Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies

Hua, Wen-Yu; Ghosh, Debashis

doi:10.1111/biom.12314

Cited by 30 publications

(39 citation statements)

References 22 publications

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“…Hua and Ghosh (2014) pointed out its equivalence to KMR; by the close connections among MDMR, KMR and the SPU(2) (i.e. SSU) test, we obtain its equivalence to other tests.…”

Section: Methodsmentioning

confidence: 56%

Comparison of statistical tests for group differences in brain functional networks

et al. 2014

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Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that, Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can be also applied to adjust for co-variates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

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“…Hua and Ghosh (2014) pointed out its equivalence to KMR; by the close connections among MDMR, KMR and the SPU(2) (i.e. SSU) test, we obtain its equivalence to other tests.…”

Section: Methodsmentioning

confidence: 56%

Comparison of statistical tests for group differences in brain functional networks

et al. 2014

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“…Since previous work has performed extensive simulations to characterize the overall score test for the semiparametric model [Hua and Ghosh, 2014; Liu et al, 2007], we focused our simulations on testing for the interaction effect. Our major concern is to assess whether the main effects “bleed” into the interaction, yielding false positives, or “cloud” the interaction, reducing sensitivity.…”

Section: Methodsmentioning

confidence: 99%

A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application

Nichols

Ghosh

et al. 2015

NeuroImage

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Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks.

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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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Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies

Cited by 30 publications

References 22 publications

Comparison of statistical tests for group differences in brain functional networks

Comparison of statistical tests for group differences in brain functional networks

A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application

Sequence Kernel Association Test of Multiple Continuous Phenotypes

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