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PubMed · 9632166

Efficient, robust, and unified method for mapping complex traits (I): two-point linkage analysis.

L P Zhao·F Quiaoit·L Hsu·C Aragaki
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Abstract

The completion of a preliminary human genome map and development of molecular methods have enabled researchers to assay a large number of polymorphic markers that are evenly spaced along the entire human genome. Among many applications, marker data are valuable for mapping complex traits through linkage or linkage-disequilibrium analysis, the former of which is the focus of this paper, the first in a series on this subject. Formalizing the concept and computation for linkage analysis, Elston and Stewart [1971; Human Heredity 21:523-542] introduced a likelihood function to capture relevant genetic information and a recursive algorithm for computing the likelihood function. However, the computing burden is prohibitive in processing complex pedigrees. Since that fundamental development, improving the computational algorithm and extending the method has been a dynamic area of research. The primary objective of this communication is to introduce a semiparametric method for linkage analysis. It is a particularly suitable approach with desirable properties for mapping complex traits that may be binary, continuous, and partially observed (i.e., censored). It incorporates candidate genes, environmental factors, and their interactions with the putative gene and is expected to be robust and efficient in comparison with likelihood-based methods. The properties of the estimates have been studied in finite samples with a limited simulation study. This method is illustrated with an application to family data contributed to the Breast Cancer Consortium.

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AlgorithmsBreast NeoplasmsChromosome MappingData Interpretation, StatisticalGenes, BRCA1Genetic MarkersHumansModels, StatisticalPedigreePhenotypeJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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L P Zhao, F Quiaoit, L Hsu, C Aragaki. 1998-06-05. Efficient, robust, and unified method for mapping complex traits (I): two-point linkage analysis.. https://pubmed.ncbi.nlm.nih.gov/9632166/

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