فهرس المقالات GBLUP

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  1 - A Comparison of the Sensitivity of the BayesC and Genomic Best Linear Unbiased Prediction(GBLUP) Methods of Estimating Genomic Breeding Values under Different Quantitative Trait Locus(QTL) Model Assumptions
  M. Shirali S.R. Miraei-Ashtiani A. Pakdel C. Haley P. Navarro R. Pong-Wong
  The objective of this study was to compare the accuracy of estimating and predicting breeding values using two diverse approaches, GBLUP and BayesC, using simulated data under different quantitative trait locus(QTL) effect distributions. Data were simulated with three d أکثر

  The objective of this study was to compare the accuracy of estimating and predicting breeding values using two diverse approaches, GBLUP and BayesC, using simulated data under different quantitative trait locus(QTL) effect distributions. Data were simulated with three different distributions for the QTL effect which were uniform, normal and gamma (1.66, 0.4). The number of QTL was assumed to be either 5, 10 or 20. In total, 9 different scenarios were generated to compare the markers estimated breeding values obtained from these scenarios using t-tests. In comparisons between GBLUP and BayesC within different scenarios for a trait of interest, the genomic estimated breeding values produced and the true breeding values in a training set were highly correlated (r>0.80), despite diverse assumptions and distributions. BayesC produced more accurate estimations than GBLUP in most simulated traits. In all scenarios, GBLUP had a consistently high accuracy independent of different distributions of QTL effects and at all numbers of QTL. BayesC produced estimates with higher accuracies in traits influenced by a low number of QTL and with gamma QTL effects distribution. In conclusion, GBLUP and BayesC had persistent high accuracies in all scenarios, although BayesC performed better in traits with low numbers of QTL and a Gamma effect distribution. تفاصيل المقالة
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  2 - Assessment of Alternative Single Nucleotide Polymorphism (SNP) Weighting Methods for Single-Step Genomic Prediction of Traits with Different Genetic Architecture
  S. Moghaddaszadeh-Ahrabi M. Bazrafshan
  We investigated the prediction accuracy and bias of single-step genomic BLUP (ssGBLUP) with or without weights for single-nucleotide polymorphisms (SNPs). The SNP weights were calculated using population Fixation Index (WssGBLUPFST) and a nonlinear method called nonline أکثر

  We investigated the prediction accuracy and bias of single-step genomic BLUP (ssGBLUP) with or without weights for single-nucleotide polymorphisms (SNPs). The SNP weights were calculated using population Fixation Index (WssGBLUPFST) and a nonlinear method called nonlinearA (WssGBLUPNLA). The results of these two weighted methods were compared with a non-weighted method. The individuals of the reference population were sorted based on their estimated breeding values and the top 5% and bottom 5% of individuals based on their estimated breeding values (EBVs) were considered as subpopulations 1 and 2. The FST values for all SNPs between subpopulations 1 and 2 were scaled between zero and one and used as weights. The prediction accuracy and bias of predictions in WssGBLUPFST, WssGBLUPNLA and ssGBLUP methods were compared considering varying the numbers of quantitative trait locus (QTL) (10, 50 and 500), heritability (0.1 and 0.4) and size of reference population (1500, 5000 and 12500). In 10 and 50 QTL, both weighting methods outperformed regular ssGBLUP and with simulation, WssGBLUPFST outperformed WssGBLUPNLA. By increasing the number of QTL to 500 QTL, the WssGBLUPFST was no longer superior to WssGBLUPNLA and ssGBLUP. Our results suggest usefulness of weighting genomic relationship matrix by using FST, especially when the trait is affected by a few numbers of QTL. The prediction accuracy of WssGBLUP methods is expected to increase by identifying and giving appropriate weight to QTL with major effects. Combining different test statistics into a single framework such as decomposition of multiple signals may help reduce false positives and pinpoint the QTL position with more precision. تفاصيل المقالة
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  3 - Effects of Marker Density, Number of Quantitative Trait Loci and Heritability of Trait on Genomic Selection Accuracy
  ف. علاء نوشهر س.ع. رأفت ر. ایمانی-نبئی ص. علیجانی ک. روبرت گرنیه
  The success of genomic selection mainly depends on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), number of QTL and heritability (h2) of the traits. The extent of LD depends on the genetic structure of the population and mar أکثر

  The success of genomic selection mainly depends on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), number of QTL and heritability (h2) of the traits. The extent of LD depends on the genetic structure of the population and marker density. This study was conducted to determine the effects of marker density, level of heritability, number of QTL, and to compare the accuracy of predicting breeding values using two diverse approaches: GBLUP and BayesA using simulated data under two different distributions of the QTL effect. Thereby, three traits (milk production, carcass weight and mature body weight) were simulated with the heritability of 0.10, 0.30 and 0.50, respectively; for each ovine animal, a genome with three chromosomes, 100 cM each. Three different marker densities was considered (1000, 2000 and 3000 markers) and the number of QTL was assumed to be either 100, 200 or 300. Data were simulated with two different distributions of the QTL effect which were uniform and gamma (α=1.66 and β=0.4) the marker density, number of the QTL, the QTL effect distributions and heritability levels significantly affected the accuracy of genomic breeding values (P<0.05). The BayesA produced estimates with greater accuracies in traits influenced by a low number of the QTL and with the gamma QTL effects distribution. Based on the findings of this simulation, heritability, as well as dense marker panels, aiming to increase the level of LD between the markers and QTL, is likely to be needed for successful implementation of the genomic selection. تفاصيل المقالة