Public Release: 

New screening method may speed hunt for disease-causing genes

Purdue University

WEST LAFAYETTE, Ind. -- Time and money spent in the quest for disease-causing genes may be drastically cut using a new identification method developed by Purdue University and University of Florida scientists.

By merging two established genetic-screening techniques, Lauren McIntyre, Purdue assistant professor of agronomy, and Marta Wayne, Florida assistant professor of zoology, may have found a way to save researchers thousands of dollars and years of work in finding candidate genes -- those most likely involved with specific inherited diseases.

The two authors report their work in the Oct. 22 issue of the Proceedings of the National Academy of Sciences.

The new technique narrows the pool of genes in a study from thousands of possibilities to fewer than 100 -- perhaps as few as 20, said McIntyre, a researcher in Purdue's Computational Genomics Group. The results should help scientists pinpoint target genes in any species for which the genome is complete.

But this is a daunting task: Fruit flies have about 14,000 genes; people have about 30,000 genes. When scientists hunt for genes responsible for inherited traits for such maladies as Alzheimer's or Parkinson's diseases, they use a combination of intuition and screening techniques to narrow the field of possibilities. Mapping studies alone can identify between hundreds and thousands of genes, leaving researchers to sort through long lists of potentially causative genes at a substantial cost in time and money.

"Although the genomes of many species have been mapped, the function of those genes is still unknown," McIntyre said. "If you have 500 genes and they are unannotated, you won't have the resources to follow up on all of them.

"Scientists spend the money and do the work, and they often wind up with a false positive. Our approach adds an additional quantitative step, giving scientists options instead of forcing them to rely solely on subjective information."

Wayne, an experimental biological geneticist, centers her work on one of sciences' classic research tools: the fruit fly -- Drosophila melanogaster, which was the model for Wayne and McIntyre's study.

Female fruit flies have varying numbers of ovarioles -- ovary chambers through which eggs pass. The number of ovarioles depends on where the flies' ancestors were born: Flies from populations born near the equator have fewer ovarioles than flies in the north or south. Wayne's goal in this study was to determine the genes responsible for this variation. This is significant to her investigation of fruit fly evolution because it relates to the number of eggs a fly lays.

McIntyre and Wayne used a popular screening method, called quantitative trait locus mapping, or QTL, to identify the regions in the fruit fly genome that might contain the genes responsible for the number of ovarioles. QTL, which screens genes for differences in their DNA, reduced the number of candidate genes from the total of 14,000 to around 5,000. The scientists then used fine mapping techniques to reduce the list to several hundred genes. However, this list is too long for an individual scientist to follow up.

Using microarray technology, which screens genes based on their RNA amounts, McIntyre and Wayne combed this smaller gene pool for differential gene expression.

The two researchers propose that genes that differ in the amount of RNA -- a critical element in cell activities, are more likely to be involved in different traits. The result of using this method reduced the pool of possible genes to between 20 and 50.

"We hypothesize that a gene showing evidence for differences at both the DNA and the RNA level is a reasonable choice for a candidate gene," McIntyre said.

Sergey Nuzhdin, an associate professor of evolution and ecology at the University of California-Davis, said other scientists saw the need to combine QTL and microarray mapping but that Wayne and McIntyre were the first to pull it off.

"They were able to merge these two emerging disciplines to derive nontrivial conclusions," he said.

The research required Wayne and the technicians in her laboratory to spend hundreds of hours dissecting thousands of flies. The goal was to count each of the fly's artichoke-shaped ovary's ovarioles in order to exactly locate the genes in the genome.

McIntyre and Wayne expect to continue their investigation of this new technique by studying whether the genes they have identified are indeed responsible for the number of ovarioles present in various flies.

They believe that the method, if confirmed, will aid in the search for genes that cause diseases in many species, including people.

Grants from the National Institutes of Health and the U.S. Department of Agriculture provided funding for this study.

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Writers: Purdue -- Susan A. Steeves, 765-496-7481, ssteeves@purdue.edu
University of Florida -- Aaron Hoover, 352-392-0186, ahoover@ufl.edu

Sources: Lauren McIntyre, 765-496-3662, lmcintyre@purdue.edu
Marta Wayne, 352-392-9925, mlwayne@zoo.ufl.edu

Related Web sites:

Purdue Computational Genomics: http://www.genomics.purdue.edu/
Purdue Department of Agronomy: http://www.agry.purdue.edu
Lauren McIntyre: http://www.agry.purdue.edu/staffbio/lmmbio.htm
Marta Wayne: http://www.zoo.ufl.edu/mlwayne
U.S. Dept. of Energy, Human Genome Program: http://www.ornl.gov/hgmis/

ABSTRACT

Combining mapping and arraying: An approach to candidate gene identification

M.L. Wayne and L.M. McIntyre

A combination of quantitative trait locus (QTL) mapping and microarray analysis was developed and used to identify 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 apriori candidate genes exist. A set of recombinant inbred lines were assayed for ovarioles 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.

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