Strategies for Conditional Two-Locus Nonparametric Linkage Analysis

Lars Ängquist, Ola Hössjer and Leif Groop

Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,

ISSN 1403-9338
In this article we deal with two-locus nonparametric linkage (NPL) analysis, mainly in the context of conditional analysis. This means that one incorporates single-locus analysis information through conditioning when performing a two-locus analysis. Here we describe different strategies for using this approach
In Cox et al. (1999) they implemented this as follows: (i) Calculate the one-locus NPL process over the included genome region(s). (ii) Weight the individual pedigree NPL scores using a weighting function depending on the NPL scores for the corresponding pedigrees at specific conditioning loci. We generalize this by conditioning with respect to the inheritance vector rather than the NPL score and by separating between the case of known (predefined) and unknown (estimated) conditioning loci. In the latter case we choose conditioning locus, or loci, according to predefined criterions. The most general approach results in a random number of selected loci, depending on the results from the previous one-locus analysis.
Major topics in this article include discussions on optimal score functions with respect to the noncentrality parameter (NCP), and how to calculate adequate p-values and perform power calculations. We also discuss issues related to multiple tests which arise from the two-step procedure with several conditioning loci as well as from the genome-wide tests.
Key words:
Nonparametric linkage analysis, two-locus linkage analysis, conditional linkage analysis, score functions, conditioning loci, two-step procedure, noncentrality parameter, genome-wide significance and power calculations, ROC curves, Monte Carlo simulation