistic takes advantages of all available information about the genetic association, while, by virtue of its design, it achieves complete robustness against confounding due to population stratification. The approach is suitable for the analysis of almost any trait type for which FBATs are available, e.g. binary, continuous, time to-onset, multivariate, etc. The efficiency and the validity of the new approach depend on the specification of a nuisance/tuning parameter and the weight parameters in the modified Liptak's method. For different trait types and ascertainment conditions, we discuss general guidelines for the optimal specification of the tuning parameter and the weight parameters. Our simulation experiments and an application to an Alzheimer study show the validity and the efficiency of the new method, which achieves power levels that are comparable to those of population-based approaches.

Highlights From The Literature

Highlights From The Literature

Global similarity with local differences in linkage disequilibrium between the Dutch and HapMap–CEU populations

The HapMap project has facilitated the selection of tagging single nucleotide polymorphisms (tagSNPs) for genome-wide association studies (GWAS) under the assumption that linkage disequilibrium (LD) in the HapMap populations is similar to the populations under investigation. Earlier reports support this assumption, although in most of these studies only a few loci were evaluated. We compared pair-wise LD and LD block structure across autosomes between the Dutch population and the CEU-HapMap reference panel. The impact of sampling distribution on the estimation of LD blocks was studied by bootstrapping. A high Pearson correlation (genome-wide; 0.93) between pair-wise r(2) for the Dutch and the CEU populations was found, indicating that tagSNPs from the CEU-HapMap panel capture common variation in the Dutch population. However, some genomic regions exhibited, significantly lower correlation than the genome-wide estimate. This might decrease the validity of HapMap tagSNPs in these regions and the power of GWAS. The LD block structure differed considerably between the Dutch and CEU-HapMap populations. This was not explained by demographic differences between the CEU and Dutch samples, as testing for population stratification was not significant. We also found that sampling variation had a large effect on the estimation of LD blocks, as shown by the bootstrapping analysis. Thus, in small samples, most of the observed differences in LD blocks between populations are most likely the result of sampling variation. This poor concordance in LD block structure suggests that large samples are required for robust estimations of local LD block structure in populations.