Title: Nonparametric Linkage Analysis via Sequential Imputation

Abstract: Multilocus calculations using all available information on all pedigree members are important in linkage analysis. Exact calculation methods for analysis are limited in either the number of loci or the number of pedigree members they can handle. Alternatives to exact calculation are Monte Carlo approaches, such as sequential imputation. Unlike exact methods, sequential imputation can handle both a moderate number of loci and a large number of pedigree members. Sequential imputation is an application of importance sampling in which ordered genotypes are sequentially imputed locus by locus and then the inheritance vectors are imputed conditional on these genotypes. The resulting inheritance vectors together with the importance sampling weights are used to derive consistent estimators of the linkage statistics of interest. This presentation will focus on the use of sequential imputation in calculating nonparametric linkage statistics, such as S_pairs or S_all. Results from a simulation study to illustrate the potential gain in power using larger pedigrees will be discussed. The results from sequential imputation will be compared with analyses performed by the popular analysis package GENEHUNTER. The GENEHUNTER analysis needed to drop between 38% to 54% of the pedigree members whereas sequential imputation was able to use all pedigree members. The power gains from using all pedigree members were substantial under two of the three genetic models studied.