ArticleHuman heredity2007
Testing genetic linkage with relative pairs and covariates by quasi-likelihood score statistics.
Article in Human heredity, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- PedWiz: a web-based tool for pedigree informatics.Frontiers in genetics · 2013Article
- Program update and novel use of the DESPAIR program to design a genome-wide linkage study using relative pairs.Human heredity · 2010Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
BACKGROUND/
aimsGenetic linkage analysis of common diseases is complicated by the heterogeneity of genetic and environmental factors that increase disease risk, and possibly interactions among them. Most linkage methods that account for covariates are restricted to sib pairs, with the exception of the conditional logistic regression model [1] implemented in LODPAL in the S.A.G.E. software [2]. Although this model can be applied to arbitrary pedigrees, at times it can be difficult to maximize the likelihood due to model constraints, and it does not account for the dependence among the different types of relative pairs in a pedigree.
methodsTo overcome these limitations, we developed a new approach based on score statistics for quasi- likelihoods, implemented as weighted least squares. Our methods can be used to test three different hypotheses: (1) a test for linkage without covariates; (2) a test for linkage with covariates, and (3) a test for effects of covariates on identity by descent sharing (i.e., heterogeneity). Furthermore, our methods are robust because they account for the dependence among different relative pairs within a pedigree. RESULTS AND
conclusionAlthough application of our methods to a prostate cancer linkage study did not find any critical covariates in our data, the results illustrate the utility and interpretation of our methods, and suggest, nonetheless, that our methods will be useful for a broad range of genetic linkage heterogeneity analyses.
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