Submitted to: Genome Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/1998
Publication Date: N/A
Citation: N/A Interpretive Summary: Presently, agriculture is experiencing another revolution as biotechnology and genomics enable new approaches and products. For genomics to be successful, agriculturally important genes need to be identified. Typically, this is done by generating a population that is varying for your trait of interest and then determining whether particular alleles of genes are associated with the trait. Assessment of the latter step is using performed statistical methods. As there are a variety of ways to approach this problem, it is desirable to use the simplest and least computationally demanding one that gives the desired result. In this paper, we describe and demonstrate the use of a new technique that seems to give good results while limiting the computer time required. This method is widely applicable and will facilitate the discovery of new genes.
Technical Abstract: A typical problem in mapping quantitative trait loci (QTL) comes from the missing QTL genotype. A routine method for paramter estimation involving missing data is the mixture model maximum likelihood method implemented via the EM algorithm. We developed an alternaitve QTL mapping method that describes a mixture of several distributions by a single model with a heterogeneous residual variance. The two methods produce virtually identical results, but the heterogeneous variance method is computationally much faster than the mixture model approach. We derive the new method in the content of QTL mapping for Binary traits in a F2 population. Using the heterogeneous variance model, we identified a QTL on chromosome IV that control Marek's disease susceptibility in chickens. The QTL alone explains 7.2% of the total trait variation.