Traits In Wheat Research Articles

Cadmium Reduction in Spring Wheat: Root Exudate Composition Affects Cd Partitioning Between Roots and Shoots

Cadmium (Cd) in cereals is one of the major sources of Cd intake by human diets, and solutions to reduce Cd concentrations in wheat still need to be developed. Plant breeding, by selecting low-Cd varieties, can be an important tool to reduce Cd in crops. Knowing the genotypic variation in Cd accumulation and furthering our understanding of the impact of root exudates composition on Cd accumulation in crops may provide valuable information for plant breeding. In this study, we selected nine spring wheat varieties and analysed the accumulation and distribution of Cd in shoots, roots, root surfaces and kernels in relation to their qualitative and quantitative composition of root exudates, determined by 1H-NMR (Proton Nuclear Magnetic Resonance). Results showed that the Cd concentration in shoots at an early stage could be used as a predictor for Cd concentration in kernels. Total Cd uptake was not correlated to the mobility of Cd in the rhizosphere, but total Cd was negatively correlated to Cd adsorbed at the root surface. Furthermore, (i) exudation of organic acids (primarily succinate and acetate) increased Cd concentration in shoots, and (ii) exudation of nucleosides, DNA (deoxyribonucleic acid) degradation products, increased Cd adsorption at the root surface. Therefore, root exudates composition should be taken into account when selecting for low-Cd wheat traits.

Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat.

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy and , respectively. Forward validation for DON indicated a predictive accuracy of and , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of and , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of and , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.

Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.

Multivariate genomic selection models improve prediction accuracy of agronomic traits in soft red winter wheat

AbstractUnivariate genomic selection (UVGS) is an important tool for increasing genetic gain and multivariate GS (MVGS), where correlated traits are included in genomic selection, which can improve genomic prediction accuracy. The objectives for this study were to evaluate MVGS approaches to improve prediction accuracy for four agronomic traits using a training population of 351 soft red winter wheat (Triticum aestivum L.) genotypes, evaluated over six site‐years in Arkansas from 2014 to 2017. Genotypes were phenotyped for grain yield, heading date, plant height, and test weight in both the training and test populations. In cross‐validations, various combinations of traits in MVGS models significantly improved prediction accuracy for test weight in comparison to a UVGS model. Marginal increases in predictive accuracy were also observed for grain yield, plant height, and heading date. Multivariate models which were identified as superior to the univariate case in cross‐validations were forward validated by predicting the advanced breeding nurseries of 2018 and 2020. In forward validation, consistent increases in accuracy were observed for test weight, plant height, and heading date using MVGS instead of UVGS. Overall, MVGS models improved prediction accuracies when correlated traits were included with the predicted response. The methods outlined in this study may be used to achieve higher prediction accuracies in unbalanced datasets over multiple environments.

Intelligent reprogramming of wheat for enhancement of fungal and nematode disease resistance using advanced molecular techniques.

Wheat (Triticum aestivum L.) diseases are major factors responsible for substantial yield losses worldwide, which affect global food security. For a long time, plant breeders have been struggling to improve wheat resistance against major diseases by selection and conventional breeding techniques. Therefore, this review was conducted to shed light on various gaps in the available literature and to reveal the most promising criteria for disease resistance in wheat. However, novel techniques for molecular breeding in the past few decades have been very fruitful for developing broad-spectrum disease resistance and other important traits in wheat. Many types of molecular markers such as SCAR, RAPD, SSR, SSLP, RFLP, SNP, and DArT, etc., have been reported for resistance against wheat pathogens. This article summarizes various insightful molecular markers involved in wheat improvement for resistance to major diseases through diverse breeding programs. Moreover, this review highlights the applications of marker assisted selection (MAS), quantitative trait loci (QTL), genome wide association studies (GWAS) and the CRISPR/Cas-9 system for developing disease resistance against most important wheat diseases. We also reviewed all reported mapped QTLs for bunts, rusts, smuts, and nematode diseases of wheat. Furthermore, we have also proposed how the CRISPR/Cas-9 system and GWAS can assist breeders in the future for the genetic improvement of wheat. If these molecular approaches are used successfully in the future, they can be a significant step toward expanding food production in wheat crops.

Multi-Environment Analysis of Biofortification Traits in Pre-bred Bread Wheat (Triticum aestivum L.)

Multi-Environment Analysis of Biofortification Traits in Pre-bred Bread Wheat (Triticum aestivum L.)

Genomic prediction with haplotype blocks in wheat.

Haplotype blocks might carry additional information compared to single SNPs and have therefore been suggested for use as independent variables in genomic prediction. Studies in different species resulted in more accurate predictions than with single SNPs in some traits but not in others. In addition, it remains unclear how the blocks should be built to obtain the greatest prediction accuracies. Our objective was to compare the results of genomic prediction with different types of haplotype blocks to prediction with single SNPs in 11 traits in winter wheat. We built haplotype blocks from marker data from 361 winter wheat lines based on linkage disequilibrium, fixed SNP numbers, fixed lengths in cM and with the R package HaploBlocker. We used these blocks together with data from single-year field trials in a cross-validation study for predictions with RR-BLUP, an alternative method (RMLA) that allows for heterogeneous marker variances, and GBLUP performed with the software GVCHAP. The greatest prediction accuracies for resistance scores for B. graminis, P. triticina, and F. graminearum were obtained with LD-based haplotype blocks while blocks with fixed marker numbers and fixed lengths in cM resulted in the greatest prediction accuracies for plant height. Prediction accuracies of haplotype blocks built with HaploBlocker were greater than those of the other methods for protein concentration and resistances scores for S. tritici, B. graminis, and P. striiformis. We hypothesize that the trait-dependence is caused by properties of the haplotype blocks that have overlapping and contrasting effects on the prediction accuracy. While they might be able to capture local epistatic effects and to detect ancestral relationships better than single SNPs, prediction accuracy might be reduced by unfavorable characteristics of the design matrices in the models that are due to their multi-allelic nature.

Genetic Attributes and Correlation Studies for Important Traits in Wheat Under Irrigated and Rainfed Conditions

Genetic Attributes and Correlation Studies for Important Traits in Wheat Under Irrigated and Rainfed Conditions

Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration.

Grain quality traits are the key factors that determine the economic value of wheat and are largely influenced by genetics and the environment. In this study, using a meta-analysis of quantitative trait loci (QTLs) and a comprehensive in silico transcriptome assessment, we identified key genomic regions and putative candidate genes for the grain quality traits protein content, gluten content, and test weight. A total of 508 original QTLs were collected from 41 articles on QTL mapping for the three quality traits in wheat published from 2003 to 2021. When these original QTLs were projected onto a high-density consensus map consisting of 14,548 markers, 313 QTLs resulted in the identification of 64 MQTLs distributed across 17 of the 21 chromosomes. Most of the meta-QTLs (MQTLs) were distributed on sub-genomes A and B. Compared with the original QTLs, the confidence interval (CI) of the MQTLs was smaller, with an average CI of 4.47 cM, while the projected QTLs CI was 11.13 cM (2.49-fold lower). The corresponding physical length of the MQTL ranged from 0.45 to 239.01 Mb. Thirty-one of these 64 MQTLs were validated in at least one genome-wide association study. In addition, five of the 64 MQTLs were selected and designated as core MQTLs. The 211 quality-related genes from rice were used to identify wheat homologs in MQTLs. In combination with transcriptional and omics analyses, 135 putative candidate genes were identified from 64 MQTL regions. The findings should contribute to a better understanding of the molecular genetic mechanisms underlying grain quality and the improvement of these traits in wheat breeding.

Genetic Variability, Heritability, Genetic Advance and Divergence Analysis in Wheat (Triticum aestivum L.)

Background: Wheat is a cereal crop that is widely farmed across the world and ranks first in global crop production. Wheat cultivation is the world’s most important food crop.The assessment of genetic divergence existing in the germplasm collections is very important to select genetically divergent parents for hybridization in a transgressive breeding programme for development of high yielding wheat varieties coupled with quality, multiple resistance and wider adaptability. Methods: The experiment on 21genotypes including two checks viz., HD 2967 and PBW 824 was conducted to work out the genetic variability, heritability, genetic advance and divergence analysis for yield and yield contributing traits in wheat (Triticum aestivum L.). The experiment was laid out in randomised block design (RBD) during Rabi, 2021-2022 at Lovely Professional University, Phagwara, Punjab. Result: The analysis of variance revealed mean sum of square due to genotypes were significant for all the characters indicating sufficient genetic variability among the genotypes. The magnitude of PCV and GCV were moderate for grain yield per plant and harvesting index, which suggests greater phenotypic and genotypic variance among the genotypes and responsiveness of the attributes for making further improvement by selection and indicating limited scope for improvement. High heritability coupled with high genetic advance observed for harvest-index thus these characters show additive gene action in their expression. In divergence analysis, maximum intra-cluster distance exhibited by cluster 2 followed by cluster 1among the different genotypes. The maximum inter-cluster distance between cluster 2 and 3 followed by cluster 2 and 7 and the minimum inter cluster distance was recorded between cluster 5 and 6 followed by cluster 1 and 3. It indicated that for crop improvement in wheat diversified genotypes may be selected from cluster 2 and cluster 3 to received maximum transgressive recombinants from segregating generations.

Coupled soil water stress and environmental effects on changing photosynthetic traits in wheat and maize

Coupled soil water stress and environmental effects on changing photosynthetic traits in wheat and maize

Analysis of the effect of GE interaction on the grain yield and its related traits in rain-fed Algerian durum wheat (Triticum turgidum L. var. durum) grown in contrasting environments

Selection for higher yield and wider adaptability are the most important tasks in crop breeding programs. (GE) interactions are commonly seen as one of the major barriers in plant breeding. The present work aims to assess the effects of GE interaction for the grain yield of 14 durum wheat varieties grown in rain-fed environments during 2014-2017 cropping seasons, and to analyze the relationships between 15 traits intra and inter-environments. Field trials were carried out in a randomized complete block design with four replicates. Grain yield data were analyzed using AMMI model. The combined analysis of variance showed that the effects of genotype, environment and their interactions were highly significant on the grain yield. Using CV and Pi index, GTA dur was the high yielding (32.5 q ha-1) and most stable variety across all the environments. Based on the inter-character correlation, the indirect selection of grain yield via the number of grains per m2 would be effective. Moreover, the inter-environment correlation of the studied variables confirms there was GE interaction and suggests that the best varieties should be chosen according to their specific adaptation. Cold environments differed from warm and moderate ones in the ranking of varieties. Indeed, Sétif site offers better possibilities for producing the Ofanto variety (39.9 q ha-1). Whereas, GTA dur and Simeto (30.9 q ha-1 and 29.7 q ha-1, respectively) prove to be the most efficient in terms of grain yield at Oued Smar and Khemis Miliana sites together.

Genomics‐assisted breeding: The next‐generation wheat breeding era

AbstractCommon wheat provides approximately 20% of the total dietary calorie intake of human beings. Recent technological advances in whole‐genome sequencing and their application in wheat and its progenitor species provide new opportunities to uncover the genetic variation of wheat traits and to accelerate the traditional breeding (TB) strategies in the context of genomics‐assisted breeding (GAB). Integration of TB, marker‐assisted selection (MAS) and genomic selection (GS) with high‐density SNP markers is expected to accelerate the breeding process and to further enhance genetic gain. With the assistance of the next‐ or third‐generation sequencing technologies and high‐throughput phenotyping platforms, GAB can now realistically be considered in the following area: (i) genome sequencing and high‐quality assembly to uncover new variations, (ii) whole‐genome sequence‐based association studies, (iii) gene function (or functional gene) identification and (iv) integration of whole genomic breeding information, utilizing multi‐omics data and different breeding strategies. We argue that GAB is becoming the preferred strategy in pursuit of new wheat cultivars with superior traits on high yielding, high nutritional quality, climate‐resilience and so on.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations)using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

Foliar application of putrescine alleviates terminal drought stress by modulating water status, membrane stability, and yield- related traits in wheat (Triticum aestivum L.).

Drought stress is one of the major limitations to the growth and yield productivity of cereal crops. It severely impairs the early growing and grain -filling stages of wheat. Therefore, cost- effective and eco-friendly approaches for alleviating drought stress in cereal crops are in high demand. Polyamines, such as putrescine, have a significant effect on improving crop yield under drought- stress conditions. Therefore, the current study was executed with the aim of exploring the significance of putrescine in alleviating drought stress and improving yield- related traits in wheat. Two distinct wheat cultivars (Fakhar-e-Bhakkar and Anaj-2017) were treated with the foliar application of different concentrations (control, 0.5, 1.0, and 1.5 PPM) of putrescine (put) under two moisture conditions (well- watered and terminal drought stress). The results demonstrate that the imposition of terminal drought stress significantly reduces different physiological and yield- related traits of both wheat cultivars. The reduction of relative water content (RWC%), membrane stability index (MSI), leaf area, tillers per plant, biomass yield, number of spikelets per spike, 100-grain weight, grain yield per plant, and straw yield was greater in Anaj-2017 than in Fakhar-e-Bhakkar cultivar. The results further explain that the foliar application of increased concentrations of putrescine from 0.0 to 1.0 PPM gradually improved physiological and yield traits, whereas these traits declined with the application of putrescine at the highest dose (1.5 PPM). The exogenous application of 1.0 PPM putrescine improved the relative water content (19.76%), specific leaf area (41.47%), and leaf area ratio (35.84%) compared with the controlled treatment. A higher grain yield (28.0g plant-1) and 100-grain weight (3.8g) were obtained with the foliar application of 1.0 PPM putrescine compared with controlled treatments. The findings of this study confirm the protective role of putrescine against terminal drought stress. It is therefore recommended to use putrescine at a concentration of 1.0 PPM, which could help alleviate terminal drought stress and attain better wheat yield.

Identification of Novel QTLs Associated with Frost Tolerance in Winter Wheat (Triticum aestivum L.).

Low temperature (cold) and freezing stress is a major problem during winter wheat growth. Low temperature tolerance (LT) is an important agronomic trait in winter wheat and determines the plants' ability to cope with below-freezing temperatures; thus, the development of cold-tolerant cultivars has become a major goal of breeding in various regions of the world. In this study, we sought to identify quantitative trait loci (QTL) using molecular markers related to freezing tolerance in winter. Thirty-four polymorphic markers among 425 SSR markers were obtained for the population, including 180 inbred lines of F12 generation wheat, derived from crosses (Norstar × Zagros) after testing with parents. LT50 is used as an effective selection criterion for identifying frost-tolerance genotypes. The progeny of individual F12 plants were used to evaluate LT50. Several QTLs related to wheat yield, including heading time period, 1000-seed weight, and number of surviving plants after overwintering, were identified. Single-marker analysis illustrated that four SSR markers with a total of 25% phenotypic variance determination were linked to LT50. Related QTLs were located on chromosomes 4A, 2B, and 3B. Common QTLs identified in two cropping seasons based on agronomical traits were two QTLs for heading time period, one QTL for 1000-seed weight, and six QTLs for number of surviving plants after overwintering. The four markers identified linked to LT50 significantly affected both LT50 and yield-related traits simultaneously. This is the first report to identify a major-effect QTL related to frost tolerance on chromosome 4A by the marker XGWM160. It is possible that some QTLs are closely related to pleiotropic effects that control two or more traits simultaneously, and this feature can be used as a factor to select frost-resistant lines in plant breeding programs.

On-farm assessment of yield and quality traits in durum wheat.

Durum wheat is key source of calories and nutrients for many regions of the world. Demand for it is predicted to increase. Further efforts are therefore needed to develop new cultivars adapted to different future scenarios. Developing a novel cultivar takes, on average, 10 years and advanced lines are tested during the process, in general, under standardized conditions. Although evaluating candidate genotypes for commercial release under different on-farm conditions is a strategy that is strongly recommended, its application for durum wheat and particularly for quality traits has been limited. This study evaluated the grain yield and quality performance of eight different genotypes across five contrasting farmers' fields over two seasons. Combining different analysis strategies, the most outstanding and stable genotypes were identified. The analyses revealed that some traits were mainly explained by the genotype effect (thousand kernel weight, flour sodium dodecyl sulfate sedimentation volume, and flour yellowness), others by the management practices (yield and grain protein content), and others (test weight) by the year effect. In general, yield showed the highest range of variation across genotypes, management practices, and years and test weight the narrowest range. Flour yellowness was the most stable trait across management conditions, while yield-related traits were the most unstable. We also determined the most representative and discriminative field conditions, which is a beneficial strategy when breeders are constrained in their ability to develop multi-environment experiments. We concluded that assessing genotypes in different farming systems is a valid and complementary strategy for on-station trials for determining the performance of future commercial cultivars in heterogeneous environments to improve the breeding process and resources.

TaSPL14-7A is a conserved regulator controlling plant architecture and yield traits in common wheat (Triticum aestivum L.).

Plant architecture is a crucial influencing factor of wheat yield and adaptation. In this study, we cloned and characterized TaSPL14, a homologous gene of the rice ideal plant architecture gene OsSPL14 in wheat. TaSPL14 homoeologs (TaSPL14-7A, TaSPL14-7B and TaSPL14-7D) exhibited similar expression patterns, and they were all preferentially expressed in stems at the elongation stage and in young spikes. Moreover, the expression level of TaSPL14-7A was higher than that of TaSPL14-7B and TaSPL14-7D. Overexpression of TaSPL14-7A in wheat resulted in significant changes in plant architecture and yield traits, including decreased tiller number and increased kernel size and weight. Three TaSPL14-7A haplotypes were identified in Chinese wheat core collection, and haplotype-based association analysis showed that TaSPL14-7A-Hap1/2 were significantly correlated with fewer tillers, larger kernels and higher kernel weights in modern cultivars. The haplotype effect resulted from a difference in TaSPL14-7A expression levels among genotypes, with TaSPL14-7A-Hap1/2 leading to higher expression levels than TaSPL14-7A-Hap3. As favorable haplotypes, TaSPL14-7A-Hap1/2 underwent positive selection during global wheat breeding over the last century. Together, the findings of our study provide insight into the function and genetic effects of TaSPL14 and provide a useful molecular marker for wheat breeding.

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

Key messageLinkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models.Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.

The Connection Between Molecular Indicators and Morphological Traits in Plants and Other Organisms by using A Special Indicator

Molecular and morphological indicators were measured to study the relationship, the RAPD marker was used as a molecular indicator, plant height, grain weight, and grain density were used as morphological traits in bread wheat, and 10 primers which have a type (op) were used in RAPD marker, 10 wheat cultivars were used as genotypes for studying. In study calculated, the number of bands which were appeared in every genotype, plant height, grain weight, and grain density as morphological traits, the results were: the C9 treatment gave a higher number of bands, but C6 which gave a higher value in plant height, C7 superior in grain weight and grain density. Mun indicator appeared high relationship between the C7 genotype and the number of fragments or genes in plant height while the C5 genotype gave less relationship, C7 genotype appeared high relationship between the number of fragments and grain weight trait according to Mun indicator while C5 genotype gave a less relationship. In a grain density trait, the C7 genotype gave a high value in Mun indicator comparison with the C5 genotype which gave less relationship, which means that all genotypes had variations in relationship with molecular indicator effects.