Detection of stable QTLs for grain protein content in rice (Oryza sativa L.) employing high throughput phenotyping and genotyping platforms

Abstract

Lack of appropriate donors, non-utilization of high throughput phenotyping and genotyping platforms with high genotype × environment interaction restrained identification of robust QTLs for grain protein content (GPC) in rice. In the present investigation a BC 3 F 4 mapping population was developed using grain protein donor, ARC10075 and high-yielding cultivar Naveen and 190 lines were genotyped using 40 K Affimetrix custom SNP array with the objective to identify stable QTLs for protein content. Three of the identified QTLs, one for GPC ( qGPC1 . 1 ) and the other two for single grain protein content ( qSGPC 2. 1 , qSGPC7 . 1 ) were stable over the environments explaining 13%, 14% and 7.8% of the phenotypic variances, respectively. Stability and repeatability of these additive QTLs were supported by the synergistic additive effects of multi-environmental-QTLs. One epistatic-QTL, independent of the main effect QTL was detected over the environment for SGPC. A few functional genes governing seed storage protein were hypothesised inside these identified QTLs. The qGPC1 . 1 was validated by NIR Spectroscopy-based high throughput phenotyping in BC 3 F 5 population. Higher glutelin content was estimated in high-protein lines with the introgression of qGPC1 . 1 in telomeric region of short arm of chromosome 1. This was supported by the postulation of probable candidate gene inside this QTL region encoding glutelin family proteins.

Genetic Analysis of Agronomic Traits and Grain Iron and Zinc Concentrations in a Doubled Haploid Population of Rice (Oryza sativa L.)

“…Genomics based breeding approaches including MAS are becoming more common and to be efficient, research breeding programs need robust and stable QTLs across environments and genetic backgrounds 29 . Genotype by environment interactions (GEI) are the expression of QTLs and GEI QTLs are important as they significantly influence the total phenotypic variance and additive effect of the main effect QTL 30 . Moreover, haplotype-based allele mining was used to detect allelic variation in genes controlling agronomic traits 31 .…”

Mapping and Validation of QTLs for the Amino Acid and Total Protein Content in Brown Rice

“…Gray blocks indicate the loci where two more QTLs were co-located. Each prefix of trait indicates the previous study (J, this study; C, Chattopadhyay et al, 2019;K, Kinoshita et al, 2017;L, Lou et al, 2009;Q, Qin et al, 2009;T, Tan et al, 2001;W, Wang et al, 2007;Y1, Yun et al, 2014;Y2, Yu et al, 2009;Y3, Yoo, 2017;Z, Zhong et al, 2011). the markers and an average of 0.93 markers within a 1-cM distance.…”

Genetic Manipulation for Improved Nutritional Quality in Rice

“…Moreover, these QTLs were detected as environmentally stable. A glutelin encoded gene (Os01g0111900) was found to be associated with this QTL, was upregulated during seed development (Chattopadhaya et al, 2019).…”

“…1, qK4.1, qK4.2, qK5.1, qK9.1, qK3.1, qK3.2, qK3.3, qK4.1, qK5.1, Garcia-Oliveira et al, 2009;Swamy et al, 2018a;Descalsota-Empleo et al, 2019 Boron ( Stangoulis et al, 2007 Grain protein content (GPC) QTL for PC (Protein content). qPC1, qPC2, qPC3, qPC6.1, qPC6.2, qPC8, qPC12.1, qPC1.1, qPC11.1, and qPC11.2, qPC-3, qPC-4, qPC-5, qPC-6 and qPC-10, qPr1 and qPr7, qPro-8, qPro-9 and qPro-10, qGPC1.1, qSGPC2.1 and qSGPC7.1 Tan et al, 2001;Qin et al, 2009;Yu et al, 2009;Zhong et al, 2011;Yun et al, 2014;Mahender et al, 2016;Kinoshita et al, 2017;Chattopadhaya et al, 2019 Amino acid content (AAC) qAa1, qAa7 Zhong et al, 2011 Lysine qAa9, qPC1 Zhong et al, 2011;Peng et al, 2014 Cys/Leu/Ile/Phe qAA. 10 Wang et al, 2008 sources of protein for human food.…”