Study on Analysis of Molecular Diversity and Trait Association for Zinc Deficiency Tolerance in Rice under Submerged Conditions

Rice is the most important human food crop in the world and is the source of sustenance and livelihood for about half of the world’s population. The rice production is affected by various biotic stresses like pests and diseases and abiotic stresses such as drought and low soil fertility which often prevent crops from reaching their true yield potential (Nanda and Wissuwa, 2016). After nitrogen (N), phosphorus (P) and potassium (K), widespread zinc (Zn) deficiency has been found responsible for yield reduction in rice and it is the most important and most frequently occurring micronutrient deficiency.

Breeding for zinc deficiency tolerance is hampered similar to breeding for other abiotic stress tolerance and nutrient use efficiency due to the poor understanding of the tolerance mechanism which in case is severely affected by the complexity involved in screening. The field screening for zinc deficiency tolerance can be variable based on soil factors like soil heterogeneity and interaction of zinc with other nutrients in the soil. The response of zinc deficiency exhibited by rice plants might also be due to other stress factors like high carbonate- bicarbonate content. Hence, Sadeghzadeh (2013) emphasized on requirement of reliable alternative screening methods.

With the advent of molecular markers, association mapping strategy also known as linkage disequilibrium (LD) mapping, which is based on LD (Linkage disequilibrium) non-random associations between causative loci and phenotype could be employed to resolve the complex traits like zinc deficiency tolerance, using natural population. Wissuwa et al., (2006), suggested that by identifying QTLs associated with symptoms of Zn deficiency, it will ultimately be possible to dissect the overall response to Zn deficiency into distinct genetic factors, each associated with a physiological mechanism conferring tolerance. Hence, in this study an attempt had been made to identify quantitative trait loci associated with zinc deficiency tolerance using Simple Sequence Repeat (SSR) markers-based association mapping.

A set of forty-four accessions were used for molecular analysis using 40 Simple Sequence Repeat (SSR) primers. The accessions were randomly selected based on their zinc deficiency score in field screening experiment which was conducted in Regional Research Station, Paiyur during Rabi 2014-15. Eight accessions from each score group of 1(highly tolerant), 3(moderately tolerant), 5 (moderately susceptible), 7 (susceptible) and 9 (highly susceptible) along with four other genotypes namely Savulu Samba, Kotta Nel, Paiyur 1 and ADT 39 constituted 44 accessions used in this study. The details of 44 rice accessions and 40 SSR primers used for the molecular marker analysis are given in Table 1 and Annexure 1 respectively.





Fresh leaf samples were collected from 44 genotypes at three leaf stage was used for isolation of genomic DNA. DNA was extracted following the modified CTAB method developed by Saghai-Maroof et al., (1984) with suitable modifications suggested by Hoisington et al., (1994). The DNA concentration was quantified and diluted to 30 ng/µl. PCR analysis was carried out using regular protocol for SSR primers in rice. The PCR products were checked for amplification in agarose gel electrophoresis (3 per cent) before loading them on Poly Acrylamide Gel Electrophoresis (PAGE). The PCR products were then run on PAGE at 150 and resolved by ethidium bromide staining procedure and bands were visualized under UV light.  The number of alleles for each of the SSR markers across the 44 accessions were identified and used for determining the Polymorphism Information Content (PIC). The PIC value for each SSR markers was calculated based on the formula Hn = 1 - Spi2, where pi is the allele frequency for the ith allele (Nei, 1973). Cluster analysis was done using distance-based approach by calculating pair wise distance matrix to generate a dissimilarity matrix using a shared allele index with DARwin software (Perrier and Collet, 2006).

To understand the population structure, the genotypic data for 40 SSR markers were analyzed by employing a model-based approach available in Structure 2.3.2 (Pritchard et al., 2000). The analysis was carried out using the online version of Structure harvester (http://tayloro.biologyucla.edu/Struct_harvest) developed by Earl and von Holdt, (2012). A value of K=2 was selected when the estimate of delta K peaked in the range of 1 to 10 sub-populations and inferred ancestry estimates of individuals (Q-matrix) were derived for the selected sub-population (Pritchard et al., 2000). Association between markers and traits was performed using structured association analysis which is implemented in TASSEL v4.1 as a general linear model (GLM) method (Bradbury et al., 2007). Genotypic data of the 44 rice accessions generated from SSR marker analysis and phenotypic data of the six characters observed in the experiment served as the input data for association mapping. The significant marker-trait associations were declared by P≤0.05 and the magnitude of the QTL effects were evaluated by R² -marker parameter.

The marker assisted breeding for zinc deficiency tolerance is in the primitive stage due to the poor understanding of the underlying mechanisms which vary drastically among the tolerant lines which are identified. Most of the studies to identify QTLs for zinc deficiency tolerance are based on the population developed from cross between single tolerant and susceptible plants and are associated with the traits like zinc content in the grains, tissues and root traits, which are not easy for observations and screening when dealing with huge populations like F2, RILS, NILs or DHs. In this study we have attempted to understand the association of molecular markers to zinc deficiency scores and few growth and yield characters using association mapping.

A total of 40 SSR markers were evaluated across a subset of 44 genotypes. The levels of polymorphism among the 44 accessions were evaluated by calculating allele number and polymorphism information content (PIC) values for each of the 40 SSR loci. The allelic range for a marker across the population is a deciding factor in understanding the genetic diversity of a population. Higher the number of alleles greater is the extent of genetic diversity. The SSR primer pairs used for the analysis, the number of alleles for each SSR locus, gene diversity and PIC values are given in Table 2.



The 40 primer pairs detected a total of 143 alleles, with an average of 3.58 alleles per locus. The number of alleles observed at each locus ranged from three to five. Out of the 40 SSR markers, 26 markers were with three alleles, 16 markers were with four alleles and two markers were with five alleles. The average PIC value was 0.56 and it ranged from a minimum of 0.22 (RM 211) to a maximum of 0.75 (RM1, RM413). The PIC value estimated based on the number of alleles is subject to the frequency of individuals under each category across the population. Subsequent to the PIC value estimation, the marker data generated was used to assess the extent of genetic diversity adopting cluster analysis.

Clustering analysis based on Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method using DARwin separated the accessions into two main clusters and three sub clusters in each cluster. Cluster is depicted in Fig 1.



The cluster analysis separated the genotypes in to two major clusters indicated the existence of two groups and the possibility of using the population for LD mapping to identify the QTL associated with zinc deficiency tolerance, though the allelic frequencies for the marker loci did not have whole genome coverage, a pre-requisite for the LD mapping in a population. The primers were randomly selected for this study. Structure analysis was carried out to establish the population structure using the allelic frequency of 40 SSR markers employed. The population structure was determined based on the survey of 40 SSR markers across the subset of 44 accessions. The results are presented in Table 3.



Optimum number of populations was inferred using the correlated allele frequencies. The analysis resulted with optimum K value as two, indicating two possible populations out of 44 accessions (Fig 2a and 2b). 

a

b

Association analysis using TASSEL v2.0.1 revealed putative association of four markers viz., RM5, RM237, RM256 and RM341. The associated markers and explained variances are presented in Table 4.



RM5 and RM237 were putatively associated with zinc deficiency score and plant height respectively. Wissuwa et al., 2006 reported a QTL Zbz1b at 124 cM in chromosome 1 with the flanking markers RG220–RG109, with the R2 value of 16.5. Bekele et al., (2013) observed that in single marker analysis using 176 RILs of cross Azucena x Moro mutant, the marker RM212 located on chromosome 1 was closely associated with zinc concentration and plant height with adjusted R2 value of 4.50 and 5.20 respectively. Stangoulis et al., (2007) identified QTL for zinc content on chromosome 1 flanked by markers RM34-RM237. Xu et al., 2016 reported an association of RM237 with plant height in chromosome 1 which corresponded to the gene encoding DGL1, which is important for cell and organ elongation in rice, this suggests the possibility that zinc deficiency score and plant height may be controlled by similar genomic regions which suggests the close association between the two. 

The markers RM 256 at chromosome 8 and RM 341 at chromosome 12 were associated with number of productive tillers. Swamy et al., 2014 reported a QTL nsp12.1 for number of spikelets per plant in a cross between O.nivara and Swarna with R2 value 33.7 at the marker interval of RM341-RM519. Wissuwa et al., (2006) reported a QTL Zmt12 for zinc deficiency induced mortality on chromosome 12 flanked by markers CDO344-1–RG543-1 with adjusted R2 value of 11.60. This suggests that genes governing number of productive tillers and single plant yield under zinc deficiency could possibly be co-localized with that of zinc deficiency tolerance, which requires further studies for confirmation.

In the current study, it is evident that there was exists ample variation at molecular level for zinc deficiency tolerance under submerged conditions. Identification of putative associations RM5 and RM237 with zinc deficiency score and plant height, respectively and that of markers RM256 and RM341 were associated with number of productive tillers suggests that association mapping could be a viable strategy for mapping QTL zinc deficiency tolerance under submerged tolerance.

The authors acknowledge the Department of Biotechnology, Government of India financial sanctions for carrying out the research. And the first author individually acknowledges Department of Biotechnology, Government of India for the research fellowship provided during the research.