The Musa Germplasm Information System (MGIS) contains key information on Musa germplasm diversity, including passport data, botanical classification, morpho-taxonomic descriptors, molecular studies, plant photographs and GIS information on 4616 accessions managed in 21 collections around the world, making it the most extensive source of information on banana genetic resources.
A genetic correlation is the proportion of shared variance between two traits that is due to genetic causes; a phenotypic correlation is the degree to which two traits co-vary among individuals in a population. In the genomics era, while gene expression, genetic association, and network analysis provide unprecedented means to decode the genetic basis of complex phenotypes, it is important to recognize the possible effects genetic progress in one trait can have on other traits. This database is designed to collect all published livestock genetic/phenotypic trait correlation data, aimed at facilitating genetic network analysis or systems biology studies.
The Animal Quantitative Trait Loci (QTL) Database (Animal QTLdb) strives to collect all publicly available trait mapping data, i.e. QTL (phenotype/expression, eQTL), candidate gene and association data (GWAS), and copy number variations (CNV) mapped to livestock animal genomes, in order to facilitate locating and comparing discoveries within and between species. New data and database tools are continually developed to align various trait mapping data to map-based genome features such as annotated genes.
Animal genetic resources underpin the livestock sector's production and profitability. As a result livestock industries contribute billions of dollars to national economies. The Animal Germplasm Resources Information Network (A-GRIN) mission is to acquire, evaluate and preserve these strategic resources so that industry and the research community can have access to a broad array of genetic variability for: national security, introduction of genetic variation, corrective breeding, and various research initiatives.
Objectives for this study were to compare switchgrass yields from 2010–2011 on eight widely used and experimental upland and lowland genotype (whole plot) at two locations in Tennessee, to determine: (i) which harvest timing (split-plot) provides maximum yield; (ii) effects of harvest timing (mid-Sep, Oct, Nov, and late Oct) on overall total P and K removal; and, (iii) how results are affected by cultivar.
Genome datasets for Calonectria henricotiae and C. pseudonaviculata causing boxwood blight disease and related fungal species
This database includes genome datasets from Calonectria pathogens of boxwood and related species.
Genome analysis of the ubiquitous boxwood pathogen Pseudonectria foliicola: A small fungal genome with an increased cohort of genes associated with loss of virulence
Boxwood plants are affected by many different diseases caused by fungi. Some boxwood diseases are deadly and quickly kill the infected plants, but with others, the plant can survive and even thrive when infected. The fungus that causes volutella blight is the most common of these weak boxwood pathogens. Even the healthiest boxwood plants are infected by the volutella fungus, and often there are no signs that the plants are hurt by the infection. In order to understand why the volutella blight fungus is such a weak pathogen and to understand the genetic mechanisms it uses to interact with boxwood, the complete genome of the volutella fungus was sequenced and characterized. These datasets are generated from the genome sequence of Pseudonectria foliicola, strain ATCC13545, the fungus responsible for volutella disease of boxwood. Datasets include the nuclear genome and mitochondrial genome assemblies (sequenced using Illumina technology), the predicted gene model dataset generated using MAKER, the multiple sequence alignment of single-copy orthologs used for phylogenetic analysis, CMAP files generated from SimpleSynteny analysis of mitogenomes, and high quality photographic images.
The i5k Workspace @ NAL is a platform for communities around ‘orphaned’ arthropod genome projects to access, visualize, curate and disseminate their data.
The Next Generation Cassava Breeding (NEXTGEN Cassava) project aims to significantly increase the rate of genetic improvement in cassava breeding and unlock the full potential of cassava, a staple crop central to food security and livelihoods across Africa. The project will implement and empirically test a new breeding method known as Genomic Selection that relies on statistical modeling to predict cassava performance before field-testing, and dramatically accelerates the breeding cycle.
Panzea is an NSF-funded project called "Biology of Rare Alleles in Maize and its Wild Relatives". We are investigating the connection between phenotype (what we see) and genotype (the genes underlying the phenotype) - of complex traits in maize and its wild relative, teosinte, and specifically in how rare genetic variations contribute to overall plant function. These studies will enrich our knowledge of evolution, sustainable agriculture, and genetic diversity and conservation. Over the 10 years of the project, we have trained many new scientists at all levels and generated key resources for the public, teachers, and scientific researchers.