Immune cells lacking Y chromosome show dysregulation of autosomal gene expression.

Epidemiological investigations show that mosaic loss of chromosome Y (LOY) in leukocytes is associated with earlier mortality and morbidity from many diseases in men. LOY is the most common acquired mutation and is associated with aberrant clonal expansion of cells, yet it remains unclear whether this mosaicism exerts a direct physiological effect. We studied DNA and RNA from leukocytes in sorted- and single-cells in vivo and in vitro.

Longitudinal changes in the frequency of mosaic chromosome Y loss in peripheral blood cells of aging men varies profoundly between individuals.

Mosaic loss of chromosome Y (LOY) is the most common somatic genetic aberration and is associated with increased risk for all-cause mortality, various forms of cancer and Alzheimer's disease, as well as other common human diseases. By tracking LOY frequencies in subjects from which blood samples have been serially collected up to five times during up to 22 years, we observed a pronounced intra-individual variation of changes in the frequency of LOY within individual men over time. We observed that in some individuals the frequency of LOY in blood clearly progressed over time and that in other men, the frequency was constant or showed other types of longitudinal development.

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results.

ACES: a machine learning toolbox for clustering analysis and visualization.

Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers.

PiiL: visualization of DNA methylation and gene expression data in gene pathways.

Background: DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation, and the specific effects of most sites have not been characterized to date.

Combinatorial identification of DNA methylation patterns over age in the human brain.

Background: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways.

Results: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages.

Scaling predictive modeling in drug development with cloud computing.

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud.

The mutational landscape in pediatric acute lymphoblastic leukemia deciphered by whole genome sequencing.

Genomic characterization of pediatric acute lymphoblastic leukemia (ALL) has identified distinct patterns of genes and pathways altered in patients with well-defined genetic aberrations. To extend the spectrum of known somatic variants in ALL, we performed whole genome and transcriptome sequencing of three B-cell precursor patients, of which one carried the t(12;21)ETV6-RUNX1 translocation and two lacked a known primary genetic aberration, and one T-ALL patient. We found that each patient had a unique genome, with a combination of well-known and previously undetected genomic aberrations.

An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts.

The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.

Rule-based models of the interplay between genetic and environmental factors in childhood allergy.

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning.