Leveraging genomics and phenomics to accelerate improvement in mungbean: A case study in how to go from GWAS to selection

Abstract

Abstract Grown on 7 million ha, mungbean is a warm‐season grain legume with regional importance in parts of Asia and Africa. Under forecasted climate change, due to its tolerance to drought and heat, the short crop duration, and its nutritional properties, mungbean could serve to fill an important need for human diets. However, selection of accessions becomes difficult where plant and consumer market variation is large. We performed selection on genebank accessions, specifically the mini‐core collection at the World Vegetable Center, for yield and yield component traits. Our selection index uses refined accuracy by leveraging genomics, phenomics, and genotype‐by‐environment interactions. Best linear unbiased prediction (BLUP) is used to predict the genotypic effects of the 292 mini‐core accessions toward seed yield based on genomic relationships formed from ∼200,000 SNPs. We expanded BLUP analysis to predict phenotypic effects based on the phenomic relationships formed from ∼75,000 measurements from three‐dimensional multispectral data. While this method is restricted to a single environment, our multi‐environment trials across eight countries and 4 years serve to quantify the genotype‐by‐environment effect. K‐fold cross‐validation finds predictive ability to vary by methods but to be related to the narrow‐sense heritability of the yield component trait. Our weighted rank sum index (WRSI) linearly combines yield component traits to proxy yield within our single environment phenomics trial by first ranking genomic and/or phenomic BLUPs, then weighting by predictive accuracy from the cross‐validated model, and then summing the component weighted ranks for each accession. Selections were made from the predicted random effects in each location, identifying three accessions overlapping across both methodologies: PI 369787 (VI001339A‐G) and EG‐MD‐6D (VI000380A‐G) from the Philippines, and PI 363534 (VI003220A‐G) from India.