Determining Optimal Nutrient Value through Leaf Tissue Analysis on Tomato

The number of vegetable producers using controlled environments and hydroponics for production in the United States has grown significantly in the last decade. There are many different hydroponic nutrient solutions on the market and these solutions have recommended rates of application that are specifically designed to suit the needs of newer hybrid lines. The management of heirloom tomatoes is different as each heirloom line has specific nutritional needs that may require more or less of an individual nutrient than the standard hybrid line. The optimization of Total Dissolved Solids (i.e. fertilizer solution) for each heirloom line is required to achieve the maximum production of saleable fruit. If not optimized, excess fertilization has the potential to become an economic cost and reduce sustainability. Each variety can require a significantly different solution strength in order to reach its maximum potential yield. This work developed methodologies required to determine optimal nutrient plans for three of the tomato varieties being grown at the Tennessee Tech University Oakley Farm greenhouses. Through leaf analysis the tomatoes were tested with an industry standard nutrient package. By testing the leaves, we found that the nutrients were in the normal range for “greenhouse tomatoes”, but it was obvious from observation that the heirloom varieties were all lacking individual nutrients like Calcium, Nitrogen and Magnesium. We feel that further research is necessary utilizing individual nutrients instead of a standard nutrient package to determine the appropriate levels of macro and micronutrients needed for the most popular commercial heirloom varieties

UNCOVERING THE ETIOLOGY OF AUTISM SPECTRUM DISORDERS: GENOMICS, BIOINFORMATICS, ENVIRONMENT, DATA COLLECTION AND EXPLORATION, AND FUTURE POSSIBILITIES

A clear and predictive understanding of the etiology of autism spectrum disorders (ASD), a group of neurodevelopmental disorders characterized by varying deficits in social interaction and communication as well as repetitive behaviors, has not yet been achieved. There remains active debate about the origins of autism, and the degree to which genetic and environmental factors, and their interplay, produce the range and heterogeneity of cognitive, developmental, and behavioral features seen in children carrying a diagnosis of ASD. Unlocking the causes of these complex developmental disorders will require a collaboration of experts in many disciplines, including clinicians, environmental exposure experts, bioinformaticists, geneticists, and computer scientists. For this workshop we invited prominent researchers in the field of autism, covering a range of topics from genetic and environmental research to ethical considerations. The goal of this workshop: provide an introduction to the current state of autism research, highlighting the potential for multi-disciplinary collaborations that rigorously evaluate the many potential contributors to ASD. It is further anticipated that approaches that successfully advance the understanding of ASD can be applied to the study of other common, complex disorders. Herein we provide a short review of ASD and the work of the invited speakers

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BACKGROUND: The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information. RESULTS: Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories. Via Biofilter 2.0 researchers can: • Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation • Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest • Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing. CONCLUSIONS: Biofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes

Limited Systemic Sclerosis Patients with Pulmonary Arterial Hypertension Show Biomarkers of Inflammation and Vascular Injury

Pulmonary arterial hypertension (PAH) is a common complication for individuals with limited systemic sclerosis (lSSc). The identification and characterization of biomarkers for lSSc-PAH should lead to less invasive screening, a better understanding of pathogenesis, and improved treatment.Forty-nine PBMC samples were obtained from 21 lSSc subjects without PAH (lSSc-noPAH), 15 lSSc subjects with PAH (lSSc-PAH), and 10 healthy controls; three subjects provided PBMCs one year later. Genome-wide gene expression was measured for each sample. The levels of 89 cytokines were measured in serum from a subset of subjects by Multi-Analyte Profiling (MAP) immunoassays. Gene expression clearly distinguished lSSc samples from healthy controls, and separated lSSc-PAH from lSSc-NoPAH patients. Real-time quantitative PCR confirmed increased expression of 9 genes (ICAM1, IFNGR1, IL1B, IL13Ra1, JAK2, AIF1, CCR1, ALAS2, TIMP2) in lSSc-PAH patients. Increased circulating cytokine levels of inflammatory mediators such as TNF-alpha, IL1-beta, ICAM-1, and IL-6, and markers of vascular injury such as VCAM-1, VEGF, and von Willebrand Factor were found in lSSc-PAH subjects.The gene expression and cytokine profiles of lSSc-PAH patients suggest the presence of activated monocytes, and show markers of vascular injury and inflammation. These genes and factors could serve as biomarkers of PAH involvement in lSSc

Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships.</p> <p>Results</p> <p>The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect.</p> <p>Conclusions</p> <p>To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see <url>https://chgr.mc.vanderbilt.edu/synthesisview</url>.</p

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico

Background The development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses. Results We conducted a large-scale simulation of randomly selected low-frequency protein-coding regions using twelve different balanced samples with an equal number of cases and controls as well as twenty-one unbalanced sample scenarios. We further explored statistical performance of different minor allele frequency thresholds and a range of genetic effect sizes. Our simulation results demonstrate that using an unbalanced study design has an overall higher type I error rate for both burden and dispersion tests compared with a balanced study design. Regression has an overall higher type I error with balanced cases and controls, while SKAT has higher type I error for unbalanced case-control scenarios. We also found that both type I error and power were driven by the number of cases in addition to the case to control ratio under large control group scenarios. Based on our power simulations, we observed that a SKAT analysis with case numbers larger than 200 for unbalanced case-control models yielded over 90% power with relatively well controlled type I error. To achieve similar power in regression, over 500 cases are needed. Moreover, SKAT showed higher power to detect associations in unbalanced case-control scenarios than regression. Conclusions Our results provide important insights into rare variant association study designs by providing a landscape of type I error and statistical power for a wide range of sample sizes. These results can serve as a benchmark for making decisions about study design for rare variant analyses