Interspecies phenotype mapping strategies.

<p>This review highlights three major methodologies to identify phenotypes in the mouse that are relevant to a human disease. (<b>A</b>) Classical approach. A mouse model is made or identified that possesses a genotype equivalent to a penetrant mutation that in human underlies the disease of interest (termed construct validity). The mouse model is examined for phenotypes that resemble those that define the human disorder (face validity). (<b>B</b>) Phenolog mapping. A group is formed containing candidate genes for a disease of interest. The respective mouse models for the orthologues of these genes are then examined for any unusually overrepresented phenotypes among them and these phenotypes (termed phenologs) are deemed relevant to the disease. (<b>C</b>) Direct phenotype mapping. Given the phenotype(s) that describe a human disease, the corresponding phenotypes in mouse are inferred by means of computational reasoning using interspecies phenotype ontology analysis. In the example shown, the HPO term <i>Aortic stenosis</i> is defined on the basis of the PATO term <i>constricted</i> and <i>aortic valve</i> (term from the Foundational Model of Anatomy ontology of human anatomy <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Rosse1" target="_blank">[35]</a>). Similarly, the MPO term <i>aortic valve stenosis</i> is defined using the same PATO term <i>constricted</i> and <i>aortic valve</i> (term from the Mouse Anatomy ontology <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Hayamizu1" target="_blank">[77]</a>). Since both the Mouse Anatomy and FMA terms for aortic valve are children of the cross-species anatomy ontology (Uberon <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Mungall2" target="_blank">[40]</a>) term for aortic valve, automatic reasoning places the HPO term Aortic stenosis and the MPO term aortic valve stenosis in the direct vicinity of one another in a cross-species phenotype ontology <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Khler2" target="_blank">[42]</a>. Therefore, these terms display a high semantic similarity to one another.</p

Phenotype ontologies.

<p>Phenotype ontologies (an excerpt from the Human Phenotype Ontology is shown here) consist of thousands of terms describing phenotypes arranged in a hierarchical system of subclasses and superclasses. The structure of an ontology enables annotation propagation whereby more specific phenotypic terms are also described by more general parent terms, and thus all ancestral terms. The terms are related to one another by subclass (“is a”) relations, such that the ontology can be represented as a so-called directed acyclic graph. The terms themselves do not describe any specific disease. Instead, annotations to terms are used to state that a certain disease is characterised by a certain phenotypic feature.</p

Predicting human genotype-phenotype relations from functional genomics data.

<p>The mouse phenotypes associated with the orthologues of human genes are a better predictor of genes that share human phenotypes than other popular gene annotations of the same genes, such as GO or KEGG. As both GO and KEGG include information derived from multiple sources, including annotations from the mouse, the success of the mouse phenotypes is likely due both to the genetic relevance of the mouse models and the fact that human and mouse phenotypic annotations both describe abnormalities (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen-1004268-g001" target="_blank">Figure 1C</a>). Resnik's <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Resnik1" target="_blank">[78]</a> measure, together with the GraSM approach <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004268#pgen.1004268-Couto1" target="_blank">[79]</a>, was used to calculate the similarity of terms organised in these hierarchical ontologies, defining the semantic similarity between any two terms as the average information content of their disjunct common-ancestor terms. Gene pairs were ordered by their semantic similarity scores based on either the human KEGG pathway annotations (pink circles), human GO biological process (grey circles), or MPO annotations to genes (blue circles). For each of KEGG, GO, and MPO annotations, gene pairs were ordered in decreasing annotation similarity and grouped into bins of 2,000, and then the median semantic similarity score between gene pairs' Human Phenotype Ontology annotations was calculated. The dashed line marks the degree of similarity expected from pairs of random genes.</p

Coding sequence (CDS) lengths of genes with <i>de novo</i> variants.

<p>(A) ‘All genes’ denotes all translated human genes, ‘Siblings’ denotes genes with <i>de novo</i> mutations in non-autistic siblings of ASD cases published by O'Roak <i>et al.</i> and Sanders <i>et al.</i> Even the genes mutated in the healthy siblings are significantly longer than all coding genes (Mann–Whitney U test, P<2×10⁻¹⁶). The box plots depict the values between the 1^st and 3^rd quartile of a distribution, the 2^nd quartile (thick band) represents the median. (B) Mutational burden strongly correlates with coding sequence length in the Exome Variant Server (Spearman's ρ = 0.710, P<2×10⁻¹⁶; <a href="http://evs.gs.washington.edu/EVS" target="_blank">http://evs.gs.washington.edu/EVS</a>). All nonsynonymous mutations were considered across all human chromosomes. (C) The median CDS length of a gene's connections correlates with its CDS length (Spearman's ρ = 0.508, P<2×10⁻¹⁶). We considered the strongest 100,000 links from the integrated phenotypic-linkage network.</p

Clustering of genes hit by <i>de novo</i> nonsynonymous substitutions.

<p>(A) We have examined the network properties of whole sets of genes with nonsynonymous mutations implicated by recent exome-sequencing studies in autism (ASD), severe intellectual disability (ID), epilepsy or schizophrenia (S). We calculated the sum of link weights among genes from a set and compared this sum to that calculated for randomized gene sets in order to assess the degree of functional clustering. (B and C) The implicated genes are significantly more strongly interconnected with each other by means of functional genomics data than random gene sets of the same size, but controlling for coding sequence (CDS) length considerably affects the p-values. The genes mutated in the same disease cluster most significantly in the integrated phenotypic-linkage network, while genes mutated in healthy controls do not cluster.</p

Processing and comparison of functional genomics data.

<p>(A) Terms in a phenotype ontology have an information content (IC) which is inversely proportional to the number of genes annotated with them. The semantic similarity between any two terms equals to the IC of their closest common ancestor term(s). (B) Gene–gene linkages derived from a data type are assessed and rescored according to the semantic similarity of the linked genes' mouse phenotype annotations. (C) The similarity in human phenotype annotations from the HPO is a benchmark on which all the data types can be compared, revealing their relative accuracy and coverage.</p

Synergistic interactions in <i>Drosophila</i> between <i>shibire</i> and <i>alpha spectrin</i>, the orthologues of ASD-candidate genes from a <i>de novo</i> gain CNV 12235_chr9_gain_129907917_l.

<p><b>A.</b> The Locus of the CNV with mapped <i>Drosophila</i> orthologues (Target, green; control, red). <b>B.</b> Synaptic alterations were characterised by NMJ type IB bouton number. Individual heterozygous mutants of candidate gene orthologues gave no significant change in NMJ morphology over <i>w</i>^<i>1118</i> controls. However, <i>Shibire</i> and <i>alpha-spectrin</i> double over expressers display reduced bouton numbers (using 1032-GAL4; UAS-Dynamin/UAS-alpha-spectrin; n>20, Kruskal-Wallis test, * P<0.05). Non-candidate gene controls <i>Su(P)</i> (using <i>Su(P)</i>^{<i>EY13245</i>}) and <i>CG14104</i> (using <i>CG14104</i>^{<i>f07593</i>}) selected from genes found within CNV gave no significant NMJ phenotype singularly or when crossed to form transheterozygotes with candidate genes. <b>C.</b> Circadian rhythm analysis of candidate genes. Negative controls and candidate gene orthologue overexpression of <i>alpha-spectrin</i> displayed normal light/dark differences in sleeping patterns singularly or when crossed. However, <i>Shibire</i> overexpression, and co-overexpression with <i>alpha-spectrin</i> lost the dark bias, and displayed no significant difference between light/dark sleeping patterns (<b>t</b>).</p

Relative Frequency Histograms of Distances from Human CNVs to the Nearest Centromere or Telomere

<p>Relative frequency histograms (striped blue bars) are compared to their expected distributions if CNVs were distributed randomly within the genome (grey bars); these expected distributions are fitted to Gaussian distributions (grey lines). Red lines represent 99.9999% prediction confidence intervals from the fitted curves.</p

Different genetic interactions effect distinct synaptic defects suggesting that distinct molecular aetiologies underlie ASD.

<p><b>A.</b> The <i>Drosophila</i> NMJ contains presynaptic active zones (labelled by Bruchpilot, BRP) and postsynaptic glutamate receptor (labelled by GluRIIA). <b>B.</b> Representative images of BRP staining demonstrate a reduced number of active zone (BRP) puncta in the transheterozygotes <i>dlg/pak, NrxIV/dlg</i> and <i>NrxIV/pak</i> as compared to control (<i>w</i>^<i>1118</i>), <b>C.</b> The number of active zone (BRP) puncta (normalised to bouton size) were significantly reduced in <i>dlg/pak, NrxIV/Dlg</i> and <i>NrxIV/Pak</i> transheterozygotes. <b>D.</b> The fluorescence of the post-synaptic glutamate receptors were scored and normalised to bouton size HRP levels. <i>Pasha/Sep4</i> transheterozygotes were the only genotype to demonstrate decreased glutamate receptor abundance. <b>E.</b> Representative images of GluRIIA staining demonstrating the reduced fluorescence in the transheterozygote <i>Pasha/Sep4</i> when compared to control. <b>F.</b> A schematic showing the sub-types of genetic interactions supporting distinct molecular aetiologies underlying ASD that converge to yield defects at the synapse.</p

Synergistic Interactions between <i>Drosophila</i> Orthologues of Genes Spanned by <i>De Novo</i> Human CNVs Support Multiple-Hit Models of Autism

<div><p>Autism spectrum disorders (ASDs) are highly heritable and characterised by deficits in social interaction and communication, as well as restricted and repetitive behaviours. Although a number of highly penetrant ASD gene variants have been identified, there is growing evidence to support a causal role for combinatorial effects arising from the contributions of multiple loci. By examining synaptic and circadian neurological phenotypes resulting from the dosage variants of unique human:fly orthologues in <i>Drosophila</i>, we observe numerous synergistic interactions between pairs of informatically-identified candidate genes whose orthologues are jointly affected by large <i>de novo</i> copy number variants (CNVs). These CNVs were found in the genomes of individuals with autism, including a patient carrying a 22q11.2 deletion. We first demonstrate that dosage alterations of the unique <i>Drosophila</i> orthologues of candidate genes from <i>de novo</i> CNVs that harbour only a single candidate gene display neurological defects similar to those previously reported in <i>Drosophila</i> models of ASD-associated variants. We then considered pairwise dosage changes within the set of orthologues of candidate genes that were affected by the same single human <i>de novo</i> CNV. For three of four CNVs with complete orthologous relationships, we observed significant synergistic effects following the simultaneous dosage change of gene pairs drawn from a single CNV. The phenotypic variation observed at the <i>Drosophila</i> synapse that results from these interacting genetic variants supports a concordant phenotypic outcome across all interacting gene pairs following the direction of human gene copy number change. We observe both specificity and transitivity between interactors, both within and between CNV candidate gene sets, supporting shared and distinct genetic aetiologies. We then show that different interactions affect divergent synaptic processes, demonstrating distinct molecular aetiologies. Our study illustrates mechanisms through which synergistic effects resulting from large structural variation can contribute to human disease.</p></div