Abstract: Neuregulin 1 (NRG1) is a strong candidate for involvement in the aetiology of schizophrenia. A haplotype, initially identified as showing association in the Icelandic and Scottish populations, has shown a consistent effect size in multiple European populations. Additionally, NRG1 has been implicated in susceptibility to bipolar disorder. In this first study to select markers systematically on the basis of linkage disequilibrium across the entire NRG1 gene, we used haplotype-tagging single-nucleotide polymorphisms to identify single markers and haplotypes associated with schizophrenia and bipolar disorder in an independently ascertained Scottish population. Haplotypes in two regions met an experiment-wide significance threshold of P=0.0016 (Nyholt's SpD) and were permuted to correct for multiple testing. Region A overlaps with the Icelandic haplotype and shows nominal association with schizophrenia (P=0.00032), bipolar disorder (P=0.0011), and the combined case group (P=0.0017). This region includes the 5' exon of the NRG1 GGF2 isoform and overlaps the expressed sequence tag (EST) cluster Hs.97362. However, no haplotype in Region A remains significant after permutation analysis (P>0.05). Region B contains a haplotype associated with both schizophrenia (P=0.00014), and the combined case group (P=0.000062), although it does not meet Nyholt's threshold in bipolar disorder alone (P=0.0022). This haplotype remained significant after permutation analysis in both the schizophrenia and combined case groups (P=0.024 and P=0.016, respectively). It spans a approximately 136 kb region that includes the coding sequence of the sensory and motor neuron derived factor (SMDF) isoform and 3' exons of all other known NRG1 isoforms. Our study identifies a new of NRG1 region involved in schizophrenia and bipolar disorder in the Scottish population.

Abstract: SUMMARY: SUSPECTS is a web-based server which combines annotation and sequence-based approaches to prioritize disease candidate genes in large regions of interest. It uses multiple lines of evidence to rank genes quickly and effectively while limiting the effect of annotation bias to significantly improve performance. AVAILABILITY: SUSPECTS is freely available at http://www.genetics.med.ed.ac.uk/suspects/ SUPPLEMENTARY INFORMATION: A quick-start guide in Macromedia Flash format is available at http://www.genetics.med.ed.ac.uk/suspects/help.shtml and Excel spreadsheets detailing the comparative performance of the software are included as Supplementary material.

Abstract: Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Abstract: BACKGROUND: Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning. RESULTS: We examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time. CONCLUSION: PROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies.