Strategies for Conditional Two-Locus Nonparametric Linkage Analysis
Lars Ängquist, Ola Hössjer and Leif Groop
Centre for Mathematical Sciences
Lund Institute of Technology,
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
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.
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