Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Symptoms do not helpfully distinguish unipolar and bipolar depression Authors' reply

We are in full agreement with Carroll about the limited utility of clinical symptoms for `diagnostic tests' and the consequent importance of efforts to discover biomarkers, endophenotypes or genetic markers. In fact, the main focus of our research is molecular genetic epidemiological investigation of mood disorders and psychoses that has precisely this aim.1–4 Further, we have a keen interest in using findings to provide biological validators for psychiatric nosology, classification and clinical diagnosis.5 However, for the moment, psychiatrists have to make do with the clinical tools available and be alert to diagnostic clues that can help in the delivery of optimal care to their patients. We stand by the statements in our paper: `It is commonly, but wrongly, assumed that there are no important differences in the clinical presentation of unipolar and bipolar depression... The clinical features of depression are not, of course, a definitive guide to diagnosis but can help alert the clinician to a possible bipolar course... This is important because optimal management varies between bipolar and unipolar depression.

Re: Symptoms do not helpfully distinguish unipolar and bipolar depression [eLetter]

We are in full agreement with Carroll about the limited utility of clinical symptoms for “diagnostic tests” and the consequent importance of efforts to discover biomarkers, endophenotypes or genetic markers. In fact, the main focus of our research is molecular genetic epidemiological investigation of mood disorders and psychoses that has precisely this aim (1-4). Further, we have a keen interest in using findings to provide biological validators for psychiatric nosology, classification and clinical diagnosis (5). However, for the moment psychiatrists have to make do with the clinical tools available and be alert to diagnostic clues that can help in the delivery of optimal care to their patients. We stand by the statements in our paper: “It is commonly, but wrongly, assumed that there are no important differences in the clinical presentation of unipolar and bipolar depression... The clinical features of depression are not, of course, a definitive guide to diagnosis but can help alert the clinician to a possible bipolar course... This is important because optimal management varies between bipolar and unipolar depression.

Clinical differences between bipolar and unipolar depression

It is commonly – but wrongly – assumed that there are no important differences between the clinical presentations of major depressive disorder and bipolar depression. Here we compare clinical course variables and depressive symptom profiles in a large sample of individuals with major depressive disorder (n=593) and bipolar disorder (n=443). Clinical characteristics associated with a bipolar course included the presence of psychosis, diurnal mood variation and hypersomnia during depressive episodes, and a greater number of shorter depressive episodes. Such features should alert a clinician to a possible bipolar course. This is important because optimal management is not the same for bipolar and unipolar depression

Age-at-onset in bipolar-I disorder: mixture analysis of 1369 cases identifies three distinct clinical sub-groups

Background To assess whether bipolar disorder type I segregates into three clinically distinct sub-groups defined by age-at-onset. Methods Clinical data were available on 1369 individuals with DSM-IV bipolar I disorder. Mixture analysis was performed on the age-at-onset (AAO) data to determine whether they were composed of more than one normal distribution. Individuals were allocated to groups according to the results of the mixture analysis. Categorical logistic regression was then used to investigate relationships between AAO and nine clinical characteristics. Results The distribution of AAOs in our sample comprised a mixture of three normal distributions with means of 18.7 (SD = 3.7), 28.3 (SD = 5.5) and 43.3 (SD = 9.1) years, with relative proportions of 0.47, 0.39 and 0.14 respectively. Individuals were allocated into three groups dependent on their AAO: ≤ 22; 25–37; and ≥ 40 years, producing a sample of 1225 individuals (144 with borderline values were excluded). Eight out of the nine clinical characteristics showed evidence for a statistical association with AAO group. Limitations Systematic and non-systematic recruitment of participants. Some data relied on retrospective recall. Conclusions Our results provide further robust evidence to suggest that the AAO distribution of individuals affected with bipolar disorder is composed of three normal distributions. Substantial clinical heterogeneity between the three AAO groups may reflect genetic heterogeneity within bipolar I disorder. Future genetic studies should consider AAO grouping as potential sub-phenotypes

Variation at the GABA(A) Receptor Gene, Rho 1 (GABRR1) Associated With Susceptibility to Bipolar Schizoaffective Disorder

We have previously reported evidence that variation at GABAA receptor genes is associated with susceptibility to bipolar disorder with schizophrenia-like psychotic features (Research Diagnostic Criteria (RDC) schizoaffective disorder, bipolar type) with gene-wide significance at GABRB1, GABRA4, GABRB3, GABRA5, and GABRR3. Here we provide suggestive evidence implicating a sixth member of the gene family, GABRR1 (gene-wide P = 0.0058; experiment-wide corrected significance P = 0.052)