Unraveling the Complex Trait of Harvest Index with Association Mapping in Rice (Oryza sativa L.)

Harvest index is a measure of success in partitioning assimilated photosynthate. An improvement of harvest index means an increase in the economic portion of the plant. Our objective was to identify genetic markers associated with harvest index traits using 203 O. sativa accessions. The phenotyping for 14 traits was conducted in both temperate (Arkansas) and subtropical (Texas) climates and the genotyping used 154 SSRs and an indel marker. Heading, plant height and weight, and panicle length had negative correlations, while seed set and grain weight/panicle had positive correlations with harvest index across both locations. Subsequent genetic diversity and population structure analyses identified five groups in this collection, which corresponded to their geographic origins. Model comparisons revealed that different dimensions of principal components analysis (PCA) affected harvest index traits for mapping accuracy, and kinship did not help. In total, 36 markers in Arkansas and 28 markers in Texas were identified to be significantly associated with harvest index traits. Seven and two markers were consistently associated with two or more harvest index correlated traits in Arkansas and Texas, respectively. Additionally, four markers were constitutively identified at both locations, while 32 and 24 markers were identified specifically in Arkansas and Texas, respectively. Allelic analysis of four constitutive markers demonstrated that allele 253 bp of RM431 had significantly greater effect on decreasing plant height, and 390 bp of RM24011 had the greatest effect on decreasing panicle length across both locations. Many of these identified markers are located either nearby or flanking the regions where the QTLs for harvest index have been reported. Thus, the results from this association mapping study complement and enrich the information from linkage-based QTL studies and will be the basis for improving harvest index directly and indirectly in rice.

[1]  A. McClung,et al.  Mapping QTLs for improving grain yield using the USDA rice mini-core collection , 2011, Planta.

[2]  T. Sinclair,et al.  Historical changes in harvest index and crop nitrogen accumulation , 1998 .

[3]  C. Brondani,et al.  Evaluation of the number and information content of fluorescent-labeled SSR markers for rice germplasm characterization. , 2005 .

[4]  E. Ross,et al.  Philosophy of Science Association , 2022 .

[5]  Zhanguo Xin,et al.  High-throughput DNA extraction method suitable for PCR. , 2003, BioTechniques.

[6]  M. Thomson,et al.  Genetic Diversity of Isolated Populations of Indonesian Landraces of Rice (Oryza sativa L.) Collected in East Kalimantan on the Island of Borneo , 2009, Rice.

[7]  R. Bernardo,et al.  In silico mapping of quantitative trait loci in maize , 2004, Theoretical and Applied Genetics.

[8]  Ming-Hsuan Chen,et al.  Genetic Assessment of a Mini‐Core Subset Developed from the USDA Rice Genebank , 2009 .

[9]  C. Donald,et al.  The Biological Yield and Harvest Index of Cereals as Agronomic and Plant Breeding Criteria , 1976 .

[10]  S. Murray,et al.  Genetic diversity and population structure analysis of accessions in the US historic sweet sorghum collection , 2009, Theoretical and Applied Genetics.

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

[12]  P. Christou,et al.  ‘Green revolution’ genes encode mutant gibberellin response modulators , 1999, Nature.

[13]  T. Toojinda,et al.  Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice1 , 2004, Plant Physiology.

[14]  X. Yang,et al.  Genetic modification of plant architecture and variety improvement in rice , 2008, Heredity.

[15]  Michael J. Thomson,et al.  Genetic diversity analysis of traditional and improved Indonesian rice (Oryza sativa L.) germplasm using microsatellite markers , 2007, Theoretical and Applied Genetics.

[16]  M. Purugganan,et al.  Genomic Variation in Rice: Genesis of Highly Polymorphic Linkage Blocks during Domestication , 2006, PLoS genetics.

[17]  L. Lipovich,et al.  Diversity of microsatellites derived from genomic libraries and GenBank sequences in rice (Oryza sativa L.) , 2000, Theoretical and Applied Genetics.

[18]  M. Jia,et al.  Genotypic and phenotypic characterization of genetic differentiation and diversity in the USDA rice mini-core collection , 2010, Genetica.

[19]  A. Saxton.  A Macro for Converting Mean Separation Output to Letter Groupings in PROC MIXED , 1998 .

[20]  H. Sabouri,et al.  Genetic dissection of biomass production, harvest index and panicle characteristics in indica-indica crosses of Iranian rice (Oryza sativa L.) cultivars. , 2009 .

[21]  G. Eizenga,et al.  Molecular diversity and genome-wide linkage disequilibrium patterns in a worldwide collection of Oryza sativa and its wild relatives , 2008, Euphytica.

[22]  Tomohiko Yoshida,et al.  Genotypic and Phenotypic Variances and Covariances in Early Maturing Grain Sorghum in a Double Cropping , 1999 .

[23]  H. Cai,et al.  Analysis of QTLs for yield-related traits in Yuanjiang common wild rice (Oryza rufipogon Griff.). , 2010, Journal of genetics and genomics = Yi chuan xue bao.

[24]  M. Nei,et al.  MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. , 2007, Molecular biology and evolution.

[25]  Peter Hedden,et al.  The genes of the Green Revolution. , 2003, Trends in genetics : TIG.

[26]  M. Sorrells,et al.  Association Analysis as a Strategy for Improvement of Quantitative Traits in Plants , 2006 .

[27]  E S Buckler,et al.  Structure of linkage disequilibrium and phenotypic associations in the maize genome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[28]  N. Tomooka,et al.  The evolving story of rice evolution , 2008 .

[29]  Amanda J. Garris,et al.  Genetic structure and diversity in Oryza sativa , 2004 .

[30]  Amanda J. Garris,et al.  Genetic Structure and Diversity in Oryza sativa L. , 2005, Genetics.

[31]  Ping Li,et al.  Characterization of QTLs for harvest index and source-sink characters in a DH population of rice (Oryza sativa L.). , 2003, Yi chuan xue bao = Acta genetica Sinica.

[32]  S. Koutroubas,et al.  Genotypic differences for grain yield and nitrogen utilization in Indica and Japonica rice under Mediterranean conditions , 2003 .

[33]  吉田 昌一,et al.  Fundamentals of rice crop science , 1981 .

[34]  V. Ravi,et al.  Structural and functional analysis of rice genome , 2004, Journal of Genetics.

[35]  Xavier Draye,et al.  Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development , 2007, Genetics.

[36]  V. Singh,et al.  Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia , 2003, Theoretical and Applied Genetics.

[37]  K. Homma,et al.  Delay of heading date as an index of water stress in rainfed rice in mini-watersheds in Northeast Thailand , 2004 .

[38]  C. Brondani,et al.  Determination of genetic variability of traditional varieties of Brazilian rice using microsatellite markers , 2006 .

[39]  T. Matsumoto.  Gibberellic Acid and Benzyladenine Promote Early Flowering and Vegetative Growth of Miltoniopsis Orchid Hybrids , 2006 .

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

[41]  L. Schrader,et al.  Exploitation of physiological and genetic variability to enhance crop productivity , 1985 .

[42]  John Doebley,et al.  Maize association population: a high-resolution platform for quantitative trait locus dissection. , 2005, The Plant journal : for cell and molecular biology.

[43]  Keyan Zhao,et al.  Genome-Wide Association Mapping in Arabidopsis Identifies Previously Known Flowering Time and Pathogen Resistance Genes , 2005, PLoS genetics.

[44]  T. Hirose,et al.  A gene controlling the number of primary rachis branches also controls the vascular bundle formation and hence is responsible to increase the harvest index and grain yield in rice , 2010, Theoretical and Applied Genetics.

[45]  Zou Jiang.  Study on Individual Plant Type Character of Liangyoupeijiu Rice , 2003 .

[46]  M. Yano,et al.  Genetic dissection and pyramiding of quantitative traits for panicle architecture by using chromosomal segment substitution lines in rice , 2008, Theoretical and Applied Genetics.

[47]  Qifa Zhang,et al.  Genetic basis of 17 traits and viscosity parameters characterizing the eating and cooking quality of rice grain , 2007, Theoretical and Applied Genetics.

[48]  F. Rohlf,et al.  NTSYS-pc Numerical Taxonomy and Multivariate Analysis System, version 2.1: Owner manual , 1992 .

[49]  A. McClung,et al.  Development and evaluation of a core subset of the USDA rice germplasm collection , 2007 .

[50]  P. Gepts.  Crop Domestication as a Long‐Term Selection Experiment , 2010 .

[51]  José Crossa,et al.  Sampling Strategies for Conserving Maize Diversity When Forming Core Subsets Using Genetic Markers , 2006 .

[52]  G. Eizenga,et al.  Association mapping of yield and its components in rice cultivars , 2007, Molecular Breeding.

[53]  K. Cassman,et al.  Rice yields decline with higher night temperature from global warming. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[54]  M. Sorrells,et al.  Association Mapping of Kernel Size and Milling Quality in Wheat (Triticum aestivum L.) Cultivars , 2006, Genetics.

[55]  L. H. Allen,et al.  Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress , 2006 .

[56]  Yunbi Xu,et al.  A marker-based approach to broadening the genetic base of rice in the USA , 2004 .

[57]  D. Raes,et al.  AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description , 2009 .

[58]  R. Jain,et al.  Genetic analysis of Indian aromatic and quality rice (Oryza sativa L.) germplasm using panels of fluorescently-labeled microsatellite markers , 2004, Theoretical and Applied Genetics.

[59]  I. Cross,et al.  Genetic characterization of Plectorhinchus mediterraneus yields important clues about genome organization and evolution of multigene families , 2012, BMC Genetics.

[60]  Aziz‐ur‐Rehman,et al.  EFFECT OF TEMPERATURE ON DEVELOPMENT AND GRAIN FORMATION IN SPRING WHEAT , 2010 .

[61]  D. Heckerman,et al.  Efficient Control of Population Structure in Model Organism Association Mapping , 2008, Genetics.

[62]  Chuanxiao Xie,et al.  Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize , 2010, Euphytica.

[63]  C. Brondani,et al.  Microsatellite marker-mediated analysis of the EMBRAPA Rice Core Collection genetic diversity , 2009, Genetica.

[64]  M. U. Dahot,et al.  YIELD AND QUALITY PARAMETERS OF WHEAT GENOTYPES AS AFFECTED BY SOWING DATES AND HIGH TEMPERATURE STRESS , 2005 .

[65]  Lei Wang,et al.  Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice , 2008, Nature Genetics.

[66]  D. Neale,et al.  Association Genetics in Pinus taeda L. I. Wood Property Traits , 2007, Genetics.

[67]  R. Hay,et al.  Harvest index: a review of its use in plant breeding and crop physiology , 1995 .

[68]  Keyan Zhao,et al.  Association Mapping With Single-Feature Polymorphisms , 2006, Genetics.

[69]  Qifa Zhang,et al.  Genome-wide association studies of 14 agronomic traits in rice landraces , 2010, Nature Genetics.

[70]  O. Hardy,et al.  spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels , 2002 .

[71]  S. Hittalmani,et al.  Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.) , 2000, Euphytica.

[72]  Keyan Zhao,et al.  An Arabidopsis Example of Association Mapping in Structured Samples , 2006, PLoS genetics.

[73]  J. Bao,et al.  Association mapping of grain color, phenolic content, flavonoid content and antioxidant capacity in dehulled rice , 2011, Theoretical and Applied Genetics.

[74]  S. Chapman,et al.  Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects , 2010, Theoretical and Applied Genetics.

[75]  Shizhong Xu,et al.  Mapping Quantitative Trait Loci Using Naturally Occurring Genetic Variance Among Commercial Inbred Lines of Maize (Zea mays L.) , 2005, Genetics.

[76]  A. Blum,et al.  Heat tolerance for yield and its components in different wheat cultivars , 1990, Euphytica.

[77]  P. Marri,et al.  Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon , 2005, BMC Genetics.

[78]  P. Donnelly,et al.  Association mapping in structured populations. , 2000, American journal of human genetics.

[79]  Yang Xc,et al.  Genetic modification of plant architecture and variety improvement in rice , 2008 .

[80]  Chengsong Zhu,et al.  Nonmetric Multidimensional Scaling Corrects for Population Structure in Association Mapping With Different Sample Types , 2009, Genetics.

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