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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2007-07-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC1920557?pdf=render |
| _version_ | 1819232294305529856 |
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| author | Pengyuan Liu Haris Vikis Yan Lu Daolong Wang Ming You |
| author_facet | Pengyuan Liu Haris Vikis Yan Lu Daolong Wang Ming You |
| author_sort | Pengyuan Liu |
| collection | DOAJ |
| 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 approximately 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. |
| first_indexed | 2024-12-23T11:58:34Z |
| format | Article |
| id | doaj.art-09f3a9d3b25d42e285f0ff05ef534555 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T11:58:34Z |
| publishDate | 2007-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-09f3a9d3b25d42e285f0ff05ef5345552022-12-21T17:48:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032007-07-0127e65110.1371/journal.pone.0000651Large-scale in silico mapping of complex quantitative traits in inbred mice.Pengyuan LiuHaris VikisYan LuDaolong WangMing YouUnderstanding 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 approximately 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.http://europepmc.org/articles/PMC1920557?pdf=render |
| spellingShingle | Pengyuan Liu Haris Vikis Yan Lu Daolong Wang Ming You Large-scale in silico mapping of complex quantitative traits in inbred mice. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC1920557?pdf=render |
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