Han G. Brunner

Severe intellectual disability (IQ DYNC1H1, GATAD2B, and CTNNB1 are novel intellectual disability genes, which raised the diagnostic yield of exome sequencing to 16%. Exome sequencing can be used as a diagnostic procedure for patients with severe intellectual disability of unknown cause. The diagnostic yield of 16% may increase as methods improve to identify other genes associated with intellectual disability.

Floating-Harbor syndrome (FHS) is a rare condition characterized by short stature, delays in expressive language, and a distinctive facial appearance. Recently, heterozygous truncating mutations in SRCAP were determined to be disease-causing. With the availability of a DNA based confirmatory test, we set forth to define the clinical features of this syndrome. Clinical information on fifty-two individuals with SRCAP mutations was collected using standardized questionnaires. Twenty-four males and twenty-eight females were studied with ages ranging from 2 to 52 years. The facial phenotype and expressive language impairments were defining features within the group. Height measurements were typically between minus two and minus four standard deviations, with occipitofrontal circumferences usually within the average range. Thirty-three of the subjects (63%) had at least one major anomaly requiring medical intervention. We did not observe any specific phenotype-genotype correlations. This large cohort of individuals with molecularly confirmed FHS has allowed us to better delineate the clinical features of this rare but classic genetic syndrome, thereby facilitating the development of management protocols.

Exome sequencing is revolutionizing Mendelian disease gene identification. This results in improved clinical diagnosis, more accurate genotype-phenotype correlations and new insights into the role of rare genomic variation in disease.

Background: Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. Results: We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. Conclusion: We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.

Evidence from many sources suggests that similar phenotypes are begotten by functionally related genes. This is most obvious in the case of genetically heterogeneous diseases such as Fanconi anemia, Bardet‐Biedl or Usher syndrome, where the various genes work together in a single biological module. Such modules can be a multiprotein complex, a pathway, or a single cellular or subcellular organelle. This observation suggests a number of hypotheses about the human phenome that are now beginning to be explored. First, there is now good evidence from bioinformatic analyses that human genetic diseases can be clustered on the basis of their phenotypic similarities and that such a clustering represents true biological relationships of the genes involved. Second, one may use such phenotypic similarity to predict and then test for the contribution of apparently unrelated genes to the same functional module. This concept is now being systematically tested for several diseases. Most recently, a systematic yeast two‐hybrid screen of all known genes for inherited ataxias indicated that they all form part of a single extended protein–protein interaction network. Third, one can use bioinformatics to make predictions about new genes for diseases that form part of the same phenotype cluster. This is done by starting from the known disease genes and then searching for genes that share one or more functional attributes such as gene expression pattern, coevolution, or gene ontology. Ultimately, one may expect that a modular view of disease genes should help the rapid identification of additional disease genes for multifactorial diseases once the first few contributing genes (or environmental factors) have been reliably identified.

Background: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. Objective: To investigate whether protein–protein interactions can predict genes for genetically heterogeneous diseases. Methods: 72 940 protein–protein interactions between 10 894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. Results: Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. Conclusions: Exploiting protein–protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.