Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Background
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.

Results
Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.

Conclusion
The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

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.

Stage of perinatal development regulates skeletal muscle mitochondrial biogenesis and myogenic regulatory factor genes with little impact of growth restriction or cross-fostering

Foetal growth restriction impairs skeletal muscle development and adult muscle mitochondrial biogenesis. We hypothesized that key genes involved in muscle development and mitochondrial biogenesis would be altered following uteroplacental insufficiency in rat pups, and improving postnatal nutrition by cross-fostering would ameliorate these deficits. Bilateral uterine vessel ligation (Restricted) or sham (Control) surgery was performed on day 18 of gestation. Males and females were investigated at day 20 of gestation (E20), 1 (PN1), 7 (PN7) and 35 (PN35) days postnatally. A separate cohort of Control and Restricted pups were cross-fostered onto a different Control or Restricted mother and examined at PN7. In both sexes, peroxisome proliferator-activated receptor (PPAR)-γ coactivator-1α (PGC-1α), cytochrome c oxidase subunits 3 and 4 (COX III and IV) and myogenic regulatory factor 4 expression increased from late gestation to postnatal life, whereas mitochondrial transcription factor A, myogenic differentiation 1 (MyoD), myogenin and insulin-like growth factor I (IGF-I) decreased. Foetal growth restriction increased MyoD mRNA in females at PN7, whereas in males IGF-I mRNA was higher at E20 and PN1. Cross-fostering Restricted pups onto a Control mother significantly increased COX III mRNA in males and COX IV mRNA in both sexes above controls with little effect on other genes. Developmental age appears to be a major factor regulating skeletal muscle mitochondrial and developmental genes, with growth restriction and cross-fostering having only subtle effects. It therefore appears that reductions in adult mitochondrial biogenesis markers likely develop after weaning.