scholarly journals A Least Squares Regression Model to Detect Quantitative Trait Loci with Polar Overdominance in a Cross of Outbred Breeds: Simulation

2013 ◽  
Vol 26 (11) ◽  
pp. 1536-1544
Author(s):  
Jong-Joo Kim ◽  
Jack C. M. Dekkers
Keyword(s):  
Quantitative Trait Loci ◽  
Regression Model ◽  
Least Squares ◽  
Quantitative Trait ◽  
Least Squares Regression ◽  
Trait Loci ◽  
Polar Overdominance

scholarly journals Mapping quantitative trait loci in crosses between outbred lines using least squares.

Genetics ◽  
1994 ◽  
Vol 136 (3) ◽  
pp. 1195-1207 ◽  
Author(s):  
C S Haley ◽  
S A Knott ◽  
J M Elsen
Keyword(s):  
Quantitative Trait Loci ◽  
Linkage Group ◽  
Least Squares ◽  
Information Content ◽  
Quantitative Trait ◽  
Inbred Lines ◽  
Residual Variance ◽  
Multiple Markers ◽  
Trait Loci ◽  
Complex Models

Abstract The use of genetic maps based upon molecular markers has allowed the dissection of some of the factors underlying quantitative variation in crosses between inbred lines. For many species crossing inbred lines is not a practical proposition, although crosses between genetically very different outbred lines are possible. Here we develop a least squares method for the analysis of crosses between outbred lines which simultaneously uses information from multiple linked markers. The method is suitable for crosses where the lines may be segregating at marker loci but can be assumed to be fixed for alternative alleles at the major quantitative trait loci (QTLs) affecting the traits under analysis (e.g., crosses between divergent selection lines or breeds with different selection histories). The simultaneous use of multiple markers from a linkage group increases the sensitivity of the test statistic, and thus the power for the detection of QTLs, compared to the use of single markers or markers flanking an interval. The gain is greater for more closely spaced markers and for markers of lower information content. Use of multiple markers can also remove the bias in the estimated position and effect of a QTL which may result when different markers in a linkage group vary in their heterozygosity in the F1 (and thus in their information content) and are considered only singly or a pair at a time. The method is relatively simple to apply so that more complex models can be fitted than is currently possible by maximum likelihood. Thus fixed effects of background genotype can be fitted simultaneously with the exploration of a single linkage group which will increase the power to detect QTLs by reducing the residual variance. More complex models with several QTLs in the same linkage group and two-locus interactions between QTLs can similarly be examined. Thus least squares provides a powerful tool to extend the range of crosses from which QTLs can be dissected whilst at the same time allowing flexible and realistic models to be explored.

Identification of cis-regulated genes in quantitative trait loci driving liver fibrosis

2012 ◽  
Vol 50 (08) ◽  
Author(s):  
R Hall ◽  
R Müllenbach ◽  
S Huss ◽  
R Alberts ◽  
K Schughart ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Liver Fibrosis ◽  
Quantitative Trait ◽  
Trait Loci
Sign in / Sign up

Export Citation Format

Share Document