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Vol. 65, No. 4, 2008
Issue release date: January 2008
Free Access
Hum Hered 2008;65:199–208
(DOI:10.1159/000112367)

Ignoring Intermarker Linkage Disequilibrium Induces False-Positive Evidence of Linkage for Consanguineous Pedigrees when Genotype Data Is Missing for Any Pedigree Member

Li B. · Leal S.M.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Tex., USA
email Corresponding Author

Abstract

Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al. [1] that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for autosomal recessive consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. False-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage, and which family members aid in its reduction, is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband’s sibling-grandparents. For the situation, when parental genotypes are unavailable, false-positive evidence for linkage can be reduced by including genotype data from either unaffected siblings of the proband or the proband’s married-in-grandparents in the analysis.


 goto top of outline Key Words

  • Consanguinity
  • False positives
  • Linkage analysis
  • Linkage disequilibrium (LD)

 goto top of outline Abstract

Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al. [1] that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for autosomal recessive consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. False-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage, and which family members aid in its reduction, is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband’s sibling-grandparents. For the situation, when parental genotypes are unavailable, false-positive evidence for linkage can be reduced by including genotype data from either unaffected siblings of the proband or the proband’s married-in-grandparents in the analysis.

Copyright © 2007 S. Karger AG, Basel


 goto top of outline References
  1. Huang Q, Shete S, Amos CI: Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am J Hum Genet 2004;75:1106–1112.
  2. Weber JL, Broman KW: Genotyping for human whole-genome scans: Past, present, and future. Adv Genet 2001;42:77–96.
  3. Murray SS, Oliphant A, Shen R, McBride C, Steeke RJ, Shannon SG, Rubano T, Kermani BG, Fan JB, Chee MS, Hansen MS: A highly informative snp linkage panel for human genetic studies. Nat Methods 2004;1:113–117.
  4. Kennedy GC, Matsuzaki H, Dong S, Liu WM, Huang J, Liu G, Su X, Cao M, Chen W, Zhang J, Liu W, Yang G, Di X, Ryder T, He Z, Surti U, Phillips MS, Boyce-Jacino MT, Fodor SP, Jones KW: Large-scale genotyping of complex DNA. Nat Biotechnol 2003;21:1233–1237.
  5. Wilcox MA, Pugh EW, Zhang H, Zhong X, Levinson DF, Kennedy GC, Wijsman EM: Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the coga and simulated data sets for genetic analysis workshop 14:Presentation groups 1, 2, and 3. Genet Epidemiol 2005;29(suppl 1):S7–S28.
  6. Cottingham RW Jr, Idury RM, Schaffer AA: Faster sequential genetic linkage computations. Am J Hum Genet 1993;53:252–263.
  7. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin – rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002;30:97–101.
  8. Abecasis GR, Wigginton JE: Handling marker-marker linkage disequilibrium: Pedigree analysis with clustered markers. Am J Hum Genet 2005;77:754–767.
  9. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, P. D, Consortium IH: A haplotype map of the human genome. Nature 2005;437:1299–1320.
  10. Gudbjartsson DF, Thorvaldsson T, Kong A, Gunnarsson G, Ingolfsdottir A: Allegro version 2. Nat Genet 2005;37:1015–1016.
  11. Gudbjartsson DF, Jonasson K, Frigge ML, Kong A: Allegro, a new computer program for multipoint linkage analysis. Nat Genet 2000;25:12–13.
  12. Sobel E, Lange K: Descent graphs in pedigree analysis: Applications to haplotyping, location scores, and marker-sharing statistics. Am J Hum Genet 1996;58:1323–1337.
  13. Weeks DE, Sobel E, O’Connell JR, Lange K: Computer programs for multilocus haplotyping of general pedigrees. Am J Hum Genet 1995;56:1506–1507.
  14. Freimer NB, Sandkuijl LA, Blower SM: Incorrect specification of marker allele frequencies: Effects on linkage analysis. Am J Hum Genet 1993;52:1102–1110.
  15. Knapp M, Seuchter SA, Baur MP: The effect of misspecifying allele frequencies in incompletely typed families. Genet Epidemiol 1993;10:413–418.
  16. Huang Q, Shete S, Swartz M, Amos CI: Examining the effect of linkage disequilibrium on multipoint linkage analysis. BMC Genet 2005;6 Suppl 1:S83.
  17. Schaid DJ, McDonnell SK, Wang L, Cunningham JM, Thibodeau SN: Caution on pedigree haplotype inference with software that assumes linkage equilibrium. Am J Hum Genet 2002;71:992–995.
  18. Boyles AL, Scott WK, Martin ER, Schmidt S, Li YJ, Ashley-Koch A, Bass MP, Schmidt M, Pericak-Vance MA, Speer MC, Hauser ER: Linkage disequilibrium inflates type i error rates in multipoint linkage analysis when parental genotypes are missing. Hum Hered 2005;59:220–227.
  19. Van Camp G, Smith RJH: Hereditary hearing loss homepage: Http://webhost.Ua.Ac. Be/hhh/. 2007.
  20. Leal SM, Yan K, Muller-Myhsok B: Simped: A simulation program to generate haplotype and genotype data for pedigree structures. Hum Hered 2005;60:119–122.
  21. Ott J: Linkage analysis and family classification under heterogeneity. Ann Hum Genet 1983;47:311–320.
  22. Goddard KA, Wijsman EM: Characteristics of genetic markers and maps for cost-effective genome screens using diallelic markers. Genet Epidemiol 2002;22:205–220.
  23. Leutenegger AL, Prum B, Genin E, Verny C, Lemainque A, Clerget-Darpoux F, Thompson EA: Estimation of the inbreeding coefficient through use of genomic data. Am J Hum Genet 2003;73:516–523.
  24. Genin E, Clerget-Darpoux F: Consanguinity and the sib-pair method: An approach using identity by descent between and within individuals. Am J Hum Genet 1996;59:1149–1162.
  25. Leutenegger AL, Genin E, Thompson EA, Clerget-Darpoux F: Impact of parental relationships in maximum lod score affected sib-pair method. Genet Epidemiol 2002;23:413–425.
  26. Liu F, Elefante S, van Duijn CM, Aulchenko YS: Ignoring distant genealogic loops leads to false-positives in homozygosity mapping. Ann Hum Genet 2006;70:965–970.
  27. Miano MG, Jacobson SG, Carothers A, Hanson I, Teague P, Lovell J, Cideciyan AV, Haider N, Stone EM, Sheffield VC, Wright AF: Pitfalls in homozygosity mapping. Am J Hum Genet 2000;67:1348–1351.

 goto top of outline Author Contacts

Dr. Suzanne M. Leal
Baylor College of Medicine, Department of Molecular and Human Genetics
One Baylor Plaza, Alkek Building N1619.01
Houston, TX 77030 (USA)
Tel. +1 713 798 4001, Fax +1 713 798 5741, E-Mail sleal@bcm.edu


 goto top of outline Article Information

Received: June 19, 2007
Accepted after revision: July 30, 2007
Published online: December 11, 2007
Number of Print Pages : 10
Number of Figures : 4, Number of Tables : 1, Number of References : 27


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 65, No. 4, Year 2008 (Cover Date: January 2008)

Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (Print), eISSN: 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

Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al. [1] that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for autosomal recessive consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. False-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage, and which family members aid in its reduction, is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband’s sibling-grandparents. For the situation, when parental genotypes are unavailable, false-positive evidence for linkage can be reduced by including genotype data from either unaffected siblings of the proband or the proband’s married-in-grandparents in the analysis.



 goto top of outline Author Contacts

Dr. Suzanne M. Leal
Baylor College of Medicine, Department of Molecular and Human Genetics
One Baylor Plaza, Alkek Building N1619.01
Houston, TX 77030 (USA)
Tel. +1 713 798 4001, Fax +1 713 798 5741, E-Mail sleal@bcm.edu


 goto top of outline Article Information

Received: June 19, 2007
Accepted after revision: July 30, 2007
Published online: December 11, 2007
Number of Print Pages : 10
Number of Figures : 4, Number of Tables : 1, Number of References : 27


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 65, No. 4, Year 2008 (Cover Date: January 2008)

Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (Print), eISSN: 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. Huang Q, Shete S, Amos CI: Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am J Hum Genet 2004;75:1106–1112.
  2. Weber JL, Broman KW: Genotyping for human whole-genome scans: Past, present, and future. Adv Genet 2001;42:77–96.
  3. Murray SS, Oliphant A, Shen R, McBride C, Steeke RJ, Shannon SG, Rubano T, Kermani BG, Fan JB, Chee MS, Hansen MS: A highly informative snp linkage panel for human genetic studies. Nat Methods 2004;1:113–117.
  4. Kennedy GC, Matsuzaki H, Dong S, Liu WM, Huang J, Liu G, Su X, Cao M, Chen W, Zhang J, Liu W, Yang G, Di X, Ryder T, He Z, Surti U, Phillips MS, Boyce-Jacino MT, Fodor SP, Jones KW: Large-scale genotyping of complex DNA. Nat Biotechnol 2003;21:1233–1237.
  5. Wilcox MA, Pugh EW, Zhang H, Zhong X, Levinson DF, Kennedy GC, Wijsman EM: Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the coga and simulated data sets for genetic analysis workshop 14:Presentation groups 1, 2, and 3. Genet Epidemiol 2005;29(suppl 1):S7–S28.
  6. Cottingham RW Jr, Idury RM, Schaffer AA: Faster sequential genetic linkage computations. Am J Hum Genet 1993;53:252–263.
  7. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin – rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002;30:97–101.
  8. Abecasis GR, Wigginton JE: Handling marker-marker linkage disequilibrium: Pedigree analysis with clustered markers. Am J Hum Genet 2005;77:754–767.
  9. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, P. D, Consortium IH: A haplotype map of the human genome. Nature 2005;437:1299–1320.
  10. Gudbjartsson DF, Thorvaldsson T, Kong A, Gunnarsson G, Ingolfsdottir A: Allegro version 2. Nat Genet 2005;37:1015–1016.
  11. Gudbjartsson DF, Jonasson K, Frigge ML, Kong A: Allegro, a new computer program for multipoint linkage analysis. Nat Genet 2000;25:12–13.
  12. Sobel E, Lange K: Descent graphs in pedigree analysis: Applications to haplotyping, location scores, and marker-sharing statistics. Am J Hum Genet 1996;58:1323–1337.
  13. Weeks DE, Sobel E, O’Connell JR, Lange K: Computer programs for multilocus haplotyping of general pedigrees. Am J Hum Genet 1995;56:1506–1507.
  14. Freimer NB, Sandkuijl LA, Blower SM: Incorrect specification of marker allele frequencies: Effects on linkage analysis. Am J Hum Genet 1993;52:1102–1110.
  15. Knapp M, Seuchter SA, Baur MP: The effect of misspecifying allele frequencies in incompletely typed families. Genet Epidemiol 1993;10:413–418.
  16. Huang Q, Shete S, Swartz M, Amos CI: Examining the effect of linkage disequilibrium on multipoint linkage analysis. BMC Genet 2005;6 Suppl 1:S83.
  17. Schaid DJ, McDonnell SK, Wang L, Cunningham JM, Thibodeau SN: Caution on pedigree haplotype inference with software that assumes linkage equilibrium. Am J Hum Genet 2002;71:992–995.
  18. Boyles AL, Scott WK, Martin ER, Schmidt S, Li YJ, Ashley-Koch A, Bass MP, Schmidt M, Pericak-Vance MA, Speer MC, Hauser ER: Linkage disequilibrium inflates type i error rates in multipoint linkage analysis when parental genotypes are missing. Hum Hered 2005;59:220–227.
  19. Van Camp G, Smith RJH: Hereditary hearing loss homepage: Http://webhost.Ua.Ac. Be/hhh/. 2007.
  20. Leal SM, Yan K, Muller-Myhsok B: Simped: A simulation program to generate haplotype and genotype data for pedigree structures. Hum Hered 2005;60:119–122.
  21. Ott J: Linkage analysis and family classification under heterogeneity. Ann Hum Genet 1983;47:311–320.
  22. Goddard KA, Wijsman EM: Characteristics of genetic markers and maps for cost-effective genome screens using diallelic markers. Genet Epidemiol 2002;22:205–220.
  23. Leutenegger AL, Prum B, Genin E, Verny C, Lemainque A, Clerget-Darpoux F, Thompson EA: Estimation of the inbreeding coefficient through use of genomic data. Am J Hum Genet 2003;73:516–523.
  24. Genin E, Clerget-Darpoux F: Consanguinity and the sib-pair method: An approach using identity by descent between and within individuals. Am J Hum Genet 1996;59:1149–1162.
  25. Leutenegger AL, Genin E, Thompson EA, Clerget-Darpoux F: Impact of parental relationships in maximum lod score affected sib-pair method. Genet Epidemiol 2002;23:413–425.
  26. Liu F, Elefante S, van Duijn CM, Aulchenko YS: Ignoring distant genealogic loops leads to false-positives in homozygosity mapping. Ann Hum Genet 2006;70:965–970.
  27. Miano MG, Jacobson SG, Carothers A, Hanson I, Teague P, Lovell J, Cideciyan AV, Haider N, Stone EM, Sheffield VC, Wright AF: Pitfalls in homozygosity mapping. Am J Hum Genet 2000;67:1348–1351.