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

Li Nanxing, Chen Lili (), Zhou Yajing and Wei Qianran
Additional contact information
Li Nanxing: School of Mathematical Sciences, Heilongjiang University, Harbin 150080, P. R. China
Chen Lili: School of Mathematical Sciences, Heilongjiang University, Harbin 150080, P. R. China
Zhou Yajing: School of Mathematical Sciences, Heilongjiang University, Harbin 150080, P. R. China
Wei Qianran: School of Mathematical Sciences, Heilongjiang University, Harbin 150080, P. R. China

Statistical Applications in Genetics and Molecular Biology, 2023, vol. 22, issue 1, 11

Abstract: 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.

Keywords: association analysis; ordinal phenotype; rare variant (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2021-0068 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:22:y:2023:i:1:p:11:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2021-0068

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().