A powerful test for ordinal trait genetic association analysis

Xue Yuan, Wang Jinjuan, Ding Juan, Zhang Sanguo and Li Qizhai ()
Additional contact information
Xue Yuan: School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
Wang Jinjuan: LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Ding Juan: School of Mathematics and Statistics, Guangxi Normal University, Guilin, China
Zhang Sanguo: School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
Li Qizhai: LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, 55, Beijing 100190, China, Phone: +86-10-82541839

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 2, 9

Abstract: Response selective sampling design is commonly adopted in genetic epidemiologic study because it can substantially reduce time cost and increase power of identifying deleterious genetic variants predispose to human complex disease comparing with prospective design. The proportional odds model (POM) can be used to fit data obtained by this design. Unlike the logistic regression model, the estimated genetic effect based on POM by taking data as being enrolled prospectively is inconsistent. So the power of resulted Wald test is not satisfactory. The modified POM is suitable to fit this type of data, however, the corresponding Wald test is not optimal when the genetic effect is small. Here, we propose a new association test to handle this issue. Simulation studies show that the proposed test can control the type I error rate correctly and is more powerful than two existing methods. Finally, we applied three tests to Anticyclic Citrullinated Protein Antibody data from Genetic Workshop 16.

Keywords: ordinal response; power; proportional odds model; response selective sampling (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2017-0066 (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:18:y:2019:i:2:p:9:n:2

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

DOI: 10.1515/sagmb-2017-0066

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 ().