Scientific sessions, CRG Group Leader Seminars
Genes and Disease Programme, CRG
Stephan Ossowski is a computer scientist studying the effects of genomic and epigenomic variation on the development of disease. He obtained his PhD for his work on population genome sequencing analysis in the model plant Arabidopsis thaliana as well as computational design of artificial miRNA for highly specific gene silencing in plants at the Max Planck Institute Tübingen. Stephans’s current research focuses on sequencing-based studies of epigenomic patterns in neurodegenerative disorders, computational analysis of structural variation in leukemia as well as the detection of disease correlated variants in rare and common, Mendelian and complex disorders.
The main goal of human genetics is to establish links between genetic variations and the phenotypic diversity, particularly with regards to disease. Genome-wide association studies (GWAS) have been immensely successful in identifying both disease-causing variants and variants influencing benign traits such as height. However, as numerous as these associations are, they do not explain all cases in disease studies, nor do they account for the full phenotypic variability. This problem termed ‘missing heritability’ is often encountered in the study of complex diseases. Several hypotheses have been proposed to explain the failure of GWAS to identify the remaining genetic components of these diseases. One of them, the "rare variant-common disease" hypothesis, proposes that different rare variants affect the same gene or pathway, causing the same disease. These variants have been missed so far, because GWAS measures single independent marker nucleotides that are frequent in the population. Further it is generally expected that regulatory variants as well as epigenetic variability contribute substantially to the development of disease but have not been studied extensively yet. However, rare variants, regulatory variants and a wide range of epigenomic marks are readily detectable by next-generation sequencing (NGS) based approaches, such as whole-genome, Exome, Methylome, ChIP and DNAseI sequencing.
We have developed laboratory and computational methods for the identification and functional analysis of causal and disease associated mutations in Exome and whole-genome sequencing studies of rare and common, mendelian and complex diseases. Our studies cover various types of diseases including cardio-vascular, neurological and rheumatologic diseases as well as cancer (chronic lymphocytic leukemia). To facilitate the routine application of Exome-seq for diagnosis and improved personalized treatment in hospital environments we have implemented eDIVA. This pipeline performs read alignment, SNP and indel prediction, CNV identification, functional annotation of coding variants and adds OMICs information from e.g. dbSNP, 1000genomes and OMIM. To improve data accessibility and to facilitate comparison between studies we integrated the newly developed database and disease variant retrieval tool eDIVA-DB.
We have further extended our approach to the association of regulatory variants with disease through sequencing experiments targeting regulatory regions like enhancers, promoters and transcription start sites (termed Regulome-seq), as several GWAS studies have indicated that regulatory variants might play a fundamental role in the development of complex diseases. We propose to combine Exome-seq and Regulome-seq with expression data in order to identify expression quantitative trait loci (eQTL), i.e. regulatory or coding variants linked to changes in expression.
Applying our approach we have recently identified KLHL3 as a gene responsible for Familial Hyperkalemic Hypertension (FHHt). A novel damaging missense mutation in a family with three affected members was identified by the analysis of Exome-seq data.