Genome wide association study (GWAS) for grain yield in rice cultivated under water deficit

Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach

The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7 cM. The average CI of the identified MQTLs (4.68 cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0 cM. The mean CI of the obtained MQTLs (2.33 cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1 cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.

Genome-wide association study of drought tolerance traits in sugar beet germplasms at the seedling stage

Introduction: Sugar beets are an important crop for global sugar production. Intense drought and the increasing lack of water resources pose a great threat to sugar beet cultivation. It is a priority to investigate favourable germplasms and functional genes to improve the breeding of drought tolerant plants. Methods: Thus, in this study, 328 sugar beet germplasms were used in a genome-wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with drought tolerance. Results: The results showed that under drought stress (9% PEG-6000), there were 11 significantly associated loci on chromosomes 2, 3, 5, 7, and 9 from the 108946 SNPs filtered using a mixed linear model (MLM). Genome-wide association analysis combined with qRT-PCR identified 13 genes that were significantly differentially expressed in drought-tolerant extreme materials. Discussion: These candidate genes mainly exhibited functions such as regulating sugar metabolism, maintaining internal environmental stability and participating in photosystem repair. This study provides valuable information for exploring the molecular mechanisms of drought tolerance and improvement in sugar beet.

Genome-Wide Association Mapping for Yield and Yield-Related Traits in Rice (Oryza Sativa L.) Using SNPs Markers

Rice (Oryza sativa L.) is a staple food for more than 50% of the world's population. Rice cultivar improvement is critical in order to feed the world's growing population. Improving yield is one of the main aims of rice breeders. However, yield is a complex quantitative trait controlled by many genes. The presence of genetic diversity is the key factor to improve the yield hence, the presence of diversity in any germplasm is important for yield improvement. In the current study, the rice germplasm was collected from Pakistan and the United States of America and a panel of 100 diverse genotypes was utilized to identify important yield and yield-related traits. For this, a genome-wide association study (GWAS) was performed to identify the genetic loci related to yield. The GWAS on the diverse germplasm will lead to the identification of new genes which can be utilized in the breeding program for improvement of yield. For this reason, firstly, the germplasm was phenotypically evaluated in two growing seasons for yield and yield-related traits. The analysis of variance results showed significant differences among traits which showed the presence of diversity in the current germplasm. Secondly, the germplasm was also genotypically evaluated using 10K SNP. Genetic structure analysis showed the presence of four groups which showed that enough genetic diversity was present in the rice germplasm to be used for association mapping analysis. The results of GWAS identified 201 significant marker trait associations (MTAs. 16 MTAs were identified for plant height, 49 for days to flowering, three for days to maturity, four for tillers per plant, four for panicle length, eight for grains per panicle, 20 unfilled grains per panicle, 81 for seed setting %, four for thousand-grain weight, five for yield per plot and seven for yield per hectare. Apart from this, some pleiotropic loci were also identified. The results showed that panicle length (PL) and thousand-grain weight (TGW) were controlled by a pleiotropic locus OsGRb23906 on chromosome 1 at 10,116,371 cM. The loci OsGRb25803 and OsGRb15974 on chromosomes 4 and 8 at the position of 14,321,111 cM and 6,205,816 cM respectively, showed pleiotropic effects for seed setting % (SS) and unfilled grain per panicle (UG/P). A locus OsGRb09180 on chromosome 4 at 19,850,601 cM was significantly linked with SS and yield/ha. Furthermore, gene annotation was performed, and results indicated that the 190 candidate genes or QTLs that closely linked with studied traits. These candidate genes and novel significant markers could be useful in marker-assisted gene selection and QTL pyramiding to improve rice yield and the selection of potential parents, recombinants and MTAs which could be used in rice breeding programs to develop high-yielding rice varieties for sustainable food security.

Candidate genes affecting stomatal density in rice (Oryza sativa L.) identified by genome-wide association

Stomata regulate photosynthesis and water loss. They have been an active subject of research for centuries, but our knowledge of the genetic components that regulate stomatal development in crops remains very limited in comparison to the model plant Arabidopsis thaliana. Leaf stomatal density was found to vary by over 2.5-fold across a panel of 235 rice accessions. Using GWAS, we successfully identified five different QTLs associated with stomatal density on chromosomes 2, 3, 9, and 12. Forty-two genes were identified within the haplotype blocks corresponding to these QTLs. Of these, nine genes contained haplotypes that were associated with different stomatal densities. These include a gene encoding a trehalose-6-phosphate synthase, an enzyme that has previously been associated with altered stomatal density in Arabidopsis, and genes encoding a B-BOX zinc finger family protein, a leucine-rich repeat family protein, and the 40 S ribosomal protein S3a, none of which have previously been linked to stomatal traits. We investigated further and show that a closely related B-BOX protein regulates stomatal development in Arabidopsis. The results of this study provide information on genetic associations with stomatal density in rice. The QTLs and candidate genes may be useful in future breeding programs for low or high stomatal density and, consequently, improved photosynthetic capacity, water use efficiency, or drought tolerance.

GmFtsH25 overexpression increases soybean seed yield by enhancing photosynthesis and photosynthates

Increasing plant photosynthetic capacity is a promising approach to boost yields, but it is particularly challenging in C₃ crops, such as soybean (Glycine max (L.) Merr.). Here, we identified GmFtsH25, encoding a member of the filamentation temperature-sensitive protein H protease family, as a major gene involved in soybean photosynthesis, using linkage mapping and a genome-wide association study. Overexpressing GmFtsH25 resulted in more grana thylakoid stacks in chloroplasts and increased photosynthetic efficiency and starch content, while knocking out GmFtsH25 produced the opposite phenotypes. GmFtsH25 interacted with photosystem I light harvesting complex 2 (GmLHCa2), and this interaction may contribute to the observed enhanced photosynthesis. GmFtsH25 overexpression lines had superior yield traits, such as yield per plant, compared to the wild type and knockout lines. Additionally, we identified an elite haplotype of GmFtsH25, generated by natural mutations, which appears to have been selected during soybean domestication. Our study sheds light on the molecular mechanism by which GmFtsH25 modulates photosynthesis and provides a promising strategy for improving the yields of soybean and other crops.

Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations

With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Climate Change Impact on Wheat Performance—Effects on Vigour, Plant Traits and Yield from Early and Late Drought Stress in Diverse Lines

Global climate change is threatening wheat productivity; improved yield under drought conditions is urgent. Here, diverse spring-wheat lines (modern, old and wheat-rye introgressions) were examined in an image-based early-vigour assay and a controlled-conditions (Biotron) trial that evaluated 13 traits until maturity. Early root vigour was significantly higher in the old Swedish lines (root length 8.50 cm) and introgressed lines with 1R (11.78 cm) and 1RS (9.91 cm) than in the modern (4.20 cm) and 2R (4.67 cm) lines. No significant correlation was noted between early root and shoot vigour. A higher yield was obtained under early drought stress in the 3R genotypes than in the other genotype groups, while no clear patterns were noted under late drought. Evaluating the top 10% of genotypes in terms of the stress-tolerance index for yield showed that root biomass, grains and spikes per plant were accountable for tolerance to early drought, while 1000-grain weight and flag-leaf area were accountable for tolerance to late drought. Early root vigour was determined as an important focus trait of wheat breeding for tolerance to climate-change-induced drought. The responsible genes for the trait should be searched for in these diverse lines. Additional drought-tolerance traits determined here need further elaboration to identify the responsible genes.

Genome-wide association study of cassava starch paste properties

An understanding of cassava starch paste properties (CSPP) can contribute to the selection of clones with differentiated starches. This study aimed to identify genomic regions associated with CSPP using different genome-wide association study (GWAS) methods (MLM, MLMM, and Farm-CPU). The GWAS was performed using 23,078 single-nucleotide polymorphisms (SNPs). The rapid viscoanalyzer (RVA) parameters were pasting temperature (PastTemp), peak viscosity (PeakVisc), hot-paste viscosity (Hot-PVisc), cool-paste viscosity (Cold-PVisc), final viscosity (FinalVis), breakdown (BreDow), and setback (Setback). Broad phenotypic and molecular diversity was identified based on the genomic kinship matrix. The broad-sense heritability estimates (h²) ranged from moderate to high magnitudes (0.66 to 0.76). The linkage disequilibrium (LD) declined to between 0.3 and 2.0 Mb (r² <0.1) for most chromosomes, except chromosome 17, which exhibited an extensive LD. Thirteen SNPs were found to be significantly associated with CSPP, on chromosomes 3, 8, 17, and 18. Only the BreDow trait had no associated SNPs. The regional marker-trait associations on chromosome 18 indicate a LD block between 2907312 and 3567816 bp and that SNP S18_3081635 was associated with SetBack, FinalVis, and Cold-PVisc (all three GWAS methods) and with Hot-PVisc (MLM), indicating that this SNP can track these four traits simultaneously. The variance explained by the SNPs ranged from 0.13 to 0.18 for SetBack, FinalVis, and Cold-PVisc and from 0.06 to 0.09 for PeakVisc and Hot-PVisc. The results indicated additive effects of the genetic control of Cold-PVisc, FinalVis, Hot-PVisc, and SetBack, especially on the large LD block on chromosome 18. One transcript encoding the glycosyl hydrolase family 35 enzymes on chromosome 17 and one encoding the mannose-p-dolichol utilization defect 1 protein on chromosome 18 were the most likely candidate genes for the regulation of CSPP. These results underline the potential for the assisted selection of high-value starches to improve cassava root quality through breeding programs.

Genome-Wide Association Analyses to Identify SNPs Related to Drought Tolerance

Drought stress is a serious agronomic problem resulting in significant yield losses globally. Breeding cultivars with drought tolerance is an important strategy that can be used to address this problem. Drought tolerance, however, is a complex multigenic trait, making advancements with conventional breeding approaches very challenging. This emphasizes the importance of dissecting the genetics of this trait and the identification and cloning of genes responsible for drought tolerance. With the rapid development of sequencing technologies and analytic methodologies, genome-wide association study (GWAS) has become an important tool for detecting natural variations underlying complex traits in crops. Identified loci can serve as targets for genomic selection or precise editing that enables the molecular design of new cultivars. This chapter describes the pipeline of statistical methods used in GWAS analysis, and covers field design, quality control, population structure control, association tests, and visualization of data. GWAS methodology used to dissect the genetic basis of drought tolerance is presented, and perspectives for optimizing the design and analysis of GWAS are discussed. The provided information serves as a valuable resource for researchers interested in GWAS technology.

Automatic Measurement of Morphological Traits of Typical Leaf Samples

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.

Jasmonates and Plant Salt Stress: Molecular Players, Physiological Effects, and Improving Tolerance by Using Genome-Associated Tools

Soil salinity is one of the most limiting stresses for crop productivity and quality worldwide. In this sense, jasmonates (JAs) have emerged as phytohormones that play essential roles in mediating plant response to abiotic stresses, including salt stress. Here, we reviewed the mechanisms underlying the activation and response of the JA-biosynthesis and JA-signaling pathways under saline conditions in Arabidopsis and several crops. In this sense, molecular components of JA-signaling such as MYC2 transcription factor and JASMONATE ZIM-DOMAIN (JAZ) repressors are key players for the JA-associated response. Moreover, we review the antagonist and synergistic effects between JA and other hormones such as abscisic acid (ABA). From an applied point of view, several reports have shown that exogenous JA applications increase the antioxidant response in plants to alleviate salt stress. Finally, we discuss the latest advances in genomic techniques for the improvement of crop tolerance to salt stress with a focus on jasmonates.

Drought Responsive Putative Marker-Trait Association in Tall Fescue as Influenced by the Presence of a Novel Endophyte

Tall fescue (Festuca arundinacea Schreb.) is one of the most important cool-season perennial obligatory outcrossing forage grasses in the United States. The production and persistence of tall fescue is significantly affected by drought in the south-central United States. Shoot-specific endophyte (Epichloë coenophiala)-infected tall fescue showed superior performance under both biotic and abiotic stress conditions. We performed a genome-wide association analysis using clonal pairs of novel endophyte AR584-positive (EP) and endophyte-free (EF) tall fescue populations consisting of 205 genotypes to identify marker-trait associations (MTAs) that contribute to drought tolerance. The experiment was performed through November 2014 to June 2018 in the field, and phenotypic data were taken on plant height, plant spread, plant vigor, and dry biomass weight under natural summer conditions of sporadic drought. Genotyping-by-sequencing of the population generated 3,597 high quality single nucleotide polymorphisms (SNPs) for further analysis. We identified 26 putative drought responsive MTAs (17 specific to EP, eight specific to EF, and one in both EP and EF populations) and nine of them (i.e., V.ep_10, S.ef_12, V.ep_27, HSV.ef_31, S.ep_30, SV.ef_32, V.ep_68, V.ef_56, and H.ef_57) were identified within 0.5 Mb region in the tall fescue genome (44.5-44.7, 75.3-75.8, 77.5-77.9 and 143.7-144.2 Mb). Using 26 MTAs, 11 tall fescue genotypes were selected for subsequent study to develop EP and EF drought tolerant tall fescue populations. Ten orthologous genes (six for EP and four for EF population) were identified in Brachypodium genome as potential candidates for drought tolerance in tall fescue, which were also earlier reported for their involvement in abiotic stress tolerance. The MTAs and candidate genes identified in this study will be useful for marker-assisted selection in improving drought tolerance of tall fescue as well opening avenue for further drought study in tall fescue.

GWAS to Identify Genetic Loci for Resistance to Yellow Rust in Wheat Pre-Breeding Lines Derived From Diverse Exotic Crosses

Yellow rust (YR) or stripe rust, caused by Puccinia striformis f. sp tritici Eriks (Pst), is a major challenge to resistance breeding in wheat. A genome wide association study (GWAS) was performed using 22,415 single nucleotide polymorphism (SNP) markers and 591 haplotypes to identify genomic regions associated with resistance to YR in a subset panel of 419 pre-breeding lines (PBLs) developed at International Center for Maize and Wheat Improvement (CIMMYT). The 419 PBLs were derived from an initial set of 984 PBLs generated by a three-way crossing scheme (exotic/elite1//elite2) among 25 best elites and 244 exotics (synthetics, landraces) from CIMMYT's germplasm bank. For the study, 419 PBLs were characterized with 22,415 high-quality DArTseq-SNPs and phenotyped for severity of YR disease at five locations in Mexico. A population structure was evident in the panel with three distinct subpopulations, and a genome-wide linkage disequilibrium (LD) decay of 2.5 cM was obtained. Across all five locations, 14 SNPs and 7 haplotype blocks were significantly (P < 0.001) associated with the disease severity explaining 6.0 to 14.1% and 7.9 to 19.9% of variation, respectively. Based on average LD decay of 2.5 cM, identified 14 SNP-trait associations were delimited to seven quantitative trait loci in total. Seven SNPs were part of the two haplotype blocks on chromosome 2A identified in haplotypes-based GWAS. In silico analysis of the identified SNPs showed hits with interesting candidate genes, which are related to pathogenic process or known to regulate induction of genes related to pathogenesis such as those coding for glunolactone oxidase, quinate O-hydroxycinnamoyl transferase, or two-component histidine kinase. The two-component histidine kinase, for example, acts as a sensor in the perception of phytohormones ethylene and cytokinin. Ethylene plays a very important role in regulation of multiple metabolic processes of plants, including induction of defense mechanisms mediated by jasmonate. The SNPs linked to the promising genes identified in the study can be used for marker-assisted selection.

A Genome-Wide Association Study Revealed Key SNPs/Genes Associated With Salinity Stress Tolerance In Upland Cotton

Millions of hectares of land are too saline to produce economically valuable crop yields. Salt tolerance in cotton is an imperative approach for improvement in response to ever-increasing soil salinization. Little is known about the genetic basis of salt tolerance in cotton at the seedling stage. To address this issue, a genome-wide association study (GWAS) was conducted on a core collection of a genetically diverse population of upland cotton (Gossypium hirsutum L.) comprising of 419 accessions, representing various geographic origins, including China, USA, Pakistan, the former Soviet Union, Chad, Australia, Brazil, Mexico, Sudan, and Uganda. Phenotypic evaluation of 7 traits under control (0 mM) and treatment (150 mM) NaCl conditions depicted the presence of broad natural variation in the studied population. The association study was carried out with the efficient mixed-model association eXpedited software package. A total of 17,264 single-nucleotide polymorphisms (SNPs) associated with different salinity stress tolerance related traits were found. Twenty-three candidate SNPs related to salinity stress-related traits were selected. Final key SNPs were selected based on the r² value with nearby SNPs in a linkage disequilibrium (LD) block. Twenty putative candidate genes surrounding SNPs, A10_95330133 and D10_61258588, associated with leaf relative water content, RWC_150, and leaf fresh weight, FW_150, were identified, respectively. We further validated the expression patterns of twelve candidate genes with qRT-PCR, which revealed different expression levels in salt-tolerant and salt-sensitive genotypes. The results of our GWAS provide useful knowledge about the genetic control of salt tolerance at the seedling stage, which could assist in elucidating the genetic and molecular mechanisms of salinity stress tolerance in cotton plants.

Analysis of Drought Tolerance and Associated Traits in Upland Cotton at the Seedling Stage

(1) Background: Upland cotton (Gossypium hirsutum L.) is the most important natural fiber worldwide, and it is extensively planted and plentifully used in the textile industry. Major cotton planting regions are frequently affected by abiotic stress, especially drought stress. Drought resistance is a complex, quantitative trait. A genome-wide association study (GWAS) constitutes an efficient method for dissecting the genetic architecture of complex traits. In this study, the drought resistance of a population of 316 upland cotton accessions was studied via GWAS. (2) Methods: GWAS methodology was employed to identify relationships between molecular markers or candidate genes and phenotypes of interest. (3) Results: A total of 8, 3, and 6 SNPs were associated with the euphylla wilting score (EWS), cotyledon wilting score (CWS), and leaf temperature (LT), respectively, based on a general linear model and a factored spectrally transformed linear mixed model. For these traits, 7 QTLs were found, of which 2 each were located on chromosomes A05, A11, and D03, and of which 1 was located on chromosome A01. Importantly, in the candidate regions WRKY70, GhCIPK6, SnRK2.6, and NET1A, which are involved in the response to abscisic acid (ABA), the mitogen-activated protein kinase (MAPK) signaling pathway and the calcium transduction pathway were identified in upland cotton at the seedling stage under drought stress according to annotation information and linkage disequilibrium (LD) block analysis. Moreover, RNA sequencing analysis showed that WRKY70, GhCIPK6, SnRK2.6, and NET1A were induced by drought stress, and the expression of these genes was significantly different between normal and drought stress conditions. (4) Conclusions: The present study should provide some genomic resources for drought resistance in upland cotton. Moreover, the germplasm of the different phenotypes, the detected SNPs and, the potential candidate genes will be helpful for molecular marker-assisted breeding studies about increased drought resistance in upland cotton.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality

BACKGROUND

The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.

RESULTS

The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI 2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds.

CONCLUSION

This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.

RNA-seq reveals the involvement of key genes for aerobic adaptation in rice

Adaptation of rice to the aerobic condition is needed to cope with the water scarcity as well as to ensure sustainable yield in future. To understand the molecular mechanisms responsible for aerobic adaptation in rice, we performed RNA-seq analysis of root and shoot i.e. developing panicle tissues at panicle initiation stage in two cultivars adapted to aerobic (CR Dhan 202) and traditional transplanted anaerobic (BPT 5204) conditions. The RNA-seq data emanated from 1.65 billion clean reads with approximately 37 million reads per sample. The number of differentially expressed transcripts was higher in the root than that in the shoot under both aerobic and anaerobic conditions. The transcription factors viz. MADS4, MADS5, MADS6, MADS7, MADS15 and transporters involved in sugar (SWEET3A) and nutrient uptake (PHT1;6, MDR-like ABC and vacuolar iron transporter homolog 2) were highly and uniquely expressed in the aerobic adapted cultivar (AAC) CR Dhan 202 under aerobic condition indicating their role in adaptation. The hormones such as ethylene and abscisic acid might be significantly involved in imparting aerobic adaptation. The higher expression of root related genes in the AAC under aerobic conditions suggests the involvement and sensitivity of roots to the water limiting condition. The metabolic activities are also more pronounced in the roots which impart rigorous plant establishment under the aerobic condition. The presence of alternative splice variants in the transcripts viz. Tetratrico peptide repeat (TPR) domain containing protein and GOLDEN2-LIKE1 (GLK1) additionally confirms that post transcriptional regulation is also crucial for aerobic adaptation. The QTLs related to root traits and stress tolerance harboring the uniquely expressed genes, which were identified in the present study can be deployed in molecular breeding programs to develop elite, high yielding aerobic rice cultivars.

Genome-wide association study revealed that the TaGW8 gene was associated with kernel size in Chinese bread wheat

Using Wheat 90 K SNP assay, kernel-related traits of Chinese bread wheat were used to perform association mapping in 14 environments by GWAS. Results indicated that 996 and 953 of 4417 and 3172 significant SNPs for kernel length and thousand-kernel weight were located on the chromosome 7B. Haplotype analysis of these SNPs on 7B generated the block containing the predicted TaGW8-B1 gene. TaGW8-B1 gene was further cloned by sequencing in bread wheat and a 276-bp InDel was found in the first intron. TaGW8-B1 without and with the 276-bp InDel were designated as TaGW8-B1a and TaGW8-B1b, respectively. Analysis of agronomic traits indicated that cultivars with TaGW8-B1a possessed significantly wider kernel width, significantly more kernel number per spike, longer kernel length, higher thousand-kernel weight and more spikelet number per spike than cultivars with TaGW8-B1b. Furthermore, cultivars with TaGW8-B1a possessed significantly higher yield than cultivars with TaGW8-B1b. Therefore, TaGW8-B1a was considered as a potentially superior allele. Meanwhile, TaGW8-B1a possessed a significantly higher expression level than TaGW8-B1b in mature seeds by qRT-PCR. It possibly suggested that the high expression of TaGW8-B1 was positively associated with kernel size in bread wheat. Distribution of TaGW8-B1 allele indicated that TaGW8-B1a has been positively selected in Chinese wheat.

Overexpression of a phospholipase (OsPLDα1) for drought tolerance in upland rice (Oryza sativa L.)

This work aimed to evaluate the drought tolerance of transformed plants of the cultivar BRSMG Curinga that overexpress the rice phospholipase D α1 (OsPLDα1) gene. The productivity of independent transformation event plants of the OsPLDα1 gene was evaluated in an experiment where 19 days of water deficit were applied at the reproductive stage, a very strict growing condition for upland rice. The non-genetically modified cultivar (NGM) under drought treatment reduced productivity by 89% compared with that under irrigated treatment, whereas transformed plants (PLDα1_E2) reduced productivity by only 41%. After the drought treatment, the PLDα1_E2 plants productivity was five times greater than that of the NGM plant. Moreover, no adverse effects on growth and development of the transgenic plants were observed. Seven days after the resumption of irrigation, PLDα1_E2 plants had higher stomatal conductance, greater photosynthetic rate, and transpiration rate than did NGM plants, as well as a higher expression level of the OsPLDα1 gene. A delay in the senescence process was observed in these PLDα1_E2 plants, and this was determined for the recovery of photosynthesis, with greater expression of the Rubisco and lower expression of the SOD. This finding was suggestive of decreased oxidative stress, probably due to gas exchange by the partial closure of the stomata of these transformed plants, which prevented the formation of reactive oxygen species. OsPLDα1 gene overexpression resulted in a reduction in production loss under severe water deficit and revealed a possibility for the development of upland rice cultivars that are more tolerant to extreme drought conditions.

Genome-wide association reveals novel genomic loci controlling rice grain yield and its component traits under water-deficit stress during the reproductive stage

A diversity panel comprising of 296 indica rice genotypes was phenotyped under non-stress and water-deficit stress conditions during the reproductive stage in the 2013 and 2014 dry seasons (DSs) at IRRI, Philippines. We investigated the genotypic variability for grain yield, yield components, and related traits, and conducted genome-wide association studies (GWAS) using high-density 45K single nucleotide polymorphisms. We detected 38 loci in 2013 and 64 loci in 2014 for non-stress conditions and 69 loci in 2013 and 55 loci in 2014 for water-deficit stress. Desynchronized flowering time confounded grain yield and its components under water-deficit stress in the 2013 experiment. Statistically corrected grain yield and yield component values using days to flowering helped to detect 31 additional genetic loci for grain yield, its components, and the harvest index in 2013. There were few overlaps in the detected loci between years and treatments, and when compared with previous studies using the same panel, indicating the complexity of yield formation under stress. Nevertheless, our analyses provided important insights into the potential links between grain yield with seed set and assimilate partitioning. Our findings demonstrate the complex genetic architecture of yield formation and we propose exploring the genetic basis of less complex component traits as an alternative route for further yield enhancement.

Genome-Wide Association Studies Reveal Genetic Variation and Candidate Genes of Drought Stress Related Traits in Cotton (Gossypium hirsutum L.)

Cotton is an important industrial crop worldwide and upland cotton (Gossypium hirsutum L.) is most widely cultivated in the world. Due to ever-increasing water deficit, drought stress brings a major threat to cotton production. Thus, it is important to reveal the genetic basis under drought stress and develop drought tolerant cotton cultivars. To address this issue, in present study, 319 upland cotton accessions were genotyped by 55,060 single nucleotide polymorphisms (SNPs) from high-density CottonSNP80K array and phenotyped nine drought tolerance related traits. The two datasets were used to identify quantitative trait nucleotides (QTNs) for the above nine traits using multi-locus random-SNP-effect mixed linear model method. As a result, a total of 20 QTNs distributed on 16 chromosomes were found to be significantly associated with six drought tolerance related traits. Of the 1,326 genes around the 20 QTNs, 205 were induced after drought stress treatment, and 46 were further mapped to Gene ontology (GO) term "response to stress." Taken genome-wide association study (GWAS) analysis, RNA-seq data and qRT-PCR verification, four genes, RD2 encoding a response to desiccation 2 protein, HAT22 encoding a homeobox-leucine zipper protein, PIP2 encoding a plasma membrane intrinsic protein 2, and PP2C encoding a protein phosphatase 2C, were proposed to be potentially important for drought tolerance in cotton. These results will deepen our understanding of the genetic basis of drought stress tolerance in cotton and provide candidate markers to accelerate the development of drought-tolerant cotton cultivars.

Multiple similarly effective solutions exist for biomedical feature selection and classification problems

Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.