Efficient two-trait-locus linkage analysis through program optimization and parallelization: application to hypercholesterolemia.

摘要

We have optimized and parallelized the GENEHUNTER-TWOLOCUS program that allows to perform linkage analysis with two trait loci in the multimarker context. The optimization of the serial program, before parallelization, results in a speedup of a factor of more than 10. The parallelization affects the two-locus-score calculation, which is predominant in terms of computation time. We obtain perfect speedup, that is, the computation time decreases exactly by a factor of the number of processors. In addition, two-locus LOD and NPL scores are now calculated for varying genetic positions of both disease loci, not just one locus varied and the position of the other disease locus fixed, as before. This results in easily interpretable 3-D plots. We have reanalyzed a pedigree with hypercholesterolemia using our new version of GENEHUNTER-TWOLOCUS. Whereas originally, two individuals had to be discarded due to excessive computation-time demands, the entire 17-bit pedigree could now be analyzed as a whole. We obtaina two-trait-locus LOD score of 5.49 under a multiplicative model, compared to LOD scores of 3.08 and 2.87 under a heterogeneity and additive model, respectively. This further increases evidence for linkage to both 1p36.1-p35 and 13q22-q32 regions, and corroborates the hypothesis that the two genes act in a multiplicative way on LDL cholesterol level. Furthermore, we compare the computation times for two-trait-locus analysis needed by the programs GENEHUNTER-TWOLOCUS, TLINKAGE, and SUPERLINK. Altogether, our algorithmic improvements of GENEHUNTER-TWOLOCUS allow researchers to analyze complex diseases under realistic two-trait-locus models with pedigrees of reasonable size and using many markers.