TAILIEUCHUNG - Báo cáo sinh học: "An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops | Genetics Selection Evolution BioMed Central Research An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops Liviu RTotir1 Rohan L Fernando 2 and Joseph Abraham3 Address 1Pioneer Hi-Bred International A Dupont Business 7250 NW 62nd Ave Johnston Iowa 5013 USA 2Department of Animal Science and Center for Integrated Animal Genomics Iowa State University Ames Iowa 50011 USA and 3Case Western Reserve University Cleveland Ohio 44106 USA Email Liviu RTotir - Rohan L Fernando - rohan@ Joseph Abraham - jabraham@ Corresponding author Open Access Published 3 December 2009 Received 22 April 2009 Genetics Selection Evolution 2009 41 52 doi 1297-9686-41 -52 Accepted 3 December 2009 This article is available from http content 41 1 52 2009 Totir et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Marginal posterior genotype probabilities need to be computed for genetic analyses such as geneticcounseling in humans and selective breeding in animal and plant species. Methods In this paper we describe a peeling based deterministic exact algorithm to compute efficiently genotype probabilities for every member of a pedigree with loops without recourse to junction-tree methods from graph theory. The efficiency in computing the likelihood by peeling comes from storing intermediate results in multidimensional tables called cutsets. Computing marginal genotype probabilities for individual i requires recomputing the likelihood for each of the possible genotypes of individual i. This can be done efficiently by storing intermediate results in two types of cutsets called anterior and .

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