Genomic-enabled prediction model with genotype × environment interaction in elitechickpea lines uri icon

abstract

  • Genomic selection (GS) allows safe phenotyping and reducescost and shortening selection cycles. Incorporating of genotype× environment (G×E) interactions in genomic prediction modelsimproves the predictive ability of lines performance across environmentsand in target environments. Phenotyping data on a setof 320 elite chickpea breeding lines on different traits (e.g., plantheight, days to maturity, and seed yield), from three consecutiveyears for two different treatments at two locations were recorded.These lines were genotyped on DArTseq(1.6K) and Genotyping-by-Sequencing (GBS; 89K SNPs) platforms. Five differentmodels were fitted, four of which included genomic informationas main effects (baseline model) and/or G×E interactions. Threedifferent cross-validation schemes that mimic real scenarios thatbreeders might face on fields were considered to assess the predictiveability of the models (CV2: incomplete field trials; CV1:newly developed lines; and CV0: new previously untested environments).Different prediction models gave different results forthe different traits; however, some interesting patterns were observed.For CV1, analyzing yield seed interaction models improvedbaseline counterparts on an average between 55 and 92% usingDArT and DArT combined with GBS data, respectively (between9 and 112% for all traits). While for CV2 these improvements variedb tween 65 and 102% (between 8 and 130% remaining traits).In CV0, no clear advantage was observed considering the interactionterm. These results suggest that GS models hold potential forbreeder?s applications on chickpea cultivar improvements

publication date

  • 2017