Quantitative genetics theory for non-inbred populations in linkage disequilibrium

Crop Breed. Appl. Biotechnol.

Silva³

et al. 2017

Self Cite

“…The LD is maximized (|Δ| = 0.25) when θ = 0 and |p 1 − p 2 | = 1. In this case, the LD value is positive with coupling and negative with repulsion (Azevedo et al 2015, Viana 2004.…”

Section: Simulated Datasetsmentioning

confidence: 94%

“…The resultant population is an advanced generation composite, which presents Hardy-Weinberg equilibrium and LD. According to Viana (2004), the LD value (�) in a composite population is…”

Section: Simulated Datasetsmentioning

confidence: 99%

Population structure correction for genomic selection through eigenvector covariates

Crop Breed. Appl. Biotechnol.

Silva³

et al. 2017

Self Cite

“…where a and b are two single nucleotide polymorphisms (SNPs), two quantitative trait loci (QTLs), or one SNP and one QTL, θ is the frequency of recombinant gametes, and p 1 and p 2 are the allele frequencies in the parental populations 1 and 2, respectively (Viana, 2004). Consequently, the LD value depends on the allele frequencies in the parental populations.…”

Section: Research Articlementioning

confidence: 99%

Triple categorical regression for genomic selection: application to cassava breeding

Lima

Sci. agric. (Piracicaba, Braz.)

et al. 2019

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Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.

“…) (Kempthorne, 1973;Viana, 2004;Viana et al, 2016), where a and b are two SNPs, or two QTLs, or one SNP and one QTL, q the frequency of recombinant gametes, and p 1 and p 2 the allele frequencies in the parental populations 1 and 2, respectively.…”

Section: Simulated Datasetsmentioning

confidence: 99%

New insights into genomic selection through population-based non-parametric prediction methods

Lima

Sci. agric. (Piracicaba, Braz.)

et al. 2019

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Genome-wide selection (GWS) is based on a large number of markers widely distributed throughout the genome. Genome-wide selection provides for the estimation of the effect of each molecular marker on the phenotype, thereby allowing for the capture of all genes affecting the quantitative traits of interest. The main statistical tools applied to GWS are based on random regression or dimensionality reduction methods. In this study a new non-parametric method, called Delta-p was proposed, which was then compared to the Genomic Best Linear Unbiased Predictor (G-BLUP) method. Furthermore, a new selection index combining the genetic values obtained by the G-BLUP and Delta-p, named Delta-p/G-BLUP methods, was proposed. The efficiency of the proposed methods was evaluated through both simulation and real studies. The simulated data consisted of eight scenarios comprising a combination of two levels of heritability, two genetic architectures and two dominance status (absence and complete dominance). Each scenario was simulated ten times. All methods were applied to a real dataset of Asian rice (Oryza sativa) aiming to increase the efficiency of a current breeding program. The methods were compared as regards accuracy of prediction (simulation data) or predictive ability (real dataset), bias and recovery of the true genomic heritability. The results indicated that the proposed Delta-p/G-BLUP index outperformed the other methods in both prediction accuracy and predictive ability.