Allowing for missing genotypes in any members of the nuclear families in transmission disequilibrium test

Gülhan Alpargu

Computational Statistics & Data Analysis, 2011, vol. 55, issue 3, 1236-1249

Abstract: The Transmission Disequilibrium Test (TDT) detects linkage between a marker and a disease-susceptibility locus in the presence of linkage disequilibrium. The TDT requires data on the genotypes of affected offspring and their parents, which might not always be available. For example, for late onset diseases it might be difficult to find parents still alive, or genotypes of offspring might not be available. Genotyping unaffected siblings, combining different genotype data sets, or assuming a model mechanism for missing parents have all been proposed to deal with missing genotypes in parents but not in offspring. In this paper, we propose a Mendel Inheritance-Transmission Disequilibrium Test (MI-TDT) to impute missing genotypes in any members of a family with two affected offspring. Our method does not require any of the remedies mentioned above but simply utilizes the fundamental property of Mendel Inheritance on the transmission of alleles from parents to offspring. Most importantly, the MI-TDT reassures researchers about the declared significant genes when incomplete data is ignored. We illustrate the MI-TDT by identifying significant genes in type 1 diabetes from the Warren families in the United Kingdom.

Keywords: Affected; offspring; Incomplete; genotype; Linkage; disequilibrium; Mendel; inheritance; Transmission; disequilibrium; Type; 1; diabetes (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00347-6
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:55:y:2011:i:3:p:1236-1249

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().