A bioeconomic quantitative genetic model for assessing milling yields in rice

Abstract

Rice production holds global significance; however, the sustainability of milling yield traits has yet to receive sufficient attention. Therefore, the current study proposed a bioeconomic model integrated with genomic best linear unbiased prediction (GBLUP) to rank elite rice genotypes for head rice yield percentages (HRY%). To underpin the elite genotypes, we used a recombinant inbred lines (RIL) population (F7) developed by crossing two medium grain rice cultivars, 'M2O5' x 'Baru', with six biological replicates. Both parents had contrasting phenotypic expressions for HRY% [(M2O5; 40%) and (Baru; 54%)]. RIL were under the genetic influence as an additive genetic variance (σ2a) of up to 37% for HRY% with high narrow-sense heritability (h2n>40%) and genetic advance of > 4% per generation with a predicted genetic gain of up to 7%. The measured traits had a moderate to strong genotypic correlation (rg; +0.3 to -0.7; P < 0.05). Bioeconomic adjusted genomic estimated breeding values (Bioeconomic-GEBV) identified the best-performing nineteen (19) individuals with high HRY% and reduced percentage losses (Husk loss, broken brown rice loss and broken white rice loss). We developed an improved version of the linkage mapping software ‘SimpleMapV2’, but QTL mapping did not detect any significant (LOD threshold > 3) QTL associated with HRY% and associated losses. The current study had a dearth of potential for implementing a bioeconomic genetic model at a larger scale across the diversity panel and breeding populations to improve milling yield traits in rice and across different grain/fruit crops that have been neglected so far.