Dataset Information

Combining mapping and arraying: An approach to candidate gene identification.

ABSTRACT: A combination of quantitative trait locus (QTL) mapping and microarray analysis was developed and used to identify 34 candidate genes for ovariole number, a quantitative trait, in Drosophila melanogaster. Ovariole number is related to evolutionary fitness, which has been extensively studied, but for which few a priori candidate genes exist. A set of recombinant inbred lines were assayed for ovariole number, and QTL analyses for this trait identified 5,286 positional candidate loci. Forty deletions spanning the QTL were employed to further refine the map position of genes contributing to variation in this trait between parental lines, with six deficiencies showing significant effects and reducing the number of positional candidates to 548. Parental lines were then assayed for expression differences by using Affymetrix microarray technology, and ANOVA was used to identify differentially expressed genes in these deletions. Thirty-four genes were identified that showed evidence for differential expression between the parental lines, one of which was significant even after a conservative Bonferroni correction. The list of potential candidates includes 5 genes for which previous annotations did not exist, and therefore would have been unlikely choices for follow-up from mapping studies alone. The use of microarray technology in this context allows an efficient, objective, quantitative evaluation of genes in the QTL and has the potential to reduce the overall effort needed in identifying genes causally associated with quantitative traits of interest.

SUBMITTER: Wayne ML

PROVIDER: S-EPMC137517 | biostudies-literature | 2002 Nov

REPOSITORIES: biostudies-literature

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Combining mapping and arraying: An approach to candidate gene identification.

Wayne M L ML McIntyre L M LM

Proceedings of the National Academy of Sciences of the United States of America 20021101 23

A combination of quantitative trait locus (QTL) mapping and microarray analysis was developed and used to identify 34 candidate genes for ovariole number, a quantitative trait, in Drosophila melanogaster. Ovariole number is related to evolutionary fitness, which has been extensively studied, but for which few a priori candidate genes exist. A set of recombinant inbred lines were assayed for ovariole number, and QTL analyses for this trait identified 5,286 positional candidate loci. Forty deletio ...[more]

PMID: 12415114

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Project description:Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective.We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin.In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

Dataset Information

Combining mapping and arraying: An approach to candidate gene identification.

Publications

Combining mapping and arraying: An approach to candidate gene identification.

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