How to visualize multidimensional networks and prioritize candidate genes?
2018-08-09T18:03:56Z (GMT) by
<p>(A) Classical network visualization methods strongly depend on the layout algorithm used for positioning nodes, making structure interpretation and reproducibility difficult. (B) Hiveplot network visualization and structure strategy. See text for details. (C) The classical network visualization for the 3 phenotypes (blue nodes 1–3) in panel A can be represented with our method with 1 hiveplot per phenotype. Phenotype 1 showed more cortex–liver correlations than the 2 other phenotypes through 1 metabolite, connecting up- and down-regulated genes in cortex after SD and down-regulated genes in liver. Phenotype 2 shows genomic regions with strong allelic effect over multiple genes in liver and cortex through a high number of <i>trans-e</i>QTLs. Phenotype 3 was mostly connected to cortically expressed genes correlating strongly with up-regulated metabolites; most <i>cis/trans-e</i>QTLs affected only cortical genes. The number of significant (labeled sf) and suggestive (labeled sg) <i>ph</i>QTLs detected for each phenotype are indicated on bottom left. The 3 phenotypes were related to active wake behaviors during recovery (Phenotype 1 and 2: LMA per hour awake and time in TDW, respectively, both during ZT12–24; Phenotype 3: Gain in time spent awake during ZT24–6). (D) Gene prioritization strategy to identify candidate genes associated with phenotype/metabolite variation, illustrated for 6 genes. Five types of analyses were integrated into a single score for each gene to reflect its strength as candidate gene, namely from left to right (i) and (ii) QTL mapping for gene expression (<i>e</i>QTLs) and <i>ph</i>- or <i>m</i>QTLs, respectively, (iii) DE after SD, (iv) gene expression/phenotype correlations, and (v) analysis of protein-damaging genetic variations relating genes to an allelic effect. See text for further details. (E) To illustrate and validate our scoring, strategy, genes in liver were prioritized for levels of α-AAA after SD. <i>Dhtkd1</i> was identified as top-ranked candidate gene. Results from QTL mapping (red line) and prioritization analysis (green line); red and black horizontal lines indicate significant thresholds for the QTL and prioritization, respectively. α-AAA, alpha-aminoadipic acid; DE, differential expression; <i>e</i>QTL, expression quantitative trait locus; FDR, false discovery rate; LMA, locomotor activity; LOD, logarithm of odds ratio; <i>m</i>QTL, metabolic quantitative trait locus; QTL, quantitative trait locus; <i>ph</i>QTL, phenotypic quantitative trait locus; SD, sleep deprivation; TDW, theta-dominated waking; ZT, zeitgeber time.</p>