Association detection between ordinal trait and rare variants based on adaptive combination of P values

Association detection between multiple traits and rare variants based on family data via a nonparametric method

Background

The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits.

Methods

In this article, we construct a new non-parametric statistic by the generalized Kendall's τ theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases.

Results

We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect.

Gene-level association analysis of bivariate ordinal traits with functional regressions

In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.

A fast and efficient approach for gene-based association studies of ordinal phenotypes

Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.

Gene-level association analysis of ordinal traits with functional ordinal logistic regressions

In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.

A two-stage testing strategy for detecting genes×environment interactions in association studies

Identifying gene×environment (G×E) interactions, especially when rare variants are included in genome-wide association studies, is a major challenge in statistical genetics. However, the detection of G×E interactions is very important for understanding the etiology of complex diseases. Although currently some statistical methods have been developed to detect the interactions between genes and environment, the detection of the interactions for the case of rare variants is still limited. Therefore, it is particularly important to develop a new method to detect the interactions between genes and environment for rare variants. In this study, we extend an existing method of adaptive combination of P-values (ADA) and design a novel strategy (called iSADA) for testing the effects of G×E interactions for rare variants. We propose a new two-stage test to detect the interactions between genes and environment in a certain region of a chromosome or even for the whole genome. First, the score statistic is used to test the associations between trait value and the interaction terms of genes and environment and obtain the original P-values. Then, based on the idea of the ADA method, we further construct a full test statistic via the P-values of the preliminary tests in the first stage, so that we can comprehensively test the interactions between genes and environment in the considered genome region. Simulation studies are conducted to compare our proposed method with other existing methods. The results show that the iSADA has higher power than other methods in each case. A GAW17 data set is also applied to illustrate the applicability of the new method.

A permutation method for detecting trend correlations in rare variant association studies

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Gene association detection via local linear regression method

The development of next-generation sequencing technology has provided us with great convenience in genetic association studies and many effective analysis methods were proposed continuously. However, population stratification is still a major issue in current genetic association studies. Many existing methods have been developed to remove the bias due to population stratification for common variant association studies, but such methods may be not effective for rare variant, which will lead to power reduction. Therefore, in this paper, we develop a principal component analysis strategy (called PC-LLR) based on local linear regression method to eliminate population stratification effect in both rare variant and common variant association studies. Simulation results indicate that the new PC-LLR method can eliminate population stratification effect well. It has correct type I error rates in all cases and higher powers in most cases, while most existing methods have inflated type I error rates at least in some cases. We also demonstrate that the PC-LLR is more effective to eliminate population stratification effect through applying the PC-LLR to the whole-exome sequencing data set from genetic analysis workshop 19 (GAW19).

A modified association test for rare and common variants based on affected sib-pair design

Current genome-wide association analysis has identified a great number of rare and common variants associated with common complex traits, however, more effective approaches for detecting associations between rare and common variants with common diseases are still demanded. Approaches for detecting rare variant association analysis will compromise the power when detecting the effects of rare and common variants simultaneously. In this paper, we extend an existing method of testing for rare variant association based on affected sib pairs (TOW-sib) and propose a variable weight test for rare and common variants association based on affected sib pairs (abbreviated as VW-TOWsib). The VW-TOWsib can be used to achieve the purpose of detecting the association of rare and common variants with complex diseases. Simulation results in various scenarios show that our proposed method is more powerful than existing methods for detecting effects of rare and common variants. At the same time, the VW-TOWsib also performs well as a method for rare variant association analysis.

Whole-exome sequencing and gene-based rare variant association tests suggest that PLA2G4E might be a risk gene for panic disorder

Panic disorder (PD) is characterized by recurrent and unexpected panic attacks, subsequent anticipatory anxiety, and phobic avoidance. Recent epidemiological and genetic studies have revealed that genetic factors contribute to the pathogenesis of PD. We performed whole-exome sequencing on one Japanese family, including multiple patients with panic disorder, which identified seven rare protein-altering variants. We then screened these genes in a Japanese PD case-control group (384 sporadic PD patients and 571 controls), resulting in the detection of three novel single nucleotide variants as potential candidates for PD (chr15: 42631993, T>C in GANC; chr15: 42342861, G>T in PLA2G4E; chr20: 3641457, G>C in GFRA4). Statistical analyses of these three genes showed that PLA2G4E yielded the lowest p value in gene-based rare variant association tests by Efficient and Parallelizable Association Container Toolbox algorithms; however, the p value did not reach the significance threshold in the Japanese. Likewise, in a German case-control study (96 sporadic PD patients and 96 controls), PLA2G4E showed the lowest p value but again did not reach the significance threshold. In conclusion, we failed to find any significant variants or genes responsible for the development of PD. Nonetheless, our results still leave open the possibility that rare protein-altering variants in PLA2G4E contribute to the risk of PD, considering the function of this gene.