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Sökning: swepub > (1995-2009) > Annan publikation > Lunds universitet > Ängquist Lars

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  • Ängquist, Lars, et al. (författare)
  • Unconditional two-locus nonparametric linkage analysis
  • 2005
  • Annan publikation (övrigt vetenskapligt)abstract
    • We discuss different aspects of unconditional two-locus nonparametric linkage (NPL) analysis with special emphasis on gene-gene interaction. We interpret this as identical-by-descent (IBD) sharing correlation between two disease loci both having marginal effect. We relate this to the concept of two-locus NPL score functions, the possible importance of using a composite rather than a simple null hypothesis and the corresponding calculation of statistical power. Moreover, we define several classes of score functions and give multiple suggestions on how to incorporate a composite null hypothesis into the analysis. The least favourable two-locus IBD-distribution is discussed, resulting in an upper bound of the two-locus p-value.
  • Ängquist, Lars, et al. (författare)
  • Using importance sampling to improve simulation in linkage analysis
  • 2003
  • Annan publikation (övrigt vetenskapligt)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 /> We illustrate the performance of the method for several pedigree examples, discuss implementation including choice of sampling parameters and describe some possible generalizations.
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Luthman, Holger (1)
Anevski, Dragi (1)
Hössjer, Ola (1)
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