Genetic model fitting results for variation in liver function test proteins.

<p>The best fitting model is highlighted in grey. For each protein, full models (ACE & ADE) was compared to nested models (AE, CE, E) using a chi-squared test ΔX² = (X² sub model)−(X² full model) with the degrees of freedom equal to ΔDf = (Df sub model)−(Df full model). The degrees of freedom increases from the full to sub or nested models due to drop in the numbers of parameters estimated as one moves down the model hierarchy. To be judged a good-fit, models should have a non-significant chi-squared goodness-of-fit statistic (p>0.05). Note, C and D cannot be included together in the same model as in quantitative genetic studies of human populations they are confounded thus the full model is either ACE or ADE. Comparisons with the ACE full model are shown here. In all cases, ACE provided a better model fit than ADE with a smaller chi-squared goodness-of-fit statistic (data not shown).</p><p>Abbreviations: X² = chi-squared goodness-of-fit statistic; Df = degrees of freedom; ΔDf = (df sub model)−(df full model); Δ X² = (X² sub model)−(X² full model); P = P-Value; A = Additive genetic influence; C = Shared environmental variance; E = Unique environmental variance.</p

Results of the linear regression analyses of age, BMI and alcohol consumption on liver function test proteins.

<p>Abbreviations: N = number of individuals, F = F test statistic, Chi² = chi-square test, p = level of significance, R² = Adjusted R² explaining the proportion of the total variance explained.</p>¶<p>We report F-test statistics with 2 degrees of freedom from all regression analyses taking into account the relatedness between the twin pairs.</p>†<p>For alcohol consumption truncated Gaussian regression was used due to the half normal distribution of the alcohol data.</p

Descriptive statistics of the raw data for female and male MZ and DZ twins including total number of individuals (n), means and standard deviations (s.d.) for the liver function test proteins and the major covariates included in the study.

<p>Note AST was not measured in male twins.</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

Characteristics of the individuals in the TUK1 and TUK2 samples.

<p>The mean (standard deviation) is shown for the TUK1 and TUK2 samples.</p><p>N represents sample size; BMI, body mass index; HPT, heat pain threshold; HPST, heat pain suprathreshold.</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

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

The Angiotensin II regulatory network was identified by causal reasoning from 138 genes associated with pain sensitivity.

<p>Causal reasoning uses directed molecular interactions to work upstream from the genes in this study (green) to identify regulators such as angiotensin II (blue) that have a causally correct regulatory role for a significant number of input genes. Correctness is determined by giving each input gene a direction of effect. Here, we presumed a loss of function (e.g. down regulation in activity) to all of our genes. Angiotensin II has direct causal connections to 12 of the genes from our 138, which can be increased to 30 if one intermediary node is allowed in the network (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003095#pgen.1003095.s001" target="_blank">Figure S1</a>). Distribution of novel rare variants identified according to minor allele frequency in a) TUK1 and b) TUK2 datasets.</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