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Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe...

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Autores principales: Liu, Pengyuan, Vikis, Haris, Lu, Yan, Wang, Daolong, You, Ming
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920557/
https://www.ncbi.nlm.nih.gov/pubmed/17653278
http://dx.doi.org/10.1371/journal.pone.0000651
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author Liu, Pengyuan
Vikis, Haris
Lu, Yan
Wang, Daolong
You, Ming
author_facet Liu, Pengyuan
Vikis, Haris
Lu, Yan
Wang, Daolong
You, Ming
author_sort Liu, Pengyuan
collection PubMed
description Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with ∼40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.
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spelling pubmed-19205572007-07-25 Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice Liu, Pengyuan Vikis, Haris Lu, Yan Wang, Daolong You, Ming PLoS One Research Article Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with ∼40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community. Public Library of Science 2007-07-25 /pmc/articles/PMC1920557/ /pubmed/17653278 http://dx.doi.org/10.1371/journal.pone.0000651 Text en Liu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Liu, Pengyuan
Vikis, Haris
Lu, Yan
Wang, Daolong
You, Ming
Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title_full Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title_fullStr Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title_full_unstemmed Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title_short Large-Scale In Silico Mapping of Complex Quantitative Traits in Inbred Mice
title_sort large-scale in silico mapping of complex quantitative traits in inbred mice
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920557/
https://www.ncbi.nlm.nih.gov/pubmed/17653278
http://dx.doi.org/10.1371/journal.pone.0000651
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