Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population

Abstract

Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C0) training population. A total of 1000 ear-to-row C0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C1). Predictions of the genotyped individuals forming cycle C1 were made, and the best predicted grain yielders were selected as parents of C2; this was repeated for more cycles (C2, C3, and C4), thereby achieving two cycles per year. Multi-environment trials of individuals from populations C0, C1, C2, C3, and C4, together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C1 to C4 reached 0.225 ton ha−1 per cycle, which is equivalent to 0.100 ton ha−1 yr−1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C0), genetic diversity narrowed only slightly during the last GS cycles (C3 and C4). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…Recently, in tropical maize, by employing RCGS on eight bi-parental populations evaluated under drought stress environments, Beyene et al (2015) reported an average gain of 0.086 ton ha −1 per cycle, and hybrids derived from cycle 3 produced 7.3% (0.176 ton ha −1 ) higher grain yield than those from cycle 0 and conventional pedigree breeding methods. Further, Zhang et al (2017) using RCGS in a tropical maize multi-parental population reported a genetic gain for grain yield from cycle 1 to cycle 4 reached 0.225 ton ha −1 per cycle, which is equivalent to 0.100 ton ha −1 year −1 over a 4.5 year breeding period from the initial cross to the last cycle. While similar rates of gain have not been reported in legumes, recent reports on over three generations per year in traditional breeding for pigeonpea offer new opportunities to apply RCGS to accelerate rates of genetic gain in grain legumes.…”

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

“…Fifteen elite tropical maize lines were crossed in diallelic fashion to form cycle 0 (C 0 ), which was genotyped using GBS markers and phenotyped at two locations in Mexico; plants with the best phenotype were selected to form the parents for GS cycle 1 (C 1 ). The C 1 parents were intercrossed and the progeny genotyped with the same GBS markers used for the C 0 population [73].…”

Genetic study and molecular breeding for high phosphorus use efficiency in maize

“…Bernardo and Yu [98] performed a simulation analysis in maize DH breeding and demonstrated that genome-wide selection has greater genetic advances than MAS. Subsequently, GS has been widely carried out in the study of maize inbred line selection, hybrid phenotypic and heterosis prediction, and has achieved great efficiencies in important agronomic traits, quality and yield of maize [99][100][101][102][103][104][105][106] . The prediction accuracy of GS is affected by multiple factors, such as the genetic structure of a trait, the number of markers, the size of the training population, and the kinship among individuals [107][108][109][110] .…”