scholarly journals Mapping quantitative trait loci for principal components of bone measurements and osteochondrosis scores in a wild boar × Large White intercross

2000 ◽  
Vol 75 (2) ◽  
pp. 223-230 ◽  
Author(s):  
L. ANDERSSON-EKLUND ◽  
H. UHLHORN ◽  
N. LUNDEHEIM ◽  
G. DALIN ◽  
L. ANDERSSON
Keyword(s):  
Principal Component Analysis ◽  
Quantitative Trait Loci ◽  
Wild Boar ◽  
Quantitative Trait ◽  
Marker Assisted Selection ◽  
Principal Component ◽  
Interval Mapping ◽  
Substantial Effect ◽  
Large White ◽  
Trait Loci

Data on osteochondrosis and femur dimensions from 195 F2 pigs from a wild boar × Large White intercross were analysed with the aim of detecting quantitative trait loci (QTLs) for normal and disturbed bone formation. The information from numerous recorded traits was summarized by principal component analysis and analysed by least-squares interval mapping. An increase in the proportion of wild boar alleles across the genome increased length versus width of femur and reduced the prevalence of osteochondrosis. The presence of QTLs with an impact on femur dimensions was indicated on chromosomes 2, 4, 16 and 17 and on osteochondrosis on chromosomes 5, 13 and 15. A substantial effect of the chromosome 5 QTL calls for further studies within commercial populations to evaluate whether marker-assisted selection could be used to reduce the prevalence of osteochondrosis.

scholarly journals Principal component analysis of quantitative trait loci for immune response to adenovirus in mice

Hereditas ◽  
2006 ◽  
Vol 143 (2006) ◽  
pp. 189-197 ◽  
Author(s):  
Solomon K. Musani ◽  
Huang-Ge Zhang ◽  
Hui-Chen Hsu ◽  
Nengjun Yi ◽  
Bernard S. Gorman ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Immune Response ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Principal Component ◽  
Component Analysis ◽  
Trait Loci

scholarly journals Genetic Architecture of Mandible Shape in Mice: Effects of Quantitative Trait Loci Analyzed by Geometric Morphometrics

Genetics ◽  
2001 ◽  
Vol 157 (2) ◽  
pp. 785-802
Author(s):  
Christian Peter Klingenberg ◽  
Larry J Leamy ◽  
Eric J Routman ◽  
James M Cheverud
Keyword(s):  
Quantitative Trait Loci ◽  
Geometric Morphometrics ◽  
Quantitative Trait ◽  
Principal Component ◽  
Inbred Strains ◽  
Interval Mapping ◽  
Morphological Integration ◽  
Developmental Genetics ◽  
Shape Variation ◽  
Trait Loci

Abstract This study introduces a new multivariate approach for analyzing the effects of quantitative trait loci (QTL) on shape and demonstrates this method for the mouse mandible. We quantified size and shape with the methods of geometric morphometrics, based on Procrustes superimposition of five morphological landmarks recorded on each mandible. Interval mapping for F2 mice originating from an intercross of the LG/J and SM/J inbred strains revealed 12 QTL for size, 25 QTL for shape, and 5 QTL for left-right asymmetry. Multivariate ordination of QTL effects by principal component analysis identified two recurrent features of shape variation, which involved the positions of the coronoid and angular processes relative to each other and to the rest of the mandible. These patterns are reminiscent of the knockout phenotypes of a number of genes involved in mandible development, although only a few of these are possible candidates for QTL in our study. The variation of shape effects among the QTL showed no evidence of clustering into distinct groups, as would be expected from theories of morphological integration. Further, for most QTL, additive and dominance effects on shape were markedly different, implying overdominance for specific features of shape. We conclude that geometric morphometrics offers a promising new approach to address problems at the interface of evolutionary and developmental genetics.

scholarly journals Mapping quantitative trait loci for carcass and meat quality traits in a wild boar x Large White intercross.

1998 ◽  
Vol 76 (3) ◽  
pp. 694 ◽  
Author(s):  
L Andersson-Eklund ◽  
L Marklund ◽  
K Lundström ◽  
C S Haley ◽  
K Andersson ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Wild Boar ◽  
Meat Quality ◽  
Quantitative Trait ◽  
Quality Traits ◽  
Large White ◽  
Meat Quality Traits ◽  
Trait Loci

scholarly journals Mapping quantitative trait loci underlying function-valued traits using functional principal component analysis and multi-trait mapping

2015 ◽  
Author(s):  
Il-Youp Kwak ◽  
Candace R. Moore ◽  
Edgar P. Spalding ◽  
Karl W Broman
Keyword(s):  
Principal Component Analysis ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Recombinant Inbred Lines ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Smooth Approximation ◽  
Functional Principal Component ◽  
Trait Loci

We previously proposed a simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative trait locus mapping is applied to these dimension-reduced data, either with a multi-trait method or by considering the traits individually and then taking the average or maximum LOD score across traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl.

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.

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