Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

Abstract

Thousands of maize landraces are stored in seed banks worldwide. Doubled-haploid libraries (DHL) produced from landraces harness their rich genetic diversity for future breeding. We investigated the prospects of genomic prediction (GP) for line per se performance in DHL from six European landraces and 53 elite flint (EF) lines by comparing four scenarios: GP within a single library (sL); GP between pairs of libraries (LwL); and GP among combined libraries, either including (cLi) or excluding (cLe) lines from the training set (TS) that belong to the same DHL as the prediction set. For scenario sL, with N = 50 lines in the TS, the prediction accuracy (r) among seven agronomic traits varied from 20.53 to 0.57 for the DHL and reached up to 0.74 for the EF lines. For LwL, r was close to zero for all DHL and traits. Whereas scenario cLi showed improved r values compared to sL, r for cLe remained at the low level observed for LwL. Forecasting r with deterministic equations yielded inflated values compared to empirical estimates of r for the DHL, but conserved the ranking. In conclusion, GP is promising within DHL, but large TS sizes (N . 100) are needed to achieve decent prediction accuracy because LD between QTL and markers is the primary source of information that can be exploited by GP. Since production of DHL from landraces is expensive, we recommend GP only for very large DHL produced from a few highly preselected landraces.

Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings

“…However, in the current study, genomic prediction was shown to be efficient in predicting early biomass accumulation, with accuracies somewhat lower, but comparable to those obtained for biomass accumulation in the later growth stages [47]. Contrarily, Brauner et al [48] reported comparable prediction accuracies for early vigor when the genomic predictions were performed in lines combined from several genetic pools. Significant differences between means of prediction accuracies obtained with and without HKW as covariate confirmed what was expected after the correlation analysis.…”

Selective Loss of Diversity in Doubled-Haploid Lines from European Maize Landraces

“…To study whether outliers affect functional phenotypic trait variation more than random sites, we computed trait effect sizes using a BayesB ( Meuwissen et al 2001 ) genomic prediction model implemented in GCTB ( Zeng et al 2018 ). We used arithmetic means over four locations of published phenotypes from 351 individuals of six European landrace DH line libraries (GB, RT, SF, Campan Galade, Walliser, Satu Mare) and 53 elite flint lines ( Brauner et al 2018 ). We calculated effect sizes based on the pooled dataset of these populations and additional parameters–chain-length, 30000–burn-in 5000.…”

“…We quantify the changes in genetic diversity due to DH production and investigate the role of drift and selection in creating the observed patterns. Combining published genotype and phenotype data from a number of sources ( Melchinger et al 2017 ; Mayer et al 2017 ; Brauner et al 2018 ), we analyze and compare samples from five populations of European maize landrace accessions and their derived DH lines. In contrast to previous reports ( Melchinger et al 2017 ), we find that landrace genetic diversity is not fully captured by DH line libraries.…”

Selective loss of diversity in doubled-haploid lines from European maize landraces

“…To study whether outliers affect functional phenotypic trait variation more than random sites, we computed trait effect sizes using a BayesB (Meuwissen et al 2001) genomic prediction model implemented in GCTB (Zeng et al 2018). We used arithmetic means over four locations of published phenotypes and 351 individuals of six European landrace DH line libraries (GB, RT, SF, Campan Galade, Walliser, Satu Mare) and 53 elite flint lines (Brauner et al 2018) and calculated effect sizes based on the pooled dataset of these populations and additional parameters -chain-length 30000 -burn-in 5000. We used BEAGLE 5.0 (Browning and Browning 2009) to impute missing data after filtering based on the same cutoffs as the 50k dataset, resulting in 37,884 SNPs for 404 individuals.…”

“…We quantify the changes in genetic diversity due to DH production and investigate the role of drift and selection in creating the observed patterns. Combining published genotype and phenotype data from a number of sources Mayer et al 2017;Brauner et al 2018), we analyze and compare samples from five populations of European maize landrace accessions and their derived DH lines. In contrast to previous reports , we find that landrace genetic diversity is not fully captured by DH line libraries.…”