Combined Association and Linkage Analysis for General Pedigrees and Genetic Models

Hössjer Ola
Additional contact information
Hössjer Ola: University of Stockholm, Sweden

Statistical Applications in Genetics and Molecular Biology, 2005, vol. 4, issue 1, 42

Abstract: A combined score test for association and linkage analysis is introduced, based on a biologically plausible model with association between markers and causal genes and penetrance between phenotypes and the causal gene. The test is based on a retrospective likelihood of marker data given phenotypes, treating the alleles of the causal gene as hidden data. It is defined for arbitrary outbred pedigrees, a wide class of genetic models including polygenic and shared environmental effects and allows for missing marker data. It is multipoint, taking marker genotypes from several loci into account simultaneously. The score vector has one association and one linkage component, which can be used to define separate tests for association and linkage. For complete marker data, we give closed form expressions for the efficiency of the linkage, association and combined tests. These are examplified for binary and quantitative phenotypes with or without polygenic effects. The conclusion is that association tests are comparatively more efficient than linkage tests for strong association, weak penetrance models, small families and non-extreme phenotypes, whereas the linkage test is more efficient for weak association, strong penetrance models, large families and extreme phenotypes. The combined test is a robust alternative, which never performs much worse than the best of the linkage and association tests, and sometimes significantly better than both of them. It should be particularly useful when little is known about the genetic model.

Keywords: Association; linkage; multipoint test; noncentrality parameter; score test (search for similar items in EconPapers)
Date: 2005
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1116 (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:4:y:2005:i:1:n:11

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

DOI: 10.2202/1544-6115.1116

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 ().