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Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.

ABSTRACT: Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipoprotein cholesterol (HDL) in mice, we demonstrate a strategy for narrowing QTL that utilizes comparative genomics and bioinformatics techniques. We show how QTL detected in multiple crosses are subjected to both combined cross analysis and haplotype block analysis; how QTL from one species are mapped to the concordant regions in another species; and how genomewide scans associating haplotype groups with their phenotypes can be used to prioritize the narrowed regions. Then we illustrate how these individual methods for narrowing QTL can be systematically integrated for mouse chromosomes 12 and 15, resulting in a significantly reduced number of candidate genes, often from hundreds to <10. Finally, we give an example of how additional bioinformatics resources can be combined with experiments to determine the most likely quantitative trait genes.

SUBMITTER: Burgess-Herbert SL

PROVIDER: S-EPMC2600954 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.

Burgess-Herbert Sarah L SL Cox Allison A Tsaih Shirng-Wern SW Paigen Beverly B

Genetics 20081009 4

Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipopr ...[more]

PMID: 18845850

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Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.

Dataset Information

Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.

Publications

Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.

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