Improvement of Rice Biomass Yield through QTL-Based Selection

Abstract

Biomass yield of rice (Oryza sativa L.) is an important breeding target, yet it is not easy to improve because the trait is complex and phenotyping is laborious. Using progeny derived from a cross between two high-yielding Japanese cultivars, we evaluated whether quantitative trait locus (QTL)-based selection can improve biomass yield. As a measure of biomass yield, we used plant weight (aboveground parts only), which included grain weight and stem and leaf weight. We measured these and related traits in recombinant inbred lines. Phenotypic values for these traits showed a continuous distribution with transgressive segregation, suggesting that selection can affect plant weight in the progeny. Four significant QTLs were mapped for plant weight, three for grain weight, and five for stem and leaf weight (at α = 0.05); some of them overlapped. Multiple regression analysis showed that about 43% of the phenotypic variance of plant weight was significantly explained (P < 0.0001) by six of the QTLs. From F2 plants derived from the same parental cross as the recombinant inbred lines, we divergently selected lines that carried alleles with positive or negative additive effects at these QTLs, and performed successive selfing. In the resulting F6 lines and parents, plant weight significantly differed among the genotypes (at α = 0.05). These results demonstrate that QTL-based selection is effective in improving rice biomass yield.

Quantitative trait loci (QTLs) for water use and crop production traits co-locate with major QTL for tolerance to water deficit in a fine-mapping population of pearl millet (Pennisetum glaucum L. R.Br.)

“…Here, it should be noticed that while the alleles from both ICMR1004 and ICMR1029 contributed to crop production traits under non-stress conditions, only the allele from 1029 specifically contributed to crop production traits under severe water stress. Earlier studies report the co-localization of QTLs for biomass, GY, and component traits (Liang et al 2009;Shi et al 2009;Matsubara et al 2016). QTLs for biomass-related traits within the pearl millet LG02 have been reported earlier (Kholova et al 2012;Kakkera et al 2015).…”

Molecular Mapping of QTLs Associated with Lodging Resistance in Dry Direct-Seeded Rice (Oryza sativa L.)

“…Previously, Mu et al ( 2004 ) identified six additive QTLs associated with culm length on chromosomes 2, 3, 4, 5, and 6 with a phenotypic variation of 39% collectively. Matsubara et al ( 2016 ) mapped four QTLs for culm length on chromosome 1, 2, 5, and 6. In present study, the QTL qCL 1.1 identified at physical position of 36.1 Mbp on chromosome 1 with 26% of phenotypic variance was 4 Mbp closer from large effect QTL for culm length (with R 2 of 73%) reported by Matsubara et al ( 2016 ).…”

QTL Mapping of Novel Genomic Regions for Yield Related Traits in Doubled Haploid (DH) Population Derived from the Popular Rice Hybrid KRH-2

“…The process of molecular mapping and utilization of quantitative trait loci (QTL) is extensively employed for enhancing rice yields [15,16,17,18]. This has been possible because a number of novel QTL loci those govern the expression of yield and its related traits were identi ed along with their precise chromosomal position and trait-enhancing effects [19,20].…”