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Identifying mutation regions for closely related individuals without a known pedigree
BACKGROUND: Linkage analysis is the first step in the search for a disease gene. Linkage studies have facilitated the identification of several hundred human genes that can harbor mutations leading to a disease phenotype. In this paper, we study a very important case, where the sampled individuals a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3507658/ https://www.ncbi.nlm.nih.gov/pubmed/22731852 http://dx.doi.org/10.1186/1471-2105-13-146 |
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author | Cui, Wenjuan Wang, Lusheng |
author_facet | Cui, Wenjuan Wang, Lusheng |
author_sort | Cui, Wenjuan |
collection | PubMed |
description | BACKGROUND: Linkage analysis is the first step in the search for a disease gene. Linkage studies have facilitated the identification of several hundred human genes that can harbor mutations leading to a disease phenotype. In this paper, we study a very important case, where the sampled individuals are closely related, but the pedigree is not given. This situation happens very often when the individuals share a common ancestor 6 or more generations ago. To our knowledge, no algorithm can give good results for this case. RESULTS: To solve this problem, we first developed some heuristic algorithms for haplotype inference without any given pedigree. We propose a model using the parsimony principle that can be viewed as an extension of the model first proposed by Dan Gusfield. Our heuristic algorithm uses Clark’s inference rule to infer haplotype segments. CONCLUSIONS: We ran our program both on the simulated data and a set of real data from the phase II HapMap database. Experiments show that our program performs well. The recall value is from 90% to 99% in various cases. This implies that the program can report more than 90% of the true mutation regions. The value of precision varies from 29% to 90%. When the precision is 29%, the size of the reported regions is three times that of the true mutation region. This is still very useful for narrowing down the range of the disease gene location. Our program can complete the computation for all the tested cases, where there are about 110,000 SNPs on a chromosome, within 20 seconds. |
format | Online Article Text |
id | pubmed-3507658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35076582012-12-03 Identifying mutation regions for closely related individuals without a known pedigree Cui, Wenjuan Wang, Lusheng BMC Bioinformatics Software BACKGROUND: Linkage analysis is the first step in the search for a disease gene. Linkage studies have facilitated the identification of several hundred human genes that can harbor mutations leading to a disease phenotype. In this paper, we study a very important case, where the sampled individuals are closely related, but the pedigree is not given. This situation happens very often when the individuals share a common ancestor 6 or more generations ago. To our knowledge, no algorithm can give good results for this case. RESULTS: To solve this problem, we first developed some heuristic algorithms for haplotype inference without any given pedigree. We propose a model using the parsimony principle that can be viewed as an extension of the model first proposed by Dan Gusfield. Our heuristic algorithm uses Clark’s inference rule to infer haplotype segments. CONCLUSIONS: We ran our program both on the simulated data and a set of real data from the phase II HapMap database. Experiments show that our program performs well. The recall value is from 90% to 99% in various cases. This implies that the program can report more than 90% of the true mutation regions. The value of precision varies from 29% to 90%. When the precision is 29%, the size of the reported regions is three times that of the true mutation region. This is still very useful for narrowing down the range of the disease gene location. Our program can complete the computation for all the tested cases, where there are about 110,000 SNPs on a chromosome, within 20 seconds. BioMed Central 2012-06-25 /pmc/articles/PMC3507658/ /pubmed/22731852 http://dx.doi.org/10.1186/1471-2105-13-146 Text en Copyright ©2012 Cui and Wang; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Cui, Wenjuan Wang, Lusheng Identifying mutation regions for closely related individuals without a known pedigree |
title | Identifying mutation regions for closely related individuals without a known pedigree |
title_full | Identifying mutation regions for closely related individuals without a known pedigree |
title_fullStr | Identifying mutation regions for closely related individuals without a known pedigree |
title_full_unstemmed | Identifying mutation regions for closely related individuals without a known pedigree |
title_short | Identifying mutation regions for closely related individuals without a known pedigree |
title_sort | identifying mutation regions for closely related individuals without a known pedigree |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3507658/ https://www.ncbi.nlm.nih.gov/pubmed/22731852 http://dx.doi.org/10.1186/1471-2105-13-146 |
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