Extended exome sequencing identifies BACH2 as a novel major risk locus for Addison's disease.

BACKGROUND: Autoimmune disease is one of the leading causes of morbidity and mortality worldwide. In Addison's disease, the adrenal glands are targeted by destructive autoimmunity. Despite being the most common cause of primary adrenal failure, little is known about its aetiology. METHODS: To understand the genetic background of Addison's disease, we utilized the extensively characterized patients of the Swedish Addison Registry. We developed an extended exome capture array comprising a selected set of 1853 genes and their potential regulatory elements, for the purpose of sequencing 479 patients with Addison's disease and 1394 controls. RESULTS: We identified BACH2 (rs62408233-A, OR = 2.01 (1.71-2.37), P = 1.66 × 10-15 , MAF 0.46/0.29 in cases/controls) as a novel gene associated with Addison's disease development. We also confirmed the previously known associations with the HLA complex. CONCLUSION: Whilst BACH2 has been previously reported to associate with organ-specific autoimmune diseases co-inherited with Addison's disease, we have identified BACH2 as a major risk locus in Addison's disease, independent of concomitant autoimmune diseases. Our results may enable future research towards preventive disease treatment.

Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes.

With the aim of evaluating the usefulness of an in vitro system for assessing the potential hepatotoxicity of compounds, the paper describes several methods of obtaining mathematical models for the prediction of compound-induced toxicity in vivo. These models are based on data derived from treating rat primary hepatocytes with various compounds, and thereafter using microarrays to obtain gene expression 'profiles' for each compound. Predictive models were constructed so as to reduce the number of 'probesets' (genes) required, and subjected to rigorous cross-validation. Since there are a number of possible approaches to derive predictive models, several distinct modelling strategies were applied to the same data set, and the outcomes were compared and contrasted. While all the strategies tested showed significant predictive capability, it was interesting to note that the different approaches generated models based on widely disparate probesets. This implies that while these models may be useful in ascribing relative potential toxicity to compounds, they are unlikely to provide significant information on underlying toxicity mechanisms. Improved predictivity will be obtained through the generation of more comprehensive gene expression databases, covering more 'toxicity space', and by the development of models that maximize the observation, and combination, of individual differences between compounds.