Abstract:Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicted and true breeding values. Typically, for individuals (so-called test individuals), breeding values are predicted by … Show more
“…4, the observed accuracies were lower than the expected accuracies according to the formula derived by Daetwyler et al [32] when the approximation for (i.e. number of independent chromosome segments) was [48] but greater than the expected accuracy when was [49]. Our results agree with those of Neves et al [77] who reported that expected accuracies based on were higher than realized accuracies across traits; however, expected accuracies using were lower than realized accuracies in the case of within-family predictions.Fig.…”
Section: Resultsmentioning
confidence: 75%
“…This formula depends on (heritability of the trait), (number of animals in the training population) and (the number of independent chromosome segments). was calculated by using two different approximations: (1) [48] and (2) [49], where is the effective population size, is the genome length and is the average chromosome length. Therefore, these two approximations of lead to two different estimates of .…”
BackgroundHanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS).MethodsAccuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs).ResultsAccuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW.ConclusionsThe predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0283-0) contains supplementary material, which is available to authorized users.
“…4, the observed accuracies were lower than the expected accuracies according to the formula derived by Daetwyler et al [32] when the approximation for (i.e. number of independent chromosome segments) was [48] but greater than the expected accuracy when was [49]. Our results agree with those of Neves et al [77] who reported that expected accuracies based on were higher than realized accuracies across traits; however, expected accuracies using were lower than realized accuracies in the case of within-family predictions.Fig.…”
Section: Resultsmentioning
confidence: 75%
“…This formula depends on (heritability of the trait), (number of animals in the training population) and (the number of independent chromosome segments). was calculated by using two different approximations: (1) [48] and (2) [49], where is the effective population size, is the genome length and is the average chromosome length. Therefore, these two approximations of lead to two different estimates of .…”
BackgroundHanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS).MethodsAccuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs).ResultsAccuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW.ConclusionsThe predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0283-0) contains supplementary material, which is available to authorized users.
“…As a consequence, . We deduce that the variance of can be decomposed in the following way: By definition, according to Rabier et al (), we have As a consequence, we can obtain the following estimations: We have analogue estimates for and .…”
Section: How To Improve the Quality Of The Predictionmentioning
confidence: 77%
“…According to formula (5) of Rabier, Barre, Asp, Charmet, and Mangin (), assuming that x 1 , …, x n are known and that ε , x new , and ε new are random, the genotypic accuracy has the following expression: where is the L 2 ‐norm and is the covariance matrix of size p × p . Note that this accuracy can be viewed as a conditional accuracy, since this expression was obtained conditionally on the TRN design matrix X .…”
Section: General Expression For the Accuracymentioning
Genomic selection is today a hot topic in genetics. It consists in predicting breeding values of selection candidates, using the large number of genetic markers now available owing to the recent progress in molecular biology. One of the most popular methods chosen by geneticists is ridge regression. We focus on some predictive aspects of ridge regression and present theoretical results regarding the accuracy criteria, that is, the correlation between predicted value and true value. We show the influence of singular values, the regularization parameter, and the projection of the signal on the space spanned by the rows of the design matrix. Asymptotic results in a high‐dimensional framework are given; in particular, we prove that the convergence to optimal accuracy highly depends on a weighted projection of the signal on each subspace. We discuss on how to improve the prediction. Last, illustrations on simulated and real data are proposed.
“…The identified QTL for economically useful traits in aquaculture have been summarized recently (Abdelrahman et al, 2017). Linkage analysis to detect QTL includes family and progeny data (Rabier et al, 2016). Segregation of QTL has been studied within family.…”
-An increase in aquatic animal production can be achieved by extending aquaculture areas geographically, utilizing new species for culture, and using new technologies. Among new technologies useful for the increase of aquaculture production is the application of genetics and genomics. New molecular tools that benefit aquaculture have been developed. There has been a large number of experimental and review papers published concerning molecular markers and the range of their applications, including aquaculture and food product analyses. Analysis of single nucleotide polymorphisms (SNPs) has emerged as genotyping technology with wide and significant applications in aquaculture. SNPs can be used for construction of genetic linkage maps, finding quantitative trait loci (QTL) for useful traits like growth, body weight, grilsing, thermal and low oxygen tolerance, resistance to stress and diseases, mapping sex determination loci and identification of progeny in selection and chromosome manipulation experiments, assessment of genomic selectionand marker assisted selection in aquaculture. Genome-wide association studies (GWAS) facilitate the finding associations between SNPs and a trait in related or unrelated specimens. However, many traits are complex and can be controlled by number of QTL. Genotyping by genome reduction complexity sequencing emerged as an efficient and applicable technology in genomic selection. Identification of genes, sequences and nucleotides (substitutions) directly influencing phenotypic variations opens the possibility of marker-assisted selection for desirable characters in culture. SNP and QTL associations can be enhanced using genome editing technology. Examples of successful applications of SNPs in aquaculture of fish, crustacean and mollusk species, representing most geographic areas, and ecological risks assessment are reviewed.
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