Computationally Efficient Multipoint Linkage Analysis on Extended Pedigrees for Trait Models With Two Contributing Major Loci

We have developed a computationally efficient method for multipoint linkage analysis on extended pedigrees for trait models having a two-locus quantitative trait loci (QTL) effect. The method has been implemented in the program, hg_lod, which uses the Markov chain Monte Carlo (MCMC) method to sample realizations of descent patterns conditional on marker data, then calculates the trait likelihood for each realization by efficient exact computation. Given its computational efficiency, hg_lod can handle data on large pedigrees with a lot of unobserved individuals, and can compute accurate estimates of logarithm of odds (lod) scores at a much larger number of hypothesized locations than can any existing method. We have compared hg_lod to lm_twoqtl, the first publically available linkage program for trait models with two major loci, using simulated data. Results show that our method is orders of magnitude faster while the accuracy of QTL localization is retained. The efficiency of our method also facilitates analyses with multiple trait models, for example, sensitivity analysis. Additionally, since the MCMC sampling conditions only on the marker data, there is no need to resample the descent patterns to compute likelihoods under alternative trait models. This achieves additional computational efficiency.