Title: Towards Understanding Bias in Clinical Data
Comparative Effectiveness Research (CER) using electronic clinical data has the potential to allow studies which assess the impacts of drugs and procedures on treated patient populations for less time and cost than prospective research studies.[1,2,9] However, clinical data from electronic health records (EHRs) may not be as controlled for accuracy and completeness as data primarily collected for research, potentially resulting in biases in these data. The result is a risk that CER using clinical data may lead to invalid conclusions. We will be presenting preliminary results of a comparison between research data collected in the community and clinically-acquired data from the same population.
Tilte: Diverse types of genetic variation converge on functional gene networks involved in schizophrenia
Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.