Strategies for Conditional TwoLocus Nonparametric Linkage Analysis
Lars Ängquist, Ola Hössjer and Leif Groop
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,
2007
ISSN 14039338

Abstract:

In this article we deal with twolocus nonparametric linkage (NPL) analysis,
mainly in the context of conditional analysis. This means that one incorporates
singlelocus analysis information through conditioning when performing a
twolocus analysis. Here we describe different strategies for using this
approach


In Cox et al. (1999) they implemented this as follows: (i) Calculate the
onelocus 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 onelocus analysis.


Major topics in this article include discussions on optimal score functions
with respect to the noncentrality parameter (NCP), and how to calculate adequate
pvalues and perform power calculations. We also discuss issues related to
multiple tests which arise from the twostep procedure with several conditioning
loci as well as from the genomewide tests.



Key words:

Nonparametric linkage analysis, twolocus linkage analysis, conditional linkage
analysis, score functions, conditioning loci, twostep procedure, noncentrality
parameter, genomewide significance and power calculations, ROC curves, Monte
Carlo simulation
