Additional file 2: Table S2. of BRCA2 carriers with male breast cancer show elevated tumour methylation

Methylation specific high resolution melting condition and primers (XLSX 36 kb

Data_Sheet_1_Multi-environment genomic prediction for soluble solids content in peach (Prunus persica).docx

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.</p

Additional file 1: Table S1. of BRCA2 carriers with male breast cancer show elevated tumour methylation

REMARK patient flow through study (XLSX 34 kb

Additional file 3: Figure S1. of BRCA2 carriers with male breast cancer show elevated tumour methylation

a) BRCA2 subgroup cluster analysis, b) BRCAX subgroup cluster analysis, c) Numbers and sizes of clusters within BRCA2 and BRCAX subgroups using various correlation coefficient cut-offs (listed on the x-axis), d) age of diagnosis of patient within Cluster A, B and other BRCA2 tumours (DOCX 234 kb

Additional file 1: of Copy number analysis by low coverage whole genome sequencing using ultra low-input DNA from formalin-fixed paraffin embedded tumor tissue

Figure S1. Profile of chromosome 7 for LPS1; Figure S2. Profile of chromosome 4 for LPS1; Figure S3.Comparison of measurement variability (MAPD); Figure S4. Alignment of reads from a WGA sample; Figure S5. Clustering of MCT-4 and MCT-6 5 ng, 20 ng, 100 ng (UA) and WGA; Figure S6. Correlation of FFPE block age with QC score. (PDF 823 kb

Heat map showing the most abundant operational taxonomic units (OTUs, at least 500 total) in the control (n = 4), peach (n = 4) and plum (n = 4) groups.

<p>Colors represent differences in relative abundance within samples (red: higher; white: median; blue: lower).</p

UPGMA hierarchical clustering using the unweighted Unifrac distance matrix.

<p>The colors represent different jackknife support: red (75–100% support); yellow (50–75%); green (25–50%); blue (<25% support). The bar represents community dissimilarity.</p

Principal Coordinate Analysis (PCoA) plots of the unweighted Unifrac distance matrix.

<p>The plots show each combination of the first three principal coordinates. Red (square): control; orange (circle): plum; blue (upright triangle): peach.</p

Rarefaction plots of 16S rRNA gene sequences obtained from fecal samples.

<p>Lines denote the average of each group; error bars represent the standard deviation. This analysis was carried out using a randomly selected 2489 sequences per sample.</p

Oligonucleotides used in this study for qPCR analysis.

<p>Oligonucleotides used in this study for qPCR analysis.</p