Statistical Methods for Association Tests of Multiple Continuous Traits in Genome‐Wide Association Studies

Wu, Baolin; Pankow, James S.

doi:10.1111/ahg.12110

Cited by 19 publications

(21 citation statements)

References 18 publications

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“…Many methods have been developed for testing the association of common variants with multiple traits (see Avery et al., ; Ferreira and Purcell, ; He et al., ; Hua and Ghosh, ; Liu et al., ; Maity et al., ; OReilly et al., ; Rasmussen‐Torvik et al., ; Schifano et al., ; van der Sluis et al., ; Wu and Pankow, ; Yang et al., , e.g.). In GWAS, identified common variants only explained a small proportion of the phenotypic variance for most complex traits studied to date.…”

Section: Introductionmentioning

confidence: 99%

Sequence Kernel Association Test of Multiple Continuous Phenotypes

Pankow

2016

Genet. Epidemiol.

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Genetic studies often collect multiple correlated traits, which could be analyzed jointly to increase power by aggregating multiple weak effects and provide additional insights into the etiology of complex human diseases. Existing methods for multiple trait association tests have primarily focused on common variants. There is a surprising dearth of published methods for testing the association of rare variants with multiple correlated traits. In this paper, we extend the commonly used sequence kernel association test (SKAT) for single trait analysis to test for the joint association of rare variant sets with multiple traits. We investigate the performance of the proposed method through extensive simulation studies. We further illustrate its usefulness with application to the analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study. We identified an exome-wide significant rare variant set in the gene YAP1 worthy of further investigations.

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Section: Introductionmentioning

confidence: 99%

Sequence Kernel Association Test of Multiple Continuous Phenotypes

Pankow

2016

Genet. Epidemiol.

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“…The null hypothesis of multi-trait association is H 0 : γ = 0. When modeling the population genotype distribution Pr( G | X ) with a logistic regression model (it holds when, e.g., the genotypes follow the Hardy-Weinberg equilibrium within each ancestry population), we can derive an adjacent-category logit model (ACL) (Wu and Pankow, 2015)…”

Section: Methodsmentioning

confidence: 99%

“…In general the POM provides a good approximation to the ACL, and two approaches have similar performance for directly genotyped/observed SNPs (Wu and Pankow, 2015). We want to remark that the inverted regression approach has assumed that the genotypes are directly observed, and all existing methods have implicitly used the “best-guess” genotypes for imputed SNPs.…”

Section: Methodsmentioning

confidence: 99%

“…Another novel approach is based on the inverted regression model, where the genotypes are regressed on the covariates and multivariate outcomes to estimate and test the multi-trait associations and typically some ordinal multinomial regression model is used. For example, O’Reilly et al (2012) adopted the proportional odds model (POM), and Wu and Pankow (2015) proposed the adjacent category logit (ACL) model. For the multivariate regression based approach, it is straightforward to accommodate the imputed SNPs by using their imputation dosages as the covariate.…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide association test of multiple continuous traits using imputed SNPs

Pankow

2017

Statistics and Its Interface

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More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the “best-guess” genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.

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“…There have been a number of publications documenting ways to detect and analyze pleiotropic data, most recently for genome-wide association studies [23,29–58], and also reporting methods to determine power and or MSSN for association mapping [43,45,46,59–66]. If one broadens the search to allow for multiple phenotypes that may not be pleiotropic, the list of published methods increases [34,67–85].…”

Section: Introductionmentioning

confidence: 99%

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Gordon

Londoño

Patel³

et al. 2016

Hum Hered

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Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

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Statistical Methods for Association Tests of Multiple Continuous Traits in Genome‐Wide Association Studies

Cited by 19 publications

References 18 publications

Sequence Kernel Association Test of Multiple Continuous Phenotypes

Sequence Kernel Association Test of Multiple Continuous Phenotypes

Genome-wide association test of multiple continuous traits using imputed SNPs

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

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