Numerical methods for genetic analysis of complex traits

Participants

Alumni

  • Mahen Jayawardena (PhD thesis 2010, Also at University of Colombo School of Computing, Sri Lanka. Principal advisor: S. Holmgren. Co-advisors: Ö. Carlborg, SLU and R. Weerasinghe, UCSC)
  • Kateryna Mishchenko (PhD thesis 2008, Mälardalen University College. Principal advisor: S. Holmgren. Co-advisors: L. Rönnegård, LCB and D. Silvestrov, MdH).
  • Kajsa Harling (f. Ljungberg) (PhD thesis 2005, Principal advisor: S. Holmgren. Co-advisor: Örjan Carlborg)

The group collaborates closely with Örjan Carlborg's group at the Swedish University of Agricultural Sciences (SLU) and Lars Rönnegård at the Statistics group of Dalarna University. The group also has contacts with the research groups of Leif Andesson and Dietrich von Rosen at SLU.

Research

Background: Most traits of medical or economical importance, such as blood pressure and cholesterol levels in humans, body weight of broiler chickens, and crop yield for maise and corn, are quantitative in nature. These traits are normally determined by the joint effect of multiple genes and the environment. Quantitative Trait Locus (QTL) mapping is a method for finding the genetic regions governing quantitative traits. Computationally, QTL mapping involves employing statistical models of the relation between phenotypes and genotypes of experimental data from populations for locating the most probable positions of the QTL and determining the statistical significance of the result. Such results can be useful in marker-assisted breeding, or as a candidate screening tool in basic research.

Deterministic global optimization methods for mapping of multiple QTL: The computations involve solving multidimensional global optimization problems for finding the most likely positions of the QTL. Using structure and knowledge from the application field, efficient deterministic schemes for the global optimization problems are derived, examined, and used for problems of interest in genetics. Also, versions of the schemes that can perform significance testing of a putative set of QTL positions very efficiently are derived.

Numerical methods for computing the QTL model fit: Different types of statistical models are used for evaluating the fit of a given set of QTL positions. In the most straight-forward scheme a linear model is used and the residual sum of squares is computed by solving a least-squares problem. Using alternative settings, a weighted least-square or a non-linear maximum-likelihood problem are solved. Introducing orthogonal model might facilitate model selection, i.e. selecting not only the most likely QTL positions, but through automated means determining the total number of QTL and where significance interactions can be found. The use of variance component models is rapidly increasing in the field of QTL analysis, and in this case a rather demanding non-linear optimization problem must be solved for each set of QTL positions. Using the structure of the QTL analysis problems, efficient algorithms for different types of model fit computations and model
selection settings are derived, analyzed and implemented.

High Performance Implementations: Even if efficient algorithms are used for the QTL search and QTL model evaluations, the computations are still demanding for models with many QTL. Parallel implementations on different types of high performance computers are developed and studied. Also, a web-based QTL portal using the software in the project is being developed, providing a basis for straightforward and practical use of the QTL mapping tool by genetics researchers.

HMMs for genotype probabilities: Existing tools for computing genotype probabilities, the basis for all models for QTL analysis, have proven unsufficient for our needs. We are therefore presenting the tool cnF2freq which, based on Hidden Markov Models, can provide genotype and haplotype probabilities. Contact Carl Nettelblad for more information, a how-to and sample data will be presented here soon.

Articles and Refereed Conference Contributions

Technical Reports and Preprints

Theses

Supervised M.Sc. Theses and Project Reports

  • S. Toor, A Grid Portal Implementation for Genetic Mapping of Multiple QTL. MSc Thesis in Scientific Computing, Uppsala University, 2007. (Advisor: S. Holmgren)

Software

The numerical methods developed in the project are implemented in C and Matlab. Some of the codes have been incorporated into the publicly available and widely used WebQTL and R/qtl packages. Some code is available at the Software page. We also have a specific page with information and code related to the cnF2freq codebase in different versions.

Contact Information

Sverker Holmgren