Prevalence of rearrangements in the 22q11.2 region and population-based risk of neuropsychiatric and developmental disorders in a Danish population: a case-cohort study.

Background: Although the pathogenic nature of copy number variants (CNVs) on chromosome 22q11.2 has been recognised for decades, unbiased estimates of their population prevalence, mortality, disease risks, and diagnostic trajectories are absent. We aimed to provide the true population prevalence of 22q11.

Altered auditory processing and effective connectivity in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11.2DS) is one of the most common copy number variants and confers a markedly increased risk for schizophrenia.

Evaluation of shared genetic susceptibility loci between autoimmune diseases and schizophrenia based on genome-wide association studies.

Background: Epidemiological studies have documented higher than expected comorbidity (or, in some cases, inverse comorbidity) between schizophrenia and several autoimmune disorders. It remains unknown whether this comorbidity reflects shared genetic susceptibility loci.

Aims: The present study aimed to investigate whether verified genome wide significant variants of autoimmune disorders confer risk of schizophrenia, which could suggest a common genetic basis.

The Danish 22q11 research initiative.

Background: Neurodevelopmental brain disorders such as schizophrenia, autism and attention deficit hyperactivity disorder are complex disorders with heterogeneous etiologies. Schizophrenia and autism are difficult to treat and often cause major individual suffering largely owing to our limited understanding of the disease biology. Thus our understanding of the biological pathogenesis needs to be substantiated to enable development of more targeted treatment options with improved efficacy.

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients.

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories.

Using electronic patient records to discover disease correlations and stratify patient cohorts.

Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics.