QTL Methods & Applications

What is QTL Mapping?

Quantitative Trait Loci (QTL) analysis is the identification of genes or regions of the genome which influence complex, or quantitative, traits. Unlike discrete traits (e.g. eye color, or the presence or absence of disease), quantitative traits (e.g. blood pressure, weight) vary continuously over a range of distribution in a population and are influenced by multiple genes as well as gene-gene and gene-environment interactions. Using statistical methods it is possible to identify regions of the genome which are responsible for such traits.

Developing New Statistical Methods for QTL Analysis

A major goal of the Churchill Group is to improve the way QTL analysis is performed. We have developed methods for combining data from multiple inbred line crosses, selectively phenotyping crosses, quantitative analysis of information content in genetic mapping study designs, Bayesian methods for mapping epistatic loci, mapping of multi-trait epistatic networks, mapping on the X chromosome, permutation testing in novel cross designs, and we have proposed new experimental strategies for gene identification. We introduced a major advance in multi-trait analysis that uses genetic loci to anchor and direct causal pathways and applied it to body composition traits.

Identifying QTL Underlying Disease

We analyze crosses spanning a wide range of traits including HDL-cholesterol, depression, blood pressure, albuminuria and kidney function, anemia, bone density, asthma, developmental abnormalities, diabetes, and digestion. Several of these studies provided evidence to support specific candidate genes or have narrowed the plausible candidates to a small number of genes.

Improving Accessibility to QTL Data via the QTL Archive

Imagine that you are a statistician with a new idea for genetic analysis and yet you have no close colleagues who are geneticists. Where will you get the data to test your ideas? Unlike other types of data that are generated with public funds, QTL mapping data are rarely made available for further analysis. The QTL Archive at The Jackson Laboratory is a growing resource that provides access to raw data from QTL studies using rodent crosses. Submissions by the community are encouraged and the data are curated by members of the Churchill group. Sharing raw QTL cross data not only protects that data from loss, but allows new advances in QTL analysis to be applied to previously generated crosses. Meta-analysis and gene discovery has been hindered to some extent by a lack of consistency in the genetic maps used in different studies. To address this problem we have recently undertaken an extensive revision of the standard mouse genetic map and are updating our QTL data to the new standard.

Introducing High School Students to QTL Analysis

In a unique experiment in high school education, we have introduced students to genetics and bioinformatics through a series of synthetic and real data problems. In the GeniQuest program students are trained to carry out genetic studies using simulated cross data from the genomes of “dragons”. Once students have mastered the basics, they can analyze QTL data from our QTL Archive. GeniQuest is a team effort combining the talents of The Jackson Laboratory, the Maine Mathematics and Science Alliance, and the Concord Consortium to develop classroom modules to teach computational biology. The project aims to introduce students to genetics, quantitative trait loci analysis, and the relationship between phenotypes and genotypes.


QTL Methods & Applications


Short Course on Systems Genetics
High School Course: GENIQUEST




QTL Archive

Example Publications

Quantitative trait loci for BMD in an SM/J by NZB/BlNJ intercross population and identification of Trps1 as a probable candidate gene
Ishimori N, Stylianou IM, Korstanje R, Marion MA, Li R, Donahue LR, Rosen CJ, Beamer WG, Paigen B, Churchill GA.
J Bone Miner Res. 2008 Sep;23(9):1529-37.
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Sex- and lineage-specific inheritance of depression-like behavior in the rat
Solberg LC, Baum AE, Ahmadiyeh N, Shimomura K, Li R, Turek FW, Churchill GA, Takahashi JS, Redei EE.
Mamm Genome. 2004 Aug;15(8):648-62.

Empirical threshold values for quantitative trait mapping
Churchill GA, Doerge RW.
Genetics. 1994 Nov;138(3):963-71.
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