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

  • Wang, Meida1
  • Ma, Weijun1
  • Zhou, Ying1
  • 1 School of Mathematical Sciences, Heilongjiang University, Department of Statistics, Harbin, 150080, China , Harbin (China)
Published Article
Journal of Human Genetics
Springer Nature
Publication Date
Nov 07, 2017
DOI: 10.1038/s10038-017-0354-2
Springer Nature


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.

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