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Vol. 73, No. 3, 2012
Issue release date: July 2012
Hum Hered 2012;73:148–158
(DOI:10.1159/000338439)

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Guo W.a, b · Shugart Y.Y.a
aDivision of Intramural Division Program, National Institute of Mental Health, National Institute of Health, Bethesda, Md., USA; bKey Laboratory for Applied Statistics of Ministry of Education and School of Mathematics and Statistics, Northeast Normal University, Changchun, China
email Corresponding Author

Abstract

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.


 goto top of outline Key Words

  • Rare variants
  • Nuclear families
  • Quantitative traits
  • Weights
  • Penalized regression

 goto top of outline Abstract

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.

Copyright © 2012 S. Karger AG, Basel


 goto top of outline References
  1. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
  2. Schork NJ, Murray SS, Frazer KA, Topol EJ: Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 2009;19:212–219.
  3. Bansal V, Libiger O, Torkamani A, Schork NJ: Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 2010;11:773–785.
  4. Li B, Leal SM: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 2008;83:311–321.
  5. Madsen BE, Browning SR: A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 2009;5:e1000384.
  6. Liu DJ, Leal SM: A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associating with rare variants due to gene main effects and interactions. PLoS Genet 2010;6:e1001156.

    External Resources

  7. Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei LJ, Sunyaev SR: Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 2010;86:832–838.
  8. Asimit J, Zeggini E: Rare variant association analysis methods for complex traits. Annu Rev Genet 2010;44:293–308.
  9. Lin D, Tang Z: A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 2011;89:354–367.
  10. Guo W, Lin SL: Generalized linear modeling with regularization for detecting common disease rare haplotype association. Genet Epidemiol 2009;33:308–316.

    External Resources

  11. Zhou H, Sehl ME, Sinsheimer JS, Lange K: Association screening of common and rare genetic variants by penalized regression. Bioinformatics 2010;26:2375–2382.
  12. Yip WK, De G, Raby BA, Laird N: Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set. BMC Proc 2011;5:S21.

    External Resources

  13. Xu X, Rakovski C, Xu X, Laird N: An efficient family-based association test using multiple markers. Genet Epidemiol 2006;30:620–626.
  14. Laird NM, Horvath S, Xu X: Implementing a unified approach to family-based tests of association. Genet Epidemiol 2000;19(suppl 1):S36–S42.
  15. Rakovski C, Xu X, Lazarus R, Laird NM: A new multimarker test for family-based association studies. Genet Epidemiol 2007;31:9–17.
  16. Zou H, Hastie T: Regularization and variable selection via the elasticnet. J R Stat Soc B 2005;67:301–320.

    External Resources

  17. Friedman J, Hastie T, Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1–22.

    External Resources

  18. Tibshirani R: Regression shrinkage and selection via the lasso. J R Stat Soc B 1996;58:267–288.
  19. Malo N, Libiger O, Schork NJ: Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am J Hum Genet 2008;82:375–385.

 goto top of outline Author Contacts

Yin Yao Shugart
Division of Intramural Research Program, National Institute of Mental Health National Institute of Health, Building 35, Room 3A 1000, 35 Convent Drive
Bethesda, MD 20892 (USA)
Tel. +1 301 496 4341, E-Mail kay1yao@mail.nih.gov


 goto top of outline Article Information

Received: December 8, 2011
Accepted after revision: March 30, 2012
Published online: June 12, 2012
Number of Print Pages : 11
Number of Figures : 1, Number of Tables : 3, Number of References : 19


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 73, No. 3, Year 2012 (Cover Date: July 2012)

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

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.



 goto top of outline Author Contacts

Yin Yao Shugart
Division of Intramural Research Program, National Institute of Mental Health National Institute of Health, Building 35, Room 3A 1000, 35 Convent Drive
Bethesda, MD 20892 (USA)
Tel. +1 301 496 4341, E-Mail kay1yao@mail.nih.gov


 goto top of outline Article Information

Received: December 8, 2011
Accepted after revision: March 30, 2012
Published online: June 12, 2012
Number of Print Pages : 11
Number of Figures : 1, Number of Tables : 3, Number of References : 19


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 73, No. 3, Year 2012 (Cover Date: July 2012)

Journal Editor: Devoto M. (Philadelphia, Pa./Rome)
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. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
  2. Schork NJ, Murray SS, Frazer KA, Topol EJ: Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 2009;19:212–219.
  3. Bansal V, Libiger O, Torkamani A, Schork NJ: Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 2010;11:773–785.
  4. Li B, Leal SM: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 2008;83:311–321.
  5. Madsen BE, Browning SR: A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 2009;5:e1000384.
  6. Liu DJ, Leal SM: A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associating with rare variants due to gene main effects and interactions. PLoS Genet 2010;6:e1001156.

    External Resources

  7. Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei LJ, Sunyaev SR: Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 2010;86:832–838.
  8. Asimit J, Zeggini E: Rare variant association analysis methods for complex traits. Annu Rev Genet 2010;44:293–308.
  9. Lin D, Tang Z: A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 2011;89:354–367.
  10. Guo W, Lin SL: Generalized linear modeling with regularization for detecting common disease rare haplotype association. Genet Epidemiol 2009;33:308–316.

    External Resources

  11. Zhou H, Sehl ME, Sinsheimer JS, Lange K: Association screening of common and rare genetic variants by penalized regression. Bioinformatics 2010;26:2375–2382.
  12. Yip WK, De G, Raby BA, Laird N: Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set. BMC Proc 2011;5:S21.

    External Resources

  13. Xu X, Rakovski C, Xu X, Laird N: An efficient family-based association test using multiple markers. Genet Epidemiol 2006;30:620–626.
  14. Laird NM, Horvath S, Xu X: Implementing a unified approach to family-based tests of association. Genet Epidemiol 2000;19(suppl 1):S36–S42.
  15. Rakovski C, Xu X, Lazarus R, Laird NM: A new multimarker test for family-based association studies. Genet Epidemiol 2007;31:9–17.
  16. Zou H, Hastie T: Regularization and variable selection via the elasticnet. J R Stat Soc B 2005;67:301–320.

    External Resources

  17. Friedman J, Hastie T, Tibshirani R: Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1–22.

    External Resources

  18. Tibshirani R: Regression shrinkage and selection via the lasso. J R Stat Soc B 1996;58:267–288.
  19. Malo N, Libiger O, Schork NJ: Accommodating linkage disequilibrium in genetic-association analyses via ridge regression. Am J Hum Genet 2008;82:375–385.