Genetic Association Tests Research Articles

CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates

We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan.

Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation.

The relationship between genetic variability and individual phenotypes is usually investigated by testing for association relying on called genotypes. Allele counts obtained from next-generation sequence data could be used for this purpose too. Genetic association can be examined by treating alternative allele counts (AACs) as the response variable in negative binomial regression. AACs from sequence data often contain an excess of zeros, thus motivating the use of Hurdle and zero-inflated models. Here we examine rough type I error rates and the ability to pick out variants with small probability values for 7 different testing approaches that incorporate AACs as an explanatory or as a response variable. Model comparisons relied on chromosome 3 DNA sequence data from 407 Hispanic participants in the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) project 1 with complete information on diastolic blood pressure and related medication. Our results suggest that in the investigation of the relationship between AAC as response variable and individual phenotypes as explanatory variable, Hurdle-negative binomial regression has some advantages. This model showed a good ability to discriminate strongly associated variants and controlled overall type I error rates. However, probability values from Hurdle-negative binomial regression were not obtained for approximately 25 % of the investigated variants because of convergence problems, and the mass of the probability value distribution was concentrated around 1.

A multi-locus genetic association test for a dichotomous trait and its secondary phenotype.

Genetic association studies often collect information on secondary phenotypes related to the primary disease status. In many situations, the secondary phenotypes are only measured in subjects with the disease condition. It would be advantageous to model the primary trait and the secondary phenotype together if they share certain level of genetic heritability. We propose a family of multi-locus testing procedures to detect the composite association between a set of genetic markers and two traits (the primary trait and a secondary phenotype), in order to identify genes influencing both traits. The proposed test is derived from a random effect model with two variance components, with each presenting the genetic effect on one trait, and incorporates a model selection procedure for seeking the optimal model to represent the two sources of genetic effects. We conduct simulation studies to evaluate performance of the proposed procedure and apply the method to a genome-wide association study of prostate cancer with the Gleason score as the secondary phenotype.

A translational systems biology approach in both animals and humans identifies a functionally related module of accumbal genes involved in the regulation of reward processing and binge drinking in males.

The mesolimbic dopamine system, composed primarily of dopaminergic neurons in the ventral tegmental area that project to striatal structures, is considered to be the key mediator of reinforcement-related mechanisms in the brain. Prompted by a genome-wide association meta-analysis implicating the Ras-specific guanine nucleotide-releasing factor 2 (RASGRF2) gene in the regulation of alcohol intake in men, we have recently shown that male Rasgrf2(-/-) mice exhibit reduced ethanol intake and preference accompanied by a perturbed mesolimbic dopamine system. We therefore propose that these mice represent a valid model to further elucidate the precise genes and mechanisms regulating mesolimbic dopamine functioning. Transcriptomic data from the nucleus accumbens (NAcc) of male Rasgrf2(-/-) mice and wild-type controls were analyzed by weighted gene coexpression network analysis (WGCNA). We performed follow-up genetic association tests in humans using a sample of male adolescents from the IMAGEN study characterized for binge drinking (n = 905) and ventral striatal activation during an fMRI reward task (n = 608). The WGCNA analyses using accumbal transcriptomic data revealed 37 distinct "modules," or functionally related groups of genes. Two of these modules were significantly associated with Rasgrf2 knockout status: M5 (p < 0.001) and M6 (p < 0.001). In follow-up translational analyses we found that human orthologues for the M5 module were significantly (p < 0.01) enriched with genetic association signals for binge drinking in male adolescents. Furthermore, the most significant locus, originating from the EH-domain containing 4 (EHD4) gene (p < 0.001), was also significantly associated with altered ventral striatal activity in male adolescents performing an fMRI reward task (pempirical < 0.001). It was not possible to determine the extent to which the M5 module was dysregulated in Rasgrf2(-/-) mice by perturbed mesolimbic dopamine signalling or by the loss of Rasgrf2 function in the NAcc. Taken together, our findings indicate that the accumbal M5 module, initially identified as being dysregulated in male Rasgrf2(-/-) mice, is also relevant for human alcohol-related phenotypes potentially through the modulation of reinforcement mechanisms in the NAcc. We therefore propose that the genes comprising this module represent important candidates for further elucidation within the context of alcohol-related phenotypes.

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

In many case-control genetic association studies, a secondary phenotype that may have common genetic factors with disease status can be identified. When information on the secondary phenotype is available only for the case group due to cost and different data sources, a fitting linear regression model ignoring supplementary phenotype data may provide limited knowledge regarding genetic association. We set up a joint model and use a Bayesian framework to estimate and test the effect of genetic covariates on disease status considering the secondary phenotype as an instrumental variable. The application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the 16th Genetic Analysis Workshop.

A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci.

BackgroundCharacterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs.MethodsWe apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1.ResultsWe demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes.ConclusionsWe conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.

Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease

In genetic association testing, failure to properly control for population structure can lead to severely inflated type 1 error and power loss. Meanwhile, adjustment for relevant covariates is often desirable and sometimes necessary to protect against spurious association and to improve power. Many recent methods to account for population structure and covariates are based on linear mixed models (LMMs), which are primarily designed for quantitative traits. For binary traits, however, LMM is a misspecified model and can lead to deteriorated performance. We propose CARAT, a binary-trait association testing approach based on a mixed-effects quasi-likelihood framework, which exploits the dichotomous nature of the trait and achieves computational efficiency through estimating equations. We show in simulation studies that CARAT consistently outperforms existing methods and maintains high power in a wide range of population structure settings and trait models. Furthermore, CARAT is based on a retrospective approach, which is robust to misspecification of the phenotype model. We apply our approach to a genome-wide analysis of Crohn disease, in which we replicate association with 17 previously identified regions. Moreover, our analysis on 5p13.1, an extensively reported region of association, shows evidence for the presence of multiple independent association signals in the region. This example shows how CARAT can leverage known disease risk factors to shed light on the genetic architecture of complex traits.

Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.

The number of methods for genome-wide testing of gene-environment (G-E) interactions continues to increase, with the aim of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods, assessed on the basis of family-wise type I error rate and power, depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting G-E interactions by evaluating the impact of exposure misclassification. We consider 7 single-step and modular screening methods for identifying G-E interaction at a genome-wide level and 7 joint tests for genetic association and G-E interaction, for which the goal is to discover new genetic susceptibility loci by leveraging G-E interaction when present. In terms of statistical power, modular methods that screen on the basis of the marginal disease-gene relationship are more robust to exposure misclassification. Joint tests that include main/marginal effects of a gene display a similar robustness, which confirms results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide searches for G-E interaction and joint tests in the presence of exposure misclassification.

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests

Background: In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem. Methods: We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation. Results: Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause. Conclusion: The PEGI method may improve power to detect an association over both CC and GI under many circumstances.

Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been examined and documented. Due to the difficulties of modeling multiple responses, nonparametric tests such as generalized Kendall's Tau have been developed to assess the association between multiple responses and risk factors. These procedures have been applied to genomewide association studies of multiple complex traits. Unfortunately, those nonparametric tests only provide the significance of the association but not the magnitude. We propose a Gaussian copula model with discrete margins for modeling multivariate binary responses. This model separates marginal effects from between-trait correlations. We use a bootstrapping margins approach to constructing Wald's statistic for the association test. Although our derivation is based on the fully parametric Gaussian copula framework for simplicity, the underlying assumptions to apply our method can be weakened. The bootstrapping margins approach only requires the correct specification of the model margins. Our simulation and real data analysis demonstrate that our proposed method not only increases power over some existing association tests, but also provides further insight into genetic association studies of multivariate traits.

A probabilistic method for testing and estimating selection differences between populations.

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection.

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer's Disease.

Results from Genome-Wide Association Studies (GWAS) have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer’s Disease (AD) ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.

Increase in Dickkopf-1 Serum Level in Recent Spondyloarthritis. Data from the DESIR Cohort.

ObjectivesTo investigate DKK-1 and SOST serum levels among patients with recent inflammatory back pain (IBP) fulfilling ASAS criteria for SpA and associated factors.MethodsThe DESIR cohort is a prospective, multicenter French cohort of 708 patients with early IBP (duration >3 months and <3 years) suggestive of AxSpA. DKK-1 and SOST serum levels were assessed at baseline and were compared between the subgroup of patients fulfilling ASAS criteria for SpA (n = 486; 68.6%) and 80 healthy controls.ResultsMean SOST serum levels were lower in ASAS+ patients than healthy controls (49.21 ± 25.9 vs. 87.8 ± 26 pmol/L; p<0.0001). In multivariate analysis, age (p = 5.4 10−9), CRP level (p<0.0001) and serum DKK-1 level (p = 0.001) were associated with SOST level. Mean DKK-1 serum levels were higher in axial SpA patients than controls (30.03 ± 15.5 vs. 11.6 ± 4.2 pmol/L; p<0.0001). In multivariate analysis, DKK-1 serum levels were associated with male gender (p = 0.03), CRP level (p = 0.006), SOST serum level (p = 0.002) and presence of sacroiliitis on radiography (p = 0.05). Genetic association testing of 10 SNPs encompassing the DKK-1 locus failed to demonstrate a significant contribution of genetics to control of DKK-1 serum levels.ConclusionsDKK-1 serum levels were increased and SOST levels were decreased among a large cohort of patients with early axial SpA compared to healthy controls. DKK-1 serum levels were mostly associated with biological inflammation and SOST serum levels.

An exposure‐weighted score test for genetic associations integrating environmental risk factors

Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene-environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome-wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.

SPS: A Simulation Tool for Calculating Power of Set-Based Genetic Association Tests.

Set-based association tests, combining a set of single-nucleotide polymorphisms into a unified test, have become important approaches to identify weak-effect or low-frequency risk loci of complex diseases. However, there is no comprehensive and user-friendly tool to estimate power of set-based tests for study design. We developed a simulation tool to estimate statistical power of multiple representative set-based tests (SPS). SPS has a graphic interface to facilitate parameter settings and result visualization. Advanced functions include loading real genotypes to define genetic architecture, set-based meta-analysis for risk loci with or without heterogeneity, and parallel simulations. In proof-of-principle examples, SPS took no more than 3 sec on average to estimate the power in a conventional setting. The SPS has been integrated into a user-friendly software tool (KGG) as an independent functional module and it is freely available at http://statgenpro.psychiatry.hku.hk/limx/kgg/.

The functional basis of wing patterning in Heliconius butterflies: the molecules behind mimicry.

Wing-pattern mimicry in butterflies has provided an important example of adaptation since Charles Darwin and Alfred Russell Wallace proposed evolution by natural selection >150 years ago. The neotropical butterfly genus Heliconius played a central role in the development of mimicry theory and has since been studied extensively in the context of ecology and population biology, behavior, and mimicry genetics. Heliconius species are notable for their diverse color patterns, and previous crossing experiments revealed that much of this variation is controlled by a small number of large-effect, Mendelian switch loci. Recent comparative analyses have shown that the same switch loci control wing-pattern diversity throughout the genus, and a number of these have now been positionally cloned. Using a combination of comparative genetic mapping, association tests, and gene expression analyses, variation in red wing patterning throughout Heliconius has been traced back to the action of the transcription factor optix. Similarly, the signaling ligand WntA has been shown to control variation in melanin patterning across Heliconius and other butterflies. Our understanding of the molecular basis of Heliconius mimicry is now providing important insights into a variety of additional evolutionary phenomena, including the origin of supergenes, the interplay between constraint and evolvability, the genetic basis of convergence, the potential for introgression to facilitate adaptation, the mechanisms of hybrid speciation in animals, and the process of ecological speciation.

Genetic risk alleles associated with serologic autoimmunity in normal individuals (BA3P.113)

Abstract We screened 2628 normal individuals for antibodies binding to the mix of antigens in a human nuclear extract and identified 579 (26.05%) individuals with significant levels of IgG antibodies to self-antigens. Next, we analyzed 639 representative sera from ANA-, ANA+, ILE and SLEs on an autoantigen proteomic array, which identified 33 antigens strongly recognized by IgG autoantibodies in ANA+ group as compared to ANA- normals. Of these 33, 19 antigens were non-nuclear or cytoplasmic proteins and 14 were nuclear proteins, including known SLE associated antigens such as dsDNA and Histones. Antigen cluster analysis showed that DNA and RNA clusters are more frequent in SLE and ILE group, while matrix proteins and antiphospholipid antibodies clusters are more frequent in ANA+ normals. Next, all the individuals were genotyped with immunochip and subset by target sequencing. Quantitative genetic association test identified SNPs associated with ANA and auto-antigens. The strongest association signal was observed at HLA, where SNP rs3117103 near BTNL2, rs9268832 near HLA-DRA and rs2395252 in HLA-DQA2 gene showed strongest (10-7) association. Other signals were in/near BANK1, PTTG1 and CR1L genes. RNA sequencing analysis identified ANA risk alleles associated with up regulation of HLA-DRB1 in monocytes and BANK1 in B cells. Our data suggest that functional variations in or near HLA DRB1 and BANK1 gene may be associated with development of humoral autoimmunity in general population.

Abstract P1-03-02: CYP2D6 intermediate metabolizers includes patient groups with distinct metabolic activity

Abstract Background: Tamoxifen is a selective estrogen receptor modulator that is the most commonly used and cost effective hormonal agent for pre-menopausal hormone-receptor positive breast cancer patients. CYP2D6 activity phenotype, which is classified by genotype, predicts the extent of metabolic activation of tamoxifen to endoxifen. We previously reported that increasing the daily dose to 40 mg/day in intermediate metabolizers (IMs), but not poor metabolizers (PMs), achieves target endoxifen concentrations, defined as that of extensive metabolizers (EMs) on 20 mg/day. There was substantial endoxifen variability in the IM phenotype group, which is composed of several discrete diplophenotypes (EM/IM, EM/PM, IM/IM, IM/PM). We enrolled a second, larger cohort of patients in order to determine whether these diplophenotypes should be combined into a single IM phenotype or segregated. Methods: 380 patients on tamoxifen ≥ 4 months and not on potent CYP2D6 inhibiting medications enrolled in Lineberger Comprehensive Cancer Center (LCCC) trial 0801. Genotyping was performed using the Amplichip® CYP450 test (Roche Diagnostics) for CYP2D6, followed by systematic assignment of phenotype based on diplophenotype. Tamoxifen was increased from 20 to 40 mg/day in PMs and IMs. Endoxifen concentrations in IM diplophenotypes were compared with EM/EMs and PM/PMs at baseline and at 4 months (after dose increase in patients with IM and PM phenotypes). Results: After exclusion of UM patients and patients missing endoxifen data at baseline and/or 4 months, 295 patients were included in this analysis. At baseline the EM/IM patients had similar endoxifen level to the EM/EM patients while the IM/IM and IM/PM patients had similar levels to the PM/PMs. After 4 months on 40 mg/day the endoxifen concentrations in EM/IM patients were significantly greater than EM/EMs; EM/PM and IM/IM patients were similar to EM/EMs; but IM/PM patients remained significantly lower than EM/EMs and similar to PM/PMs (See Table 1 for results). Conclusions: The large group of patients currently defined as CYP2D6 intermediate metabolizers is comprised of four distinct CYP2D6 diplophentoypes. The most metabolically active diplophenotype (EM/IM) are very similar to EM/EMs while the least active diplophenotype (IM/PM) are similar to PM/PMs. A more accurate CYP2D6 activity classification system may be necessary if genetic association testing and genotype-guided therapy are pursued. Endoxifen Level at Baseline and 4 Months by CYP2D6 Diplophenotype Baseline Endoxifen 4-Month Endoxifen DiplophenotypenMedian (SD)P-val vs. EM/EMP-val vs. PM/PMMedian (SD)P-val vs. EM/EMP-val vs. PM/PMEM/EM11038.67 (6.01)NAp=0.00018.23 (5.09)NAp=0.007EM/IM2568.02 (4.75)p=0.09p=0.00213.11 (9.38)p&amp;lt;0.0001p&amp;lt;0.0001EM/PM2745.72 (4.45)p=0.0001p=0.028.91 (5.28)p=0.42p=0.003IM/IM2174.29 (4.10)p=0.001p=0.266.52 (5.53)p=0.27p=0.24IM/PM2323.90 (3.17)p&amp;lt;0.0001p=0.485.82 (3.47)p=0.0009p=0.77PM/PM3133.33 (2.89)p=0.0001NA6.08 (2.57)p=0.007NA1Diplophenotype classified as extensive metabolizer phenotype, continued on 20 mg/day. 2Diplophenotypes classified as intermediate metabolizer phenotype, changed to 40 mg/day. 3Diplophenotype classified as poor metabolizer phenotype, changed to 40 mg/day. Citation Format: Daniel L Hertz, Anna C Snavely, Howard L McLeod, Christine M Walko, Joseph G Ibrahim, Steven Anderson, Karen E Weck, Peter Rubin, Oludamilola Olajide, Susan Moore, Rachel Raab, Daniel R Carrizosa, Steven Corso, Gary Schwartz, Jeffrey M Peppercorn, James P Evans, Zeruesenay Desta, David A Flockhart, Lisa A Carey, William J Irvin Jr. CYP2D6 intermediate metabolizers includes patient groups with distinct metabolic activity [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P1-03-02.

Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.