ABSTRACT: Genome-wide association studies are a powerful tool for unravelling the genetic background of complex disorders such as major depression.
We conducted a genome-wide association study of 604 patients with major depression and 1364 population based control subjects. The top hundred findings were followed up in a replication sample of 409 patients and 541 control subjects.
Two SNPs showed nominally significant association in both the genome-wide association study and the replication samples: 1) rs9943849 (p(combined) = 3.24E-6) located upstream of the carboxypeptidase M (CPM) gene and 2) rs7713917 (p(combined) = 1.48E-6), located in a putative regulatory region of HOMER1. Further evidence for HOMER1 was obtained through gene-wide analysis while conditioning on the genotypes of rs7713917 (p(combined) = 4.12E-3). Homer1 knockout mice display behavioral traits that are paradigmatic of depression, and transcriptional variants of Homer1 result in the dysregulation of cortical-limbic circuitry. This is consistent with the findings of our subsequent human imaging genetics study, which revealed that variation in single nucleotide polymorphism rs7713917 had a significant influence on prefrontal activity during executive cognition and anticipation of reward.
Our findings, combined with evidence from preclinical and animal studies, suggest that HOMER1 plays a role in the etiology of major depression and that the genetic variation affects depression via the dysregulation of cognitive and motivational processes.
Biological psychiatry 09/2010; 68(6):578-85. · 8.93 Impact Factor
ABSTRACT: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
Nature Biotechnology 08/2010; 28(8):827-38. · 29.50 Impact Factor
ABSTRACT: To evaluate the impact of a predefined gene expression-based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients.
Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies.
The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 +/- 0.03 v 0.38 +/- 0.04; 5-year overall survival [OS] 0.98 +/- 0.01 v 0.56 +/- 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P <or= .001 for both EFS and OS for each). In subgroups with clinical low-, intermediate-, and high-risk of death from disease, the PAM predictor significantly separated patients with divergent outcome (low-risk 5-year OS: 1.0 v 0.75 +/- 0.10, P < .001; intermediate-risk: 1.0 v 0.82 +/- 0.08, P = .042; and high-risk: 0.81 +/- 0.08 v 0.43 +/- 0.05, P = .001). In multivariate Cox regression models based on both EFS and OS, PAM was a significant independent prognostic marker (EFS: hazard ratio [HR], 3.375; 95% CI, 2.075 to 5.492; P < .001; OS: HR, 11.119, 95% CI, 2.487 to 49.701; P < .001). The highest potential clinical impact of the classifier was observed in patients currently considered as non-high-risk (n = 289; 5-year EFS: 0.87 +/- 0.02 v 0.44 +/- 0.07; 5-year OS: 1.0 v 0.80 +/- 0.06; both P < .001).
Gene expression-based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.
Journal of Clinical Oncology 07/2010; 28(21):3506-15. · 18.37 Impact Factor