Meta-QTL and haplo-pheno analysis reveal superior haplotype combinations associated with low grain chalkiness under high temperature in rice

Abstract

Chalk, an undesirable grain quality trait in rice, is primarily formed due to high temperatures during the grain-filling process. Owing to the disordered starch granule structure, air spaces and low amylose content, chalky grains are easily breakable during milling thereby lowering head rice recovery and its market price. Availability of multiple QTLs associated with grain chalkiness and associated attributes, provided us an opportunity to perform a meta-analysis and identify candidate genes and their alleles contributing to enhanced grain quality. From the 403 previously reported QTLs, 64 Meta-QTLs encompassing 5262 non-redundant genes were identified. MQTL analysis reduced the genetic and physical intervals and nearly 73% meta-QTLs were narrower than 5cM and 2Mb, revealing the hotspot genomic regions. By investigating expression profiles of 5262 genes in previously published datasets, 49 candidate genes were shortlisted on the basis of their differential regulation in at least two of the datasets. We identified non-synonymous allelic variations and haplotypes in 39 candidate genes across the 3K rice genome panel. Further, we phenotyped a subset panel of 60 rice accessions by exposing them to high temperature stress under natural field conditions over two Rabi cropping seasons. Haplo-pheno analysis uncovered haplotype combinations of two starch synthesis genes, GBSSI and SSIIa, significantly contributing towards the formation of grain chalk in rice. We, therefore, report not only markers and pre-breeding material, but also propose superior haplotype combinations which can be introduced using either marker-assisted breeding or CRISPR-Cas based prime editing to generate elite rice varieties with low grain chalkiness and high HRY traits.

Current Status and Future Prospects of Head Rice Yield

“…For example, [118] observed a lower head rice yield under high relative humidity and a high negative correlation between RH and HRY (r = −0.693). In contrast, [23,129] found a very weak positive correlation between RH and HRY (r = 0.168) over two-crop growing seasons across multiple growing locations. Both studies measured the RH following the entire period, grain ripening, and maturity stages (from heading/50% flowering to harvesting).…”

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

“…Meta-analyses of QTLs associated with a variety of traits have been recently conducted in different crops such as wheat ( Kumar et al, 2021 ; Kumar et al, 2022 A. C. ; Kumar et al, 2023 S. ; Saini et al, 2021 ; Saini et al, 2022 ; Tanin et al, 2022 ), rice ( Sandhu et al, 2021 ; Kumari et al, 2023 ), barley ( Akbari et al, 2022 ), common bean ( Shafi et al, 2022 ), pigeon pea ( Halladakeri et al, 2023 ), including maize ( Kaur et al, 2021 ; Sheoran et al, 2022 ; Wang et al, 2022 ; Gupta et al, 2023 ; Karnatam et al, 2023 ), for diverse traits, including both yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) and quality traits ( Jin et al, 2013 ; Dong et al, 2015 ). However, there is currently no comprehensive study on the genomic regions influencing both grain quality and yield in maize.…”

Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis

“…Meta-analysis of QTLs retrieved from different independent studies, is an alternate method that can help in precise mapping of traits ( Sharma et al., 2023 ). MQTL analysis is a relatively new concept and is rapidly emerging an efficient method for narrowing the confidence intervals (CI) of overlapping QTLs, allowing for rapid and efficient discovery of candidate markers and genes linked to the trait of interest ( Kumari et al., 2023 ; Sharma et al., 2023 ). Meta-analysis has already been performed for various traits in wheat ( Kumar et al., 2021 ; Pal et al., 2021 , Saini et al., 2022b ) including resistance to different diseases such as leaf rust ( Soriano and Royo, 2015 ; Amo and Soriano, 2022 ), stem rust ( Yu et al., 2014 ), tan spot ( Liu et al., 2020 ), Fusarium head blight ( Liu et al., 2009 ; Löffler et al., 2009 ; Venske et al., 2019 ; Zheng et al., 2021 ), stripe rust ( Jan et al., 2021 ; Kumar et al., 2023 ), multiple disease resistance ( Pal et al., 2022 ; Saini et al., 2022a ) and PM resistance ( Marone et al., 2013 ).…”