Bian_2016_GWAS_and_Genomic_Prediction_Data_Supplement

Bian_2016_GWAS_and_Genomic_Prediction_Data_Supplemen

Ampliseq workflow.

<p>Target regions are selected and amplicons are designed to cover the region. Amplicons are then partially digested, and adapters and barcodes are ligated onto amplicons. Samples are then equalized and pooled. Sequencing was completed on an Ion Torrent PGM™.</p

Simulations and alignments.

<p>The first two columns show the alignment statistics for the Bowtie2 and BWA-MEM alignments of the simulated data. The third column shows the alignment statistics for the actual data. For the actual data, the alignment statistics were averaged across all samples so that the per sample average is shown in the table.</p

Regions targeted by Ampliseq design.

<p>Targeted regions of interest are shown, along with the number of amplicons and coverage for each region.</p

Multidimensional scaling applied to all samples at all resequenced loci.

<p>The plot includes five samples per accession and control samples.</p

Venn diagram comparing different SNP datasets.

<p>Venn diagram representing the relationships among SNPs called using GATK and SAMtools, and the HapMap3 SNPs in the same genomic regions. Novel variants are those unique to the GATK and SAMtools datasets.</p

Number of reads per amplicon versus amplicon length.

<p>The number of reads per amplicon has little relationship with amplicon length. Long amplicons are represented.</p

Supplemental material for Ovenden et al., 2018

<div>The supplementary files contain the data used in the study "Accounting for genotype-by-environment interactions and residual genetic variation in genomic selection for water-soluble carbohydrate concentration in wheat" by Ben Ovenden, Andrew Milgate, Len J. Wade, Greg J. Rebetzke and James B. Holland. </div><div>File S1 contains the SNP genotype information, File S2 contains the relationship matrix and File S3 contains a PCA plot of the first two eigenvectors of the relationship matrix. File S4 contains the phenotype dataset for WSCC. File S5 is script for the models used for genomic selection and cross-validation and File S6 contains the cross-validation sets.</div

Raw and filtered trial data

Data from multi-environment trial. Raw and filtered trial data. Includes brief description of content. See publication for additional details

A Genome-Wide Association Study of the Maize Hypersensitive Defense Response Identifies Genes That Cluster in Related Pathways

<div><p>Much remains unknown of molecular events controlling the plant hypersensitive defense response (HR), a rapid localized cell death that limits pathogen spread and is mediated by resistance (R-) genes. Genetic control of the HR is hard to quantify due to its microscopic and rapid nature. Natural modifiers of the ectopic HR phenotype induced by an aberrant auto-active R-gene (<i>Rp1-D21</i>), were mapped in a population of 3,381 recombinant inbred lines from the maize nested association mapping population. Joint linkage analysis was conducted to identify 32 additive but no epistatic quantitative trait loci (QTL) using a linkage map based on more than 7000 single nucleotide polymorphisms (SNPs). Genome-wide association (GWA) analysis of 26.5 million SNPs was conducted after adjusting for background QTL. GWA identified associated SNPs that colocalized with 44 candidate genes. Thirty-six of these genes colocalized within 23 of the 32 QTL identified by joint linkage analysis. The candidate genes included genes predicted to be in involved programmed cell death, defense response, ubiquitination, redox homeostasis, autophagy, calcium signalling, lignin biosynthesis and cell wall modification. Twelve of the candidate genes showed significant differential expression between isogenic lines differing for the presence of <i>Rp1-D21</i>. Low but significant correlations between HR-related traits and several previously-measured disease resistance traits suggested that the genetic control of these traits was substantially, though not entirely, independent. This study provides the first system-wide analysis of natural variation that modulates the HR response in plants.</p></div