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

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

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

Composition of fecal microbiota in the control (n = 4), peach (n = 4) and plum (n = 4) groups at the phylum level.

<p>Bars represent median percentage of sequences. The y axis (percentage of sequences) was modified to also show the low abundant phyla.</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

Oligonucleotides used in this study for qPCR analysis.

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

Median (minimum-maximum) indices of bacterial diversity (Shannon Weaver and Chao1 3%) and richness (OTUs 3%) obtained from fecal samples of the control, peach and plum groups. <i>P</i> values come from the non-parametric Kruskal-Wallis.

<p>These estimates are based on 2489-sequences subsamples.</p

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

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

Quantitative real-time PCR results for Ruminococcaceae (family, A), <i>Faecalibacterium</i> (B), <i>Lactobacillus</i> (C), <i>Turicibacter</i> (D), Bacteroidetes (phylum, E) and <i>Bifidobacterium</i> (F) in the lean (n = 6), control obese (n = 8), peach (n = 7), and plum (n = 9) groups.

<p>Error bars represent the median and interquartile ranges (all results were normalized to qPCR data for total bacteria). Columns not sharing the same superscript are significantly different (p<0.05). *Significantly higher than all other groups.</p