On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Corresponding author: Chen-Hung Kao

Communicating editor: Z-B. ZENG

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.

This article has been cited by other articles:

				B. Feenstra, I. M. Skovgaard, and K. W. Broman Mapping Quantitative Trait Loci by an Extension of the Haley-Knott Regression Method Using Estimating Equations Genetics, August 1, 2006; 173(4): 2269 - 2282. [Abstract] [Full Text] [PDF]

				C. A. Anderson, A. F. McRae, and P. M. Visscher A Simple Linear Regression Method for Quantitative Trait Loci Linkage Analysis With Censored Observations Genetics, July 1, 2006; 173(3): 1735 - 1745. [Abstract] [Full Text] [PDF]

				S. Sen, J. M. Satagopan, and G. A. Churchill Quantitative Trait Locus Study Design From an Information Perspective Genetics, May 1, 2005; 170(1): 447 - 464. [Abstract] [Full Text] [PDF]

				Y. Liu, G. B. Jansen, and C. Y. Lin Quantitative Trait Loci Mapping for Dairy Cattle Production Traits Using a Maximum Likelihood Method J Dairy Sci, February 1, 2004; 87(2): 491 - 500. [Abstract] [Full Text] [PDF]

				J. O. Borevitz, J. N. Maloof, J. Lutes, T. Dabi, J. L. Redfern, G. T. Trainer, J. D. Werner, T. Asami, C. C. Berry, D. Weigel, et al. Quantitative Trait Loci Controlling Light and Hormone Response in Two Accessions of Arabidopsis thaliana Genetics, February 1, 2002; 160(2): 683 - 696. [Abstract] [Full Text] [PDF]

				C. Lange and J. C. Whittaker Mapping Quantitative Trait Loci Using Generalized Estimating Equations Genetics, November 1, 2001; 159(3): 1325 - 1337. [Abstract] [Full Text] [PDF]

				a. Sen and G. A. Churchill A Statistical Framework for Quantitative Trait Mapping Genetics, September 1, 2001; 159(1): 371 - 387. [Abstract] [Full Text] [PDF]

				R. Nakamichi, Y. Ukai, and H. Kishino Detection of Closely Linked Multiple Quantitative Trait Loci Using a Genetic Algorithm Genetics, May 1, 2001; 158(1): 463 - 475. [Abstract] [Full Text]