Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data

Abstract

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits-single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.

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

“…As discussed in Zhang et al (2014) and Kim et al (2016), the score test is similar to CCA and MANOVA, hence we expect that T Sco will perform similarly to metaCCA, as to be confirmed.…”

“…neuroimaging endophenotypes (Shen et al, 2010;Zhang et al, 2014), can be used to boost power and illuminate on underlying biological mechanisms as compared to popular disease-based single trait analyses; see a review by Yang and Wang (2013). Most of the existing association tests for multiple traits are based on individual-level data (Basu et al, 2013;Fan et al, 2015Fan et al, , 2016Tang and Ferreira, 2012;Wang et al, 2015;Wang et al, 2016;Yang et al, 2010;Zhang et al, 2014) with only few exceptions such as MGAS (Van der Sluis et al, 2015) and metaCCA (Cichonska et al, 2016). Third, to increase the sample size, large consortia are being formed, aiming for meta analysis of multiple GWAS, for which often only summary statistics for single SNP-single trait associations, rather than individual-level genotypic and phenotypic data, are available.…”

“…MGAS (Van der Sluis et al, 2015) uses an extended Simes procedure and behaves like a univariate minimum P value approach, while metaCCA (Cichonska et al, 2016) is based on canonical correlation analysis (CCA) of multiple traits and multiple SNPs, which is related to MANOVA and the GEE-score test (Kim et al, 2016;Zhang et al, 2014); the latter two may lose power in some situations with multiple but relatively sparse and weak association signals between the traits and SNPs (Pan et al, 2014;Zhang et al, 2014).…”

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

“…In particular, the SPU (2) However, it is not clear how to choose an optimal g a priori for given data. Hence, Zhang et al (2014) proposed an aSPU test to extract association evidence from multiple SPU(g) tests. The statistic of the aSPU test is the minimum P-value of SPUðgÞs for some candidate values of g,…”

“…We introduce a novel gene-based adaptive sum of powered score test for a set of multiple traits, denoted as aSPUset, by extending the single SNP-based test of Zhang et al (2014). Suppose that there are p SNPs in a gene and k traits of interests.…”