อิทธิพลของแบบจำลองทางสถิติและจำนวนเครื่องหมายที่ส่งผลต่อความแม่นยำของการทำนายค่าจีโนมสำหรับลักษณะผลผลิตน้ำยางในต้นยางพารา - นิลพลับ ภัทรวี; อุโฆษกิจ กิตติพัฒน์ (Thai Journal of Science and Technology, 2020)
Genomic prediction is a method for predicting genomic breeding values (GEBVs) of quantitative traits for plants and animals, using nucleotide variation throughout the genome. This approach can increase genetic gains by accelerating the breeding cycle. Several factors affecting prediction accuracy should be well evaluated if breeders exploit genomic selection to its full potential. In this study, a panel of 170 natural rubber trees genotyped with 14,155 single nucleotide polymorphism (SNP) markers to investigate the effect of marker density and statistical models was examined for genomic prediction accuracy of latex yield in the dry (YD) and wet (YW) seasons. The performance of two different genomic prediction methods that differ with respect to assumptions regarding distribution of marker effects, including ridge regression-best linear unbiased prediction (RR–BLUP), and Bayesian LASSO (least absolute shrinkage and selection operator, BL) was evaluated. The predictive ability of the methods was evaluated using a cross-validation approach. RR-BLUP had higher predictive ability than BL for both YD and YW. This suggests that latex yield in the dry and wet seasons is controlled by many genes of equal contribution of all markers to the observed variation. Accuracy can be improved by increasing the optimal marker diversity. These findings represent a resource for plant breeders and contribute to the collective knowledge for genomic selection in rubber tree.