Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

Comparison of real world and core laboratory lupus anticoagulant results from the Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) clinical database and repository

Background: Variability remains a challenge in lupus anticoagulant (LA) testing. Objective: To validate LA test performance between Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) Core laboratories and examine agreement in LA status between Core and local/hospital laboratories contributing patients to this prospective registry. Methods: Five Core laboratories used the same reagents, analyzer type, protocols, and characterized samples for LA validation. Non-anticoagulated registry samples were retested at the corresponding regional Core laboratories and anticoagulated samples at a single Core laboratory. Categorical agreement and discrepancies in LA status between Core and local/hospital laboratories were analyzed. Results: Clotting times for the reference/characterized plasmas used for normalized ratios were similar between Core laboratories (CV <4%); precision and agreement for LA positive/negative plasma were similar (all CV ≤5%) in the four laboratories that completed both parts of the validation exercise; 418 registry samples underwent LA testing. Agreement for LA positive/negative status between Core and local/hospital laboratories was observed in 87% (115/132) non-anticoagulated and 77% (183/237) anticoagulated samples. However, 28.7% (120/418) of samples showed discordance between the Core and local/hospital laboratories or equivocal LA results. Some of the results of the local/hospital laboratories might have been unreliable in 24.7% (41/166) and 23% (58/252) of the total non-anticoagulated and anticoagulated samples, respectively. Equivocal results by the Core laboratory might have also contributed to discordance. Conclusions: Laboratories can achieve good agreement in LA performance by use of the same reagents, analyzer type, and protocols. The standardized Core laboratory results underpin accurate interpretation of APS ACTION clinical data

Genome-wide association study reveals a set of genes associated with resistance to the Mediterranean corn borer (Sesamia nonagrioides L.) in a maize diversity panel

[Background] Corn borers are the primary maize pest; their feeding on the pith results in stem damage and yield losses. In this study, we performed a genome-wide association study (GWAS) to identify SNPs associated with resistance to Mediterranean corn borer in a maize diversity panel using a set of more than 240,000 SNPs.[Results] Twenty five SNPs were significantly associated with three resistance traits: 10 were significantly associated with tunnel length, 4 with stem damage, and 11 with kernel resistance. Allelic variation at each significant SNP was associated with from 6 to 9% of the phenotypic variance. A set of genes containing or physically close to these SNPs are proposed as candidate genes for borer resistance, supported by their involvement in plant defense-related mechanisms in previously published evidence. The linkage disequilibrium decayed (r2 < 0.10) rapidly within short distance, suggesting high resolution of GWAS associations.[Conclusions] Most of the candidate genes found in this study are part of signaling pathways, others act as regulator of expression under biotic stress condition, and a few genes are encoding enzymes with antibiotic effect against insects such as the cystatin1 gene and the defensin proteins. These findings contribute to the understanding the complex relationship between plant-insect interactions.This work was supported by the National Plan for Research and Development of Spain (projects AGL2012-33415). L.F. Samayoa acknowledges a contract JAE-Predoc from the Spanish Council for Scientific Research (CSIC).Peer reviewe

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Abstract Objectives Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed