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Next-generation sequencing technology not only presents a new method for the detection of human genomic structural variation, but also provides a large number of genetic data of rare variants for us. Currently, how to detect association between human complex diseases and rare variants using genetical data has attracted extensive attention. In the field of medicine, many people's health and disease conditions are measured by ordinal response variables, namely, the trait value reflects the development stage or severity of a certain condition. However, most existing methods to test for association between rare variants and complex diseases are designed to deal with dichotomous or quantitative traits. Association analysis methods of ordinal traits are relatively fewer, and considering ordinal traits as dichotomous and quantitative traits will inevitably lose some valuable information in the original data. Therefore, in this paper, we extend an existing method of adaptive combination of P values (ADA) and propose a new method of association analysis for ordinal trait based on it (called OR-ADA) to test for possible association between ordinal trait and rare variants. In our method, we establish a cumulative logistic regression model, in which the regression coefficients are estimated by the Newton-Raphson algorithm and the likelihood ratio test is used to test the association. Through a large number of simulation studies and an example, we demonstrate the performance of the new method and compare it with several methods. The analysis results show that the OR-ADA strategy is robust to the signs of effects of causal variants and more powerful under many scenarios.

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared. [ABSTRACT FROM AUTHOR]