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. 2000 Oct;156(2):855–865. doi: 10.1093/genetics/156.2.855

On the differences between maximum likelihood and regression interval mapping in the analysis of quantitative trait loci.

C H Kao 1
PMCID: PMC1461291  PMID: 11014831

Abstract

The differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

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Selected References

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  1. Doerge R. W., Churchill G. A. Permutation tests for multiple loci affecting a quantitative character. Genetics. 1996 Jan;142(1):285–294. doi: 10.1093/genetics/142.1.285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Dupuis J., Siegmund D. Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics. 1999 Jan;151(1):373–386. doi: 10.1093/genetics/151.1.373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Grattapaglia D., Bertolucci F. L., Penchel R., Sederoff R. R. Genetic mapping of quantitative trait loci controlling growth and wood quality traits in Eucalyptus grandis using a maternal half-sib family and RAPD markers. Genetics. 1996 Nov;144(3):1205–1214. doi: 10.1093/genetics/144.3.1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Hackett C. A., Weller J. I. Genetic mapping of quantitative trait loci for traits with ordinal distributions. Biometrics. 1995 Dec;51(4):1252–1263. [PubMed] [Google Scholar]
  5. Haley C. S., Knott S. A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity (Edinb) 1992 Oct;69(4):315–324. doi: 10.1038/hdy.1992.131. [DOI] [PubMed] [Google Scholar]
  6. Haley C. S., Knott S. A., Elsen J. M. Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics. 1994 Mar;136(3):1195–1207. doi: 10.1093/genetics/136.3.1195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Henshall J. M., Goddard M. E. Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression. Genetics. 1999 Feb;151(2):885–894. doi: 10.1093/genetics/151.2.885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Jansen R. C. Interval mapping of multiple quantitative trait loci. Genetics. 1993 Sep;135(1):205–211. doi: 10.1093/genetics/135.1.205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Jiang C., Zeng Z. B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics. 1995 Jul;140(3):1111–1127. doi: 10.1093/genetics/140.3.1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Kao C. H., Zeng Z. B., Teasdale R. D. Multiple interval mapping for quantitative trait loci. Genetics. 1999 Jul;152(3):1203–1216. doi: 10.1093/genetics/152.3.1203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lander E. S., Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989 Jan;121(1):185–199. doi: 10.1093/genetics/121.1.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Lebreton C. M., Visscher P. M., Haley C. S., Semikhodskii A., Quarrie S. A. A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci. Genetics. 1998 Oct;150(2):931–943. doi: 10.1093/genetics/150.2.931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Li Z., Pinson S. R., Park W. D., Paterson A. H., Stansel J. W. Epistasis for three grain yield components in rice (Oryza sativa L.). Genetics. 1997 Feb;145(2):453–465. doi: 10.1093/genetics/145.2.453. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Rebaï A., Goffinet B. More about quantitative trait locus mapping with diallel designs. Genet Res. 2000 Apr;75(2):243–247. doi: 10.1017/s0016672399004358. [DOI] [PubMed] [Google Scholar]
  15. Satagopan J. M., Yandell B. S., Newton M. A., Osborn T. C. A bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics. 1996 Oct;144(2):805–816. doi: 10.1093/genetics/144.2.805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Weber K., Eisman R., Morey L., Patty A., Sparks J., Tausek M., Zeng Z. B. An analysis of polygenes affecting wing shape on chromosome 3 in Drosophila melanogaster. Genetics. 1999 Oct;153(2):773–786. doi: 10.1093/genetics/153.2.773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Xu S. A comment on the simple regression method for interval mapping. Genetics. 1995 Dec;141(4):1657–1659. doi: 10.1093/genetics/141.4.1657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Xu S. Further investigation on the regression method of mapping quantitative trait loci. Heredity (Edinb) 1998 Mar;80(Pt 3):364–373. doi: 10.1046/j.1365-2540.1998.00307.x. [DOI] [PubMed] [Google Scholar]
  19. Xu S. Iteratively reweighted least squares mapping of quantitative trait loci. Behav Genet. 1998 Sep;28(5):341–355. doi: 10.1023/a:1021617618150. [DOI] [PubMed] [Google Scholar]
  20. Zeng Z. B., Liu J., Stam L. F., Kao C. H., Mercer J. M., Laurie C. C. Genetic architecture of a morphological shape difference between two Drosophila species. Genetics. 2000 Jan;154(1):299–310. doi: 10.1093/genetics/154.1.299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Zeng Z. B. Precision mapping of quantitative trait loci. Genetics. 1994 Apr;136(4):1457–1468. doi: 10.1093/genetics/136.4.1457. [DOI] [PMC free article] [PubMed] [Google Scholar]

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