Skip to main content
Log in

Correcting for biases in affected sib-pair linkage analysis caused by uncertainty in sibling relationship

  • Articles
  • Published:
Science China Mathematics Aims and scope Submit manuscript

Abstract

When there is uncertainty in sibling relationship, the classical affected sib-pair (ASP) linkage tests may be severely biased. This can happen, for example, if some of the half sib-pairs are mixed with full sib-pairs. The genomic control method has been used in association analysis to adjust for population structures. We show that the same idea can be applied to ASP linkage analysis with uncertainty in sibling relationship. Assuming that, in addition to the candidate marker, null markers that are unlinked to the disease locus are also genotyped, we may use the information on these loci to estimate the proportion of half sib-pairs and to correct for the bias and variance distortion caused by the heterogeneity of sibling relationship. Unlike in association studies, the null loci are not required to be matched with the candidate marker in allele frequency for ASP linkage analysis. This makes our approach flexible in selecting null markers. In our simulations, using a number of 30 or more null loci can effectively remove the bias and variance distortion. It is also shown that, even the null loci are weakly linked to the disease locus, the proposed method can also provide satisfactory correction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Amrita R, Weeks D E. Relationship uncertainty linkage statistics (RULS): Affected relative pair statistics that model relationship uncertainty. Genet Epidemiol, 2008, 32: 313–324

    Article  Google Scholar 

  2. Blackwelder C, Elston R C. A comparison of sib-pair linkage tests for disease susceptibility loci. Genet Epidemiol, 1985, 2: 85–97

    Article  Google Scholar 

  3. Boehnke M, Cox N J. Accurate inference of relationship in sib-pair linkage studies. Amer J Hum Genet, 1997, 61: 423–429

    Article  Google Scholar 

  4. Chakraborty R, Jin L. Determination of relatedness between individuals using DNA fingerprinting. Hum Biol, 1993, 65: 875–895

    Google Scholar 

  5. Chakraborty R, Jin L. A unified approach to study hypervariable polymorphisms: Statistical considerations of determining relatedness and population distances. EXS, 1993, 67: 153–175

    Google Scholar 

  6. Devlin B, Roeder K. Genomic control for association studies. Biometrics, 1999, 55: 997–1004

    Article  MATH  Google Scholar 

  7. Devlin B, Roeder K, Wasserman L. Genomic control, a new approach to genetic-based association studies. Theor Popul Biol, 2001, 60: 155–166

    Article  MATH  Google Scholar 

  8. Ehm M G, Wagner M. Test statistic to detect errors in sib-pair relationships. Amer J Hum Genet Suppl, 1996, 59: A217

    Google Scholar 

  9. Ehm M G, Wagner M. A test statistic to detect errors in sib-pair relationships. Amer J Hum Genet, 1998, 62: 181–188

    Article  Google Scholar 

  10. Goring H H, Ott J. Relationship estimation in affected sib pair analysis of late-onset diseases. Eur J Genet, 1997, 5: 69–77

    Google Scholar 

  11. Gorroochurn P, Heiman G A, Hodge S E, et al. Centralizing the non-central chi-square: a new method to correct for population stratification in genetic case-control association studies. Genet Epidemiol, 2006, 30: 277–289

    Article  Google Scholar 

  12. Risch N. Linkage strategies for genetically complex traits. II. The power of affected relative pairs. Ann Hum Genet, 1990, 46: 229–241

    Google Scholar 

  13. Stivers D N, Zhong Y, Hanis C L, et al. REL-TYPE: a computer program for determining biological re-latedness between individuals based on allele sharing at mi-crosatellite loci. Amer J Hum Genet Suppl, 1996, 59: A190

    Google Scholar 

  14. Schaid D J, Elston R C, Tran L, et al. Model-free sib-pair linkage analysis: combining full-sib and half-sib pairs. Genet Epidemiol, 2000, 19: 30–51

    Article  Google Scholar 

  15. Setakis E, Stirnadel H, Balding D J. Logistic regression protects against population structure in genetic association studies. Genome Res, 2006, 16: 290–296

    Article  Google Scholar 

  16. Shih M C, Whittemore A S. Allele-sharing among affected relatives: non-parametric methods for identifying genes. Stat Meth Med Res, 2001, 10: 27–55

    Article  MATH  Google Scholar 

  17. Whittemore A S, Tu I P. Simple, robust linkage tests for affected sibs. Amer J Hum Genet, 1998, 62: 1228–1242

    Article  Google Scholar 

  18. Zheng G, Freidlin B, Li Z, et al. Genomic control for association studies under various genetic models. Biometrics, 2005, 61: 186–192

    Article  MathSciNet  MATH  Google Scholar 

  19. Zheng G, Freidlin B, Gastwirth J L. Robust genomic control for association studies. Amer J Hum Genet, 2006, 78: 350–356

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to YaNing Yang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuan, M., Cui, W. & Yang, Y. Correcting for biases in affected sib-pair linkage analysis caused by uncertainty in sibling relationship. Sci. China Math. 55, 1127–1135 (2012). https://doi.org/10.1007/s11425-012-4373-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11425-012-4373-3

Keywords

MSC(2010)

Navigation