Abstract
Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen (O’ Reilly et al. 2012), employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study.
Highlights
Genome-wide association studies (GWASs) have emerged as a common tool for identifying the genetic variants for numerous complex diseases
Even though MultiPhen makes no assumption on the phenotypic distributions, it does not necessarily mean that it is efficient
We first pointed out and prove that a recent method for multiple phenotypes association testing, MultiPhen, is equivalent to an earlier test proposed for the same purpose
Summary
Genome-wide association studies (GWASs) have emerged as a common tool for identifying the genetic variants for numerous complex diseases. The conventional GWASs focus on a single phenotype, aiming to identify the associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype [1,2,3]. Complex human disorders, such as mental disorders, are often characterized by multiple intermediate phenotypes [4, 5]. Many phenotypes, such as body-mass-index and refractive error, are derived from other. Chinese Government, and the International Collaborative Research Fund from NSFC (11328103)
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