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Vol. 60, No. 1, 2005
Issue release date: 2005
Hum Hered 2005;60:36–42
(DOI:10.1159/000087917)

Empirical Bayes Method for Incorporating Data from Multiple Genome Scans

Beasley T.M.a · Wiener H.b · Zhang K.a · Bartolucci A.A.a · Amos C.I.e · Allison D.a, c
Departments of aBiostatistics, Section of Statistical Genetics, bEpidemiology, and cNutrition Sciences and Clinical Nutrition Research Center, The University of Alabama at Birmingham, Birmingham, Ala.; eDepartment of Epidemiology, University of Texas, M.D. Anderson Cancer Center, Houston, Tex., USA
email Corresponding Author

Abstract

Individual genome scans tend to have low power and can produce markedly biased estimates of QTL effects. Further, the confidence interval for their location is often prohibitively large for subsequent fine mapping and positional cloning. Given that a large number of genome scans have been conducted, not to mention the large number of variables and subsets tested, it is difficult to confidently rule out type 1 error as an explanation for significant effects even when there is apparent replication in a separate data set. We adapted Empirical Bayes (EB) methods [1] to analyze data from multiple genome scans simultaneously and alleviate each of these problems while still allowing for different QTL population effects across studies. We investigated the effects of using the EB method to include data from background studies to update the results of a single study of interest via simulation and demonstrated that it has a stable confidence level over a wide range of parameters defining the background studies and increased the power to detect linkage, even when some of the background studies were null or had QTL effect at other markers. This EB method for incorporating data from multiple studies into genome scan analyses seems promising.


 goto top of outline Key Words

  • Empirical Bayes
  • Genome scan
  • Data sharing

 goto top of outline Abstract

Individual genome scans tend to have low power and can produce markedly biased estimates of QTL effects. Further, the confidence interval for their location is often prohibitively large for subsequent fine mapping and positional cloning. Given that a large number of genome scans have been conducted, not to mention the large number of variables and subsets tested, it is difficult to confidently rule out type 1 error as an explanation for significant effects even when there is apparent replication in a separate data set. We adapted Empirical Bayes (EB) methods [1] to analyze data from multiple genome scans simultaneously and alleviate each of these problems while still allowing for different QTL population effects across studies. We investigated the effects of using the EB method to include data from background studies to update the results of a single study of interest via simulation and demonstrated that it has a stable confidence level over a wide range of parameters defining the background studies and increased the power to detect linkage, even when some of the background studies were null or had QTL effect at other markers. This EB method for incorporating data from multiple studies into genome scan analyses seems promising.

Copyright © 2005 S. Karger AG, Basel


 goto top of outline References
  1. Morris CN: Parametric empirical Bayes inference: Theory and applications. J Am Stat Assoc 1983;78:47–55.

    External Resources

  2. Blacker D, et al: Results of a high-resolution genome screen of 437 Alzheimer’s Disease families. Hum Molec Genet 2003;12:23–32.
  3. Haseman JK, Elston RC: The investigation of linkage between a quantitative trait and marker locus. Behav Genet 1972;2:3–19.
  4. Carlin BP, Louis TA: Empirical Bayes: Past, present and future. J Am Stat Assoc 2000;95:1286–1289.

    External Resources

  5. Efron B, Morris CN: Stein’s estimator and its competitors – An empirical Bayes approach. J Am Stat Assoc 1973;68:117–130.

    External Resources

  6. Carlin BP, Louis TA: Bayes and Emprirical Bayes Methods for Data Analysis, ed 2. Boca Raton, Chapman and Hall, 2000.
  7. Bonney GE, Amfoth KK, Sherman SL, Keats BJB: An application of empirical Bayes methods to updating linkage information on chromosome 21. Cytogenet Cell Genet 1992;59:112–113.
  8. Li Z, Rao DC: Random effects model for meta-analysis of multiple quantitative sib pair linkage studies. Genet Epidemiol 1996;13:377–383.
  9. Lockwood JR, Roeder K, Devlin: A Bayesian hierarchical model for allele frequencies. Genet Epidemiol 2001;20:17–33.
  10. Witte JS: Genetic analysis with hierarchical models. Genet Epidemiol 1997;14:1137–1142.
  11. Efron B, Morris CN: Data Analysis using Stein’s estimator and its generalizations. J Am Stat Assoc 1975;70:379–421.
  12. Tang R: Fitting and evaluating certain two-level hierarchical models. PhD dissertation, Harvard University, Cambridge, MA, 2002.
  13. Fulker DW, Cherney SS, London CR: Multipoint interval mapping of quantitative trait loci using sib pairs. Am J of Hum Genet 1995;56:1224–1333.
  14. Etzel CJ, Guerra R: Meta-analysis of genetic-linkage analysis of quantitative-trait-loci. Am J Hum Genet 2002;71:56–65.

 goto top of outline Author Contacts

T. Mark Beasley, PhD
Department of Biostatistics, University of Alabama at Birmingham
1665 University Boulevard
Birmingham, AL 35294 (USA)
Tel. +1 205 975 4957, Fax +1 205 975 2540, E-Mail MBeasley@ms.soph.uab.edu


 goto top of outline Article Information

Received: February 28, 2005
Accepted: June 22, 2005
Published online: August 31, 2005
Number of Print Pages : 7
Number of Figures : 5, Number of Tables : 0, Number of References : 14


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 60, No. 1, Year 2005 (Cover Date: 2005)

Journal Editor: Devoto, M. (Wilmington, Del.)
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.com/hhe


Copyright / Drug Dosage / Disclaimer

Copyright: 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 or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
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 goverment 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.

Abstract

Individual genome scans tend to have low power and can produce markedly biased estimates of QTL effects. Further, the confidence interval for their location is often prohibitively large for subsequent fine mapping and positional cloning. Given that a large number of genome scans have been conducted, not to mention the large number of variables and subsets tested, it is difficult to confidently rule out type 1 error as an explanation for significant effects even when there is apparent replication in a separate data set. We adapted Empirical Bayes (EB) methods [1] to analyze data from multiple genome scans simultaneously and alleviate each of these problems while still allowing for different QTL population effects across studies. We investigated the effects of using the EB method to include data from background studies to update the results of a single study of interest via simulation and demonstrated that it has a stable confidence level over a wide range of parameters defining the background studies and increased the power to detect linkage, even when some of the background studies were null or had QTL effect at other markers. This EB method for incorporating data from multiple studies into genome scan analyses seems promising.



 goto top of outline Author Contacts

T. Mark Beasley, PhD
Department of Biostatistics, University of Alabama at Birmingham
1665 University Boulevard
Birmingham, AL 35294 (USA)
Tel. +1 205 975 4957, Fax +1 205 975 2540, E-Mail MBeasley@ms.soph.uab.edu


 goto top of outline Article Information

Received: February 28, 2005
Accepted: June 22, 2005
Published online: August 31, 2005
Number of Print Pages : 7
Number of Figures : 5, Number of Tables : 0, Number of References : 14


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 60, No. 1, Year 2005 (Cover Date: 2005)

Journal Editor: Devoto, M. (Wilmington, Del.)
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.com/hhe


Copyright / Drug Dosage

Copyright: 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 or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
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 goverment 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.

References

  1. Morris CN: Parametric empirical Bayes inference: Theory and applications. J Am Stat Assoc 1983;78:47–55.

    External Resources

  2. Blacker D, et al: Results of a high-resolution genome screen of 437 Alzheimer’s Disease families. Hum Molec Genet 2003;12:23–32.
  3. Haseman JK, Elston RC: The investigation of linkage between a quantitative trait and marker locus. Behav Genet 1972;2:3–19.
  4. Carlin BP, Louis TA: Empirical Bayes: Past, present and future. J Am Stat Assoc 2000;95:1286–1289.

    External Resources

  5. Efron B, Morris CN: Stein’s estimator and its competitors – An empirical Bayes approach. J Am Stat Assoc 1973;68:117–130.

    External Resources

  6. Carlin BP, Louis TA: Bayes and Emprirical Bayes Methods for Data Analysis, ed 2. Boca Raton, Chapman and Hall, 2000.
  7. Bonney GE, Amfoth KK, Sherman SL, Keats BJB: An application of empirical Bayes methods to updating linkage information on chromosome 21. Cytogenet Cell Genet 1992;59:112–113.
  8. Li Z, Rao DC: Random effects model for meta-analysis of multiple quantitative sib pair linkage studies. Genet Epidemiol 1996;13:377–383.
  9. Lockwood JR, Roeder K, Devlin: A Bayesian hierarchical model for allele frequencies. Genet Epidemiol 2001;20:17–33.
  10. Witte JS: Genetic analysis with hierarchical models. Genet Epidemiol 1997;14:1137–1142.
  11. Efron B, Morris CN: Data Analysis using Stein’s estimator and its generalizations. J Am Stat Assoc 1975;70:379–421.
  12. Tang R: Fitting and evaluating certain two-level hierarchical models. PhD dissertation, Harvard University, Cambridge, MA, 2002.
  13. Fulker DW, Cherney SS, London CR: Multipoint interval mapping of quantitative trait loci using sib pairs. Am J of Hum Genet 1995;56:1224–1333.
  14. Etzel CJ, Guerra R: Meta-analysis of genetic-linkage analysis of quantitative-trait-loci. Am J Hum Genet 2002;71:56–65.