Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics.

Abstract:

:The introduction of stochastic methods in pedigree analysis has enabled geneticists to tackle computations intractable by standard deterministic methods. Until now these stochastic techniques have worked by running a Markov chain on the set of genetic descent states of a pedigree. Each descent state specifies the paths of gene flow in the pedigree and the founder alleles dropped down each path. The current paper follows up on a suggestion by Elizabeth Thompson that genetic descent graphs offer a more appropriate space for executing a Markov chain. A descent graph specifies the paths of gene flow but not the particular founder alleles traveling down the paths. This paper explores algorithms for implementing Thompson's suggestion for codominant markers in the context of automatic haplotyping, estimating location scores, and computing gene-clustering statistics for robust linkage analysis. Realistic numerical examples demonstrate the feasibility of the algorithms.

journal_name

Am J Hum Genet

authors

Sobel E,Lange K

subject

Has Abstract

pub_date

1996-06-01 00:00:00

pages

1323-37

issue

6

eissn

0002-9297

issn

1537-6605

journal_volume

58

pub_type

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