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 Epidemioljournal_title
Genetic epidemiologyauthors
Jung J,Weeks DE,Feingold Edoi
10.1002/gepi.20177subject
Has Abstractpub_date
2006-12-01 00:00:00pages
652-65issue
8eissn
0741-0395issn
1098-2272journal_volume
30pub_type
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