Faculty Opinions recommendation of Protein Quantitative Trait Loci Analysis Identifies Genetic Variation in the Innate Immune Regulator TOLLIP in Post-Lung Transplant Primary Graft Dysfunction Risk.

Naturally occurring genetic variation is the basis for differences in performance and appearance between and within different breeds and lines of livestock. In a few instances (e.g. coat colour, polling) the genes (or loci) which control the variation between animals and breeds have a large enough effect to be individually recognisable. For many traits, however, the combined effects of many different genes act together to control quantitative differences between breeds and individuals within breeds (hence such genes are often referred to as quantitative trait loci or QTLs). Thus the dramatic successes of modern breeding result from generations of selection which has produced accumulated changes at a number of different loci. The genome contains up to 100,000 different genes and identifying those which contribute to variation in traits of interest is a difficult task. One first step is to identify regions of the genome containing loci of potential interest through their linkage to genetic markers.

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Quantitative trait loci associated with the humoral innate immune response in chickens were confirmed in a cross between Green-Legged Partridgelike and White Leghorn

Poultry Science ◽

10.3382/ps.2011-01465 ◽

2011 ◽

Vol 90 (9) ◽

pp. 1909-1915 ◽

Cited By ~ 8

Author(s):

A. Sławińska ◽

A. Witkowski ◽

M. Nieuwland ◽

G. Minozzi ◽

M. Bednarczyk ◽

...

Keyword(s):

Immune Response ◽

Quantitative Trait Loci ◽

Innate Immune Response ◽

Quantitative Trait ◽

Innate Immune ◽

White Leghorn ◽

Trait Loci

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Mapping quantitative trait loci for innate immune response in the pig

International Journal of Immunogenetics ◽

10.1111/j.1744-313x.2010.00985.x ◽

2011 ◽

Vol 38 (2) ◽

pp. 121-131 ◽

Cited By ~ 20

Author(s):

M. J. Uddin ◽

M. U. Cinar ◽

C. Große-Brinkhaus ◽

D. Tesfaye ◽

E. Tholen ◽

...

Keyword(s):

Immune Response ◽

Quantitative Trait Loci ◽

Innate Immune Response ◽

Quantitative Trait ◽

Innate Immune ◽

Trait Loci

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Genetic variation underlying pod size and color traits of common bean depends on quantitative trait loci with epistatic effects

Molecular Breeding ◽

10.1007/s11032-013-0008-9 ◽

2014 ◽

Vol 33 (4) ◽

pp. 939-952 ◽

Cited By ~ 10

Author(s):

Fernando J. Yuste-Lisbona ◽

Ana M. González ◽

Carmen Capel ◽

Manuel García-Alcázar ◽

Juan Capel ◽

...

Keyword(s):

Genetic Variation ◽

Quantitative Trait Loci ◽

Common Bean ◽

Quantitative Trait ◽

Epistatic Effects ◽

Color Traits ◽

Trait Loci

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Quantitative Trait Loci Variation for Growth and Obesity Between and Within Lines of Pigs (Sus scrofa)

Genetics ◽

10.1093/genetics/164.2.629 ◽

2003 ◽

Vol 164 (2) ◽

pp. 629-635 ◽

Cited By ~ 2

Author(s):

Yoshitaka Nagamine ◽

Chris S Haley ◽

Asheber Sewalem ◽

Peter M Visscher

Keyword(s):

Genetic Variation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Sus Scrofa ◽

Strong Support ◽

Wild Type ◽

Porcine Genome ◽

Trait Loci ◽

Sib Families ◽

Related Population

Abstract The hypothesis that quantitative trait loci (QTL) that explain variation between divergent populations also account for genetic variation within populations was tested using pig populations. Two regions of the porcine genome that had previously been reported to harbor QTL with allelic effects that differed between the modern pig and its wild-type ancestor and between the modern pig and a more distantly related population of Asian pigs were studied. QTL for growth and obesity traits were mapped using selectively genotyped half-sib families from five domesticated modern populations. Strong support was found for at least one QTL segregating in each population. For all five populations there was evidence of a segregating QTL affecting fatness in a region on chromosome 7. These findings confirm that QTL can be detected in highly selected commercial populations and are consistent with the hypothesis that the same chromosome locations that account for variation between populations also explain genetic variation within populations.

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Linkage Analysis of Molecular Markers and Quantitative Trait Loci in Populations of Inbred Backcross Lines of Brassica napus L.

Genetics ◽

10.1093/genetics/153.2.949 ◽

1999 ◽

Vol 153 (2) ◽

pp. 949-964 ◽

Cited By ~ 2

Author(s):

David V Butruille ◽

Raymond P Guries ◽

Thomas C Osborn

Keyword(s):

Genetic Variation ◽

Brassica Napus ◽

Quantitative Trait Loci ◽

Qtl Analysis ◽

Quantitative Trait ◽

Agronomic Traits ◽

Pedigree Structure ◽

Inbred Backcross Lines ◽

Trait Loci ◽

Inbred Backcross

Abstract Backcross populations are often used to study quantitative trait loci (QTL) after they are initially discovered in balanced populations, such as F2, BC1, or recombinant inbreds. While the latter are more powerful for mapping marker loci, the former have the reduced background genetic variation necessary for more precise estimation of QTL effects. Many populations of inbred backcross lines (IBLs) have been developed in plant and animal systems to permit simultaneous study and dissection of quantitative genetic variation introgressed from one source to another. Such populations have a genetic structure that can be used for linkage estimation and discovery of QTL. In this study, four populations of IBLs of oilseed Brassica napus were developed and analyzed to map genomic regions from the donor parent (a winter-type cultivar) that affect agronomic traits in spring-type inbreds and hybrids. Restriction fragment length polymorphisms (RFLPs) identified among the IBLs were used to calculate two-point recombination fractions and LOD scores through grid searches. This information allowed the enrichment of a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the IBLs were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach. The number of QTL detected for each trait and the number of effective factors calculated by using biometrical methods were of similar magnitude. Populations of IBLs were shown to be valuable for both marker mapping and QTL analysis.

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Multiple Interval Mapping for Quantitative Trait Loci

Genetics ◽

10.1093/genetics/152.3.1203 ◽

1999 ◽

Vol 152 (3) ◽

pp. 1203-1216

Author(s):

Chen-Hung Kao ◽

Zhao-Bang Zeng ◽

Robert D Teasdale

Keyword(s):

Genetic Variation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Genetic Parameters ◽

Interval Mapping ◽

Ratio Test ◽

Data Set ◽

Multiple Interval Mapping ◽

Total Genetic Variation ◽

Trait Loci

Abstract A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from ∼1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed ∼10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0.3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/~chkao/).

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