Genetic gains for heat tolerance in potato in three cycles of recurrent selection

Practically all potato cultivars grown in Brazil are native to Europe and not fully adapted to the tropical conditions.The purpose of this study was to estimate the genetic gains of three cycles of recurrent selection for heat tolerance in potato. The basepopulation in this study consisted of five Brazilian and five heat-tolerant clones. In the winter of 2006 and rainy growing season of 2007 103 clones were evaluated (eight clones of the base population, 29 of the first cycle, 32 and 30 of the second and third recurrent selection cycle, respectively, and four control cultivars). The genetic gains for tuber traits in both growing seasons were 37.8 %(yield), 13.0 % (weight), 32.4 % (percent of large tubers), 0.8 % (tuber specific gravity) and 16.6 % (general tuber appearance).The percentage of physiological disorders (second-growth tubers and cracking) was also reduced by selection

A community-based validation study of the short-form 36 version 2 philippines (tagalog) in two cities in the philippines

10.1371/journal.pone.0083794PLoS ONE812-POLN

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

10.1186/1471-2288-12-48BMC Medical Research Methodology12

Data management for prospective research studies using SAS® software

<p>Abstract</p> <p>Background</p> <p>Maintaining data quality and integrity is important for research studies involving prospective data collection. Data must be entered, erroneous or missing data must be identified and corrected if possible, and an audit trail created.</p> <p>Methods</p> <p>Using as an example a large prospective study, the Missouri Lower Respiratory Infection (LRI) Project, we present an approach to data management predominantly using SAS software. The Missouri LRI Project was a prospective cohort study of nursing home residents who developed an LRI. Subjects were enrolled, data collected, and follow-ups occurred for over three years. Data were collected on twenty different forms. Forms were inspected visually and sent off-site for data entry. SAS software was used to read the entered data files, check for potential errors, apply corrections to data sets, and combine batches into analytic data sets. The data management procedures are described.</p> <p>Results</p> <p>Study data collection resulted in over 20,000 completed forms. Data management was successful, resulting in clean, internally consistent data sets for analysis. The amount of time required for data management was substantially underestimated.</p> <p>Conclusion</p> <p>Data management for prospective studies should be planned well in advance of data collection. An ongoing process with data entered and checked as they become available allows timely recovery of errors and missing data.</p

Ethnic differences in the association of fat and lean mass with bone mineral density in the Singapore population

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An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores