Genomic prediction of zinc-biofortification potential in rice gene bank accessions

Abstract

Key message A genomic prediction model successfully predicted grain Zn concentrations in 3000 gene bank accessions and this was verified experimentally with selected potential donors having high on-farm grain-Zn in Madagascar. Abstract Increasing zinc (Zn) concentrations in edible parts of food crops, an approach termed Zn-biofortification, is a global breeding objective to alleviate micro-nutrient malnutrition. In particular, infants in countries like Madagascar are at risk of Zn deficiency because their dominant food source, rice, contains insufficient Zn. Biofortified rice varieties with increased grain Zn concentrations would offer a solution and our objective is to explore the genotypic variation present among rice gene bank accessions and to possibly identify underlying genetic factors through genomic prediction and genome-wide association studies (GWAS). A training set of 253 rice accessions was grown at two field sites in Madagascar to determine grain Zn concentrations and grain yield. A multi-locus GWAS analysis identified eight loci. Among these, QTN_11.3 had the largest effect and a rare allele increased grain Zn concentrations by 15%. A genomic prediction model was developed from the above training set to predict Zn concentrations of 3000 sequenced rice accessions. Predicted concentrations ranged from 17.1 to 40.2 ppm with a prediction accuracy of 0.51. An independent confirmation with 61 gene bank seed samples provided high correlations (r = 0.74) between measured and predicted values. Accessions from the aus sub-species had the highest predicted grain Zn concentrations and these were confirmed in additional field experiments, with one potential donor having more than twice the grain Zn compared to a local check variety. We conclude utilizing donors from the aus sub-species and employing genomic selection during the breeding process is the most promising approach to raise grain Zn concentrations in rice.

Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

“…Recently, Zn mainstreaming breeding has been initiated to incorporate Zn as a must trait in all the future rice varieties ( Stangoulis and Knez, 2022 ). Identification of diverse donors, QTLs, genes, and a better understanding of molecular basis of grain Zn concentration, agronomic and yield traits are essential for the efficient Zn biofortification of rice ( Swamy et al., 2016 , Swamy et al., 2018 ; Calayugan et al., 2020 ; Rakotondramana et al, 2022 )…”

“…Recently, Zn mainstreaming breeding has been initiated to incorporate Zn as a must trait in all the future rice varieties (Stangoulis and Knez, 2022). Identification of diverse donors, QTLs, genes, and a better understanding of molecular basis of grain Zn concentration, agronomic and yield traits are essential for the efficient Zn biofortification of rice (Swamy et al, 2016Calayugan et al, 2020;Rakotondramana et al, 2022) We used high Zn aus accession Kaliboro (IRGC 77201-1) to develop four RILs mapping populations. It has acceptable yield potential (~4900 kg/ha) and twice the amount of grain Zn (~40 ppm) in comparison to popular rice varieties (14-16ppm).…”

“…They are well-known for their wider adaptability, tolerance to biotic and abiotic stresses, and nutritional value ( Xu et al., 2006 ; Ali et al., 2011 ; Henry et al., 2011 ; Gamuyao et al., 2012 ; Zhu et al., 2016 ). Currently, aus accessions are being explored for Zn biofortification due to their high grain Zn concentration ( Swamy et al., 2018 ; Rakotondramana et al., 2022 ).…”

Proof of concept and early development stage of market-oriented high iron and zinc rice expressing dicot ferritin and rice nicotianamine synthase genes

“…The National Institute of Standards and Technology (NIST) rice flour standard 1568a and the Wageningen Evaluating Programs for Analytical Laboratories (WEPAL) IPE-135 were used as quality controls, and the samples were digested and analyzed using the same method as used for the rice samples. At Flinders University, ICP-OES of the polished rice grains was performed as it is previously described 59,60 .…”

Genomic approaches for improving grain zinc and iron content in wheat

“…The efficacy of GS has been tested for multiple traits in several crops, including wheat, for yield ( Juliana et al, 2018 ), leaf rust and yellow rust ( Juliana et al, 2017 ), spot blotch ( Juliana et al, 2022b ), and wheat blast ( Juliana et al, 2022a ). GS to improve grain mineral concentrations has been employed in maize ( Mageto et al, 2020 ), rice ( Rakotondramanana et al, 2022 ), and wheat ( Velu et al, 2018 ), with reasonable prediction accuracies. Velu et al (2018) reported prediction accuracies ranging from 0.331 to 0.694, with an average of 0.542, for grain Zn content and 0.324 to 0.734, with an average of 0.529, for grain Fe content, in the Harvest Plus Association Mapping (HPAM) panel.…”