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

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

Structural ocular findings in patients with Marfan syndrome (MFS) and controls.

<p>Total = number of patients with documented findings for the respective category, unilat. = unilateral, bilat. = bilateral.</p

Direction of lens subluxation in eyes of Marfan syndrome patients with ectopia lentis (n = 101 patients, 182 eyes).

<p>Direction of lens subluxation in eyes of Marfan syndrome patients with ectopia lentis (n = 101 patients, 182 eyes).</p

Biometry, dimensions and refractive error of eyes of Marfan patients and control eyes.

<p>Biometry, dimensions and refractive error of eyes of Marfan patients and control eyes.</p

Exomes of 85 European individuals (CEU) as well as 88 African individuals (YRI) were filtered for rare compound heterozygous candidate variants.

<p>A) In average around 230 variants pass the filter in CEU exomes and 309 in YRI exomes. B) The potential compound heterozygotes are distributed over 31 genes in CEU individuals and 67 genes in YRI individuals. C) Altogether 1998 genes harbored potential compound heterozygous variants in the tested individuals and compound heterozygotes in 1066 genes occurred only in singular cases.</p

The length of the coding sequence and the mean number of rare alleles per gene.

<p>In an average healthy individual from the 5000 exomes project there is more than one rare heterozygous variant in <i>MUC16</i> that has an allele frequency below 0.01 in the reference population. In contrast, the coding sequence of <i>PIGO</i> is much shorter and rare heterozygous variants occur in less than 8 out of 1000 exomes.</p

Compound Heterozygote Filtering Rules.

<p>If both parents of the index patient are unaffected it is not possible that one of the heterozygous disease causing mutations is present in a heterozygous state in both parents unless a recombination occurred between this variant and the second compound heterozygous mutation.</p

Filtering results for compound heterozyotes in a case study.

<p>With the filter settings for genotype frequency <0.01, effect on protein level (functional filter: missense, nonsense, stop loss, splice site, insertions or deletions), and compound heterozygous yields six variants in three genes. <i>MUC16</i> and <i>NBPR10</i> are both genes from large gene families known for their high variability and detection artifacts due to pseudogenes. The heterozygotes in <i>PIGO</i> remain as the likeliest candidates. The <i>Show</i> icon at the right end of the line links to an expert curated annotation database that indicates that the mutation in <i>PIGO</i> is causing Hyperphosphatasia with mental retardation syndrome and has been published in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070151#pone.0070151-Krawitz1" target="_blank">[9]</a>. The gene view for <i>PIGO</i> lists all variant annotations for this gene and links to further knowledge bases. The length of the coding sequence of the longest transcript (max. CDS) and the mean number of rare heterozygous variant calls per exome (MRHC) are important parameters for the assessment of candidate genes.</p

Illustration of mapping artifacts resulting in false positive variant detection.

<p>The illustrated sample carries a mutation in the maternal copy of a pseudogene of <i>NBPF10</i>. If the pseudogene is not included in the reference sequence, the reads originating from this pseudogene are mismapped. This may result in a false variant call. Indicative for false genotype calls are proportions of reads supporting the alternate allele that strongly deviate from 0.5 or 1.</p