Recent advances on genome-wide association studies (GWAS) and genomic selection (GS); prospects for Fusarium head blight research in Durum wheat.

PURPOSE: Wheat is an important cereal crop that is cultivated in different parts of the world. The biotic stresses are the major concerns in wheat-growing nations and are responsible for production loss globally. The change in climate dynamics makes the pathogen more virulent in foothills and tropical regions. There is growing concern about FHB in major wheat-growing nations, and until now, there has been no known potential source of resistance identified in wheat germplasm. The plant pathogen interaction activates the cascade of pathways, genes, TFs, and resistance genes. Pathogenesis-related genes' role in disease resistance is functionally validated in different plant systems. Similarly, Genomewide association Studies (GWAS) and Genomic selection (GS) are promising tools and have led to the discovery of resistance genes, genomic regions, and novel markers. Fusarium graminearum produces deoxynivalenol (DON) mycotoxins in wheat kernels, affecting wheat productivity globally. Modern technology now allows for detecting and managing DON toxin to reduce the risk to humans and animals. This review offers a comprehensive overview of the roles played by GWAS and Genomic selection (GS) in the identification of new genes, genetic variants, molecular markers and DON toxin management strategies. METHODS: The review offers a comprehensive and in-depth analysis of the function of Fusarium graminearum virulence factors in Durum wheat. The role of GWAS and GS for Fusarium Head Blight (FHB) resistance has been well described. This paper provides a comprehensive description of the various statistical models that are used in GWAS and GS. In this review, we look at how different detection methods have been used to analyze and manage DON toxin exposure. RESULTS: This review highlights the role of virulent genes in Fusarium disease establishment. The role of genome-based selection offers the identification of novel QTLs in resistant wheat germplasm. The role of GWAS and GS selection has minimized the use of population development through breeding technology. Here, we also emphasized the function of recent technological developments in minimizing the impact of DON toxins and their implications for food safety.

MicroRNA-related markers associated with corpus luteum tropism in buffalo (Bubalus bubalis).

The study was undertaken to decipher the microRNA (miRNA) related markers associated with corpus luteum (CL) tropism in buffalo. The data obtained from deep sequencing of CL tissue from different physiological stages was mined in silico for the identification of miRNA-related markers (SSR & SNP). From the present study, 5 annotated and 176 unannotated miRNA were deduced while comparing with Bos taurus genome. In addition, 4 SSRs and 9 SNPs were deduced from the miRNA sequences. These SSRs were on the genes viz. Eukaryotic translation initiation factor 1-like, myocyte enhancer factor 2A, beta casein, T cell receptor gamma cluster 1. The SNP positions on genes viz. PYGO1 (Pygopus family PHD finger 1), LOC100337244 (Multidrug resistance-associated protein 4), FTH1 (Ferritin heavy chain 1), LOC788634 (BOLA class I histocompatibility antigen), PLXND1 (Plexin D1) and UBC (Ubiquitin C) show that these genes play critical role in CL tropism during estrous cycle in buffalo.

Identification and characterization of trait-specific SNPs using ddRAD sequencing in water buffalo.

Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation.

It is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.

Statistical approach for selection of biologically informative genes.

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies.

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.

The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ ( https://cran.r-project.org/web/packages/GSAQ ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.

An efficient algorithm for protein structure comparison using elastic shape analysis.

BACKGROUND: Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. RESULTS: An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. CONCLUSIONS: The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.

Synergistic regulatory networks mediated by microRNAs and transcription factors under drought, heat and salt stresses in Oryza Sativa spp.

BACKGROUND: Transcription factors (TFs) and microRNAs (miRNAs) are primary gene regulators within the cell. Regulatory mechanisms of these two main regulators are of great interest to biologists and may provide insights into the abiotic and biotic stresses. However, the interaction between miRNAs and TFs in a gene regulatory network (GRN) still remains uncovered. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, therefore study of interplay among these two regulators in gene regulation is important towards explaining the mechanism of different abiotic stresses. RESULT: Oligonucleotide microarrays containing 51,279 transcripts were used to identify total 133 salt responsive target genes regulated by 11 TFs that are also differentially regulated by miRNA under salinity, heat and drought stresses in Oryza sativa. TF's-target interactions which are most enriched in their downstream regulation were also identified. Many genes whose encoded proteins are implicated in response to light and radiation stimulus, hormone stimuli, oxidative stress, copper ion binding and electron transport were found to be enriched. However the majority were novel for the combined abiotic stress, which indicates that there are a great number of genes induced after the exposure these abiotic stresses and regulated by miRNA. CONCLUSION: Analysis of the expression profile data of Oryza provides clues regarding some putative cellular and molecular processes that are undertaken in response to these stresses. The study also identified a large number of candidate functional genes that appear to be constitutively involved in salt, drought and heat stresses tolerance. Further examination of these genes may enable the molecular basis of abiotic stress tolerance in Oryza, to be elucidated.

Effects of total solar eclipse on mental patients-a clinicobiochemical correlation.

Thirteen treated psychotic cases comprising of eight schizophrenic, four M.D.P. (manic type) and one M. D. P. (depressive type), who were clinically symptom free, were studied in respect of their hormones and behavioural abnormalities under effect of total solar eclipse. Of the hormones studied viz., T(2), T(4), TSH, Cortisol and prolactin, it is prolactin which showed an increase in titre associated with behavioural abnormalities in concerned patients during and immediately after the total solar eclipse. Deflection in both prolactin and behaviour gradually seemed to normalise over the post eclipse period.

A study of psychiatric morbidity of a rural community at an interval of ten years.

A field survey of psychiatric morbidity was conducted in a village by a door to door survey. The survey was repeated after 10 years by the same team and by the same method. The aim was to compare the rates of mental morbidity of the community at the interval of a decade and to trace out during the second survey all the persons - both ill and well - found in the first survey and to assess their mental health status.Though the total morbidity did not change from 1972 to 1982 there was a definite rise in the rates of morbidity of Hysteria and Anxiety showed a slight fall in 1982. The health population of 1972 traced and assessed in 1982 showed a lower rate of morbidity than the total population of 1982. The rate of recovery of the morbid stock assessed after ten years was about 29 % and as many as 14.8% of the morbid people died during this period. This death rate is much higher than that of healthy population assessed after ten years (h.9%). 47.8% of the cases detected in the first survey were found to be ill during the second survey richer with the same diagnosis or with a new diagnosis.

Psychiatric morbidity in an urban slum-dwelling community.

The present study concerns the prevalence of psychiatric morbidity in a slum area in Calcutta and its relation to certain demographic and social variables. The survey was carried out by a team of psychiatrists by a door-to-door enquiry. Significant relationship of mental morbidity were found with age, sex, caste, socioeconomic status and family size.