### Using importance sampling to improve simulation in linkage analysis

(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)

Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/834630

- author
- Ängquist, Lars
^{LU}and Hössjer, Ola - organization
- publishing date
- 2003
- type
- Contribution to journal
- 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- unpublished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
- 2016-04-04 09:30:52
- date last changed
- 2018-11-21 20:53:38

@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}, }