Haploview output showing linkage disequilibrium relationships between the 91 eligible SNPs

<p><b>Copyright information:</b></p><p>Taken from "Common variants in the , , , and cancer susceptibility genes are unlikely to increase breast cancer risk"</p><p>http://breast-cancer-research.com/content/9/2/R27</p><p>Breast Cancer Research 2007;9(2):R27-R27.</p><p>Published online 11 Apr 2007</p><p>PMCID:PMC1868915.</p><p></p> The matrix indicates the D' value between each pair of SNPs – darker colours indicate higher values

Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis - Fig 1

<p><b>The association between Pfizer-23andMe–derived polygenic risk profiles scores for pain on chronic pain phenotypes in GS:SFHS (left panel) and UK Biobank (right panel).</b> This figure shows the association between polygenic risk scores for pain (derived from Pfizer-23andMe data) and chronic pain in GS:SFHS (left panel) and UK Biobank (right panel). Vertical <i>y</i>-axis represents the effect size as a standardised beta; horizontal axis represents the four alternative <i>p</i>-value thresholds used for the generation of polygenic scores in the discovery GWAS studies.</p

Association between MDD-related traits in GS:SFHS and UK Biobank with the Psychiatric Genomics Consortium derived MDD polygenic risk scores.

<p>Association between MDD-related traits in GS:SFHS and UK Biobank with the Psychiatric Genomics Consortium derived MDD polygenic risk scores.</p

Association of polygenic profile scores for chronic pain and MDD in GS:SFHS.

<p>Association of polygenic profile scores for chronic pain and MDD in GS:SFHS.</p

The association between Pfizer-23andMe–derived polygenic profiles scores for chronic pain and chronic pain in GS:SFHS and UK Biobank.

<p>The association between Pfizer-23andMe–derived polygenic profiles scores for chronic pain and chronic pain in GS:SFHS and UK Biobank.</p

Pathways identified by causal reasoning.

<p>Causal reasoning <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003095#pgen.1003095-Chindelevitch1" target="_blank">[11]</a> uses a large curated database of directed regulatory molecular interactions to identify the most plausible upstream regulators of a gene set with a proposed directionality (eg. down-regulated). We considered the 138 genes identified to contain loss of function mutations. One regulatory pathway (angiotensin II) is significant after correction for multiple testing when considering directionality (Correctness p) as well as when ignoring directionality of regulation (Enrichment p).</p><p>The sign (−/+) after the regulator's name indicates the loss (−) or gain (+) of activity required to explain the loss of function mutations.</p><p>Enrichment p-value indicates the significance of the number of connections apparent in our data compared to the total number of connections.</p><p>Correctness p-value also accounts for the regulatory direction (+/−) and indicates the significance of the hypothesis as a regulator.</p

Quantile–quantile plots for the six different variant burden analysis methods.

<p>Quantile–quantile plots are shown for: (a) AMELIA, (b) CCRaVAT, (c) fixed filter test, minor allele frequency <0.05, (d) Madsen-Browning with polyphen weights, (e) Han and Pan aSumtest, (f) SSU, sum-of-squares test (Han and Pan).</p

Association between polygenic risk of MDD and chronic pain phenotypes in GS:SFHS and UK Biobank.

<p>This figure shows the association between polygenic risk scores for MDD (derived from Psychiatric Genomics Consortium data) and chronic pain in GS:SFHS (left panel) and UK Biobank (right panel). Vertical <i>y</i>-axis represents the effect size as a standardised beta, horizontal axis represents the four alternative <i>p</i>-value thresholds used for the generation of polygenic scores in the discovery GWAS studies.</p

Details of the SNVs identified in TUK1 and TUK2 samples.

<p>The number of SNVs detected is shown according to their functional consequences, for the TUK1 and TUK2 samples.</p

SNVs identified in gene <i>GZMM</i>.

<p>Schematic showing number of subjects in TUK1 (top row) and TUK2 (bottom row) having nonsynonymous SNVs within the <i>GZMM</i> gene, with novel variants in black and those described in dbSNP in green. Subject counts in blue are for pain insensitive subjects and in red, pain sensitive. Squares represent homozygous and ovals heterozygous mutations. Exons are shown as dark cylinders, UTRs pale grey rectangles and introns dotted line.</p