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UNLABELLED 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(More)
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(More)
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(More)
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(More)
The genomic regions identified through genetic linkage studies of complex disease are frequently large and may contain hundreds of genes. This number can be reduced by the candidate gene approach – matching functional annotation to what is known about the etiology of the disease in question. SUSPECTS is a web based tool designed to help researchers(More)
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(More)
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