Segregation Analysis of a Complex Quantitative Trait: Approaches for Identifying Influential Data PointsIgo Jr. R.P.a · Chapman N.H.b · Wijsman E.M.b, c
aDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, Departments of bMedicine and cBiostatistics, University of Washington, Seattle, Wash., USA
Do you have an account?
- Rent for 48h to view
- Buy Cloud Access for unlimited viewing via different devices
- Synchronizing in the ReadCube Cloud
- Printing and saving restrictions apply
Rental: USD 8.50
Cloud: USD 20.00
Article / Publication Details
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
- Atwood LD, Heard-Costa NL: Limits of fine-mapping a quantitative trait. Genet Epidemiol 2003;24:99–106.
- Jarvik GP: Complex segregation analysis: uses and limitations. Am J Hum Genet 1998;63:942–946.
- MacLean CJ, Morton NE, Lew R: Analysis of family resemblance. IV. Operational characteristics of segregation analysis. Am J Hum Genet 1975;27:365–384.
- Go RCP, Elston RC, Kaplan EB: Efficiency and robustness of pedigree segregation analysis. Am J Hum Genet 1978;30:28–37.
- Dizier MH, Bonaitipellie C, Clerget-Darpoux F: Conclusions of segregation analysis for family data generated under 2-locus models. Am J Hum Genet 1993;53:1338–1346.
- Hanis CL, Chakraborty R: Nonrandom sampling in human genetics: familial correlations. IMA J Math Appl Med Biol 1984;1:193–213.
- Heath SC: Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. Am J Hum Genet 1997;61:748–760.
Igo RP Jr, Chapman NH, Berninger VW, Matsushita M, Brkanac Z, Rothstein JH, Holzman T, Nielsen K, Raskind WH, Wijsman EM: Genomewide scan for real-word reading subphenotypes of dyslexia: Novel chromosome 13 locus and genetic complexity. Am J Med Genet (Neuropsychiatr Genet) 2006;141:15–27.
- Wijsman EM, Yu D: Joint oligogenic segregation and linkage analysis using Bayesian Markov chain Monte Carlo methods. Mol Biotech 2004;28:205–226.
- Berninger VW, Abbott RD, Thomson JB: Language phenotype for reading and writing disability: A family approach. Sci Stud Reading 2001;5:59–106.
- Raskind WH, Igo RP Jr, Chapman NH, Thomson JB, Matsushita M, Brkanac Z, Holzman T, Brown M, Berninger VW, Wijsman EM: A genome-wide linkage analysis in multigenerational families with dyslexia: identification of a novel locus on chromosome 2q that contributes to phonological decoding efficiency. Mol Psychiatr 2005;10:699–711.
- Bonney G: Regressive logistic models for familial disease and other binary traits. Biometrics 1986;42:611–625.
S.A.G.E.: Statistical Analysis for Genetic Epidemiology, Release 3.1. 1997; Computer program package available from the Department of Epidemiology and Biostatistics, Rammelkamp Center for Education and Research, MetroHealth Campus, Case Western Reserve University, Cleveland, OH.
- Demenais FM, Bonney GE: Equivalence of the mixed and regressive models for genetic analysis. I. Continuous traits. Genet Epidemiol 1989;6:597–617.
Barnett V, Lewis T: Outliers in Statistical Data. New York, Wiley & Sons, 1984, pp 63–78.
Article / Publication Details
Copyright / Drug Dosage / DisclaimerCopyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.