Scanning and filling : ultra-dense SNP genotyping combining genotyping-by-sequencing, SNP array and whole-genome resequencing data

Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96– 98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at 20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses

Collaborative Design Work in Technology Education

Peer reviewe

Increased Anion Channel Activity Is an Unavoidable Event in Ozone-Induced Programmed Cell Death

Ozone is a major secondary air pollutant often reaching high concentrations in urban areas under strong daylight, high temperature and stagnant high-pressure systems. Ozone in the troposphere is a pollutant that is harmful to the plant. generation by salicylic and abscisic acids. Anion channel activation was also shown to promote the accumulation of transcripts encoding vacuolar processing enzymes, a family of proteases previously reported to contribute to the disruption of vacuole integrity observed during programmed cell death.-induced programmed cell death. Because ion channels and more specifically anion channels assume a crucial position in cells, an understanding about the underlying role(s) for ion channels in the signalling pathway leading to programmed cell death is a subject that warrants future investigation

A survey of personality, stress and mental health among coal miners

To survey the status of personality traits, the extent of stress and mental health of coal miners. 1 454 workers (844 coal miners and 610 ordinary workers) were polled to complete the questionnaires, which included a self-developed Social Stressor Questionnaire, the NEO Five-Factor Inventory (NEO-FFI), the Symptom Checklist 90 (SCL-90) and the General Health Questionnaire (GHQ). Male, relatively younger and less educated are the salient demographic characters of the coal miners comparing to the ordinary workers. As of personality, they score higher in Neuroticism, lower in both Agreeableness and Conscientiousness. Whereas, there shows no significant difference on SCL-90