binned_subsets_2_14

contains the evenly distributed genotype subsets used in Spindel et al., 201

Additional file 1: Figure S1. of Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation

Genotype accuracy based on frequency of alleles imputed. Data represent results of variants imputed at 5,010 markers in 12 pedigree members with GBS data, imputed from individuals B, H, J, and F with WGS data. Genotypes were called using the “Threshold” method for calling genotypes, as described in the main text. (PDF 237 kb

MET_crfilt_.90_outliers_removed_for_RRBlup

Post-imputation GBS dataset used specifically for GS cross-validation in Spindel et al., 2015. Dataset contains all markers with call rates >= .9 and lines that were included in the GS analysis (i.e., sub-population outliers are removed from this dataset). The data are formatted for use with the R rrBLUP package

RYT_plotdata_by_GHID_corrected_PUBLIC_ACCESS

contains the raw phenotype data for genotyped individuals for 2009-2012, dry and wet seasons, used in Spindel et al., 2015

CV experiments.

<p>*AS = all seasons, DS = dry season only, WS = wet season only</p><p>CV experiments.</p

Mean accuracies of cross-validation for prediction of grain yield (Kg/ha) (top row), flowering time (days to 50% flowering) (middle row), and plant height (cm) (bottom row) in the 2012 dry season, using 10 selections of SNP subsets either distributed evenly throughout the genome (right column) or chosen at random (left column) and five different statistical methods, error bars constructed using 1 standard error from the mean.

<p>The training population consisted of data from years 2009–2011, both seasons per year.</p

Summary of best performing GS experiments for predicting grain yield (YLD), flowering time (FL), and plant height (PH) in the 2012 dry season (2012 DS) and the 2012 WS (2012 WS)

<p>TP = Training population, all = both dry and wet seasons for each year, DS only = dry seasons only for each year. VP = validation population. Accuracy = correlation of the predicted GEBV and the phenotype in the validation population, where the training population included the validation season/year for individuals not in the validation fold. Statistical methods not connected by the same letter performed significantly different from each other across experiments by pairwise students t (α = .05).</p><p>Summary of best performing GS experiments for predicting grain yield (YLD), flowering time (FL), and plant height (PH) in the 2012 dry season (2012 DS) and the 2012 WS (2012 WS)</p

Genome-Wide Association Study for Traits Related to Plant and Grain Morphology, and Root Architecture in Temperate Rice Accessions

<div><p>Background</p><p>In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding—after data editing—57000 polymorphic and informative SNPS, among which 54% were in genic regions.</p><p>Results</p><p>In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10⁻⁷ to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (<i>FDR</i> = 3 · 10⁻⁷) and for plant height on chromosome 6 (<i>FDR</i> = 0.011).</p><p>Conclusions</p><p>We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.</p></div

Phenotypic correlations among plant morphology, grain quality and root traits.

<p>Phenotypic correlations among plant morphology, grain quality and root traits.</p

Manhattan and Q-Q plots of GWAS results for shoot and root dry weight.

<p>Manhattan and Q-Q plots of GWAS results for shoot and root dry weight.</p