Integrating genetic and gene expression data: methods and applications of eQTL mapping
The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expres...
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| Veröffentlicht in: | Yíchuán Jg. 30; H. 9; S. 1228 |
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| Sprache: | Chinesisch |
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01.09.2008
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| Abstract | The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression. Similar to traditional QTL studies, the main goal of eQTL is to identify the genomic locations to which the expression traits are linked. Although microarrays provide the expression data of thousands of transcripts, standard QTL mapping methods, which are able to handle at most tens of traits, cannot be applied directly. As a result, it is necessary to consider the statistical principles involved in the design and analysis of these experiments. In this paper, we reviewed individual selection, experimental design of microarray, normalization of gene expression data, mapping methods, and explaining of results and proposed potential methodological problems for such analyses. Finally, we discussed the application |
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| AbstractList | The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression. Similar to traditional QTL studies, the main goal of eQTL is to identify the genomic locations to which the expression traits are linked. Although microarrays provide the expression data of thousands of transcripts, standard QTL mapping methods, which are able to handle at most tens of traits, cannot be applied directly. As a result, it is necessary to consider the statistical principles involved in the design and analysis of these experiments. In this paper, we reviewed individual selection, experimental design of microarray, normalization of gene expression data, mapping methods, and explaining of results and proposed potential methodological problems for such analyses. Finally, we discussed the application |
| Author | Liu, Gang Qin, Dan-Dan Ni, Zhong-Fu Sun, Qi-Xin Peng, Hui-Ru Song, Fang-Wei Song, Guang-Shu |
| Author_xml | – sequence: 1 givenname: Gang surname: Liu fullname: Liu, Gang organization: Department of Plant Genetics & Breeding and State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization (MOE), Beijing 100193, China – sequence: 2 givenname: Hui-Ru surname: Peng fullname: Peng, Hui-Ru – sequence: 3 givenname: Zhong-Fu surname: Ni fullname: Ni, Zhong-Fu – sequence: 4 givenname: Dan-Dan surname: Qin fullname: Qin, Dan-Dan – sequence: 5 givenname: Fang-Wei surname: Song fullname: Song, Fang-Wei – sequence: 6 givenname: Guang-Shu surname: Song fullname: Song, Guang-Shu – sequence: 7 givenname: Qi-Xin surname: Sun fullname: Sun, Qi-Xin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18779184$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Animals Chromosome Mapping Gene Expression - physiology Gene Expression Profiling Mice Models, Genetic Oligonucleotide Array Sequence Analysis - instrumentation Oligonucleotide Array Sequence Analysis - methods Quantitative Trait Loci - genetics Quantitative Trait Loci - physiology |
| Title | Integrating genetic and gene expression data: methods and applications of eQTL mapping |
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