Evaluating dimensionality reduction for genomic prediction

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates

A comprehensive analysis of Trehalose-6-phosphate synthase (TPS) gene for salinity tolerance in chickpea (Cicer arietinum L.)

Soil salinity affects various crop cultivation but legumes are the most sensitive to salinity. Osmotic stress is the first stage of salinity stress caused by excess salts in the soil on plants which adversely affects the growth instantly. The Trehalose-6-phosphate synthase (TPS) genes play a key role in the regulation of abiotic stresses resistance from the high expression of different isoform. Selected genotypes were evaluated to estimate for salt tolerance as well as genetic variability at morphological and molecular level. Allelic variations were identified in some of the selected genotypes for the TPS gene. A comprehensive analysis of the TPS gene from selected genotypes was conducted. Presence of significant genetic variability among the genotypes was found for salinity tolerance. This is the first report of allelic variation of TPS gene from chickpea and results indicates that the SNPs present in these conserved regions may contribute largely to functional distinction. The nucleotide sequence analysis suggests that the TPS gene sequences were found to be conserved among the genotypes. Some selected genotypes were evaluated to estimate for salt tolerance as well as for comparative analysis of physiological, molecular and allelic variability for salt responsive gene Trehalose-6-Phosphate Synthase through sequence similarity. Allelic variations were identified in some selected genotypes for the TPS gene. It is found that Pusa362, Pusa1103, and IG5856 are the most salt-tolerant lines and the results indicates that the identified genotypes can be used as a reliable donor for the chickpea improvement programs for salinity tolerance

Chickpea (<i>Cicer arietinum</i> L.) Biology and Biotechnology: From Domestication to Biofortification and Biopharming

Chickpea (Cicer arietinum L.), the world’s second most consumed legume crop, is cultivated in more than 50 countries around the world. It is a boon for diabetics and is an excellent source of important nutrients such as vitamins A, C, E, K, B1–B3, B5, B6, B9 and minerals (Fe, Zn, Mg and Ca) which all have beneficial effects on human health. By 2050, the world population can cross 9 billion, and in order to feed the teaming millions, chickpea production should also be increased, as it is a healthy alternative to wheat flour and a boon for diabetics. Moreover, it is an important legume that is crucial for food, nutrition, and health security and the livelihood of the small-scale farmers with poor resources, in developing countries. Although marvelous improvement has been made in the development of biotic and abiotic stress-resistant varieties, still there are many lacunae, and to fulfill that, the incorporation of genomic technologies in chickpea breeding (genomics-assisted breeding, high-throughput and precise-phenotyping and implementation of novel breeding strategies) will facilitate the researchers in developing high yielding, climate resilient, water use efficient, salt-tolerant, insect/pathogen resistant varieties, acceptable to farmers, consumers, and industries. This review focuses on the origin and distribution, nutritional profile, genomic studies, and recent updates on crop improvement strategies for combating abiotic and biotic stresses in chickpea

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates

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Not AvailableFoot-and-mouth disease (FMD) endangers a large number of livestock populations across the globe being a highly contagious viral infection in wild and domestic cloven-hoofed animals. It adversely affects the socioeconomic status of millions of households. Vaccination has been used to protect animals against FMD virus (FMDV) to some extent but the effectiveness of available vaccines has been decreased due to high genetic variability in the FMDV genome. Another key aspect that the current vaccines are not favored is they do not provide the ability to differentiate between infected and vaccinated animals. Thus, RNA interference (RNAi) being a potential strategy to control virus replication, has opened up a new avenue for controlling the viral transmission. Hence, an attempt has been made here to establish the role of RNAi in therapeutic developments for FMD by computationally identifying (i) microRNA (miRNA) targets in FMDV using target prediction algorithms, (ii) targetable genomic regions in FMDV based on their dissimilarity with the host genome and, (iii) plausible anti-FMDV miRNA-like simulated nucleotide sequences (SNSs). The results revealed 12 mature host miRNAs that have 284 targets in 98 distinct FMDV genomic sequences. Wet-lab validation for anti-FMDV properties of 8 host miRNAs was carried out and all were observed to confer variable magnitude of antiviral effect. In addition, 14 miRBase miRNAs were found with better target accessibility in FMDV than that of Bostaurus. Further, 8 putative targetable regions having sense strand properties of siRNAs were identified on FMDV genes that are highly dissimilar with the host genome. A total of 16 SNSs having > 90% identity with mature miRNAs were also identified that have targets in FMDV genes. The information generated from this study is populated at http://bioinformatics.iasri.res.in/fmdisc/ to cater the needs of biologists, veterinarians and animal scientists working on FMD.Not Availabl

A Scintillating Journey of Genomics in Simplifying Complex Traits and Development of Abiotic Stress Resilient Chickpeas

Chickpea (Cicer arietinum L.) is an important cool season food legume cultivated in more than 55 countries across the globe. In the context of climate change, productivity of chickpea is hampered by higher incidence of abiotic and biotic stresses. Among abiotic stresses, drought, heat, cold and salinity are the most important yield limiting factors. Advanced genomics technologies have great potential to accelerate mapping, gene discovery, marker development and genomics-assisted breeding. Integration of precise phenotypic data along with sequence information will help in developing cultivars tolerant to various abiotic stresses. In this chapter, we discuss the impact of various abiotic stresses on chickpea production and provide an update on potential strategies to develop stress-tolerant chickpea cultivars. In addition, we also summarize the systematic efforts of simplifying the complex traits in chickpea as well as development of improved varieties with tolerance to abiotic stresses during last decade. In addition, we also highlight the emerging stresses and future strategies to combat the abiotic stresses