scholarly journals Theoretical and Empirical Power of Regression and Maximum-Likelihood Methods to Map Quantitative Trait Loci in General Pedigrees

2004 ◽  
Vol 75 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Xijiang Yu ◽  
Sara A. Knott ◽  
Peter M. Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Empirical Power ◽  
Likelihood Methods ◽  
Trait Loci ◽  
Maximum Likelihood Methods

scholarly journals Aspects of maximum likelihood methods for the mapping of quantitative trait loci in line crosses

1992 ◽  
Vol 60 (2) ◽  
pp. 139-151 ◽  
Author(s):  
S. A. Knott ◽  
C. S. Haley
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Simulated Data ◽  
Interval Mapping ◽  
Likelihood Methods ◽  
Trait Loci ◽  
Marker Loci ◽  
Maximum Likelihood Methods ◽  
Distribution Testing

SummaryMaximum likelihood methods for the mapping of quantitative trait loci (QTL) have been investigated in an F2 population using simulated data. The use of adjacent (flanking) marker pairs gave improved power for the detection of QTL over the use of single markers when markers were widely spaced and the QTL effect large. The use of flanking marker loci also always gave moreaccurate and less biassed estimates for the effect and position of the QTL and made the method less sensitive to violations of assumptions, for example non-normality of the distribution. Testing the hypothesis of a linked QTL against that of no QTL is not biassed by the presence of unlinked QTL. This test is more robust and easier to obtain than the comparison of a linked with an unlinked QTL. Fixing the recombination fraction between the markers at an incorrect value in the analyses with flanking markers does not generally bias the test for QTL or estimates of their effect. The presence of multiple linked QTL bias both tests and estimates of effect with interval mapping, leading to inflated values when QTL are in association in the lines crossed and deflated values when they are in dispersion.

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

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe 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.

scholarly journals Mapping quantitative trait loci in outcross populations via residual maximum likelihood. II. A simulation study

1996 ◽  
Vol 28 (6) ◽  
pp. 491 ◽  
Author(s):  
FE Grignola ◽  
I Hoeschele ◽  
Q Zhang ◽  
G Thaller
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Simulation Study ◽  
Quantitative Trait ◽  
Residual Maximum Likelihood ◽  
Trait Loci

scholarly journals THE GENETIC ANALYSIS OF QUANTITATIVE TRAIT DIFFERENCES BETWEEN TWO HOMOZYGOUS LINES

Genetics ◽  
1984 ◽  
Vol 108 (3) ◽  
pp. 733-744
Author(s):  
R C Elston
Keyword(s):  
Maximum Likelihood ◽  
Genetic Analysis ◽  
Quantitative Trait ◽  
Quantitative Data ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods

ABSTRACT Previous maximum likelihood methods to analyze quantitative data on two inbred parental strains, their F1 and backcross generations are extended in three directions: (1) a method is suggested to transform the data to better satisfy the assumptions of normality and homoscedasticity; (2) the likelihoods are modified to allow for litter correlations and heteroscedasticity and (3) allowance is made for the incorporation of F2 data. The problem of making a choice among a set of simple genetic hypotheses is further discussed.

scholarly journals Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning

2011 ◽  
Vol 81 (7) ◽  
pp. 792-796 ◽  
Author(s):  
Hui Wang ◽  
Sherri Rose ◽  
Mark J. van der Laan
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Quantitative Trait ◽  
Targeted Maximum Likelihood ◽  
Trait Loci
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