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Using importance sampling to improve simulation in linkage analysis

Ängquist, Lars LU and Hössjer, Ola (2003) In Preprint without journal information
Abstract
In this article we describe and discuss implementation of a weighted simulation procedure, importance sampling, in the context of nonparametric linkage analysis. The objective is to estimate genome-wide p-values, i.e. the probability that the maximal linkage score exceeds a given threshold under the null hypothesis of no linkage. In order to reduce variance of the p-value estimate for large thresholds, we simulate linkage scores under a distribution different from the null with an artificial disease locus positioned somewhere along the genome. To compensate for the fact that we simulate under the wrong distribution, the simulated scores are reweighted using a certain likelihood ratio. If design parameters of the sampling distribution are... (More)
In this article we describe and discuss implementation of a weighted simulation procedure, importance sampling, in the context of nonparametric linkage analysis. The objective is to estimate genome-wide p-values, i.e. the probability that the maximal linkage score exceeds a given threshold under the null hypothesis of no linkage. In order to reduce variance of the p-value estimate for large thresholds, we simulate linkage scores under a distribution different from the null with an artificial disease locus positioned somewhere along the genome. To compensate for the fact that we simulate under the wrong distribution, the simulated scores are reweighted using a certain likelihood ratio. If design parameters of the sampling distribution are chosen correctly, the variance of the final significance value estimate is reduced. This results in more accurate genome-wide p-value estimates for large thresholds, based on a substantially smaller number of simulations than is needed using traditional unweighted simulation.

We illustrate the performance of the method for several pedigree examples, discuss implementation including choice of sampling parameters and describe some possible generalizations. (Less)
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organization
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publication status
unpublished
subject
in
Preprint without journal information
issue
2003:34
publisher
Manne Siegbahn Institute
ISSN
0348-7911
language
English
LU publication?
yes
id
e573d956-c93c-488f-b97e-a7b0dbfdbfc4 (old id 834630)
date added to LUP
2008-01-10 14:49:56
date last changed
2016-04-16 06:49:08
@article{e573d956-c93c-488f-b97e-a7b0dbfdbfc4,
  abstract     = {In this article we describe and discuss implementation of a weighted simulation procedure, importance sampling, in the context of nonparametric linkage analysis. The objective is to estimate genome-wide p-values, i.e. the probability that the maximal linkage score exceeds a given threshold under the null hypothesis of no linkage. In order to reduce variance of the p-value estimate for large thresholds, we simulate linkage scores under a distribution different from the null with an artificial disease locus positioned somewhere along the genome. To compensate for the fact that we simulate under the wrong distribution, the simulated scores are reweighted using a certain likelihood ratio. If design parameters of the sampling distribution are chosen correctly, the variance of the final significance value estimate is reduced. This results in more accurate genome-wide p-value estimates for large thresholds, based on a substantially smaller number of simulations than is needed using traditional unweighted simulation. <br/><br>
We illustrate the performance of the method for several pedigree examples, discuss implementation including choice of sampling parameters and describe some possible generalizations.},
  author       = {Ängquist, Lars and Hössjer, Ola},
  issn         = {0348-7911},
  language     = {eng},
  number       = {2003:34},
  publisher    = {Manne Siegbahn Institute},
  series       = {Preprint without journal information},
  title        = {Using importance sampling to improve simulation in linkage analysis},
  year         = {2003},
}