Analysis Of Genetic Association Studies Research Articles

The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait.

Accurate power and sample size estimation is crucial to the design and analysis of genetic association studies. When analyzing a binary trait via logistic regression, important covariates such as age and sex are typically included in the model. However, their effects are rarely properly considered in power or sample size computation during study planning. Unlike when analyzing a continuous trait, the power of association testing between a binary trait and a genetic variant depends, explicitly, on covariate effects, even under the assumption of gene-environment independence. Earlier work recognizes this hidden factor but the implemented methods are not flexible. We thus propose and implement a generalized method for estimating power and sample size for (discovery or replication) association studies of binary traits that (i) accommodates different types of nongenetic covariates E, (ii) deals with different types of G-E relationships, and (iii) is computationally efficient. Extensive simulation studies show that the proposed method is accurate and computationally efficient for both prospective and retrospective sampling designs with various covariate structures. A proof-of-principle application focused on the understudied African sample in the UK Biobank data. Results show that, in contrast to studying the continuous blood pressure trait, when analyzing the binary hypertension trait ignoring covariate effects of age and sex leads to overestimated power and underestimated replication sample size. The simulated datasets can be found on the online web-page of this manuscript, and the UK Biobank application data can be accessed at https://www.ukbiobank.ac.uk. The R package SPCompute that implements the proposed method is available at CRAN. The genome-wide association studies are carried out using the software PLINK 2.0 [Purcell et al. (Plink: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75.)].

Non-Parametric MCMC Gibbs Sampler Approach and Misclassification Assessment of Estimating Haplotype Frequencies among Related Statistical Approaches

Introduction: Haplotype analysis allows higher resolution analysis in genetic association studies and is used as a reference panel for genotype imputation in genome-wide association studies. Haplotypes estimates from genotypes among unrelated individuals but misclassification of the haplotype reconstruction will directly affect the accuracy of the results.
 Methods: This study proposes a novel statistical method Gibbs sampler algorithm to estimate haplotype frequency and quantify the influence of misclassification bias of the estimate haplotype. The performance of the algorithm is evaluated on simulated datasets assuming that linkage phase unknown. The simulation used different minor allele frequencies at each single nucleotide polymorphism (SNP) and different linkagedisequilibrium between the SNPs.
 Results: The Gibbs sampler algorithm presents higher accuracy among over seven SNPs or less, validated, and deals with missing genotype compared to previous related statistical approaches. Misclassification of estimated haplotypes leads to non-differential bias in exposure and affects haplotype estimates in haplotype analysis. The observed odds ratio underestimates the association between haplotype and phenotype by 36% to 99%.
 Conclusion: The Gibbs sampler algorithm provides higher accuracy and robust effectiveness performance, handles missing genotypes and provides uncertain probabilities of haplotype frequencies. The misclassification bias of the estimate haplotype underestimates the genetic association by more than forty percent.

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies.

Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations. U-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS. Supplementary data are available at Bioinformatics online.

Impact of Selection Bias on Estimation of Subsequent Event Risk.

Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was <10, bias was usually <10% (downward toward the null). Despite the low bias, the probability that a confidence interval included the true effect decreased (undercoverage) with increasing sample size because of increasing precision. Importantly, false-positive rates were not affected by selection bias. In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.

Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.

We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for Single Nucleotide Polymorphism (SNP) set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing.

Massive withdrawal symptoms and affective vulnerability are associated with variants of the CHRNA4 gene in a subgroup of smokers.

Heterogeneous phenotypes of complex disorders pose a great challenge for genetic association studies and for the development of personalized treatment strategies. Cluster analysis of phenotypic data has been recently proposed as a reliable auxiliary method for such studies. A cohort of 236 treatment-seeking smokers was investigated after overnight nicotine abstinence. Alpha4 nicotinic acetylcholine receptor (nAChR) subunit-related phenotypes were assessed by the Fagerström Test for Nicotine Dependence (FTND), exhaled carbon monoxide (CO) measurements, the Minnesota Nicotine Withdrawal Scale (MNWS) and the Zung Self-Rating Depression Scale (ZSDS). Seven tag SNPs (single-nucleotide polymorphisms) across CHRNA4 (the gene encoding alpha4 subunit of the nicotinic acetylcholine receptor) were genotyped and two-step cluster analysis was used for phenotypic cluster characterization. Haplotype estimation was determined by HapStat module of R 2.0 software. Three different phenotypic clusters were identified and the C3 cluster was characterized by the highest ZSDS and MNWS scores compared to others. Furthermore, lifetime prevalence of major depression was significantly higher in the C3 cluster (p = 0.019). In genetic association tests, this cluster was also significantly associated with rs3787138 genotypes (p = 0.004) while haplotype analyses of three SNPs (rs3787138, rs1044396, rs3787140) revealed that the risk for C3 phenotype was almost three times higher in GCC haplotype carriers compared to others (pperm = 0.013). This is the first report on a significant association between CHRNA4 variants and a subgroup of smokers characterized by massive withdrawal symptoms and affective vulnerability. Identification of such a phenotypic cluster can be a pivotal step for further pharmacogenetic studies on ligands of the alpha4 nAChR subunit. Our results suggest that performing cluster analysis in genetic association studies can be proposed for complex disorders.

A general semi-parametric approach to the analysis of genetic association studies in population-based designs

BackgroundFor genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results.ResultsIn this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available.ConclusionsThe flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.

Genetic model selection in genome-wide association studies: robust methods and the use of meta-analysis

In genetic association studies (GAS) as well as in genome-wide association studies (GWAS), the mode of inheritance (dominant, additive and recessive) is usually not known a priori. Assuming an incorrect mode of inheritance may lead to substantial loss of power, whereas on the other hand, testing all possible models may result in an increased type I error rate. The situation is even more complicated in the meta-analysis of GAS or GWAS, in which individual studies are synthesized to derive an overall estimate. Meta-analysis increases the power to detect weak genotype effects, but heterogeneity and incompatibility between the included studies complicate things further. In this review, we present a comprehensive summary of the statistical methods used for robust analysis and genetic model selection in GAS and GWAS. We then discuss the application of such methods in the context of meta-analysis. We describe the theoretical properties of the various methods and the foundations on which they are based. We also present the available software implementations of the described methods. Finally, since only few of the available robust methods have been applied in the meta-analysis setting, we present some simple extensions that allow robust meta-analysis of GAS and GWAS. Possible extensions and proposals for future work are also discussed.

Loss of Power in Two‐Stage Residual‐Outcome Regression Analysis in Genetic Association Studies

In the area of genetic epidemiology, investigating the association between genetic risk factors and phenotypic outcomes is an important challenge. Often in such studies, the analysis approach needs to control for potentially confounding factors, such as clinical, demographic, or environmental variables. Multiple linear regression (MLR) is often used for such analysis when the outcome of interest is a quantitative trait. Under this model, the effects of genetic factors are estimated effectively and covariates are adjusted correctly. In large-scale genome-wide studies with a relatively large number of genetic and environmental factors present, some alternative analytic strategies are being used to address the issues of covariate adjustment. One such approach is a two-stage residual-outcome regression analysis (2SR). At stage one, the outcome is regressed on all the covariates. At stage two, the residual-adjusted outcome is then regressed on genetic factors. 2SR has been employed in genetic association studies [Choy et al., 2008], as well as genetic linkage studies [Slager and Iturria, 2003]. Zeegers and colleagues have demonstrated that the 2SR has equal power with other covariate-adjusted methods in genetic linkage studies [Zeegers et al., 2004], but the use of such a two-stage approach in association analyses is less well understood. While there may be some potential advantages with the 2SR approach in both ease of use and computational and data management efficiency, some issues, such as the bias of estimates, power, and type I error, need to be thoroughly investigated.

A Subset-Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

Cochran-Armitage Test versus Logistic Regression in the Analysis of Genetic Association Studies

Objective: The Cochran-Armitage trend test based on the linear regression model has become a standard procedure for association testing in case-control studies. In contrast, the logistic regression model is generally used for estimating effect sizes. The aim of this paper is to propose an approach that allows for association testing and parameter estimation by means of the same statistic. Methods/Results: The trend test is recommendable as a test of no association between genotype and risk of disease. It is a two-sample test for differences between cases and controls with respect to the average number of risk alleles occurring in the genotype of an individual. We argue that this difference is not of primary interest in genetic association studies. It should be replaced with the disease odds ratio, which can be assessed under both cohort sampling and case-control sampling. Conclusion: The Cochran-Armitage trend test should be replaced by the Wald statistic from a logistic regression model for hypothesis testing and estimation in genetic association studies.

Bias due to two-stage residual-outcome regression analysis in genetic association studies

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided.

Is PATCHED an important candidate gene for neural tube defects? Cranial and thoracic neural tube defects in a family with Gorlin syndrome: a case report

. The relationship of mutations in the patched gene PTCH and nevoid basal cell carcinoma (NBCC) or Gorlin syndrome is well established. Animal studies have implicated the hedgehog-patched signalling pathway in neurulation and neural tube defects (NTDs). Spina bifida occulta and bifid vertebrae are well recognized in NBCCS, but there appears to be only one report of open spina bifida. We report a father and two sons with a truncating PTCH mutation and the major features of NBCCS. One son had open thoracic spina bifida and the other had an occipital meningocoele. We believe this to be the first report of cranial NTD in NBCCS and suggest that consideration be given to including PTCH analysis in genetic association studies in NTDs as the hedgehog pathway is integral to normal human neurulation.

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

BackgroundGene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits.FindingsHere we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run.ConclusionsGENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies

BackgroundSeveral methods have been presented for the analysis of complex interactions between genetic polymorphisms and/or environmental factors. Despite the available methods, there is still a need for alternative methods, because no single method will perform well in all scenarios. The aim of this work was to evaluate the performance of three selected rule based classifier algorithms, RIPPER, RIDOR and PART, for the analysis of genetic association studies.MethodsOverall, 42 datasets were simulated with three different case-control models, a varying number of subjects (300, 600), SNPs (500, 1500, 3000) and noise (5%, 10%, 20%). The algorithms were applied to each of the datasets with a set of algorithm-specific settings. Results were further investigated with respect to a) the Model, b) the Rules, and c) the Attribute level. Data analysis was performed using WEKA, SAS and PERL.ResultsThe RIPPER algorithm discovered the true case-control model at least once in >33% of the datasets. The RIDOR and PART algorithm performed poorly for model detection. The RIPPER, RIDOR and PART algorithm discovered the true case-control rules in more than 83%, 83% and 44% of the datasets, respectively. All three algorithms were able to detect the attributes utilized in the respective case-control models in most datasets.ConclusionsThe current analyses substantiate the utility of rule based classifiers such as RIPPER, RIDOR and PART for the detection of gene-gene/gene-environment interactions in genetic association studies. These classifiers could provide a valuable new method, complementing existing approaches, in the analysis of genetic association studies. The methods provide an advantage in being able to handle both categorical and continuous variable types. Further, because the outputs of the analyses are easy to interpret, the rule based classifier approach could quickly generate testable hypotheses for additional evaluation. Since the algorithms are computationally inexpensive, they may serve as valuable tools for preselection of attributes to be used in more complex, computationally intensive approaches. Whether used in isolation or in conjunction with other tools, rule based classifiers are an important addition to the armamentarium of tools available for analyses of complex genetic association studies.

An evaluation of statistical approaches to rare variant analysis in genetic association studies

Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc.

Variations in the cannabinoid receptor 1 gene predispose to migraine

In animal models endogenous cannabinoids have an inhibitory effect on trigeminovascular activation through the cannabinoid receptor 1 (CB1), although there is no evidence of the potential role of CB1 in human migraine. In this study we applied single marker association and haplotypic trend regression analysis to investigate the relationship between the CB1 gene ( CNR1) and headache with migraine symptoms (nausea, photophobia and disability, measured by the ID-migraine questionnaire). We identified our controls (CO = 684) as those who have not reported ID-migraine symptoms at all and defined migraine headache sufferers ( M = 195) as those who reported all three symptoms. The CNR1 was covered by 10 SNPs located throughout the gene based on haplotype tagging (htSNP) and previous literature. Our results demonstrated a significant haplotypic effect of CNR1 on migraine headaches ( p = 0.008, after permutation p = 0.017). This effect was independent of reported depression or drug/alcohol abuse although using neuroticism in the analysis as covariant slightly decreased this association ( p = 0.027, permutated p = 0.052). These results suggest a significant effect of CNR1 on migraine headaches that might be related to the alteration of peripheral trigeminovascular activation. In addition, this is the first study to demonstrate the effectiveness of using trait components combinations to define extreme phenotypes with haplotype analysis in genetic association studies for migraine. However, further studies are needed to elucidate the role of CNR1 and the cannabinoid system in migraine.

GENESTAT: an information portal for design and analysis of genetic association studies

We present the rationale, the background and the structure for version 2.0 of the GENESTAT information portal (www.genestat.org) for statistical genetics. The fast methodological advances, coupled with a range of standalone software, makes it difficult for expert as well as non-expert users to orientate when designing and analysing their genetic studies. The ultimate ambition of GENESTAT is to guide on statistical methodology related to the broad spectrum of research in genetic epidemiology. GENESTAT 2.0 focuses on genetic association studies. Each entry provides a summary of a topic and gives links to key papers, websites and software. The flexibility of the internet is utilised for cross-referencing and for open editing. This paper gives an overview of GENESTAT and gives short introductions to the current main topics in GENESTAT, with additional entries on the website. Methods and software developers are invited to contribute to the portal, which is powered by a Wikipedia-type engine and allows easy additions and editing.

Bayesian survival analysis in genetic association studies

Motivation: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations.Results: We compare the performance of the proposed method in simulation studies to the univariate Cox regression and to dimension reduction methods, and we observe that it performs similarly in localizing the causal site, while offering a clear advantage in terms of false positive associations. Moreover, it offers computational advantages. Applying our method to a real prospective study, we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.Availability: R codes are available upon request.Contact: ioanna.tachmazidou@imperial.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.