scholarly journals Quantitative trait loci (QTL) mapping of resistance to coffee berry disease (Colletotrichum kahawae Waller Bridge) in Coffea arabica L. variety Rume Sudan

2020 ◽  
Vol 16 (8) ◽  
pp. 1184-1194 ◽  
Author(s):  
Mwita Gimase James ◽  
Muriithi Thagana Wilson ◽  
Ogutu Omondi Chrispine ◽  
Jerono Cheserek Jane ◽  
Mukiri Gichimu Bernard ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Coffea Arabica ◽  
Coffee Berry ◽  
Coffee Berry Disease ◽  
Colletotrichum Kahawae ◽  
Trait Loci ◽  
Coffea Arabica L

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.

Letter to the Editor: Methods for mapping quantitative trait loci in autotetraploid species

2020 ◽  
Author(s):  
Jing Chen ◽  
Lindsey J Leach ◽  
Zewei Luo
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Statistical Methods ◽  
Quantitative Trait ◽  
Important Species ◽  
Letter To The Editor ◽  
Trait Loci ◽  
Points Of View

Abstract Mapping quantitative trait loci (QTL) in autotetraploid species represents a timely and challenging task. Two papers published by Wu and his colleagues proposed statistical methods for QTL mapping in these evolutionarily and economically important species. In this Letter to the Editor, we present critical comments on the fundamental conceptual errors involved, from both statistical and genetic points of view.

scholarly journals A method for computing identity by descent probabilities and quantitative trait loci mapping with dominant (AFLP) markers

2002 ◽  
Vol 79 (3) ◽  
pp. 247-258 ◽  
Author(s):  
MIGUEL PÉREZ-ENCISO ◽  
ODILE ROUSSOT
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Cost Effective ◽  
Significant Loss ◽  
Outbred Population ◽  
Identity By Descent ◽  
Fluorescent Band ◽  
Length Polymorphisms ◽  
Trait Loci

Amplified fragment length polymorphisms (AFLPs) are a widely used marker system: the technique is very cost-effective, easy and rapid, and reproducibly generates hundreds of markers. Unfortunately, AFLP alleles are typically scored as the presence or absence of a band and, thus, heterozygous and dominant homozygous genotypes cannot be distinguished. This results in a significant loss of information, especially as regards mapping of quantitative trait loci (QTLs). We present a Monte Carlo Markov Chain method that allows us to compute the identity by descent probabilities (IBD) in a general pedigree whose individuals have been typed for dominant markers. The method allows us to include the information provided by the fluorescent band intensities of the markers, the rationale being that homozygous individuals have on average higher band intensities than heterozygous individuals, as well as information from linked markers in each individual and its relatives. Once IBD probabilities are obtained, they can be combined into the QTL mapping strategy of choice. We illustrate the method with two simulated populations: an outbred population consisting of full sib families, and an F2 cross between inbred lines. Two marker spacings were considered, 5 or 20 cM, in the outbred population. There was almost no difference, for the practical purpose of QTL estimation, between AFLPs and biallelic codominant markers when the band density is taken into account, especially at the 5 cM spacing. The performance of AFLPs every 5 cM was also comparable to that of highly polymorphic markers (microsatellites) spaced every 20 cM. In economic terms, QTL mapping with a dense map of AFLPs is clearly better than microsatellite QTL mapping and little is lost in terms of accuracy of position. Nevertheless, at low marker densities, AFLPs or other biallelic markers result in very inaccurate estimates of QTL position.

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