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A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits.

ABSTRACT: Genetic pleiotropy refers to the situation in which a single gene influences multiple traits and so it is considered as a major factor that underlies genetic correlation among traits. To identify pleiotropy, an important focus in genome-wide association studies (GWAS) is on finding genetic variants that are simultaneously associated with multiple traits. On the other hand, longitudinal designs are often employed in many complex disease studies, such that, traits are measured repeatedly over time within the same subject. Performing genetic association analysis simultaneously on multiple longitudinal traits for detecting pleiotropic effects is interesting but challenging. In this paper, we propose a 2-step method for simultaneously testing the genetic association with multiple longitudinal traits. In the first step, a mixed effects model is used to analyze each longitudinal trait. We focus on estimation of the random effect that accounts for the subject-specific genetic contribution to the trait; fixed effects of other confounding covariates are also estimated. This first step enables separation of the genetic effect from other confounding effects for each subject and for each longitudinal trait. Then in the second step, we perform a simultaneous association test on multiple estimated random effects arising from multiple longitudinal traits. The proposed method can efficiently detect pleiotropic effects on multiple longitudinal traits and can flexibly handle traits of different data types such as quantitative, binary, or count data. We apply this method to analyze the 16th Genetic Analysis Workshop (GAW16) Framingham Heart Study (FHS) data. A simulation study is also conducted to validate this 2-step method and evaluate its performance.

SUBMITTER: Wang W

PROVIDER: S-EPMC4202779 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits.

Wang Weiqiang W Feng Zeny Z Bull Shelley B SB Wang Zuoheng Z

Frontiers in genetics 20141020

Genetic pleiotropy refers to the situation in which a single gene influences multiple traits and so it is considered as a major factor that underlies genetic correlation among traits. To identify pleiotropy, an important focus in genome-wide association studies (GWAS) is on finding genetic variants that are simultaneously associated with multiple traits. On the other hand, longitudinal designs are often employed in many complex disease studies, such that, traits are measured repeatedly over time ...[more]

PMID: 25368629

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Project description:BackgroundThe common non-synonymous mutation of the glucokinase regulator (GCKR) gene, namely rs1260326, is widely reported to have pleiotropic effects on cardio-metabolic traits and hematological parameters.ObjectiveThis study aimed to identify whether other GCKR variants may have pleiotropic effects independent of the rs1260326 genotypes.MethodsIn total, 81,097 Taiwan Biobank participants were enrolled for the regional plot association studies and candidate variant analysis of the region around the GCKR gene.ResultsThe initial candidate variant approach showed the significant association of the rs1260326 genotypes with multiple phenotypes. Regional plot association analysis of the GCKR gene region further revealed genome-wide significant associations between GCKR variants and serum total and low-density lipoprotein cholesterol; triglyceride, uric acid, creatinine, aspartate aminotransferase, γ-Glutamyl transferase, albumin, and fasting plasma glucose levels; estimated glomerular filtration rate; leukocyte and platelet counts; microalbuminuria, and metabolic syndrome, with rs1260326 being the most common lead polymorphism. Serial conditional analysis identified genome-wide significant associations of two low-frequency exonic mutations, rs143881585 and rs8179206, with high serum triglyceride and albumin levels. In five rare GCKR exonic non-synonymous or nonsense mutations available for analysis, GCKR rs146175795 showed an independent association with serum triglyceride and albumin levels and rs150673460 showed an independent association with serum triglyceride levels. Weighted genetic risk scores from the combination of GCKR rs143881585 and rs146175795 revealed a significant association with metabolic syndrome.ConclusionIn addition to the rs1260326 variant, low-frequency and rare GCKR exonic mutations exhibit pleiotropic effects on serum triglyceride and albumin levels and the risk of metabolic syndrome. These results provide evidence that both common and rare GCKR variants may play a critical role in predicting the risk of cardiometabolic disorders.

Project description:IntroductionParticipant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point.MethodsAcross time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation.ResultsIn the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities.DiscussionCompared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point.

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A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits.

Publications

A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits.

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