Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

The molecular basis of copper-transport diseases

Copper (Cu) is a potentially toxic yet essential element. Menkes disease, a copper deficiency disorder, and Wilson disease, a copper toxicosis condition, are two human genetic disorders, caused by mutations of two closely related Cu-transporting ATPases. Both molecules efflux copper from cells. Quite diverse clinical phenotypes are produced by different mutations of these two Cu-transporting proteins. The understanding of copper homeostasis has become increasingly important in clinical medicine as the metal could be involved in the pathogenesis of some important neurological disorders such as Alzheimer's disease, motor neurone diseases and prion diseases.

Genetic variation in selenoprotein S influences inflammatory response

Chronic inflammation has a pathological role in many common diseases and is influenced by both genetic and environmental factors. Here we assess the role of genetic variation in selenoprotein S (SEPS1, also called SELS or SELENOS), a gene involved in stress response in the endoplasmic reticulum and inflammation control. After resequencing SEPS1, we genotyped 13 SNPs in 522 individuals from 92 families. As inflammation biomarkers, we measured plasma levels of IL-6, IL-1b and TNF-a. Bayesian quantitative trait nucleotide analysis identified associations between SEPS1 polymorphisms and all three proinflammatory
cytokines. One promoter variant, 105G-A, showed strong evidence for an association with each cytokine (multivariate P = 0.0000002). Functional analysis of this polymorphism showed that the A variant significantly impaired SEPS1 expression after exposure to endoplasmic reticulum stress agents (P = 0.00006). Furthermore, suppression of SEPS1 by short interfering RNA in macrophage cells increased the release of IL-6 and TNF-a. To investigate further the significance of the observed associations, we genotyped 105G-A in 419 Mexican American individuals from 23 families for replication. This analysis confirmed a significant
association with both TNF-a (P = 0.0049) and IL-1b (P = 0.0101). These results provide a direct mechanistic link between SEPS1 and the production of inflammatory cytokines and suggest that SEPS1 has a role in mediating inflammation.

Genetic variation in PARL influences mitochondrian content

Given their involvement in processes necessary for life, mitochondrial damage and subsequent dysfunction can lead to a wide range of human diseases. Previous studies of both animal models and humans have suggested that presenilins-associated rhomboid-like protein (PARL) is a key regulator of mitochondrial integrity and function, and plays a role in cellular apoptosis. As a surrogate measure of mitochondrial integrity, we previously measured mitochondrial content in a Caucasian population consisting of large extended pedigrees, with results highlighting a substantial genetic component to this trait. To assess the inXuence of variation in the PARL gene on mitochondrial content, we re-sequenced 6.5 kb of the gene, identifying 16 SNPs and genotyped these in 1,086 Caucasian individuals, distributed across 170 families. Statistical genetic analysis revealed that one promoter variant, T-191C, exhibited signiWcant eVects (after correction for multiple testing) on mitochondrial content levels. Comparison of the transcription factor binding characteristics of the T-191C promoter SNP by EMSA indicates preferential binding of nuclear factors to the T allele, suggesting functional variation in PARL expression. These results suggest that genetic variation within PARL inXuences mitochondrial abundance and integrity.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

An association rule analysis framework for complex physiological and genetic data

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.

Population genetic structure of the red fox (Vulpes vulpes) in the UK

The red fox (Vulpes vulpes) is common and widely distributed within the UK. It is a carrier or potential carrier of numerous zoonotic diseases. Despite this, there are no published reports on the population genetics of foxes in Britain. In this study, we aim to provide an insight into recent historical movement of foxes within Britain, as well as a current assessment of the genetic diversity and gene flow within British populations. We used 14 microsatellite markers to analyse 501 red fox samples originating from England, southern Scotland and northern France. High genetic diversity was evident within the sample set as a whole and limited population genetic structure was present in British samples analysed. Notably, STRUCTURE analysis found support of four population clusters, one of which grouped two southern England sampling areas with the nearby French samples from Calais, indicating recent (post-formation of the Channel) mixing of British and French populations. This may coincide with reports of large-scale translocations of foxes into Britain during the nineteenth century for sport hunting. Other STRUCTURE populations may be related to geographic features or to cultural practices such as fox hunting. In addition, the two British urban populations analysed showed some degree of differentiation from their local rural counterparts.

Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.