Additional file 1 of npInv: accurate detection and genotyping of inversions using long read sub-alignment

Supplementary Figure. Figure S1. Error rates distribution around a wrong mapping homozygous inversion region. Figure S2. Evaluation of BWA-MEM [1], Minimap2 [2] and NGMLR [3] using npInv with short NHEJ inversions. Figure S3. Error rates distribution of 5 aligners. Figure S4. The performance of genotyping inversion from simulated and real data. Figure S5. PCR products validating three inversions (4q35.2, 3q21.3 and 10q11.22). Figure S6. IGV [7] view for left breakpoint on inversion 4q35.2. Supplementary Information. Algorithm 1. Program pseudocode. Supplementary Table. Table S1. PCR primers for validation. Table S2. NA12878 inversion combined from npInv, Validated [6] (Val), Assembly [8] (Asse) and Delly [9]. (PDF 325 kb

Most associated linear combinations of phenotypes at genome-wide significant SNPs.

*<p>indicates that the SNP did not have a univariate genome-wide significant <i>P</i> value. Each row indicates the linear combination of phenotypes (given by the corresponding regression coefficients) which is most associated with the given SNP under the MultiPhen regression, after removing the most associated phenotype. The regression coefficients have been scaled so that the CHOL coefficient is always equal to one. The last row contains the expected coefficients according to the Friedewald Formula (Equation 1).</p

Results under standard GWAS and MultiPhen approaches for genome-wide significant SNPs.

<p>¶ Nyholt-Šidák corrected for 4 comparisons. § Nyholt-Šidák corrected for 3 comparisons. Results compare univariate and MultiPhen <i>P</i> values, presented on the -log10 scale for ease of comparison, for all SNPs with genome-wide significant <i>P</i> values (>7.301 on the -log10 scale) from either approach. Genome-wide significant results shown in bold. The difference in terms of orders of magnitude of the MultiPhen <i>P</i> value on all phenotypes is relative to the most associated univariate phenotype; and the order of magnitude difference for MultiPhen where the most associated phenotype is excluded is relative to the univariate result also excluding the most associated phenotype.</p

Genome-wide significant results from standard GWAS approach and MultiPhen tested on combinations of the lipids using NFBC1966 data.

<p>Each bar shows the number of SNPs reaching genome-wide significance for a given phenotype-combination analysis (specified by the first letters of each trait, such that CHL refers to an analysis on the CHOL, HDL and LDL), with the SNPs discovered by both the univariate approach and MultiPhen shown by the white segment of the bar, the SNPs discovered by the univariate approach only shown by the grey segment, and the SNPs discovered by MultiPhen only illustrated by the black segment. The bars labelled ALL2 and ALL3 combine results across analyses on all combinations of two and three lipid traits, respectively, while ALL combines the results across the analyses of all 2, 3 and 4 combinations of the traits. A complete breakdown of these results is presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s018" target="_blank">Tables S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s019" target="_blank">S6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s020" target="_blank">S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s021" target="_blank">S8</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s022" target="_blank">S9</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s023" target="_blank">S10</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s024" target="_blank">S11</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s025" target="_blank">S12</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s026" target="_blank">S13</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s027" target="_blank">S14</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s028" target="_blank">S15</a>.</p

The correlation structure between pairs of lipids.

<p>The left panel shows the correlation structure between total cholesterol (CHOL) and low-density lipoprotein (LDL) in 5655 individuals from the Northern Finland Birth Cohort 1966. Each circle depicts the value of CHOL (X-axis) and LDL (Y-axis) in mmol/L for each individual. The right panel shows the correlation structure between low-density lipoprotein (LDL) and high-density lipoprotein (HDL), in mmol/L, in the same individuals. The arrows in each plot show the direction of effect of a variant affecting only CHOL or only HDL, such that the genotypes of individuals underlying each plotted point are more likely to contain risk alleles for the labelled lipid moving through the points in the direction of the arrow. The diagonal arrows are based on the Friedewald Formula (Friedewald.72). The arrows indicate that effects of variants can be in very different directions in the 2-dimensional spaces shown; the aim of modelling and testing linear combinations of phenotypes is to capture effects in any direction.</p

Behaviour of the different methods under the null.

<p>This table relates to the simulation study to test the type 1 error rates of MultiPhen, CCA, and the univariate approach, described in the text. The elements of the table show the number of results with <i>P</i><1e^–5 in the scenario described by the corresponding row and column (which give the minor allele frequencies) headers. Since 100000 replicates of SNP-phenotype associations were simulated under the null hypothesis of no association, the expectation for all elements of the table is 1; those with >1 indicating inflation of the type 1 error rate. Simulations with MAF = 30%, 0.5% were performed on a sample size of N = 5000. For the full results see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s001" target="_blank">Figures S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s008" target="_blank">S8</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s014" target="_blank">Table S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034861#pone.0034861.s016" target="_blank">S3</a>.</p

The power of MultiPhen in different scenarios of effect and correlation between phenotypes.

<p>Power results based on simulations described in the text for MultiPhen (red lines) and the standard single-phenotype approach (black lines). Left panel: causal variant explains 0.5% of phenotypic variance of both phenotypes. Middle panel: causal variant explains 0.5% on the phenotypic variance of the first phenotype and 0.1% of the variance in the second phenotype. Right panel: causal variant explains 0.5% of phenotypic variance of the first phenotype and 0% of the second phenotype.</p

Analysis strategy for identifying coordinated behaviour between disease dysregulated pathways.

<p>Disease genes (e.g. FYN, SRC and LCK) that are targeted by anti-inflammatory drugs and associated with biomarkers of disease-relevant biological processes provide insight into the biological function resulting from the coordinated behaviour of both dysregulated pathways identified by integrating GWAS data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Ramasamy1" target="_blank">[6]</a> and gene expression data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074821#pone.0074821-Benson1" target="_blank">[2]</a> (a) Co-enrichment analysis of Pareto-efficient pathways identify pathways that are involved in the systemic response to pollen sensitisation and involved in the cellular response to pollen allergen challenge; in this study, complement system was the top hit. (b) Coordination between disease dysregulated pathway (CD4+ T cell activation) and the pathway identified in the disease context (Complement system) is studied using inter-pathway interactions network analysis (INPAR-N). (C) Regression and correlation enrichment analysis is applied to test if the INPAR-N is associated with markers of the biological process involved in the disease onset, i.e. Th2 priming. (D) The genes of the INPAR-N are mapped to disease pathophysiology using drug target network analysis.</p

Regression analysis shows that the disease network gene expression is predictive of the Th2 differentiation markers.

<p>Regression analysis shows that the disease network gene expression is predictive of the Th2 differentiation markers.</p

Inter-pathway interactions analysis identifies disease network linking complement system to CD4+ T cell activation.

<p>(A) INPAR network has 19 genes that link Complement system to CD4+ T cell activation. Blue nodes correspond to genes involved in T cell activation while red nodes correspond to genes involved in the complement system. (B) Drug target network analysis of the INPAR-N showing that several immunosuppressive drugs target the INPAR-N network genes, particularly the Src family of tyrosine kinases, including Src, Fyn, Lck. (C) Allergens trigger the innate immune system that in turn triggers the adaptive immune system. INPAR-N includes complement system proteins that interact with T cell membrane proteins.</p