Multivariate Phenotype Association Analysis by Marker‐Set Kernel Machine Regression

Maity, Arnab; Sullivan, Patrick F.; Tzeng, Jung-Ying

doi:10.1002/gepi.21663

Cited by 81 publications

(146 citation statements)

References 25 publications

Supporting

Mentioning

142

Contrasting

Order By: Relevance

“…By considering multiple phenotypes that measure the different aspects of underlying diseases, the power of the association analysis can potentially be improved (Zhang et al, 2010;Maity et al, 2012). Several methods were recently developed to detect the joint effect of genetic variants on multivariate phenotype (Tao et al, 2015;Wang et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A generalized association test based on U statistics

Wei

2017

Bioinformatics

View full text Add to dashboard Cite

Summary:Motivation: Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotype. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes. Considering the genotype and phenotype as two complex objects, this also poses a general statistical problem of testing association between complex objects. Results: We here proposed a similarity-based test, generalized similarity U (GSU), that can test the association between complex objects. We first studied the theoretical properties of the test in a general setting and then focused on the application of the test to sequencing association studies. Based on theoretical analysis, we proposed to use Laplacian kernel based similarity for GSU to boost power and enhance robustness. Through simulation, we found that GSU did have advantages over existing methods in terms of power and robustness. We further performed a whole genome sequencing (WGS) scan for Alzherimer's Disease Neuroimaging Initiative (ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with imaging phenotype. Availability: We developed a C++ package for analysis of whole genome sequencing data using GSU. The source codes can be downloaded at https://github.com/changshuaiwei/gsu. Contact:

show abstract

Section: Discussionmentioning

confidence: 99%

“…We evaluated the performance of GSU by comparing it with variance component score (VCscore) test under univariate or multivariate linear mixed model (Wu et al, 2011;Maity et al, 2012). For each simulation, we created 1000 simulation replicates to evaluate type I error and power.…”

Section: ) and A Cauchy-distributed Phenotype (Denoted As C) Bymentioning

confidence: 99%

A generalized association test based on U statistics

Wei

2017

Bioinformatics

View full text Add to dashboard Cite

show abstract

“…with the corresponding b hj 6 ¼ 0). As discussed in more detail in Kim et al (2016), MTSPUsSet (1, 1) is like a burden test (Shen et al, 2010), while MTSPUsSet (c 1 , c 2 ) for large values of c 1 and c 2 is effectively equivalent to a univariate minimum P value test on all single SNP-single trait pairs; MTSPUsSet(2, 2) is closely related to a variance-component score test in kernel machine regression (Maity et al, 2012) A main innovation here is to use a matrix normal distribution (Gupta and Nagar, 1999;Zhou, 2014) to obtain P values based on the known asymptotic normal distribution of the Z scores under H 0 . Specifically, denote Z (i) as the ith row vector, and Z j as the jth column vector (i.e.…”

Section: Gene-based Testsmentioning

confidence: 99%

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

Kwak

Pan

2016

Bioinformatics

View full text Add to dashboard Cite

Summary: To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene-or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP-and traitlevels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a metaanalysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. Availability and Implementation: The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/.

show abstract

“…Another extreme is the burden test (Shen et al 2010;Guo et al 2013;Mukherjee et al 2014), which is powerful in the presence of a dense association pattern, in which most SNP-trait pairs are associated with almost equal effect sizes and directions; otherwise, e.g., when the association directions of some SNP-trait pairs are different, it does not perform well (as is well known for analysis of rare variants). A compromise between the above two extremes is a variance-component test (Maity et al 2012;Wang et al 2013), which is more robust to association density/ sparsity and varying association directions. Nevertheless, as shown in the context of multiple rare variants and a single trait , it may still suffer from power loss in the presence of more sparse association patterns (i.e., when there are fewer associated SNP-trait pairs).…”

mentioning

confidence: 99%

“…To date, several but not many methods have been proposed for gene-based multitrait analysis (Maity et al 2012;Guo et al 2013;Van der Sluis et al 2015;Wang et al 2015). The simplest way is to use the minimum P-value (minP) test based on the most significant single-SNP-singletrait association, which, however, may lose power in the presence of multiple weak associations between multiple SNPs and multiple traits.…”

mentioning

confidence: 99%

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

Kim¹,

Zhang²,

Pan

2016

Genetics

View full text Add to dashboard Cite

Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genomewide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.KEYWORDS adaptive association test; ADNI; default mode network; gene-based test; imaging genetics; multiple traits A LZHEIMER'S disease (AD) (MIM 104300) is the most common neurodegenerative disease, and every 67 sec, someone in the United States develops AD (Alzheimer's Association 2015a). Currently there is no cure for AD, and most cases are diagnosed in the late stage of the disease. It is projected that the number of Americans of age 65 years and older with AD will increase from 5.1 million in 2015 to 13.5 million in 2050, a growth from an estimated 11% of the U.S. senior population in 2015 to 16% in 2050, costing .$1.1 trillion in 2050 (Alzheimer's Association 2015b). To advance our...

show abstract

Multivariate Phenotype Association Analysis by Marker‐Set Kernel Machine Regression

Cited by 81 publications

References 25 publications

A generalized association test based on U statistics

A generalized association test based on U statistics

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

Contact Info

Product

Resources

About