Exome sequencing and advances in crop improvement

A Data-Distribution and Successive Spline Points based discretization approach for evolving gene regulatory networks from scRNA-Seq time-series data using Cartesian Genetic Programming

The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases.

Genetic effects on longitudinal cognitive decline during the early stages of Alzheimer's disease

Cognitive decline in early-stage Alzheimer's disease (AD) may depend on genetic variability. In the Swedish BioFINDER study, we used polygenic scores (PGS) (for AD, intelligence, and educational attainment) to predict longitudinal cognitive change (measured by mini-mental state examination (MMSE) [primary outcome] and other cognitive tests) over a mean of 4.2 years. We included 260 β-amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 121 Aβ-positive CU (preclinical AD), 50 Aβ-negative mild cognitive impairment (MCI) patients, and 127 Aβ-positive MCI patients (prodromal AD). Statistical significance was determined at Bonferroni corrected p value < 0.05. The PGS for intelligence (beta = 0.1, p = 2.9e-02) was protective against decline in MMSE in CU and MCI participants regardless of Aβ status. The polygenic risk score for AD (beta =  - 0.12, p = 9.4e-03) was correlated with the rate of change in MMSE and was partially mediated by Aβ-pathology (mediation effect 20%). There was no effect of education PGS on cognitive measures. Genetic variants associated with intelligence mitigate cognitive decline independent of Aβ-pathology, while effects of genetic variants associated with AD are partly mediated by Aβ-pathology.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects

Legumes play a vital role in ensuring global nutritional food security and improving soil quality through nitrogen fixation. Accelerated higher genetic gains is required to meet the demand of ever increasing global population. In recent years, speedy developments have been witnessed in legume genomics due to advancements in next-generation sequencing (NGS) and high-throughput genotyping technologies. Reference genome sequences for many legume crops have been reported in the last 5 years. The availability of the draft genome sequences and re-sequencing of elite genotypes for several important legume crops have made it possible to identify structural variations at large scale. Availability of large-scale genomic resources and low-cost and high-throughput genotyping technologies are enhancing the efficiency and resolution of genetic mapping and marker-trait association studies. Most importantly, deployment of molecular breeding approaches has resulted in development of improved lines in some legume crops such as chickpea and groundnut. In order to support genomics-driven crop improvement at a fast pace, the deployment of breeder-friendly genomics and decision support tools seems appear to be critical in breeding programs in developing countries. This review provides an overview of emerging genomics and informatics tools/approaches that will be the key driving force for accelerating genomics-assisted breeding and ultimately ensuring nutritional and food security in developing countries.

Plant exomics: concepts, applications and methodologies in crop improvement

Molecular breeding has a crucial role in improvement of crops. Conventional breeding techniques have failed to ameliorate food production. Next generation sequencing has established new concepts of molecular breeding. Exome sequencing has proven to be a significant tool for assessing natural evolution in plants, studying host pathogen interactions and betterment of crop production as exons assist in interpretation of allelic variation with respect to their phenotype. This review covers the platforms for exome sequencing, next generation sequencing technologies that have revolutionized exome sequencing and led toward development of third generation sequencing. Also discussed in this review are the uses of these sequencing technologies to improve wheat, rice and cotton yield and how these technologies are used in exploring the biodiversity of crops, providing better understanding of plant-host pathogen interaction and assessing the process of natural evolution in crops and it also covers how exome sequencing identifies the gene pool involved in symbiotic and other co-existential systems. Furthermore, we conclude how integration of other methodologies including whole genome sequencing, proteomics, transcriptomics and metabolomics with plant exomics covers the areas which are left untouched with exomics alone and in the end how these integration will transform the future of crops.

Genome-wide patterns of genetic variation in two domestic chickens

Domestic chickens are excellent models for investigating the genetic basis of phenotypic diversity, as numerous phenotypic changes in physiology, morphology, and behavior in chickens have been artificially selected. Genomic study is required to study genome-wide patterns of DNA variation for dissecting the genetic basis of phenotypic traits. We sequenced the genomes of the Silkie and the Taiwanese native chicken L2 at ∼23- and 25-fold average coverage depth, respectively, using Illumina sequencing. The reads were mapped onto the chicken reference genome (including 5.1% Ns) to 92.32% genome coverage for the two breeds. Using a stringent filter, we identified ∼7.6 million single-nucleotide polymorphisms (SNPs) and 8,839 copy number variations (CNVs) in the mapped regions; 42% of the SNPs have not found in other chickens before. Among the 68,906 SNPs annotated in the chicken sequence assembly, 27,852 were nonsynonymous SNPs located in 13,537 genes. We also identified hundreds of shared and divergent structural and copy number variants in intronic and intergenic regions and in coding regions in the two breeds. Functional enrichments of identified genetic variants were discussed. Radical nsSNP-containing immunity genes were enriched in the QTL regions associated with some economic traits for both breeds. Moreover, genetic changes involved in selective sweeps were detected. From the selective sweeps identified in our two breeds, several genes associated with growth, appetite, and metabolic regulation were identified. Our study provides a framework for genetic and genomic research of domestic chickens and facilitates the domestic chicken as an avian model for genomic, biomedical, and evolutionary studies.

Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.