Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice

Seed vigor is the key performance parameter of good quality seed. A panel was prepared by shortlisting genotypes from all the phenotypic groups representing seedling growth parameters from a total of 278 germplasm lines. A wide variation was observed for the traits in the population. The panel was classified into four genetic structure groups. Fixation indices indicated the existence of linkage disequilibrium in the population. A moderate to high level of diversity parameters was assessed using 143 SSR markers. Principal component, coordinate, neighbor-joining tree and cluster analyses showed subpopulations with a fair degree of correspondence with the growth parameters. Marker–trait association analysis detected eight novel QTLs, namely qAGR4.1, qAGR6.1, qAGR6.2 and qAGR8.1 for absolute growth rate (AGR); qRSG6.1, qRSG7.1 and qRSG8.1 for relative shoot growth (RSG); and qRGR11.1 for relative growth rate (RGR), as analyzed by GLM and MLM. The reported QTL for germination rate (GR), qGR4-1, was validated in this population. Additionally, QTLs present on chromosome 6 controlling RSG and AGR at 221 cM and RSG and AGR on chromosome 8 at 27 cM were detected as genetic hotspots for the parameters. The QTLs identified in the study will be useful for improvement of the seed vigor trait in rice.

[1]  S. R. Barik,et al.  Association mapping for protein, total soluble sugars, starch, amylose and chlorophyll content in rice , 2022, BMC Plant Biology.

[2]  P. Dash,et al.  Association Mapping for Quantitative Trait Loci Controlling Superoxide Dismutase, Flavonoids, Anthocyanins, Carotenoids, γ-Oryzanol and Antioxidant Activity in Rice , 2022, Agronomy.

[3]  S. K. Pradhan,et al.  Unraveling the genomic regions controlling the seed vigour index, root growth parameters and germination per cent in rice , 2022, PloS one.

[4]  S. K. Pradhan,et al.  Genetic structure and molecular markers-trait association for physiological traits related to seed vigour in rice , 2021, Plant Gene.

[5]  A. Mukherjee,et al.  Development of Submergence-Tolerant, Bacterial Blight-Resistant, and High-Yielding Near Isogenic Lines of Popular Variety, ‘Swarna’ Through Marker-Assisted Breeding Approach , 2021, Frontiers in Plant Science.

[6]  S. K. Pradhan,et al.  Population genetic structure and association mapping for iron toxicity tolerance in rice , 2021, PloS one.

[7]  S. K. Pradhan,et al.  Marker-Assisted Backcross Breeding for Improvement of Submergence Tolerance and Grain Yield in the Popular Rice Variety, ‘Maudamani’ , 2021, Agronomy.

[8]  M. Tuti,et al.  Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud. , 2020, Scientific Reports.

[9]  P. Sanghamitra,et al.  Association of molecular markers with physio-biochemical traits related to seed vigour in rice , 2020, Physiology and Molecular Biology of Plants.

[10]  S. K. Pradhan,et al.  Genetic mapping of physiological traits associated with terminal stage drought tolerance in rice , 2020, BMC Genetics.

[11]  Ning Tang,et al.  A lamin-like protein OsNMCP1 regulates drought resistance and root growth through chromatin accessibility modulation by interacting with a chromatin remodeler OsSWI3C in rice. , 2020, The New phytologist.

[12]  S. K. Pradhan,et al.  Genetic Relationship and Structure Analysis of Root Growth Angle for Improvement of Drought Avoidance in Early and Mid-Early Maturing Rice Genotypes , 2020 .

[13]  S. K. Pradhan,et al.  Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding , 2020, BMC Plant Biology.

[14]  S. K. Pradhan,et al.  Marker-assisted selection for transfer of submergence tolerance, bacterial blight resistance and yield enhancement in the rice backcross derivatives , 2020 .

[15]  Genetic Structure and Markers -Trait Association Analyses for Fe-Toxicity Tolerance, Grain-Fe Content and Yield Component Traits In Rice , 2020 .

[16]  T. Guo,et al.  Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice , 2019, Front. Plant Sci..

[17]  A. Mukherjee,et al.  Development of flash-flood tolerant and durable bacterial blight resistant versions of mega rice variety ‘Swarna’ through marker-assisted backcross breeding , 2019, Scientific Reports.

[18]  P. Gantet,et al.  Unraveling the Genetic Elements Involved in Shoot and Root Growth Regulation by Jasmonate in Rice Using a Genome-Wide Association Study , 2019, Rice.

[19]  Fei Liu,et al.  Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging , 2019, Molecules.

[20]  S. K. Pradhan,et al.  Association mapping reveals multiple QTLs for grain protein content in rice useful for biofortification , 2019, Molecular Genetics and Genomics.

[21]  T. Mohapatra,et al.  Genetic mapping of morpho-physiological traits involved during reproductive stage drought tolerance in rice , 2019, bioRxiv.

[22]  T. Mohapatra,et al.  QTL mapping for relative water content trait at reproductive stage drought stress in rice , 2018 .

[23]  Shaoqing Li,et al.  QTL Mapping of Seed Vigor of Backcross Inbred Lines Derived From Oryza longistaminata Under Artificial Aging , 2018, Front. Plant Sci..

[24]  Yan Zhao,et al.  Genetic analysis of roots and shoots in rice seedling by association mapping , 2018, Genes & Genomics.

[25]  S. Mishra,et al.  Genetic Improvement of Rainfed Shallowlowland Rice for Higher Yield and Climate Resilience , 2018 .

[26]  A. Nayak,et al.  Rice Research for Enhancing Productivity, Profitability and Climate Resilience , 2018 .

[27]  E. Pandit,et al.  Genetic diversity, population structure, marker validation and kinship analysis for seedling stage cold tolerance in indica rice , 2018 .

[28]  S. Pradhan,et al.  Genetic diversity and population structure using linked SSR markers for heat stress tolerance in rice , 2017 .

[29]  S. K. Pradhan,et al.  Genome-Wide Association Mapping Reveals Multiple QTLs Governing Tolerance Response for Seedling Stage Chilling Stress in Indica Rice , 2017, Front. Plant Sci..

[30]  S. K. Pradhan,et al.  Genetic variation and association of molecular markers for iron toxicity tolerance in rice , 2017 .

[31]  A. Mahender,et al.  Population Structure, Genetic Diversity and Molecular Marker-Trait Association Analysis for High Temperature Stress Tolerance in Rice , 2016, PloS one.

[32]  A. Mukherjee,et al.  Incorporation of Bacterial Blight Resistance Genes Into Lowland Rice Cultivar Through Marker-Assisted Backcross Breeding. , 2016, Phytopathology.

[33]  A. Anandan,et al.  Population Structure, Diversity and Trait Association Analysis in Rice (Oryza sativa L.) Germplasm for Early Seedling Vigor (ESV) Using Trait Linked SSR Markers , 2016, PloS one.

[34]  G. Bassel,et al.  Seed vigour and crop establishment: extending performance beyond adaptation. , 2016, Journal of experimental botany.

[35]  A. Anandan,et al.  Comparison of Sub1 markers and their combinations for submergence tolerance and analysis of adaptation strategies of rice in rainfed lowland ecology. , 2015, Comptes rendus biologies.

[36]  A. Anandan,et al.  Screening and classification of genotypes for seedling-stage chilling stress tolerance in rice and validation of the trait using SSR markers , 2015, Plant Genetic Resources.

[37]  A. Anandan,et al.  Pyramiding of three bacterial blight resistance genes for broad-spectrum resistance in deepwater rice variety, Jalmagna , 2015, Rice.

[38]  A. Mahender,et al.  Early seedling vigour, an imperative trait for direct-seeded rice: an overview on physio-morphological parameters and molecular markers , 2015, Planta.

[39]  Huifang Jiang,et al.  Quantitative trait locus analysis of agronomic and quality-related traits in cultivated peanut (Arachis hypogaea L.) , 2015, Theoretical and Applied Genetics.

[40]  Hongsheng Zhang,et al.  Dynamic Quantitative Trait Locus Analysis of Seed Vigor at Three Maturity Stages in Rice , 2014, PloS one.

[41]  Y. Xing,et al.  Identification and fine mapping of quantitative trait loci for seed vigor in germination and seedling establishment in rice. , 2014, Journal of integrative plant biology.

[42]  Hui Wang,et al.  Genetic diversity and association mapping of seed vigor in rice (Oryza sativa L.) , 2014, Planta.

[43]  R. Bhat,et al.  Molecular mapping of early vigour related QTLs in rice , 2013 .

[44]  C. Job,et al.  Seed germination and vigor. , 2012, Annual review of plant biology.

[45]  Satoshi Natsume,et al.  Genome sequencing reveals agronomically important loci in rice using MutMap , 2012, Nature Biotechnology.

[46]  Theunis Piersma,et al.  The interplay between habitat availability and population differentiation , 2012 .

[47]  Hang He,et al.  Development and application of a set of breeder-friendly SNP markers for genetic analyses and molecular breeding of rice (Oryza sativa L.) , 2011, Theoretical and Applied Genetics.

[48]  Hongsheng Zhang,et al.  Quantitative trait loci analysis for rice seed vigor during the germination stage , 2010, Journal of Zhejiang University SCIENCE B.

[49]  Y. Singh,et al.  Divergence analysis for quality traits in some indigenous Basmati rice genotypes , 2008 .

[50]  Edward S. Buckler,et al.  TASSEL: software for association mapping of complex traits in diverse samples , 2007, Bioinform..

[51]  Bin Han,et al.  Rice functional genomics research in China , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[52]  L. K. Bose,et al.  Genetic Variability of Quality Characters and Grain Yield in Lowland Rice Genotypes of Eastern India , 2007 .

[53]  Jun Abe,et al.  Growth of Rice (Oryza Sativa L.) Cultivars Under Upland Conditions With Different Levels of Water Supply3. Root System Development, Soil Moisture Changeand Plant Water Status , 2007 .

[54]  P. Smouse,et al.  genalex 6: genetic analysis in Excel. Population genetic software for teaching and research , 2006 .

[55]  M. McMullen,et al.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.

[56]  G. Evanno,et al.  Detecting the number of clusters of individuals using the software structure: a simulation study , 2005, Molecular ecology.

[57]  Kejun Liu,et al.  PowerMarker: an integrated analysis environment for genetic marker analysis , 2005, Bioinform..

[58]  L. K. Bose,et al.  GENETIC DIVERGENCE IN DEEPWATER RICE GENOTYPES , 2005 .

[59]  Y. Xing,et al.  Molecular dissection of seedling-vigor and associated physiological traits in rice , 2002, Theoretical and Applied Genetics.

[60]  Walter Sermeus,et al.  Data analysis methods. , 2002, Studies in health technology and informatics.

[61]  P. Donnelly,et al.  Inference of population structure using multilocus genotype data. , 2000, Genetics.

[62]  Michaël Dingkuhn,et al.  Relationships between upland rice canopy characteristics and weed competitiveness , 1999 .

[63]  R. Ellis.  The quantification of ageing and survival in orthodox seeds , 1981 .

[64]  W. F. Thompson,et al.  Rapid isolation of high molecular weight plant DNA. , 1980, Nucleic acids research.

[65]  P. J. Radford,et al.  Growth Analysis Formulae - Their Use and Abuse 1 , 1967 .

[66]  Rory A. Fisher,et al.  SOME REMARKS ON THE METHODS FORMULATED IN A RECENT ARTICLE ON “THE QUANTITATIVE ANALYSIS OF PLANT GROWTH.” , 1921 .

[67]  E. Finch-SavageW..  Seed vigour and crop establishment – extending performance beyond adaptation , 2022 .