Rice molecular markers and genetic mapping: Current status and prospects

Journal of Integrative Agriculture 2017, 16(9): 1879–1891 Available online at www.sciencedirect.com

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Rice molecular markers and genetic mapping: Current status and prospects Ghulam Shabir1, 3, Kashif Aslam1, 3, Abdul Rehman Khan2, Muhammad Shahid5, Hamid Manzoor3, Sibgha Noreen6, Mueen Alam Khan7, 8, Muhammad Baber3, Muhammad Sabar1, 4, Shahid Masood Shah2, Muhammad Arif1 1

National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Pakistan Biotechnology Program, Environmental Sciences, COMSATS Institute of Information Technology, Abbottabad 22010, Pakistan 3 Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60000, Pakistan 4 Rice Research Institute, Kala Shah Kaku 39020, Pakistan 5 Department of Bioinformatics and Biotechnology, Government College University, Faisalabad 38000, Pakistan 6 Institute of Pure and Applied Biology, Bahauddin Zakariya University, Multan 60000, Pakistan 7 Department of Plant Breeding and Genetics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 8 National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing 210095, P.R.China 2

Abstract Dramatic changes in climatic conditions that supplement the biotic and abiotic stresses pose severe threat to the sustainable rice production and have made it a difficult task for rice molecular breeders to enhance production and productivity under these stress factors. The main focus of rice molecular breeders is to understand the fundamentals of molecular pathways involved in complex agronomic traits to increase the yield. The availability of complete rice genome sequence and recent improvements in rice genomics research has made it possible to detect and map accurately a large number of genes by using linkage to DNA markers. Linkage mapping is an effective approach to identify the genetic markers which are co-segregating with target traits within the family. The ideas of genetic diversity, quantitative trait locus (QTL) mapping, and marker-assisted selection (MAS) are evolving into more efficient concepts of linkage disequilibrium (LD) also called association mapping and genomic selection (GS), respectively. The use of cost-effective DNA markers derived from the fine mapped position of the genes for important agronomic traits will provide opportunities for breeders to develop high-yielding, stress-resistant, and better quality rice cultivars. Here we focus on the progress of molecular marker technologies, their application in genetic mapping and evolution of association mapping techniques in rice. Keywords: genetic mapping, molecular markers, maker assisted selection, Oryza sativa L., quantitative trait loci

Received 23 August, 2016 Accepted 10 February, 2017 Correspondence Shahid Masood Shah, Tel: +92-333-5273893, E-mail: [email protected] © 2017 CAAS. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/) doi: 10.1016/S2095-3119(16)61591-5

1. Introduction The human population of the world is increasing day by day. According to world population data sheet (Population Institute, USA) estimate, the world population will become eight billion in 2024 (Anonymous 2011). At the current rate, about 70% more food will be required to feed about 15 billion

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people by the mid of this century. Rapidly growing population is the major issue in the world, leading to a shortage of food, resulting many challenges to the agricultural community (Borlaug 2000). Biotic stresses such as diseases and insects, and abiotic stresses including drought, salinity, temperature, cold, etc., are the main causes of low yield which lead towards billions of dollar’s losses annually in food crops especially cereal group (Borlaug 2000; Kumar et al. 2015). In cereal group, rice is the staple food source for more than half of the world’s population (Hossain 1997; Gupta and Varshney 2005). Rice is an annual plant, belonging to monocot family Poaceae and the genus Oryza. The genus Oryza consists of two cultivated and 23 wild species. The Oryza is also splited into five complexes named as Oryza sativa, Oryza ridleyi, Oryza officinalis, Oryza ridelyi, and unclassified (Jain et al. 2010). Among the cultivated species, Oryza sativa (Asian rice) is grown mostly in all rice growing areas of the world while Oryza glaberrima (African rice) is only confined to the western tropical Africa. All species of rice have basic chromosome number n=12 and classified level is diploid with 2n=24 and tetraploid with 2n=48 chromosomes. In this scenario, it is vital to increase the yield of major food crops, including rice, through the development of high yielding varieties with higher resistance to biotic and abiotic stresses. Rice has great importance around the world due to its nutritional value. It has a special glutelin made protein, having more balanced amino acid compared to other cereals that have prolamine-rich storage proteins. Rice is a good source of thiamin, niacin, riboflavin, phosphorus, magnesium, zinc, and copper. Short-grain of rice is very starchy, cooks soft and sticky while long-grain rice contains less starch. More than 477.5 million tons of rice per year are being consumed by the present world population. Though, the global production of rice is increasing, this increase is not proportional to the demand of increasing human population. The rice production should rise at least 70% in the coming years to fulfill the demand of increasing human population by 2050 (Leegood et al. 2010). The quality and quantity of rice plants have been improved by conventional plant breeding methods which are well known and still in the practice. The breeding methods in rice can be simplified in three steps: (I) plant breeding based on observed variation in different varieties; (II) plant breeding based on controlled mating by selection of desired characters; and (III) plant breeding based on monitored recombination by selection of specific genes or marker profiles (Breseghello and Coelho 2013). Conventional plant breeding approaches are time-consuming, laborious, and have several other ecological, physiological, and biological constraints.

To overcome the above mentioned problems, researchers are focusing on new modern breeding techniques such as marker assisted breeding, recombinant DNA technology, and ‘omics’ sciences (genomics, proteomics, metabolomics), to improve the rice plant yield by developing better disease resistance (Wang et al. 2015; Raboin et al. 2016) and grain quality improvement in rice plant (Feng et al. 2016). However, the precision of biotechnological approaches are, mainly genetic engineering and, genetic mapping. These approaches contribute rapidly and significantly in crop improvement by offering a wide array of novel genes and traits identification. Identified gene(s) can be effectively inserted into elite crops to raise yield, nutritional value, and confer resistance to abiotic and biotic stresses (Pandey et al. 2016). Many outstanding reviews have been published about the different types of molecular markers used in plants and their application in construction of linkage map, genetic mapping, and marker-assisted selection (MAS) techniques (Agarwal et al. 2008). This review thus focuses on: I) recent advances in molecular marker technologies for rice genetics; II) QTLs identification techniques applied in rice germplasm; and III) overview of association mapping evolution in rice.

2. Genetic marker Markers which designate genetic differences between individuals within a specie or among species are called genetic markers. In the 19th century, the idea of phenotype based genetic markers was introduced by Gregor Mendel (Austria) followed by the genetic linkage theory by T. H. Morgan (Columbia University, USA) by using Drosophila melanogaster as an experimental material (William and Michael 1996). Normally, they are not target genes but act as gene tags and are located near to gene controlling the trait of interest. So these markers do not affect the phenotype of the trait of interest. Like genes, these markers also occupy definite genomic locations within chromosomes called loci (Collard et al. 2005). These markers can be classified into various types: I) morphological markers; II) biochemical markers; and III) DNA markers.

2.1. Morphological markers These are generally characterized by phenotypic traits by means of vision as color, size, shape, etc. (Collard et al. 2005). Due to its limited application, researchers started biochemical markers to detect quantitative trait loci (QTLs) in plants.

2.2. Biochemical markers Biochemical markers or isozyme markers are enzymes

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variations that can be identified through specific staining and electrophoresis techniques. Isozymes were first used as molecular marker to detect QTLs in maize (Stuber et al. 1987). In rice, isozymes have also been used to study linkage relationships and for genetic analysis (Agarwal et al. 2008). Conventional plant breeders commonly select the superior rice plants on the basis of biochemical and morphological markers. But selection based on these markers has many drawbacks, e.g., environmental variability and developmental stages influenced the expression of desirable traits and limited in number of biochemical markers available (Collard et al. 2005). These reasons compelled scientists towards development of more efficient markers like DNA markers which could overcome these limitations (Gupta and Varshney 2005; Agrama et al. 2007). In spite of these limitations, morphological and biochemical markers still are valuable for plant breeders but the extent of variation is limited in these markers.

2.3. DNA markers The development and use of DNA or molecular markers to find the DNA polymorphisms in plant genome are great achievement in the field of biotechnology and molecular biology (Gupta and Varshney 2005). The genetic information is encoded in the plant’s genetic material, the deoxy-ribo nucleic acid (DNA). DNA is packed in chromosomes. Genes that control specific characters of plants are located on chromosomes. All the genetic material present in a haploid set of chromosomes of an organism is known as genome (Collard et al. 2005). Molecular markers are abundant in genome. These markers appear by different types of mutations such as replication errors, rearrangements (insertions or deletions) and substitution mutations (Agarwal et al. 2008). These markers are generally situated in non-coding regions of DNA that is why these are large in numbers and have no environmental influence (Gupta and Varshney 2005). Molecular markers are a specific, identifiable feature of DNA sequence on chromosomes that locate the position of a gene of interest or inheritance of particular traits (Agarwal et al. 2008). These genes of interest are linked with the molecular marker(s). Thus, the individual plant’s selection depends upon the presence or absence of molecular marker(s). The presence of molecular marker indicates the presence of the gene of interest of desirable traits. Plant molecular breeders select the superior plant at seedling stage by using molecular marker rather than waiting to observe the phenotype at maturity (Gupta and Varshney 2005). Later molecular breeders adopted conventional breeding techniques to improve the crop. DNA markers,

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tightly linked with agronomic traits, are now extensively used to find out loci controlling agronomic traits in several crop species (Gupta and Varshney 2005; Jain et al. 2010). To characterize the rice genome, molecular markers were firstly used in 1988 (Jain et al. 2010). DNA markers can expose genetic differences, checked by gel electrophoresis technique followed by silver stain or ethidium bromide staining (Agarwal et al. 2008). The DNA markers are only useful if these can disclose the differences among the individuals of different species or the same species and are named as polymorphic markers, while monomorphic markers cannot distinguish between individuals. Polymorphic markers are further divided into two categories as codominant (indicate size difference and multiple alleles) or dominant (either absent or present and show two alleles) (Collard et al. 2005). A perfect molecular marker should possess the following characteristics: I) show polymorphism; II) evenly distributed all over the genome; III) create reliable, multiple, and independent bands; IV) be quick, simple, and inexpensive; V) require lesser quantities of DNA samples; VI) link to separate phenotypes; and VII) needs no earlier information about the organism genome. Although no molecular marker fulfills all these characteristics, researchers choose the molecular marker according to their need and availability (Agarwal et al. 2008).

3. Types of DNA markers Molecular markers are divided into following three groups: i) hybridization based markers; ii) polymerase chain reaction (PCR) based markers; iii) DNA sequence based markers.

3.1. Hybridization-based markers Restriction fragment length polymorphism (RFLP) is a famous hybridization-based marker which are results of insertion or deletion of sequences in the genome and change in restriction sites (Agarwal et al. 2008). In these markers, polymorphism is detected by treating the DNA with specific restriction enzymes followed by electrophoresis and a hybridization process by labeled probe (Collard et al. 2005; Agarwal et al. 2008). Southern hybridization technique was applied to detect first group of RFLP markers. The first rice genome molecular map was constructed by a cross of indica and japonica cultivars of O. sativa using 135 RFLP markers that covered the 1 389 cM length (Jain et al. 2010). Numerous genes controlling qualitative or quantitative traits of rice could be localized and mapped by using this map. Later, many Japanese research groups started their efforts to increase the density of RFLP markers to develop the genetic map with these markers (Agarwal et al. 2008).

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These markers are robust, reliable, and transferable across populations, but have some limitations such as laborious, expensive, large amount of DNA, time-consuming, not suitable for high throughput genotyping applications and require radioactively labeled probe which have safety related issues. Molecular plant breeders need easy-to-use, simple and faster methods of analysis.

3.2. PCR-based markers The main PCR-based markers includes: i) random amplification of polymorphic DNA (RAPD); ii) amplified fragment length polymorphism (AFLP); and iii) simple sequence repeat (SSR) or microsatellite. In RAPD, arbitrary primers are used to amplify the genomic DNA. By gel electrophoresis, amplified products are separated on agarose and visualized by staining (Gupta and Varshney 2005). These markers are quick, simple, inexpensive, small amount of DNA required, and there is no need of prior sequence information of the target genome, however, these are not without some drawbacks too, like these are generally not transferable, dominant and are less reproducible (Jain et al. 2010). AFLP marker is based on selective amplification of double digested restriction products using adaptors linked to restriction fragment ends acting as a specific primer binding site for PCR amplification. For more specific amplification, one to three extra nucleotides (arbitrary chosen) are added to adapter sequence. These markers generate high level of polymorphism, but these are costly, dominant, following complicated methodology and with the large amount of DNA required (Gupta and Varshney 2005). In rice, RAPD and AFLP both are being used, but later SSRs have also been developed for rice genome (Collard et al. 2005). SSRs also known as microsatellites are tandem repeated nucleotide motifs of 1–6 bp which are present both in prokaryotic and eukaryotic genomes. At a particular locus, these motifs commonly show variation in the number of repeats (Shah et al. 2015a). Polymorphism in microsatellite is usually supposed to be the outcome of replication error which happens at a higher rate than the mutation in non-repetitive DNA (Agarwal et al. 2008). Primers can be developed from unique flanking sequences once SSRs are sequenced and cloned. These markers are multiallelic and usually resolve on agarose gel (Kalia et al. 2011). Microsatellites are classified on the basis of their size and location in the genome as nuclear SSRs (nuSSRs), chloroplastic SSRs (cpSSRs), and mitochondrial SSRs (mtSSRs) (Shah et al. 2015b). These are found both in coding and noncoding regions of nuclear genome and can also be found in the chloroplast genome as cpSSRs and in mitochondrial genome as mtSSRs (Kalia et al. 2011).

In 2002, the greatest widespread and comprehensive rice genetic map was published by using SSRs as markers (McCouch et al. 2002). Rice is used as a model organism for cereal crops and its fully sequenced genome contains a large number of SSRs. Thousands of SSR markers with their determined chromosomal location and polymorphism levels have been developed for rice research (de Oliveira Borba et al. 2010). More than 20 000 SSR primers of rice have been developed (Yonemaru et al. 2010). Due to their abundance in the rice genome and number of other advantages, these markers are highly preferred compared to other molecular markers. Some of the advantages are multi allelic, co-dominant, reliable, evenly distributed in genome, requiring less quantity of DNA, efficient, cost effective, and good for high-throughput technology (McCouch et al. 2002). These markers are used for linkage maps construction, gene mapping and MAS (Edwards and Batley 2010; Gonzaga et al. 2015). SSR markers have been used frequently for germplasm characterization but nowadays these are discouraged due to the drawbacks such as troubles in the marker development, difficulty to merge SSR data across different groups and labs, high polymorphism rate due to having many alleles and precise scoring difficulty in SSR genotyping. To avoid these problems, single nucleotide polymorphisms (SNPs) are becoming the marker of choice as they are more abundant across the genome compared to SSR, mostly bi-allelic in nature making allele calling more simple, data from different systems or groups can be easily merged in a database, genotyping can be automated, allowing for rapid, high-throughput marker genotyping (Edwards and Batley 2010; Van Inghelandt et al. 2010; Shah et al. 2016).

3.3. DNA sequence-based markers SNP SNP as a molecular marker is the difference between two DNA sequences on the basis of single nucleotide substitution. When two or more individuals are compared, SNPs are classified as either transition (Cytosine↔Thymine or Guanine↔Adenine) or transversions (Cytosine↔Guanine, Adenine↔Thymine, Cytosine↔Adenine, Thymine↔Guanine). Frequently SNPs are bi-allelic in nature due to more chance of base transitions as compared to transversions and very little occurrence of the spontaneous mutations. Theoretically, there is a chance of tri- and tetra-allelic SNPs but it is rare (Hayward et al. 2012). Haplotype is a group of SNPs found in a single gene or small section of DNA. In eukaryotic genomes, SNP is found both in coding and non-coding regions of nuclear and plastid genomes (Edwards and Batley 2010). Most of the SNPs occur in intergenic region that have no direct effect on agronomi-

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cally important traits. Presence of SNP inside the coding regions can cause non-synonymous mutations that change an amino acid sequence (Van Inghelandt et al. 2010) and can directly affect the function of the genes. In case of synonymous mutations that do not change the sequence of amino acid can, however, alter mRNA splicing, causing in phenotypic changes (Edwards and Batley 2010; Van Inghelandt et al. 2010). As a gene based perfect markers, SNPs can be used for molecular breeding (Edwards and Batley 2010; de Oliveira Borba et al. 2010). SNPs are useful for different purposes such as for QTL and association mapping; map based positional cloning, variety identification and testing of seed purity (Agarwal et al. 2008; Kalia et al. 2011). These markers are also used as DNA markers to find the population substructure (Jain et al. 2010) and are the best markers for studying the complex genetic traits due to low mutation rate. SNPs are the most plentiful type of markers in rice or in any other genomes (Begum et al. 2015). SSR loci in the rice genome are found at each 19.6 kb, while at each 100 bp or less SNPs are present (Zhou et al. 2012; Kumar et al. 2015). A large number of techniques are available to genotype SNP markers and are based on resolution power. These techniques are used to dissect genotype-phenotype association in rice crop (de Oliveira Borba et al. 2010; Shabir et al. 2013). In recent times, numerous researchers have been involved in developing breeders friendly SNPs. The development of SNP is to determine genomic composition of different rice varieties and genotype-phenotype association studies. For example, McNally et al. (2009) used about 160 000 non-redundant SNPs to study the breeding history and relationship of 20 diverse varieties. In a Japanese cultivar (Koshihikari), about 67 051 SNPs were exposed, comparing with Nipponbare sequence data which were used as references. Similarly, highly dense rice genome haplotype has been constructed after the discovery of approximately 3.6 million SNPs (Huang et al. 2010). In rice, many chromosomal regions/QTLs controlling several agronomic traits have been discovered. What are quantitative trait loci (QTLs)? In plant breeding, most of the traits of interest like yield, quality, height, resistance against salinity, drought, diseases, etc., are quantitative in nature and are controlled by many genes (Yu and Buckler 2006). These quantitative or polygenic traits have specific loci on genome called quantitative trait loci (QTLs). In other words, the regions within genomes which are associated with quantitative traits and have more effects on the expression of trait as compared to other regions are called QTLs. It may be single gene or group of linked genes that affect the specific trait (Collard et al. 2005). Overall phenotype of plant is affected by small effect of each gene controlling the trait and environmental effects (Maloof 2003).

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So, QTLs identification by conventional morphological marker based assessment is not easy because the plants with the same phenotype can carry diverse alleles at each of many genes or QTLs and plants with same QTL genotypes can display dissimilar phenotypes when grow under diverse environment. Due to these reasons, it is difficult to assume the genotype from the phenotype and one must construct particular genetic stocks and grow them in exactly controlled environments (Maloof 2003). The upgrading of polygenic traits by conventional breeding approaches is time-consuming. This problem is generally reduced by multi environmental assessment of replicated trials and identification of QTLs position in genome (Collard et al. 2005). QTLs identification To identify the QTLs, markers are placed in the order, the comparative genetic distance between these markers is specified and their linkage group on the basis of recombination value is assigned. The main objective of QTLs identification studies is to find out those neutrally inherited molecular markers which are very near to the genes controlling the complex (usually quantitative) traits (Agarwal et al. 2008). If any complex trait is phenotypically different then it may be due to the environment, influence of many QTLs, the interaction among these QTLs and the interaction between QTLs and environment (Maloof 2003). Usually two ways are used to identify QTLs in plants: I) QTL mapping/Linkage analysis; II) association mapping/ association analysis. I) QTL mapping/Linkage analysis: QTL mapping theory was first explained by Sax (1923). He detected that bean seed size which is a complex and polygenic trait was correlated with seed coat color, a simple monogenic trait. For successful QTL mapping, selection of suitable mapping population is very important. In QTL mapping commonly F2, double haploid (DH), back cross (BC), recombinant inbred lines (RILs), and near isogenic lines (NILs) populations are used to identify QTLs (Collard et al. 2005). The main steps involved in QTL mapping are (i) segregating mapping populations development (resulting from the cross of two selected parents diverse for one or more trait of interest); (ii) linkage map construction by using suitable molecular markers; (iii) phenotypic traits measurements of 50–250 progenies of the population to check the segregation of a trait of interest in diverse environments; and (iv) selection of only polymorphic DNA markers checked in parents followed by individuals of mapping population. Afterward, these ordered markers are statistically correlated with phenotypic traits of mapping population individuals for QTL detection (Collard et al. 2005). The two main objectives of QTL mapping in plants are (i) to check biological information of genetic architecture and the QTLs inheritance; (ii) to discover markers that can be used in plant breeding as indirect selection tools (Maloof 2003; Du et al. 2008). The main target of rice molecular

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breeders is to focus on rice yield and yield-related traits that have a great effect on improving rice production. Main agronomic traits are plant height, heading date, yield, and yield component traits (Du et al. 2008). There are three main yield components, i.e., number of grains per panicle, grain weight, and number of panicles per plant which are involved, to increase the rice grain yield (Edwards and Batley 2010). To improve agronomic traits related to yield, integration of the QTLs/genes from other rice species are demanded. A number of struggles have been done for valuable genes to introgressed into the best rice cultivars by the way of interspecific hybridization (Du et al. 2008). IRRI (International Rice Research Institute), WARDA (West Africa Rice Development Association), and CIAT (International Center for Tropical Agriculture) are the main institutions involved in rice breeding programs. Important genes from the O. glaberrima (African rice) into O. sativa breeding lines were introgressed at WARDA (Jones et al. 1997). In 1991 and 1992, about 1 130 germplasm were assessed for agronomic and morphological traits. Broader genetic variation was found in some of the agronomic traits, particularly growth duration in O. glaberrima. This shows that O. glaberrima germplasm contains many useful resistance genes that can be used for crop development. Six agronomic traits (number of filled grains per panicle, number of panicles per plant, total number of spikelet per panicle, grain yield per plant, thousand grain weight and spikelet fertility), were evaluated by using SSR markers to dissect the QTLs that control the yield traits on short arm of rice chromosome 6. About three yield traits related QTLs were situated on the target section (Du et al. 2008). Many other rice traits such as flag leaf length and culm length were also evaluated for QTL analysis in rice (Kobayashi et al. 2003). Leaf is a very important organ of the plant and has a major role in plant yield because basically it involves many important physiological functions for example transpiration and photosynthesis. The last leaf of a plant that appears before the panicle (inflorescence) is known as flag leaf. It is the main source that provides photosynthetic product to the panicle (Wang and Li 2005). It is a very complex trait controlled by numerous QTLs and is significantly affected by the environment. A number of studies showed that genomic regions on chromosomes 1 and 4 have a major QTL for flag leaf size (Yan et al. 1999). Flag leaf size QTL (qFL1) has shown variation in numbers of environments so by taking big segregating population its location narrowed to 31 kb region having four genes (Wang et al. 2011). Plant height is one of the main agronomic traits related to the phenotypic acceptability of the crop variety. In vegetative developmental stage, rice plant height increases slowly but in reproductive stage before heading it increases

rapidly (Wang and Li 2005). In wheat and rice, genetically improved semi-dwarf varieties have increased the yield since the 1960s and that is called as ‘Green Revolution’ in the history (Collard et al. 2005; Wang and Li 2005). Plant height is a quantitative trait and is controlled by many QTLs (Huang et al. 1996). Many QTLs for plant height have been identified in rice. About 60 dwarf mutants in rice have been recognized. In different studies, rice dwarf genes are cloned and characterized (Hong et al. 2003). Semi-dwarf (sd-1) genes in superior rice varieties are involved to increase grain yield due to their improved absorbance of sunlight and resistance to lodging (Monna et al. 2002). A few reports are available in identifying QTLs of grain quality that focuses on appearance qualities, eating, cooking amylose content, chalkiness, gel consistency, and alkali spreading scores (Kobayashi et al. 2003; Du et al. 2008). Rice grain quality, appearance, and weight depend upon grain size such as length of grain, width of grain, and the ratio between grain length and grain width. Rice grain length has a main role in milling quality and it also affects the grain weight. Grain weight is one of those three components which are involved in grain yield. So grain length has great importance in rice breeding (Agrama et al. 2007; de Oliveira Borba et al. 2010). Rice molecular breeders identify those loci controlling the grain size variation for breeding application. There is large grain size variation in many rice germplasm. Large number of grain size QTLs/ genes have been identified as GS3 (gene for grain size lie on chromosome 3), GIF1 (for grain incomplete filling on chromosome1), GW2 (gene for grain weight lie on chromosome 2), and qSW5/GW5, a gene for seed width lie on chromosome 5 (Shomura et al. 2008; Weng et al. 2008). In breeding programs, only a few reported QTLs have been used for amylose content and heading date improvement (Yano et al. 2000). A lot of reported rice QTLs have not been used extensively by researchers because they are not sure about the exact location of QTLs and these QTLs have not been tested in multiple environments. During the last few years, association mapping technique is becoming popular in plant genetics as an alternative approach to avoid the disadvantages of QTL mapping. This approach enables researcher to locate the important genes in natural population of the genome (Agrama et al. 2007; de Oliveira Borba et al. 2010). II) Association mapping/Association analysis: Association mapping also known as linkage disequilibrium (LD) mapping is a technique that depends upon LD to study the relationship between genotypic and phenotypic variation in natural populations (Agrama et al. 2007; de Oliveira Borba et al. 2010). It is based on the concept that a set of molecular markers either denotes actual genes or they are so near to the functional genes that they co-segregate. It

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is a new approach to dissect complex traits by exploiting historical recombination events at the population level and it can find the markers that are associated with specific agronomic traits in many crops such as maize, barley, and rice (Abdurakhmonov and Abdukarimov 2008). There are commonly two approaches applied to find the association mapping: i) candidate gene association mapping; ii) genome-wide association mapping. i) Candidate gene association mapping: In this method, only those markers are needed which are available in candidate genes. It is based on previous knowledge and linkage mapping results. In plant sciences, this technique is normally used to identify functional variation (Yang et al. 2011) such as flowering time in Arabidopsis thaliana (Ehrenreich et al. 2009) maturity and disease traits QTLs in potato (Gebhardt et al. 2004). ii) Genome-wide association mapping: In this approach, whole genome is scanned to identify genomic regions related to important agronomic traits on all chromosomes. LD extent is the main factor in the success and resolution of this method. Some advantages of this technique are; use of natural populations, no need to make bi-parental population, high resolution mapping due to many recombination events after many meiosis (Agrama et al. 2007). The whole genome study in Arabidopsis and maize plants had been first time introduced by using already known data on flowering time and pathogens resistant genes in plants (Aranzana et al. 2005). Now the whole genome association study, has moved from Arabidopsis and maize to other species such as barley, rice and many other crops (Huang et al. 2010; Soto-Cerda and Cloutier 2012; Kumar et al. 2015). The extent of LD in crop determines the number of markers needed, but usually a large number of markers are used in this study, depending on LD decay and genome size as compared to candidate gene approach. Genome wide association study includes (Agrama et al. 2007) (Fig. 1): • Selection of diverse germplasm from a natural population • Measuring the yield or quality related phenotypic data of the selected germplasm in different environments and multiple replication • Mapping population genotyping with accessible molecular markers; for either candidate genes/regions or as a genome-wide scan • The extent of LD measurement using marker data • Population structure assessment • Phenotypic and genotypic data correlation by using statistical methods that reveal markers located near the locus controlling trait of interest Association mapping study in rice Association mapping is potentially very suitable for dissection of complex rice

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traits. Rice has relatively small genome size, completely sequenced and is well suited to genome-wide association (Abdurakhmonov and Abdukarimov 2008). Different studies on number of rice germplasm material show that 25 cM is a reasonable resolution in rice for association mapping study (Agrama et al. 2007; Huang et al. 2010). Grain traits are highly associated with rice production and serve as key factors for grain yield and its market value (Yao et al. 2016). A diverse rice accessions of 469 were evaluated for association in grain shape with the help of 5 291 SNPs. Associated SNPs identified 27 of them may be used in MAS (Feng et al. 2016). Begum et al. (2016) studied the association in breeding lines of rice against 19 agronomic traits by applying 71 710 SNPs and identified 52 QTLs for 11 agronomic traits. Out of these 52 QTLs, some of them have large effect that may be used for future breeding programs. Agrama et al. (2007) studied the association between 123 SSR loci and the complex traits associated with yield and its components, such as 1 000-kernel weight, kernel width, kernel length, kernel width/length ratio, by using 103 rice accessions. Mixed linear model (MLM) technique was used to detect SSR marker loci associated with three agronomic traits, i.e., panicle length, plant height, and heading date on chromosome 7 from a diverse Chinese rice germplasm (Wen et al. 2009). Natural population of 128 japonica rice varieties was examined during two years, for association mapping of many important agronomic traits, e.g., heading date, filled grains per panicle, percentage seed set, grain density, 1 000-kernel weight, flag leaf length, and grain yield per plant. Out of 152 SSRs, 16 SSR markers were associated with traits under study (Zhou et al. 2012). A total 242 rice accessions were examined to find out the association of yield parameters and grain-quality traits with 86 SSR markers by using mixed linear model. Eight markers displayed meaningful association with four different parameters as panicle number, amylose content, head-milled rice, and yield (de Oliveira Borba et al. 2010). Yield is also affected by biotic and abiotic stresses. The biotic stress plays an important role in rice production along with yield and its related components. The most damaging disease of rice was evaluated by association studies for resistance. Wang et al. (2015) studied 151 rice accessions for rice blast disease by applying 156 SSR markers. Out of these 21 markers were identified as link to blast resistance and noticed that short stature was significantly correlated with blast resistance. Zhu et al. (2016) studied 373, 356, and 336 rice cultivars in Shanghang, Wuchang, and Taojiang, respectively. Genome-wide association studies (GWAS) offer a powerful strategy for understanding the genetic basis of complex traits (McCouch et al. 2016). A total of 16 loci associated

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Diverse germplasm collection

Candidate gene Genome wide association study

Genotyping using molecular markers

Phenotyping of specific traits

Paddy

Milled

Cooked

Ancestory (population structure; Relative kinship (K matrix) Q matrix) G1

Generalized linear model (GLM) Phenotype=Q matrix+Genotype

G2

G3

G4

Polymorphism in candidate gene LD

Mixed linear model (MLM) Phenotype=Q matrix+K matrix+Genotype

Fig. 1 Steps involved in association mapping for tagging a gene of interest using germplasm accessions.

with field blast resistance were identified by using GWAS. Another study was carried out to find quantitative and qualitative alleles for blast resistance that can be used for sustainable basis. A total of 340 accessions of japonica and indica background was evaluated by GWAS. In japonica two loci, and in indica only one locus was significantly associated with rice blast resistance (Raboin et al. 2016). A panel of 162 rice cultivars from African countries were analyzed with 44 000 SNP chip and 31 genomic regions were found associated with rice blast resistance (Mgonja et al. 2016). Abiotic stresses are also part of economic loss to farmers by reducing yield. A genome wide association mapping was performed in 220 rice accessions with the help of 6 000 SNPs against salinity tolerance and new QTLs on chromosomes 4, 6, and 7 were identified along with Saltol (Kumar et al. 2015). Similarly, excessive metals in soil hamper the plant growth. Aluminum tolerance traits were analyzed in 150 accessions with 274 SSR markers. A total of 23 associations was detected and can be used to devel-

op aluminum tolerant rice cultivars (Zhang et al. 2016). In order to practically get the fruit of these results, several new breeding tools are emerging. Genomic selection (GS) is served as an emerging breeding tool by predicting value of individual in a breeding population by applying genome-wide markers. Spindel et al. (2015) analyzed 363 elite breeding lines by applying 73 147 markers by using genotyping by sequencing. Their results suggested that GS could be an effective tool to enhance the efficiency of breeding programs. The other studies related to the rice association mapping are summarized in Table 1. In these days, there are some limitations in the molecular rice breeding such as real gene-based or tightly linked molecular markers for the agronomic traits are not available and per sample analysis is relatively costly (Maloof 2003; Agarwal et al. 2008; de Oliveira Borba et al. 2010; Aslam and Arif 2014). Analysis cost per sample is also reducing day by day with the high throughput genotyping techniques and

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introduction of marker multiplexing, but yet the preliminary prices of the equipment’ are high. However, after getting the genotyping service facilities set up, sample analysis is becoming relatively quick and cost effective (de Oliveira et al. 2010; Kumar et al. 2015). Limitations of association mapping In spite of many advantages of AM, it has also some limitations like Type-1 error; false positive association between marker and trait could be produced by population structure (Abdurakhmonov and Abdukarimov 2008; de Oliveira Borba et al. 2010; Kumar et al. 2015). When different statistical methods are used to correct the population structure effect during marker trait associations then subpopulation differences are ignored. Due to this the causative markers involved in subpopulation phenotypic differences remain undetected. The other limitations are epistasis, gene conversion. This

technique also requires a large number of molecular markers for GWAS. Markers strength depends upon the genome size and LD decay. Population structure The main aim of association mapping is to find the markers which have associated with QTLs-controlling complex traits. Those QTLs which have no association with the markers will cause residual error and reduce the power of statistics. The QTLs which are not physically linked with molecular marker can be a source of false association between phenotype and genotype due to family and population structure (Bradbury et al. 2007; de Oliveira Borba et al. 2010; Kumar et al. 2015). Population structure is a central part in the association mapping analysis because it can lessen Type-1 error between molecular markers and traits of interest in self-pollinated species (Yu and Buckler 2006). False positive is the major issue in as-

Table 1 Association mapping studies in rice Germplasm Diverse cultivars

Sample size 105

Marker used1) SSRs

Number of markers 18

Trait Late bright resistance

Diverse land races

577

577

Starch quality

Diverse accessions Landraces

103 170

SSRs/SNPs/ STSs SSRs SSRs

Landraces Diverse cultivars Diverse varieties Accessions Accessions

517 217 128 210 416

SNPs SSRs SSRs SSRs SSRs

155 152 86 100

Accessions Breeding lines Accessions

413 192 90

SNPs SSRs SSRs

44 100 97 108

Diverse cultivars Accessions Accessions Accessions Japonica accessions Accessions Cultivars Accessions Varieties Breeding lines Breeding lines MAGIC population Accessions

303 203 114 416 167 347 328 220 95 363 523 120

SSRs SSRs SSRs SSRs GBS SSRs SNPs SNPs SSRs SNPs SNPs SNP

24 154 21 100 148 44 100 6 000 363 71 710 5 291 4 500

151

SSR

156

Breeding lines Accessions

363 391

GBS GBS

73 147 1 66 418

Accessions Accessions

469 150

SNP SSR

5 291 274

1)

123 126

Reference Olsen and Purugganan (2002) Bao et al. (2006)

Yield and its components Agrama et al. (2007) Heading date, plant height Wen et al. (2009) and panicle length Multiple agronomic traits Huang et al. (2010) Sheath blight Jia et al. (2012) Yield and its component Zhou et al. (2012) Yield and grain quality trait de Oliveira et al. (2010) Grain color and nutritional quality Shao et al. (2011) traits genetic architecture Zhao et al. (2011) Grain quality and flowering time Ordonez et al. (2010) Stigma and spikelet Yan et al. (2009) characteristics Awn less Hu et al. (2011) Complex trait of harvest index Li et al. (2012) Bacterial blight resistance Garris et al. (2003) Starch quality traits Jin et al. (2010) Root traits Courtois et al. (2013) Cold tolerance Cui et al. (2013) Drought recover traits Al-Shugeairy et al. (2015) Salinity tolerance Kumar et al. (2015) Grain filling rate Liu et al. (2015) Yield and other agronomic traits Begum et al. (2015) Agronomic traits Lu et al. (2015) Agronomic traits Meng et al. (2016) Disease resistance and yieldrelated components Genomic selection model Plant and grain morphology and root architecture Grain shape Al tolerance traits

Wang et al. (2015) Spindel et al. (2015) Biscarini et al. (2016) Feng et al. (2016) Zhang et al. (2016)

SSR, simple sequence repeat; SNP, single nucleotide polymorphism; STS, sequence tag site; GBS, genotyping by sequencing.

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sociation analysis. There are different approaches to reduce the effect of false positive. For population structure analysis, the most frequently used methods are STRUCTURE and principle component analysis (PCA) (Yu and Buckler 2006; Bradbury et al. 2007; Yang et al. 2011). I) STRUCTURE: In case of STRUCTURE, all the individuals are accepted as unrelated and all the loci are assumed to follow Hardy-Weinberg equilibrium in a population. But in actual conditions, very little data approved this postulation (Yang et al. 2011). The Q matrix resulting from STRUCTURE is being applied in TASSEL for general linear model (GLM). II) TASSEL (trait analysis by association, evolution and linkage): It is commonly the most advanced statistical software used in association genetic study to find kinship and population structure information among diverse individuals (Yu and Buckler 2006). By this software we can calculate and graphically show LD, population structure analysis on the base of PCA or Q matrix, genetic distance tree plots. Though the software can deal with both SNP and SSR markers but the newest TASSEL ver. 3.0.146 only takes SNPs. SSR analysis, can be done by TASSEL ver. 2.1. Otherwise, GenStat can be used as alternative software for SSR analysis in AM study (Yu and Buckler 2006; Harjes et al. 2008; Soto-Cerda and Cloutier 2012; Courtois et al. 2013). In TASSEL, two approaches are applied to achieve association analysis: i) GLM and ii) mixed linear model (MLM). i) GLM: In this method associations between markers and mean phenotypic parameters are determined. For this approach there is no need of kinship as potential cause of genotype-phenotype correlation. It accounts only for population structure (Yu and Buckler 2006) and the percentages of admixture (Q matrix) as fixed effects (Courtois et al. 2013). ii) MLM: This method includes both kinship and population structure in association analysis (Ehrenreich et al. 2009). In MLM, Q+K is used in TASSEL. Now, K matrix (pairwise relatedness among all individuals) of a population is estimated by using randomly selected markers. A compound method, Q+K, that combines evidence from both Q and K, has an advantage over only Q matrix approach. It is very active approach in association mapping to remove the confusing effects of population structure (Yu and Buckler 2006). The MLM model works better than only Q model or only K model alone (Zhao J et al. 2007). In MLM method, K was effectively used in association study in plants such as maize (Harjes et al. 2008), Arabidopsis (Zhao K et al. 2007) and rice (Kumar et al. 2015).

4. Future prospects Rice is a major food crop in the cereal group around the world so major attention remains essential to improve yield

and quality of rice. This task can be achieved by harnessing novel alleles from available germplasm, including wild species and use of modern molecular techniques helpful in developing high yielding rice varieties. In this situation, it is supposed that molecular marker application in genetic mapping will enable the rice breeders to detect the genes controlling agronomically important traits. The high-throughput techniques result in discovery of molecular markers that will be useful in identifying the rice genotypes carrying desired characters as these have been effectively used to develop linkage maps and mapping genes required for varietal development. In general, the selection of a molecular marker technique is based on reliability, statistical power and level of polymorphisms. When these marker techniques reach a higher degree of automation than it will be suitable to use DNA markers directing to a new “Green Revolution” in the agricultural world. Presently, the enormous development of more efficient DNA markers will go on in future, because they can serve as an important tool for the plant breeders and geneticists to develop the cultivars of rice for food security and sustainable productivity.

Acknowledgements The authors thank Dr. Syed Bilial Hussain and Dr. Muhammad Kamran Qureshi (Bahauddin Zakariya University, Pakistan) for their critical comments and valuable suggestions for improving the quality of this manuscript.

References Abdurakhmonov I Y, Abdukarimov A. 2008. Application of association mapping to understanding the genetic diversity of plant germplasm resources. International Journal of Plant Genomics, 2008, 1–18. Agarwal M, Shrivastava N, Padh H. 2008. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Reports, 27, 617–631. Agrama H, Eizenga G, Yan W. 2007. Association mapping of yield and its components in rice cultivars. Molecular Breeding, 19, 341–356. Al-Shugeairy Z, Price A H, Robinson D. 2015. Genome wide association mapping for drought recovery trait in rice (Oryza sativa L.). International Journal of Applied Agricultural Sciences, 1, 11–18. Anonymous. 2011. From 6 billion to 7 billion: How population growth is changing and challenging our world, Population Institute. [2017-01-16]. http://apo.org.au/node/26644 Aranzana M J, Kim S, Zhao K, Bakker E, Horton M, Jakob K, Lister C, Molitor J, Shindo C, Tang C. 2005. Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genetics, 1, e60.

Ghulam Shabir et al. Journal of Integrative Agriculture 2017, 16(9): 1879–1891

Aslam K, Arif M. 2014. SSR analysis of chromosomes 3 and 7 of rice (Oryza staiva L.) associated with grain length. Pakistan Journal of Botany, 46, 1363–1372. Bao J, Corke H, Sun M. 2006. Microsatellites, single nucleotide polymorphisms and a sequence tagged site in starchsynthesizing genes in relation to starch physicochemical properties in nonwaxy rice (Oryza sativa L.). Theoretical and Applied Genetics, 113, 1185–1196. Begum H, Spindel J E, Lalusin A, Borromeo T, Gregorio G, Hernandez J, Virk P, Collard B, McCouch S R. 2015. Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLOS ONE, 10, e0119873. Biscarini F, Cozzi P, Casella L, Riccardi P, Vattari A, Orasen G, Perrini R, Tacconi G, Tondelli A, Biselli C. 2016. Genome-wide association study for traits related to plant and grain morphology, and root architecture in temperate rice accessions. PLOS ONE, 11, e0155425. Borlaug N E. 2000. Ending world hunger. The promise of biotechnology and the threat of antiscience zealotry. Plant Physiology, 124, 487–490. Bradbury P J, Zhang Z, Kroon D E, Casstevens T M, Ramdoss Y, Buckler E S. 2007. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics, 23, 2633–2635. Breseghello F, Coelho A S G. 2013. Traditional and modern plant breeding methods with examples in rice (Oryza sativa L.). Journal of Agricultural and Food Chemistry, 61, 8277–8286. Collard B, Jahufer M, Brouwer J, Pang E. 2005. An introduction to markers, quantitative trait loci (QTL) mapping and markerassisted selection for crop improvement: the basic concepts. Euphytica, 142, 169–196. Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, Droc G, Frouin J, Rouan L, Gozé E, Kilian A. 2013. Genome-wide association mapping of root traits in a japonica rice panel. PLOS ONE, 8, e78037. Cui D, Xu C Y Tang C F, Yang C G, Yu T Q, Xin X A, Cao G I Xu F R, Zhang J G Han L Z. 2013. Genetic structure and association mapping of cold tolerance in improved japonica rice germplasm at the booting stage. Euphytica, 193, 369–382. Du J H, Fan Y Y, Wu J R, Zhuang J Y. 2008. Dissection of QTLs for yield traits on the short arm of rice chromosome. Agricultural Sciences in China, 7, 513–520. Edwards D, Batley J. 2010. Plant genome sequencing: Applications for crop improvement. Plant Biotechnology Journal, 8, 2–9. Ehrenreich I M, Hanzawa Y, Chou L, Roe J L, Kover P X, Purugganan M D. 2009. Candidate gene association mapping of Arabidopsis flowering time. Genetics, 183, 325–335. Feng Y, Lu Q, Zhai R, Zhang M, Xu Q, Yang Y, Wang S, Yuan X, Yu H, Wang Y. 2016. Genome wide association mapping for grain shape traits in indica rice. Planta, 244, 1–12. Garris A J, McCOUCH S R, Kresovich S. 2003. Population

1889

structure and its effect on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics, 165, 759–769. Gebhardt C, Ballvora A, Walkemeier B, Oberhagemann P, Schüler K. 2004. Assessing genetic potential in germplasm collections of crop plants by marker-trait association: A case study for potatoes with quantitative variation of resistance to late blight and maturity type. Molecular Breeding, 13, 93–102. Gonzaga Z J, Aslam K, Septiningsih E M, Collard B C. 2015. Evaluation of SSR and SNP markers for molecular breeding in rice. Plant Breeding and Biotechnology, 3, 139–152. Gupta P K, Varshney R K. 2005. Cereal genomics: An overview. In: Cereal Genomics. Springer, The Netherlands. pp. 1–18. Harjes C E, Rocheford T R, Bai L, Brutnell T P, Kandianis C B, Sowinski S G, Stapleton A E, Vallabhaneni R, Williams M, Wurtzel E T. 2008. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science, 319, 330–333. Hayward A, Mason A, Dalton-Morgan J, Zander M, Edwards D, Batley J. 2012. SNP discovery and applications in Brassica napus. Plant Biotechnology, 39, 12. Hong Z, Ueguchi-Tanaka M, Umemura K, Uozu S, Fujioka S, Takatsuto S, Yoshida S, Ashikari M, Kitano H, Matsuoka M. 2003. A rice brassinosteroid-deficient mutant, ebisu dwarf (d2), is caused by a loss of function of a new member of cytochrome P450. The Plant Cell, 15, 2900–2910. Hossain M. 1997. Rice supply and demand in Asia: A socioeconomic and biophysical analysis. In: Applications of Systems Approaches at the Farm and Regional Levels. vol. 1. Springer, The Netherlands. pp. 263–279. Hu G, Zhang D, Pan H, Li B, Wu J, Zhou X, Zhang Q, Zhou L, Yao G, Li J. 2011. Fine mapping of the awn gene on chromosome 4 in rice by association and linkage analyses. Chinese Science Bulletin, 56, 835–839. Huang N, Courtois B, Khush G S, Lin H, Wang G, Wu P, Zheng K. 1996. Association of quantitative trait loci for plant height with major dwarfing genes in rice. Heredity, 77, 130–137. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z. 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics, 42, 961–967. Van Inghelandt D, Melchinger A E, Lebreton C, Stich B. 2010. Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theoretical and Applied Genetics, 120, 1289–1299. Jain S M, Brar D S, Ahloowalia B. 2010. Molecular Techniques in Crop Improvement. Springer, The Netherlands. Jia L, Yan W, Zhu C, Agrama H A, Jackson A, Yeater K, Li X, Huang B, Hu B, McClung A. 2012. Allelic analysis of sheath blight resistance with association mapping in rice. PLoS ONE, 7, e32703. Jin L, Lu Y, Xiao P, Sun M, Corke H, Bao J. 2010. Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theoretical and

1890

Ghulam Shabir et al. Journal of Integrative Agriculture 2017, 16(9): 1879–1891

Applied Genetics, 121, 475–487. Jones N, Ougham H, Thomas H. 1997. Markers and mapping: We are all geneticists now. New Phytologist, 137, 165–177. Kalia R K, Rai M K, Kalia S, Singh R, Dhawan A. 2011. Microsatellite markers: An overview of the recent progress in plants. Euphytica, 177, 309–334. Kobayashi S, Fukuta Y, Sato T, Osaki M, Khush G. 2003. Molecular marker dissection of rice (Oryza sativa L.) plant architecture under temperate and tropical climates. Theoretical and Applied Genetics, 107, 1350–1356. Kumar V, Singh A, Mithra S A, Krishnamurthy S, Parida S K, Jain S, Tiwari K K, Kumar P, Rao A R, Sharma S. 2015. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Research, 22, 133–145. Leegood R C, Evans J R, Furbank R T. 2010. Food security requires genetic advances to increase farm yields. Nature, 464, 831–831. Li X, Yan W, Agrama H, Jia L, Jackson A, Moldenhauer K, Yeater K, McClung A, Wu D. 2012. Unraveling the complex trait of harvest index with association mapping in rice (Oryza sativa L.). PLoS ONE, 7, e29350. Liu E, Liu X, Zeng S, Zhao K, Zhu C, Liu Y, Breria M C, Zhang B, Hong D. 2015. Time-course association mapping of the grain-filling rate in rice (Oryza sativa L.). PLOS ONE, 10, e0119959. Lu Q, Zhang M, Niu X, Wang S, Xu Q, Feng Y, Wang C, Deng H, Yuan X, Yu H. 2015. Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics, 16, 1. Maloof J N. 2003. QTL for plant growth and morphology. Current Opinion in Plant Biology, 6, 85–90. McCouch S R, Teytelman L, Xu Y, Lobos K B, Clare K, Walton M, Fu B, Maghirang R, Li Z, Xing Y. 2002. Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Research, 9, 199–207. McCouch S R, Wright M H, Tung C W, Maron L G, McNally K L, Fitzgerald M, Singh N, DeClerck G, Agosto-Perez F, Korniliev P. 2016. Open access resources for genome-wide association mapping in rice. Nature Communications, 7, doi: 10.1038/ncomms10532 McNally K L, Childs K L, Bohnert R, Davidson R M, Zhao K, Ulat V J, Zeller G, Clark R M, Hoen D R, Bureau T E. 2009. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences of the United States of America, 106, 12273–12278. Meng L, Zhao X, Ponce K, Ye G, Leung H. 2016. QTL mapping for agronomic traits using multi-parent advanced generation inter-cross (MAGIC) populations derived from diverse elite indica rice lines. Field Crops Research, 189, 19–42. Mgonja E M, Balimponya E G, Kang H, Bellizzi M, Park C H, Li Y, Mabagala R, Sneller C, Correll J, Opiyo S. 2016. Genomewide association mapping of rice resistance genes against Magnaporthe oryzae isolates from four African countries. Phytopathology, 106, 1359–1365. Monna L, Kitazawa N, Yoshino R, Suzuki J, Masuda H, Maehara

Y, Tanji M, Sato M, Nasu S, Minobe Y. 2002. Positional cloning of rice semidwarfing gene, sd-1: Rice “Green revolution gene” encodes a mutant enzyme involved in gibberellin synthesis. DNA Research, 9, 11–17. de Oliveira B T, Brondani R, Breseghello F, Coelho A, Mendonca J, Rangel P, Brondani C. 2010. Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Genetics and Molecular Biology, 33, 515. Olsen K M, Purugganan M D. 2002. Molecular evidence on the origin and evolution of glutinous rice. Genetics, 162, 941–950. Ordonez S A, Silva J, Oard J H. 2010. Association mapping of grain quality and flowering time in elite japonica rice germplasm. Journal of Cereal Science, 51, 337–343. Pandey G K, Pandey A, Prasad M, Böhmer M. 2016. Editorial: Abiotic stress signaling in plants: Functional genomic intervention. Frontiers in Plant Science, 7, 681. Raboin L M, Ballini E, Tharreau D, Ramanantsoanirina A, Frouin J, Courtois B, Ahmadi N. 2016. Association mapping of resistance to rice blast in upland field conditions. Rice, 9, 59. Sax K. 1923. The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics, 8, 552. Shabir G, Naveed S A, Arif M. 2013. Estimation of phenotypic variability and mutual association of yield and its components in rice (Oryza sativa L.) germplasm using multivariate analysis. Pakistan Journal of Agricultural Research, 51, 361–377. Shah S M, Arif M, Aslam K, Shabir G, Thomson M J. 2016. Genetic diversity analysis of Pakistan rice (Oryza sativa) germplasm using multiplexed single nucleotide polymorphism markers. Genetic Resources and Crop Evolution, 63, 1113–1126. Shah S M, Aslam K, Shabir G, Khan A R, Abbasi B H, Shinwari Z K, Arif M. 2015a. Population structure and diversity of the AA genome of rice based on simple sequence repeats variation in organelle genome. Pakistan Journal of Botany, 47, 1773–1782. Shah S M, Shabir G, Aslam K, Sabar M, Arif M. 2015b. Identification of SSR markers to find adulteration in elite basmati rice varieties. Environment and Plant Systems, 1, 4–15. Shao Y, Jin L, Zhang G, Lu Y, Shen Y, Bao J. 2011. Association mapping of grain color, phenolic content, flavonoid content and antioxidant capacity in dehulled rice. Theoretical and Applied Genetics, 122, 1005–1016. Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M. 2008. Deletion in a gene associated with grain size increased yields during rice domestication. Nature Genetics, 40, 1023–1028. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink J L, McCouch S R. 2015. Genomic selection and association mapping in rice (Oryza sativa): Effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLOS

Ghulam Shabir et al. Journal of Integrative Agriculture 2017, 16(9): 1879–1891

Genetics, 11, e1005350. Stuber C W, Edwards M, Wendel J F. 1987. Molecular markerfacilitated investigations of quantitative trait loci in maize. II. Factors influencing yield and its component traits. Crop Science, 27, 639–648. Wang L, Wang A, Huang X, Zhao Q, Dong G, Qian Q, Sang T, Han B. 2011. Mapping 49 quantitative trait loci at high resolution through sequencing-based genotyping of rice recombinant inbred lines. Theoretical and Applied Genetics, 122, 327–340. Wang X, Jia M H, Ghai P, Lee F N, Jia Y. 2015. Genome-wide association of rice blast disease resistance and yield-related components of rice. Molecular Plant-Microbe Interactions, 28, 1383–1392. Wang Y, Li J. 2005. The plant architecture of rice (Oryza sativa). Plant Molecular Biology, 59, 75–84. Wen W, Mei H, Feng F, Yu S, Huang Z, Wu J, Chen L, Xu X, Luo L. 2009. Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.). Theoretical and Applied Genetics, 119, 459–470. Weng J, Gu S, Wan X, Gao H, Guo T, Su N, Lei C, Zhang X, Cheng Z, Guo X. 2008. Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Research, 18, 1199–1209. William S K, Michael R C. 1996. Essentials of Genetics. 2nd. Prentice-Hall Inc., Upper Saddle River, USA. Yan J, Zhu J, He C, Benmoussa M, Wu P. 1999. Molecular marker-assisted dissection of genotype×environment interaction for plant type traits in rice (Oryza sativa L.). Crop Science, 39, 538–544. Yan W G, Li Y, Agrama H A, Luo D, Gao F, Lu X, Ren G. 2009. Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.). Molecular Breeding, 24, 277–292. Yang X, Xu Y, Shah T, Li H, Han Z, Li J, Yan J. 2011. Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica, 139, 1045–1054. Yano M, Katayose Y, Ashikari M, Yamanouchi U, Monna L,

1891

Fuse T, Baba T, Yamamoto K, Umehara Y, Nagamura Y. 2000. Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. The Plant Cell, 12, 2473–2483. Yao X Y, Wang J Y, Liu J, Wang W, Yang S L, Zhang Y, Xu Z J. 2016. Comparison and analysis of QTLs for grain and hull thickness related traits in two recombinant inbred line (RIL) populations in rice (Oryza sativa L.). Journal of Integrative Agriculture, 15, 2437–2450. Yonemaru J I, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M. 2010. Q-TARO: QTL annotation rice online database. Rice, 3, 194–203. Yu J, Buckler E S. 2006. Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology, 17, 155–160. Zhang P, Zhong K, Tong H, Shahid M Q, Li J. 2016. Association mapping for aluminum tolerance in a core collection of rice landraces. Frontiers in Plant Science, 7, doi: 10.3389/ fpls.2016.01415 Zhao J, Paulo M J, Jamar D, Lou P, Van Eeuwijk F, Bonnema G, Vreugdenhil D, Koornneef M. 2007. Association mapping of leaf traits, flowering time, and phytate content in Brassica rapa. Genome, 50, 963–973. Zhao K, Aranzana M J, Kim S, Lister C, Shindo C, Tang C, Toomajian C, Zheng H, Dean C, Marjoram P. 2007. An Arabidopsis example of association mapping in structured samples. PLoS Genetics, 3, e4. Zhao K, Tung C W, Eizenga G C, Wright M H, Ali M L, Price A H, Norton G J, Islam M R, Reynolds A, Mezey J. 2011. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications, 2, 467. Zhou J, You A, Ma Z, Zhu L, He G. 2012. Association analysis of important agronomic traits in japonica rice germplasm. African Journal of Biotechnology, 11, 2957–2970. Zhu D, Kang H, Li Z, Liu M, Zhu X, Wang Y, Wang D, Wang Z, Liu W, Wang G L. 2016. A genome-wide association study of field resistance to Magnaporthe oryzae in rice. Rice, 9, 44. (Managing editor WANG Ning)