Skip to main content
Log in

Influence of outliers on QTL mapping for complex traits

  • Published:
Journal of Zhejiang University SCIENCE B Aims and scope Submit manuscript

Abstract

A method was proposed for the detection of outliers and influential observations in the framework of a mixed linear model, prior to the quantitative trait locus (QTL) mapping analysis. We investigated the impact of outliers on QTL mapping for complex traits in a mouse BXD population, and observed that the dropping of outliers could provide the evidence of additional QTL and epistatic loci affecting the 1stBrain-OB and the 2ndBrain-OB in a cross of the abovementioned population. The results could also reveal a remarkable increase in estimating heritabilities of QTL in the absence of outliers. In addition, simulations were conducted to investigate the detection powers and false discovery rates (FDRs) of QTLs in the presence and absence of outliers. The results suggested that the presence of a small proportion of outliers could increase the FDR and hence decrease the detection power of QTLs. A drastic increase could be obtained in the estimates of standard errors for position, additive and additive×environment interaction effects of QTLs in the presence of outliers.

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.

Institutional subscriptions

Similar content being viewed by others

References

  • Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B, 57:289–300.

    Google Scholar 

  • Cao, G.Q., Zhu, J., He, C.X., Gao, Y.M., Wu, P., 2001. Study on epistatic effects and QTL×environment interaction effects of QTLs for panicle length in rice (Oryza sativa L.). J. Zhejiang Univ. (Agric. & Life Sci.), 27(1):55–61.

    CAS  Google Scholar 

  • Cheng, R., Park, N., Hodge, S.E., Juo, S.H.H., 2003. Comparison of the linkage results of two phenotypic constructs from longitudinal data in the Framingham Heart Study: analyses on data measured at three time points and on the average of three measurements. BMC Genet., 4(Suppl. 1):S20. [doi:10.1186/1471-2156-4-S1-S20]

    Article  PubMed  Google Scholar 

  • Cook, R.D., 1977. Detection of influential observations in linear regression. Technometrics, 19(1):15–18. [doi:10.2307/1268249]

    Article  Google Scholar 

  • Doerge, R.W., Churchill, G.A., 1996. Permutation tests for multiple loci affecting a quantitative character. Genetics, 142:285–294.

    PubMed  CAS  Google Scholar 

  • Fernandes, E., Pacheco, A., Penha-Gonçalves, C., 2007. Mapping of quantitative trait loci using the skew-normal distribution. J. Zhejiang Univ. Sci. B, 8(11):792–801. [doi:10.1631/jzus.2007.B0792]

    Article  PubMed  Google Scholar 

  • Hadi, A.S., Simonoff, J.S., 1993. Procedures for the identification of multiple outliers in linear models. J. Am. Stat. Assoc., 88(424):1264–1272. [doi:10.2307/2291266]

    Article  Google Scholar 

  • Haley, C.S., Knott, S.A., 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking marker. Heredity, 69:315–324.

    PubMed  CAS  Google Scholar 

  • Hayat, Y., Salahuddin, Mahmood, Q., Islam, E., Yang, J., 2007. Comparative study of outliers based on statistical methods to evaluate and select the optimum regression model for fertilizers utilization. Scientific Research Monthly, 3:81–84.

    Google Scholar 

  • Jansen, R.C., Stam, P., 1994. High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136:1447–1455.

    PubMed  CAS  Google Scholar 

  • Lander, E.S., Botstein, D., 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics, 121(1):185–199.

    PubMed  CAS  Google Scholar 

  • Li, Z.K., Luo, L.J., Mei, H.W., Wang, D.L., Shu, Q.Y., Tabien, R., Zhong, D.B., Ying, C.S., Stansel, J.W., Khush, G.S., Paterson, A.H., 2001. Over-dominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics, 158:1737–1753.

    PubMed  CAS  Google Scholar 

  • Pérez-Enciso, M., Toro, M.A., 1999. Robust QTL effect estimation using the Minimum Distance method. Heredity, 83(3):347–353. [doi:10.1038/sj.hdy.6885800]

    Article  PubMed  Google Scholar 

  • SAS, 1999. SAS STAT User’s Guide. Versions 7 and 8. SAS Institute Inc., Cary, NC, USA, p.2118.

    Google Scholar 

  • Schabenberger, O., 2004. Mixed Model Influence Diagnostics. Proceedings of the twenty-Ninth Annual SAS Users Group International Conference, May 9–12, Montreal. SAS Institute Inc., Cary, NC, USA, Paper 189-29, p.1–17.

    Google Scholar 

  • Searle, S.R., Casella, G., McCulloch, C.E., 1992. Variance Components. John Wiley & Sons, New York.

    Google Scholar 

  • Tilquin, P., Coppieters, W., Elsen, J.M., Lantier, F., Moreno, C., Baret, P.V., 2001. Statistical powers of QTL mapping methods applied to bacteria counts. Genet. Res. Camb., 78:303–316.

    CAS  Google Scholar 

  • Wang, D.L., Zhu, J., Li, Z.K., Paterson, H.A., 1999. Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches. Theor. Appl. Genet., 99(7–8):1255–1264. [doi:10.1007/s001220051331]

    Article  Google Scholar 

  • Williams, R.W., Airey, D.C., Kulkarni, A., Zhou, G., Lu, L., 2001. Genetic dissection of the olfactory bulbs of mice: QTLs on four chromosomes modulate bulb size. Behav. Genet., 31(1):61–77. [doi:10.1023/A:1010209925783]

    Article  PubMed  CAS  Google Scholar 

  • Yang, J., Zhu, J., William, R., 2007. Mapping genetic architecture of complex trait in experimental populations. Bioinformatics, 23(12):1527–1536. [doi:10.1093/bioinformatics/btm143]

    Article  PubMed  CAS  Google Scholar 

  • Zeng, Z.B., 1994. Precision mapping of quantitative traitloci. Genetics, 136:1457–1468.

    PubMed  CAS  Google Scholar 

  • Zewotir, T., Galpin, J.S., 2005. Influence diagnostics for linear mixed model. J. Data Sci., 3:153–177.

    Google Scholar 

  • Zewotir, T., Galpin, J.S., 2007. A unified approach on residuals, leverages and outliers in the linear mixed model. Test, 16(1):58–75. [doi:10.1007/s11749-006-0001-2]

    Article  Google Scholar 

  • Zhu, J., 1997. Analysis Methods for Genetic Models. Agricultural Publication House of China, Beijing, p.160 (in Chinese).

    Google Scholar 

  • Zhu, J., Weir, B.S., 1994a. Analysis of cytoplasmic and maternal effects. I. A genetic model for diploid plant seeds and animals. Theor. Appl. Genet., 89(2–3):153–159. [doi: 10.1007/BF00225135]

    Google Scholar 

  • Zhu, J., Weir, B.S., 1994b. Analysis of cytoplasmic and maternal effects. II. Genetic model for triploid endosperms. Theor. Appl. Genet., 89(2–3):160–166. [doi:10.1007/BF00225136]

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Zhu.

Additional information

Project supported by the National Basic Research Program (973) of China (No. 2004CB117306) and the Hi-Tech Research and Development Program (863) of China (No. 2006AA10A102)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hayat, Y., Yang, J., Xu, Hm. et al. Influence of outliers on QTL mapping for complex traits. J. Zhejiang Univ. Sci. B 9, 931–937 (2008). https://doi.org/10.1631/jzus.B0820045

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.B0820045

Key words

CLC number

Navigation