Longitudinal SNP‐set association analysis of quantitative phenotypes

Recent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT.

Section: An Lmm For Correlated Microbiome Datamentioning

confidence: 99%

mentioning

confidence: 99%

A small‐sample kernel association test for correlated data with application to microbiome association studies

Zhan

Xue

Zheng

et al. 2018

“…The tests using model-based inference assume a compound-symmetry/auto-regressive within-subject correlation structure in a mixed model, but violation of this assumption can lead to inflated type I error rate. We note that this model-based inference is similar to the longitudinal sequence kernel association test (LSKAT) and longitudinal burden test (LBT) proposed by Wang et al (2017), where they assume the within-subject correlation to be a mixture of compound-symmetry and auto-regressive structure. Their method practically reduces the type I error inflation and have equivalent power as model-based inference when the within-subject correlation is correctly specified, although the type I error rate is not theoretically justified to be robust.…”

Section: Numerical Simulationsmentioning

confidence: 76%

“…These methods are primarily proposed for modeling and testing a modest number of variants compared to the number of subjects. For gene-based analysis, several groups have recently extended the burden and dispersion tests to longitudinal studies through mixed effect models or generalized estimating equations (He, et al 2015; Wang, Xu, Zhang, Wu and Wang, 2017). The mixed effect approaches are model-based, which can lead to inflated type I error rate when the within subject correlation is misspecified.…”

Section: Introductionmentioning

confidence: 99%

“…The mixed effect approaches are model-based, which can lead to inflated type I error rate when the within subject correlation is misspecified. Wang et al (2017) proposed a practical strategy to reduce the inflation by combining multiple working correlation structures. Although it can work well for various scenarios, the type I error rate is not theoretically justified to be robust.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)

Lee

Zhang

et al. 2017

Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene-based association tests have been proposed to aggregate the genetic variants within a gene, pathway or specific genomic region as opposed to a one-at-a-time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interest. However, usual sandwich/model-based inference for sequencing studies with longitudinal outcomes and rare variants can produce deflated/inflated type I error rate without further corrections. In this paper, we develop a group of tests for rare-variant association based on outcomes with repeated measures. We propose new perturbation methods such that the type I error rate of the new tests is not only robust to misspecification of within-subject correlation, but also significantly improved for variants with extreme rarity in a study with small or moderate sample size. Through extensive simulation studies, we illustrate that substantially higher power can be achieved by utilizing longitudinal outcomes and our proposed finite sample adjustment. We illustrate our methods using data from the Multi-Ethnic Study of Atherosclerosis for exploring association of repeated measures of blood pressure with rare and common variants based on exome sequencing data on 6361 individuals.

Adaptive testing for association between two random vectors in moderate to high dimensions

Pan

2017

Testing for association between two random vectors is a common and important task in many fields, however, existing tests, such as Escoufier’s RV test, are suitable only for low-dimensional data, not for high-dimensional data. In moderate to high dimensions, it is necessary to consider sparse signals, which are often expected with only a few, but not many, variables associated with each other. We generalize the RV test to moderate to high dimensions. The key idea is to data-adaptively weight each variable pair based on its empirical association. As the consequence, the proposed test is adaptive, alleviating the effects of noise accumulation in high-dimensional data, and thus maintaining the power for both dense and sparse alternative hypotheses. We show the connections between the proposed test with several existing tests, such as a generalized estimating equationsG-based adaptive test, multivariate kernel machine regression, and kernel distance methods. Furthermore, we modify the proposed adaptive test so that it can be powerful for non-linear or non-monotonic associations. We use both real data and simulated data to demonstrate the advantages and usefulness of the proposed new test. The new test is freely available in R package at https://github.com/jasonzyx/aSPC.