Identification of genomic region(s) responsible for high iron and zinc content in rice

Abstract

Micronutrient especially iron and zinc-enriched rice hold immense promise for sustainable and cost-effective solutions to overcome malnutrition. In this context, BC 2 F 5 population derived from cross between RP-Bio226 and Sampada was used to localize genomic region(s)/QTL(s) for grain Fe (iron) and Zn (zinc) content together with yield and yield-related traits. Genotyping of mapping population with 108 SSR markers resulted in a genetic map of 2317.5 cM with an average marker distance of 21.5 cM. Mean grain mineral content in the mapping population across the two seasons ranged from 10.5–17.5 ppm for Fe and 11.3–22.1 ppm for Zn. Based on the multi-season phenotypic data together with genotypic data, a total of two major QTLs for Fe (PVE upto 17.1%) and three for Zn (PVE upto 34.2%) were identified. Comparative analysis across the two seasons has revealed four consistent QTLs for Fe ( qFe 1.1 , qFe 1.2 , qFe 6.1 and qFe 6.2 ) and two QTL for Zn content ( qZn 1.1 and qZn 6.2 ). Additionally, based on the previous and current studies three meta-QTLs for grain Fe and two for grain Zn have been identified. In-silico analysis of the identified QTL regions revealed the presence of potential candidate gene(s) such as, OsPOT , OsZIP4 , OsFDR3 , OsIAA5 etc., that were previously reported to influence grain Fe and Zn content. The identified QTLs could be utilized in developing high yielding, Fe and Zn denser varieties by marker assisted selection (MAS).

Analysis of QTL responsible for grain iron and zinc content in doubled haploid lines of rice (Oryza sativa) derived from an intra‐japonica cross

“…In general, iron content showed lower variance and higher CV than zinc content. By contrast, previous works have shown that although iron and zinc contents are highly variable (Dixit et al, ), zinc content tends to be more consistent and less variable than iron content (Anuradha et al, ). This difference may be attributed to various environmental and genetic factors.…”

“…Grain iron and zinc contents are positively correlated (Anandan, Rajiv, Eswaran, & Prakash, ; Descalsota et al, ; Descalsota‐Empleo, Amparado, et al, ; Dixit et al, ; Inabangan‐Asilo et al, ; Stangoulis et al, ; Swamy, Descalsota, et al, ; Xu et al, ). Similarly, in this work, grain iron and zinc contents were positively correlated and presented similar correlations with other traits.…”

“…The development of genotypes with high mean iron and zinc contents and yield is a major target of biofortification breeding. However, grain iron and zinc contents are negatively correlated with grain yield (Dixit et al, ; Inabangan‐Asilo et al, ; Norton et al, ), which has a diluting effect on zinc content (Descalsota et al, ). In the present work, SPP, TGW and PN were considered as proxies for grain yield because PN, a direct regulator of grain number (Ikeda et al, ), and grain weight are major components of grain yield (Huo et al, ).…”

“…Bayesian network analysis revealed that PL has a direct effect on grain iron and zinc contents (Descalsota‐Empleo, Amparado, et al, ). Dixit et al () showed that iron and zinc contents have significantly positive, albeit weak, correlations with PL. Inabangan‐Asilo et al () found that the iron and zinc contents of eight high zinc rice breeding lines cultivated in several locations in the Philippines during the 2014–2016 wet and dry seasons are positively correlated only during the dry season.…”

Genetic Manipulation for Improved Nutritional Quality in Rice

“…Respective QTL References Zinc (Zn) qZN-5, qZN-7, qSZn2, qSZn12, qZn7, qZn3.1, qZn7.1, qZn7.2, qZn7.3, qZn12.1, qZn4, qZn6, qZn1-1, qZn12-1, qZn5-1, qZn8-1, qZn12-1, qZn2.1, qZn2.1, qZn3.1, qZn6.1, qZn6.2, qZn8.1, qZn11.1, qZn12.1, qZn12.2, qZn2.2, qZn8.3, qZn12.3, qZn3.1, qZn7, qZn8.3, qZn3-1, qZn1.1, qZn5.1, qZn9.1, qZn12.1, qZn1.1, qZn6.1, qZn6.2, qZn2-1, qZn2-2, qZn5, qZn10, qZn2.1, qZn3.1, qZn5.1, qZn5.2, qZn7.1, qZn9.1, qZn11.1, qZn 1.1, qZn2.1, qZn3.1, qZn3.2, qZn5.1, qZn6.1, qZn8.1, qZn8.2, qZn9.1, qZn12.1 Lu et al, 2008;Garcia-Oliveira et al, 2009;Ishikawa et al, 2010;Zhang et al, 2011;Anuradha et al, 2012;Jin et al, 2013;Swamy et al, 2018a;Swamy et al, 2018b;Dixit et al, 2019;Descalsota-Empleo et al, 2019;Calayugan et al, 2020;Lee et al, 2020;Pradhan et al, 2020 Iron (Fe) qFE-1, qFE-9, qGFe4, qSFe1, qSFe12, qFe1, qFe3, qFe6, qFe2-1, qFe9-1, qFe4.1, qFe3.3, qFe7.3, Fe8.1, qFe12.2, qFe3-1, qFe9.1, qFe12.1, qFe1.1, qFe1.2, qFe6.1, qFe6.2, qFe5, qK6.1, qFe2.2, qFe3.1, qFe4.1., qFe6.1, qFe8.1, qFe11.2, qFe11.3, qFe12.1, Stangoulis et al, 2007;Lu et al, 2008;Garcia-Oliveira et al, 2009;Ishikawa et al, 2010;Norton et al, 2010;Jin et al, 2013;Swamy et al, 2018a;Swamy et al, 2018b;Dixit et al, 2019;Calayugan et al, 2020;Lee et al, 2020;Pradhan et al, 2020 Zn andIron qZn10, qZn2, qZn4, qZn5, qZn6, qZn7, qZn9 Zhang et al, 2014 Manganese (Mn) qMn1-1, qMn2-1, qMn3-1, qMn10-1, qMn2.1, qMn2.1, qMn7.1, qMn1.1, qMn1.2, qMn3.1, qMn3.2, qMn4.1 -Oliveira et al, 2009;...…”