Abstract
The goal of this work was to compare the effect of the accuracy and residual variance in genome wide selection using marker selection as well as using the effect of the indirect selection, using simulated and real data. In simulated data was used one sample with 200 individuals with 1,000 molecular markers in F2 population. The real data was obtained in maize with F2 population with 441 individuals and genotyping with 261 SSR markers. There was 11 traits evaluated (ear length, ear width, row number, kernels per row, 100-kernel weight, ear weight, grain yield, length of branch, number of branch, plant height and ear height). All data was analyzed using rrBLUP method and 10-fold cross-validation. In simulated and maize data the results were similar: the residual variance with few markers is lower than with the 1000 markers and the accuracy with few markers is bigger than with 1000 markers. For maize data multi trait selection, the accuracy increased when the correlation between traits is greater than 0.50 and residual variance decreased when the correlation is greater than 0.70. In this sense, these results showed that marker selection could be used as a first step in genome wide selection, improving the prediction and compute demand.
Highlights
The principle of genomic selection is simultaneously estimate the effect of all markers in a training population comprised of phenotyped and genotyped individuals (MEUWISSEN et al, 2001)
This can potentially capture all the quantitative trait loci (QTL) that contribute to the variation of a trait
It was found that all linkage groups were restored according to the parameters used in simulation including the total size (100cM), in the main distance between markers, and the order of the markers that constitute the linkage group
Summary
The principle of genomic selection is simultaneously estimate the effect of all markers in a training population comprised of phenotyped and genotyped individuals (MEUWISSEN et al, 2001). Genomic estimated breeding values (GEBVs) could be calculated as the sum of estimated marker effects for genotyped individuals in a predicted population. Fitting simultaneously all markers ensures that marker-effect estimates are unbiased, small effects are captured (BROMAN; SPEED, 2002). This can potentially capture all the quantitative trait loci (QTL) that contribute to the variation of a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. Only the marker information is required to calculate the GEBVs (HEFFNER et al, 2009)
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