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Original Paper

Segregation Analysis of a Complex Quantitative Trait: Approaches for Identifying Influential Data Points

Igo Jr. R.P.a · Chapman N.H.b · Wijsman E.M.b, c

Author affiliations

aDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, Departments of bMedicine and cBiostatistics, University of Washington, Seattle, Wash., USA

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Hum Hered 2006;61:80–86

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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: December 12, 2005
Accepted: March 22, 2006
Published online: June 15, 2006
Issue release date: June 2006

Number of Print Pages: 7
Number of Figures: 0
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

Abstract

Background/Aims: Complex traits pose a particular challenge to standard methods for segregation analysis (SA), and for such traits it is difficult to assess the ability of complex SA (CSA) to approximate the true mode of inheritance. Here we use an oligogenic Bayesian Markov chain Monte Carlo method for SA (OSA) to verify results from a single-locus likelihood-based CSA for data on a quantitative measure of reading ability. Methods: We compared the profile likelihood from CSA, maximized over the trait allele frequency, to the posterior distribution of genotype effects from OSA to explore differences in the overall parameter estimates from SA on the original phenotype data and the same data Winsorized to reduce the potential influence of three outlying data points. Results: Bayesian OSA revealed two modes of inheritance, one of which coincided with the QTL model from CSA. Winsorizing abolished the model originally estimated by CSA; both CSA and OSA identified only the second OSA model. Conclusion: Differences between the results from the two methods alerted us to the presence of influential data points, and identified the QTL model best supported by the data. Thus, the Bayesian OSA proved a valuable tool for assessing and verifying inheritance models from CSA.

© 2006 S. Karger AG, Basel


References

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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: December 12, 2005
Accepted: March 22, 2006
Published online: June 15, 2006
Issue release date: June 2006

Number of Print Pages: 7
Number of Figures: 0
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE


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