For more than 25 years, Jackson Laboratory Professor Beverly Paigen and her laboratory have been pioneers in using novel techniques to reveal the genetic bases of cardiovascular disease. In 2010 a key breakthrough was spearheaded by Dr. Ron Korstanje, who along with his crew has demonstrated that it is possible to narrow quantitative trait loci (QTL) by combining and analyzing data from mouse and rat crosses (Cox et al. 2010). The revolutionary process promises to greatly accelerate the discovery of alleles that regulate a variety of common yet complex human diseases – and to accelerate the discovery of therapies for those diseases.
Quantitative traits are phenotypic characteristics or traits that are regulated by multiple genes. Unlike monogenic traits, they do not follow Mendelian inheritance patterns. QTLs are chromosomal regions that are associated with particular quantitative traits. They are first identified by crossing closely related organisms of one species – such as two mouse or rat strains, or two corn varieties – each of which manifests a different extreme of a phenotype – such as high and low blood pressure, or high and low corn yield. By analyzing the phenotypic data from the cross with sophisticated statistical programs, the trait in question can be associated with particular genomic regions – QTLs.
Initially, QTLs can encompass hundreds of genes, but only one or a few of them actually regulate the trait in question. To identify the regulatory gene(s), a QTL must be reduced to a manageable size – one that contains only a dozen or so genes, from which a few promising "candidates" are stringently tested. Reducing the size of a QTL can take years of tedious work that involves performing more crosses and more complex analyses. The Paigen lab has become particularly adept at shortening the process – by using and refining such techniques as analyzing combined cross data, haplotype analyses and comparative genomics.
Korstanje and crew reasoned that because the mouse and the rat are such closely related species, there should be no reason why mouse cross and rat cross data that identify the same high-density lipoprotein (HDL) QTLs cannot be combined and analyzed to both narrow and increase the statistical power of the QTLs. They were right. They recoded, standardized, and combined data from two HDL QTLs identified in one rat and two mouse crosses: WOKW x DA rats (the WxDA cross), PERA/EiJ (000930) x DBA/2J (000671) mice (the PxD2 cross), and C57BL/6J (000664) x DBA/2J mice (the BxD2 cross). They found that analyzing data combined from a WxDA Chr 6 QTL and an orthologous BxD2 Chr 12 QTL increases the statistical significance of the QTL and narrows it from 44 to 22 cM, reducing the number of genes from 613 to 304. Similarly, analyzing data combined from a WxDA Chr 10 QTL and an orthologous PxD Chr 11 QTL increases the QTL's significance and narrows it from 41 to 19 cM, reducing the number of genes from 1343 to 761.
The work by Korstanje's team was the first to successfully combine QTL data from different species. This approach promises to be a powerful tool for narrowing QTL intervals, identifying genes underlying QTLs and accelerating the development of therapies for complex diseases.
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Cox A, SM, Sheehan SH, Klo¬ting I, Paigen B, Korstanje R. 2010. Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals. Heredity 105:426—32.