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Vol. 65, No. 3, 2008
Issue release date: December 2007
Section title: Original Paper
Free Access
Hum Hered 2008;65:154–165
(DOI:10.1159/000109732)

Increased Efficiency of Case-Control Association Analysis by Using Allele-Sharing and Covariate Information

Schmidt S. · Schmidt M.A. · Qin X. · Martin E.R. · Hauser E.R.
Center for Human Genetics, Duke University Medical Center, Durham, N.C., USA
email Corresponding Author

Abstract

Objective: We compared the efficiency of case selection strategies for following up a genome-wide linkage screen of multiplex families. We simulated datasets under three models by which continuous environmental or clinical covariates may contribute to disease risk or linkage heterogeneity: (i) a quantitative trait locus (QTL) underlying a continuous disease risk factor, (ii) a gene-environment interaction model, (iii) a heterogeneity model defined by distinct covariate distributions in linked and unlinked families. Methods: Marker genotypes and covariate values were generated for affected sibling pair (ASP) families, according to the three models above. We evaluated two case selection strategies relative to a reference design, which compared all family probands to a sample of unrelated controls (‘all’). The first strategy ignored covariates and selected probands from families with NPL scores ≧0 (‘linked best’). The second strategy selected probands from families identified by an ordered subset analysis (OSA), which utilizes family-specific linkage and covariate information. Results: The ‘linked best’ design provided power very similar to the ‘all’ design under all three models. Under some QTL and heterogeneity models, the OSA design was both most powerful and most efficient. Conclusions: Incorporating allele sharing and covariate information from ASP families into a case-control study design can increase power and reduce genotyping cost.

© 2007 S. Karger AG, Basel


  

Key Words

  • Gene-environment interaction
  • Quantitative trait locus
  • Genetic heterogeneity
  • Linkage analysis
  • Ordered subset analysis
  • Study design
  • SIMLA

References

  1. Li M, Boehnke M, Abecasis GR: Efficient study designs for test of genetic association using sibship data and unrelated cases and controls. Am J Hum Genet 2006;78:778–792.
  2. Risch N, Teng J: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases: DNA pooling. Genome Res 1998;8:1273–1288.
  3. Teng J, Risch N: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res 1999;9:234–241.
  4. Epstein MP, Veal CD, Trembath RC, Barker JN, Li C, Satten GA: Genetic association analysis using data from triads and unrelated subjects. Am J Hum Genet 2005;76:592–608.
  5. Weinberg CR, Umbach DM: A hybrid design for studying genetic influences on risk of diseases with onset early in life. Am J Hum Genet 2005;77:627–636.
  6. Laird NM, Lange C: Family-based designs in the age of large-scale gene-association studies. Nat Rev Genet 2006;7:385–394.
  7. Schmidt MA, Hauser ER, Martin ER, Schmidt S: Extension of the SIMLA package for generating pedigrees with complex inheritance patterns: Environmental covariates, gene-gene and gene-environment interaction. Stat Appl Genet Mol Biol 2005;4:Article 15 [Epub].
  8. 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.
  9. Kong A, Cox NJ: Allele-sharing models: LOD scores and accurate linkage tests. Am J Hum Genet 1997;61:1179–1188.
  10. Hauser ER, Watanabe RM, Duren WL, Bass MP, Langefeld CD, Boehnke M: Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 2004;27:53–63.
  11. Fingerlin TE, Boehnke M, Abecasis GR: Increasing the power and efficiency of disease-marker case-control association studies through use of allele-sharing information. Am J Hum Genet 2004;74:432–443.
  12. Schaid DJ: General score tests for associations of genetic markers with disease using cases and their parents. Genet Epidemiol 1996;13:423–449.
  13. Sturmer T, Gefeller O, Brenner H: A computer program to estimate power and relative efficiency to assess multiplicative interactions in flexibly matched case-control studies. Comput Methods Programs Biomed 2004;74:261–265.
  14. Saunders CL, Barrett JH: Flexible matching in case-control studies of gene-environment interactions. Am J Epidemiol 2004;159:17–22.
  15. Chung RH, Hauser ER, Martin ER: Interpretation of simultaneous linkage and family-based association tests in genome screens. Genet Epidemiol 2007;31:134–142.
  16. Schmidt S, Schmidt MA, Qin X, Martin ER, Hauser ER: Linkage analysis with gene-environment interaction: model illustration and performance of ordered subset analysis. Genet Epidemiol 2006;30:409–422.
  17. Schmidt S, Qin X, Schmidt M, Martin ER, Hauser ER: Interpreting analyses of continuous covariates in affected sibling pair linkage studies. Genet Epidemiol 2007;April 4 [Epub ahead of print].
  18. Qin X, Schmidt S, Schmidt M, Martin E, Hauser E: A visualization tool for genetic parameters in complex human traits. International Genetic Epidemiology Society, November 16–17, 2006, Tampa, FL.
  19. Schmidt S, Hauser MA, Scott WK, Postel EA, Agarwal A, Gallins P, Wong F, Chen YS, Spencer K, Schnetz-Boutaud N, Haines JL, Pericak-Vance MA: Cigarette smoking strongly modifies the association of LOC387715 and age-related macular degeneration. Am J Hum Genet 2006;78:852–864.
  20. Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC, Hobbs HH: A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am J Hum Genet 2006;78:410–422.
  21. Cohen JC, Boerwinkle E, Mosley TH Jr, Hobbs HH: Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354:1264–1272.
  22. Romeo S, Pennacchio LA, Fu Y, Boerwinkle E, Tybjaerg-Hansen A, Hobbs HH, Cohen JC: Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet 2007.
  23. Lou X, Schmidt S, Hauser ER: Evaluation of GIST and LAMP in the GAW15 simulated data. BMC Genetics, in press.

  

Author Contacts

Silke Schmidt, PhD
Center for Human Genetics, Duke University Medical Center
Box 3445
Durham, NC 27710 (USA)
Tel. +1 919 684 0624, Fax +1 919 684 0925, E-Mail silke.schmidt@duke.edu

  

Article Information

Received: March 23, 2007
Accepted after revision: June 13, 2007
Published online: October 12, 2007
Number of Print Pages : 12
Number of Figures : 3, Number of Tables : 2, Number of References : 23

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 65, No. 3, Year 2008 (Cover Date: December 2007)

Journal Editor: Devoto, M. (Philadelphia, Pa.)
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

Objective: We compared the efficiency of case selection strategies for following up a genome-wide linkage screen of multiplex families. We simulated datasets under three models by which continuous environmental or clinical covariates may contribute to disease risk or linkage heterogeneity: (i) a quantitative trait locus (QTL) underlying a continuous disease risk factor, (ii) a gene-environment interaction model, (iii) a heterogeneity model defined by distinct covariate distributions in linked and unlinked families. Methods: Marker genotypes and covariate values were generated for affected sibling pair (ASP) families, according to the three models above. We evaluated two case selection strategies relative to a reference design, which compared all family probands to a sample of unrelated controls (‘all’). The first strategy ignored covariates and selected probands from families with NPL scores ≧0 (‘linked best’). The second strategy selected probands from families identified by an ordered subset analysis (OSA), which utilizes family-specific linkage and covariate information. Results: The ‘linked best’ design provided power very similar to the ‘all’ design under all three models. Under some QTL and heterogeneity models, the OSA design was both most powerful and most efficient. Conclusions: Incorporating allele sharing and covariate information from ASP families into a case-control study design can increase power and reduce genotyping cost.

© 2007 S. Karger AG, Basel


  

Author Contacts

Silke Schmidt, PhD
Center for Human Genetics, Duke University Medical Center
Box 3445
Durham, NC 27710 (USA)
Tel. +1 919 684 0624, Fax +1 919 684 0925, E-Mail silke.schmidt@duke.edu

  

Article Information

Received: March 23, 2007
Accepted after revision: June 13, 2007
Published online: October 12, 2007
Number of Print Pages : 12
Number of Figures : 3, Number of Tables : 2, Number of References : 23

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 65, No. 3, Year 2008 (Cover Date: December 2007)

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

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


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 3/23/2007
Accepted: 6/13/2007
Published online: 10/12/2007
Issue release date: December 2007

Number of Print Pages: 12
Number of Figures: 3
Number of Tables: 2

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. Li M, Boehnke M, Abecasis GR: Efficient study designs for test of genetic association using sibship data and unrelated cases and controls. Am J Hum Genet 2006;78:778–792.
  2. Risch N, Teng J: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases: DNA pooling. Genome Res 1998;8:1273–1288.
  3. Teng J, Risch N: The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res 1999;9:234–241.
  4. Epstein MP, Veal CD, Trembath RC, Barker JN, Li C, Satten GA: Genetic association analysis using data from triads and unrelated subjects. Am J Hum Genet 2005;76:592–608.
  5. Weinberg CR, Umbach DM: A hybrid design for studying genetic influences on risk of diseases with onset early in life. Am J Hum Genet 2005;77:627–636.
  6. Laird NM, Lange C: Family-based designs in the age of large-scale gene-association studies. Nat Rev Genet 2006;7:385–394.
  7. Schmidt MA, Hauser ER, Martin ER, Schmidt S: Extension of the SIMLA package for generating pedigrees with complex inheritance patterns: Environmental covariates, gene-gene and gene-environment interaction. Stat Appl Genet Mol Biol 2005;4:Article 15 [Epub].
  8. 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.
  9. Kong A, Cox NJ: Allele-sharing models: LOD scores and accurate linkage tests. Am J Hum Genet 1997;61:1179–1188.
  10. Hauser ER, Watanabe RM, Duren WL, Bass MP, Langefeld CD, Boehnke M: Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 2004;27:53–63.
  11. Fingerlin TE, Boehnke M, Abecasis GR: Increasing the power and efficiency of disease-marker case-control association studies through use of allele-sharing information. Am J Hum Genet 2004;74:432–443.
  12. Schaid DJ: General score tests for associations of genetic markers with disease using cases and their parents. Genet Epidemiol 1996;13:423–449.
  13. Sturmer T, Gefeller O, Brenner H: A computer program to estimate power and relative efficiency to assess multiplicative interactions in flexibly matched case-control studies. Comput Methods Programs Biomed 2004;74:261–265.
  14. Saunders CL, Barrett JH: Flexible matching in case-control studies of gene-environment interactions. Am J Epidemiol 2004;159:17–22.
  15. Chung RH, Hauser ER, Martin ER: Interpretation of simultaneous linkage and family-based association tests in genome screens. Genet Epidemiol 2007;31:134–142.
  16. Schmidt S, Schmidt MA, Qin X, Martin ER, Hauser ER: Linkage analysis with gene-environment interaction: model illustration and performance of ordered subset analysis. Genet Epidemiol 2006;30:409–422.
  17. Schmidt S, Qin X, Schmidt M, Martin ER, Hauser ER: Interpreting analyses of continuous covariates in affected sibling pair linkage studies. Genet Epidemiol 2007;April 4 [Epub ahead of print].
  18. Qin X, Schmidt S, Schmidt M, Martin E, Hauser E: A visualization tool for genetic parameters in complex human traits. International Genetic Epidemiology Society, November 16–17, 2006, Tampa, FL.
  19. Schmidt S, Hauser MA, Scott WK, Postel EA, Agarwal A, Gallins P, Wong F, Chen YS, Spencer K, Schnetz-Boutaud N, Haines JL, Pericak-Vance MA: Cigarette smoking strongly modifies the association of LOC387715 and age-related macular degeneration. Am J Hum Genet 2006;78:852–864.
  20. Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC, Hobbs HH: A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am J Hum Genet 2006;78:410–422.
  21. Cohen JC, Boerwinkle E, Mosley TH Jr, Hobbs HH: Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354:1264–1272.
  22. Romeo S, Pennacchio LA, Fu Y, Boerwinkle E, Tybjaerg-Hansen A, Hobbs HH, Cohen JC: Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet 2007.
  23. Lou X, Schmidt S, Hauser ER: Evaluation of GIST and LAMP in the GAW15 simulated data. BMC Genetics, in press.