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

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

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

Jeesun Jung,Daniel E Weeks,Eleanor Feingold,

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.

Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics. Publishing Authors By Initials

J Jung,DE Weeks,E Feingold,

For similar investigative techniques: epidemiologic methods: statistics as topic: models, statistical: proportional hazards models research abstracts see: investigative techniques: epidemiologic methods: statistics as topic: models, statistical: proportional hazards models research

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

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Genetic epidemiology

VOLUME: 30

Page Numbers: 652-65

Journal Abbreviation: Genet. Epidemiol.

ISSN: 0741-0395

DAY: 3

MONTH: Dec

YEAR: 2006

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

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LANGUAGE: eng

NlmUniqueID: 8411723

Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics. Keywords Mesh Terms:

KEYWORDS: Proportional Hazards Models

MESH TERMS: genetics

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Grant and Affiliation Information for Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

AFFILIATION: Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, PA 15261, USA. jeesun.jung@hgen.pitt.edu

Country: United States

AGENCY: United States NIMH

GRANT: R01MH064205

ACRONYM: MH

MEDLINETA: Genet Epidemiol

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