Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection

Abstract

Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi‐site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h2), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi‐site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype‐by‐sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h2 estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost‐effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.

Genome-wide association studies dissect the G×E interaction for agronomic traits in a worldwide collection of safflower (Carthamus tinctorius L.)

“…The heatmap of the genomic relationship matrix (G) revealed a strong population structure among 318 accessions (Supplementary Figure s1), which was consistent with the previous observation (Zhao et al 2021). After further ltering, a total of 1806 SNPs were used for GWAS studies, with 1780 positioned on the 12 pseudochromosomes of the draft sa ower genome assembly, about 100-200 SNPs per pseudochromosome.…”

“…Plant material and phenotyping 406 globally diverse sa ower accessions were sourced from the Australian Grain Genebank (AGG), including elite cultivars, breeding lines, and landraces. Accessions information and the eld trial experiment design are previously described (Zhao et al 2021). In brief, with a randomized complete block design, all accessions were sown at two eld sites in two consecutive years (2017 and 2018, a total of four sites that differed in water availability) with plot size of 1m x 5m, 5 rows in each plot, and 220 seeds sowed per plot.…”

“…SNP genotyping and population structure 349 accessions were genotyped using a genotyping-by-sequencing assay as described in (Zhao et al 2021). In brief, genomic DNA was extracted from six crushed seeds per accession, digested with restriction endonucleases PstI (6-bp cutter) and MseI (4-bp cutter), followed by the ampli cation, puri cation, and sequencing by Illumina Hiseq 3000 sequencer.…”

Genome-Wide Association Study and Genomic Prediction for Soybean Cyst Nematode Resistance in USDA Common Bean (Phaseolus vulgaris) Core Collection

“…In addition, GA is affected by the trait self, such as heritability. The GH has been estimated and reported in animals and plants such as heifers (Nawaz et al, 2018 ), soybean (Xavier and Rainey, 2020 ), and safflower (Zhao et al, 2021 ). de los Campos et al ( 2015 ) developed whole-genome regression methods to estimate the GH, which was defined as the proportion of variance of a trait that can be explained (in the population) by linear regression on a set of markers.…”

Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea

“…The univariate or single-trait genomic prediction model is the most common method used in crop GS (de Los Campos et al, 2013 ; Wang et al, 2018 ; Zhao et al, 2021 ). However, additional information in genetically correlated traits can be exploited to improve prediction accuracies via multivariate models.…”