RT Journal Article
SR Electronic
T1 Efficiency of genomic prediction of non-assessed single crosses
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 141440
DO 10.1101/141440
A1 José Marcelo Soriano Viana
A1 Helcio Duarte Pereira
A1 Gabriel Borges Mundim
A1 Hans-Peter Piepho
A1 Fabyano Fonseca e Silva
YR 2017
UL http://biorxiv.org/content/early/2017/05/25/141440.abstract
AB The objective was to provide additional relevant information on efficiency of prediction of non-assessed single crosses. We derived the genetic model for genomic prediction. The SNP and QTL genotypic data for DH lines, the QTL genotypic data of SCs, and the phenotypic data for DH lines and SCs were simulated assuming 10,000 SNPs, 400 QTLs, two groups of 70 selected DH lines, and 4,900 SCs. The heritabilities for the assessed SCs were 30, 60 and 100%. The scenarios included three sampling processes of DH lines, two sampling processes of SCs for testing, two SNP densities, DH lines from distinct and same populations, DH lines from populations with lower LD, two genetic models, three statistical models, and three statistical approaches. The efficiency of prediction of untested SCs was based on the prediction accuracy and the efficacy of identification of the best 300 (7–9%) non-assessed SCs (coincidence index), computed based on the true genotypic values. Concerning the prediction accuracy and coincidence, our results proved that prediction of untested SCs is very efficient. The accuracies and coincidences ranged from approximately 0.8 and 0.5, respectively, under low heritability, to 0.9 and 0.7, assuming high heritability. Additionally, we highlighted the relevance of the overall LD and evidenced that efficient prediction of untested SCs can be achieved for crops that show no heterotic pattern, for reduced training set size (10%), for SNP density of 1 cM, and for distinct sampling processes of DH lines, based on random choice of the SCs for testing.