Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

Abstract:

:In this study, we compare the statistical properties of a number of methods for estimating P-values for allele-sharing statistics in non-parametric linkage analysis. Some of the methods are based on the normality assumption, using different variance estimation methods, and others use simulation (gene-dropping) to find empirical distributions of the test statistics. For variance estimation methods, we consider the perfect variance approximation and two empirical variance estimates. The simulation-based methods are gene-dropping with and without conditioning on the observed founder alleles. We also consider the Kong and Cox linear and exponential models and a Monte Carlo method modified from a method for finding genome-wide significance levels. We discuss the analytical properties of these various P-value estimation methods and then present simulation results comparing them. Assuming that the sample sizes are large enough to justify a normality assumption for the linkage statistic, the best P-value estimation method depends to some extent on the (unknown) genetic model and on the types of pedigrees in the sample. If the sample sizes are not large enough to justify a normality assumption, then gene-dropping is the best choice. We discuss the differences between conditional and unconditional gene-dropping.

journal_name

Genet Epidemiol

journal_title

Genetic epidemiology

authors

Jung J,Weeks DE,Feingold E

doi

10.1002/gepi.20177

subject

Has Abstract

pub_date

2006-12-01 00:00:00

pages

652-65

issue

8

eissn

0741-0395

issn

1098-2272

journal_volume

30

pub_type

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