Effects of Marker Density, Number of Quantitative Trait Loci and Heritability of Trait on Genomic Selection Accuracy

الموضوعات :

ف. علاء نوشهر ¹ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
س.ع. رأفت ² (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ر. ایمانی-نبئی ³ (Department of Animal Science, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran)
ص. علیجانی ⁴ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ک. روبرت گرنیه ⁵ (INRA-INPT-ENSAT-INPT-ENVT, Université de Toulouse, UMR 1388 GenPhySE, Castanet Tolosan, France)

تاريخ الإرسال : 23 الإثنين , شعبان, 1437 تاريخ التأكيد : 14 الخميس , ذو الحجة, 1437 تاريخ الإصدار : 13 الجمعة , ربيع الأول, 1439

الکلمات المفتاحية: GBLUP, heritability, BayesA, genomic breeding value, marker density, quantitative trait loci,

ملخص المقالة :

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.

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