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Table of Contents
Vol. 51, No. 4, 2001
Issue release date: March 2001
Hum Hered 2001;51:199–208
(DOI:10.1159/000053343)

Power to Detect Linkage Based on Multiple Sets of Data in the Presence of Locus Heterogeneity: Comparative Evaluation of Model-Based Linkage Methods for Affected Sib Pair Data

Vieland V.J.a,b · Wang K.a · Huang J.c
Departments of aBiostatistics, bPsychiatry, and cMathematical Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, USA
email Corresponding Author

Abstract

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217–225) shows further that in application to multiple data sets, familiar model-free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in power for pooled data in the presence of locus heterogeneity and other complicating factors.


 goto top of outline Key Words

  • Linkage analysis
  • Heterogeneity
  • Affected sib pairs
  • Multiple data sets

 goto top of outline Abstract

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217–225) shows further that in application to multiple data sets, familiar model-free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in power for pooled data in the presence of locus heterogeneity and other complicating factors.

Copyright © 2001 S. Karger AG, Basel


 goto top of outline References
  1. Elston RC: P values, power, and pitfalls in the linkage analysis of psychiatric disorders; in Gershon ES, Cloninger CR (eds): Genetic Approaches to Mental Disorders. Washington, American Psychiatric Press, 1994.
  2. Morton NE: Significance levels in complex inheritance. Am J Hum Genet 1998;62:690–697.
  3. Lander E, Kruglyak L: Genetic dissection of complex traits: Guidelines for interpreting and reporting linkage results. Nat Genet 1995;11:241–247.
  4. Stuart A, Ord JK: Kendall’s Advanced Theory of Statistics, ed 5. New York, Oxford University Press, 1991, vol 2.
  5. Vieland VJ: Bayesian linkage analysis, or: How I learned to stop worrying and love the posterior probability of linkage. Am J Hum Genet 1998;63:947–954.
  6. Huang J, Vieland VJ: Comparison of model-free and model-based linkage statistics in the presence of locus heterogeneity: Single data set and multiple data set applications. Hum Hered 2001;51:217–225.
  7. Wang K, Vieland VJ, Huang J: A Bayesian approach to replication of linkage findings. Genet Epidemiol 1999;17(suppl 1):S749–S754.
  8. Vieland VJ, Wang K, Huang J: A new linkage analysis method for complex disorders based on multiple sets of data. Am J Human Genet Suppl 1999;65:2554.
  9. Suarez BK, Rice J, Reich T: The generalized sib pair IBD distribution: Its use in the detection of linkage. Am J Hum Genet 1978;42:87–94.
  10. Smith CAB: Homogeneity test for linked data. Proc Sec Int Congr Hum Genet 1961, vol 1, pp 212–213.
  11. Suarez BK, Hampe CL, Van Eerdewegh P: Problems of replicating linkage claims in psychiatry; in Gershon ES, Cloninger CR (eds): Genetic Approaches to Mental Disorders. Washington, American Psychiatric Press, 1994.
  12. Greenberg DA, MacCluer JW, Spence MA, Falk CT, Hodge SE: Simulated data for a complex genetic trait (Problem 2 for GAW 11): How the model was developed, and why. Genet Epidemiol 1999;17(suppl 1):S449–S460.
  13. Badner JA, Goldin LR: Meta-analysis of linkage studies. Genet Epidemiol 1999;17(suppl 1):S449–S460.
  14. Goldstein DR, Sain SR, Guerra R, Etzel CJ: Meta-analysis by combining parameter estimates: Simulated linkage studies. Genet Epidemiol 1999;17(suppl 1):S581–S586.
  15. Guerra R, Etzel CJ, Goldstein DR, Sain SR: Meta-analysis by combining p-values: Simulated linkage studies. Genet Epidemiol 1999;17(suppl 1):S605–S610.
  16. Wise LH, Lewis CM: A method for meta-analysis of genome searches: Application to simulated data. Genet Epidemiol 1999;17(suppl):S767–S772.
  17. Risch N: Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs. Am J Hum Genet 1990;46:242–253.
  18. Holmans P: Asymptotic properties of affected-sib-pair linkage analysis. Am J Hum Genet 1993;52:362–374.
  19. Wang K, Huang J, Vieland VJ: The consistency of the posterior probability of linkeage, submitted.

 goto top of outline Author Contacts

Veronica J. Vieland, PhD
Department of Biostatistics, 2800 SB
University of Iowa College of Public Health
Iowa City, IA 52242 (USA)
Tel. +1 319 353 4782, Fax +1 319 353 3003, E-Mail veronica-vieland@uiowa.edu


 goto top of outline Article Information

Received: Received: December 22, 1999
Accepted: April 5, 2000
Revision received: March 7, 2000
Number of Print Pages : 10
Number of Figures : 0, Number of Tables : 3, Number of References : 19


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)
Founded 1950 as Acta Genetica et Statistica Medica by Gunnar Dahlberg; Continued by M. Hauge (1965–1983)

Vol. 51, No. 4, Year 2001 (Cover Date: Released March 2001)

Journal Editor: J. Ott, New York, N.Y.
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.ch/journals/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

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217–225) shows further that in application to multiple data sets, familiar model-free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in power for pooled data in the presence of locus heterogeneity and other complicating factors.



 goto top of outline Author Contacts

Veronica J. Vieland, PhD
Department of Biostatistics, 2800 SB
University of Iowa College of Public Health
Iowa City, IA 52242 (USA)
Tel. +1 319 353 4782, Fax +1 319 353 3003, E-Mail veronica-vieland@uiowa.edu


 goto top of outline Article Information

Received: Received: December 22, 1999
Accepted: April 5, 2000
Revision received: March 7, 2000
Number of Print Pages : 10
Number of Figures : 0, Number of Tables : 3, Number of References : 19


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)
Founded 1950 as Acta Genetica et Statistica Medica by Gunnar Dahlberg; Continued by M. Hauge (1965–1983)

Vol. 51, No. 4, Year 2001 (Cover Date: Released March 2001)

Journal Editor: J. Ott, New York, N.Y.
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.ch/journals/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. Elston RC: P values, power, and pitfalls in the linkage analysis of psychiatric disorders; in Gershon ES, Cloninger CR (eds): Genetic Approaches to Mental Disorders. Washington, American Psychiatric Press, 1994.
  2. Morton NE: Significance levels in complex inheritance. Am J Hum Genet 1998;62:690–697.
  3. Lander E, Kruglyak L: Genetic dissection of complex traits: Guidelines for interpreting and reporting linkage results. Nat Genet 1995;11:241–247.
  4. Stuart A, Ord JK: Kendall’s Advanced Theory of Statistics, ed 5. New York, Oxford University Press, 1991, vol 2.
  5. Vieland VJ: Bayesian linkage analysis, or: How I learned to stop worrying and love the posterior probability of linkage. Am J Hum Genet 1998;63:947–954.
  6. Huang J, Vieland VJ: Comparison of model-free and model-based linkage statistics in the presence of locus heterogeneity: Single data set and multiple data set applications. Hum Hered 2001;51:217–225.
  7. Wang K, Vieland VJ, Huang J: A Bayesian approach to replication of linkage findings. Genet Epidemiol 1999;17(suppl 1):S749–S754.
  8. Vieland VJ, Wang K, Huang J: A new linkage analysis method for complex disorders based on multiple sets of data. Am J Human Genet Suppl 1999;65:2554.
  9. Suarez BK, Rice J, Reich T: The generalized sib pair IBD distribution: Its use in the detection of linkage. Am J Hum Genet 1978;42:87–94.
  10. Smith CAB: Homogeneity test for linked data. Proc Sec Int Congr Hum Genet 1961, vol 1, pp 212–213.
  11. Suarez BK, Hampe CL, Van Eerdewegh P: Problems of replicating linkage claims in psychiatry; in Gershon ES, Cloninger CR (eds): Genetic Approaches to Mental Disorders. Washington, American Psychiatric Press, 1994.
  12. Greenberg DA, MacCluer JW, Spence MA, Falk CT, Hodge SE: Simulated data for a complex genetic trait (Problem 2 for GAW 11): How the model was developed, and why. Genet Epidemiol 1999;17(suppl 1):S449–S460.
  13. Badner JA, Goldin LR: Meta-analysis of linkage studies. Genet Epidemiol 1999;17(suppl 1):S449–S460.
  14. Goldstein DR, Sain SR, Guerra R, Etzel CJ: Meta-analysis by combining parameter estimates: Simulated linkage studies. Genet Epidemiol 1999;17(suppl 1):S581–S586.
  15. Guerra R, Etzel CJ, Goldstein DR, Sain SR: Meta-analysis by combining p-values: Simulated linkage studies. Genet Epidemiol 1999;17(suppl 1):S605–S610.
  16. Wise LH, Lewis CM: A method for meta-analysis of genome searches: Application to simulated data. Genet Epidemiol 1999;17(suppl):S767–S772.
  17. Risch N: Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs. Am J Hum Genet 1990;46:242–253.
  18. Holmans P: Asymptotic properties of affected-sib-pair linkage analysis. Am J Hum Genet 1993;52:362–374.
  19. Wang K, Huang J, Vieland VJ: The consistency of the posterior probability of linkeage, submitted.