Senior Lecturer/Associate Professor at Department of Immunology, Genetics and Pathology, Research programme: Genomics and Neurobiology; Research group Åsa Johansson
Keywords: bioinformatik bioinformatics genomics artificial intelligence statistics epidemiology genetics statistical modeling and machine learning genetik genomik pacbio bionformatics biomedical science biomedical research artificiell intelligens biostatistics artifical intelligence molecular-biology statistics cancer epidemiology statistical modeling
2021 - Senior Lecture in Medical Genetics, Dept. Immunology, Genetics and Pathology, Uppsala University, Sweden.
2017 – 2021 Associated Senior Lecture, Dept. Immunology, Genetics and Pathology, Uppsala University, Sweden.
2014 – 2017 Researcher, Dept. Immunology, Genetics and Pathology, Faculty of Medicine, Uppsala University, Sweden
2013 Docent in molecular epidemiology at Uppsala university
2010 – 2014 Bioinformatician / SSMF Post Doc, Uppsala Clinical Research Center (UCR), Uppsala Academic Hospital/Uppsala University, Sweden
2008 – 2010 Postdoctoral researcher. Collaboration between: Texas Biomedical Centre (former Southwest Foundation for Medical Research - SFBR), San Antonio, Texas, US (placement the first year) and Department of Cancer Research and Molecular Medicine, NTNU, Trondheim, Norway (placement the second year)
2007 PhD in Medical Genetics, Dept. Immunology, Genetics and Pathology, Uppsala University, Sweden
Every fourth individual in Europe suffer from a common disease, such as cancer, asthma, diabetes, or myocardial infarction. My research focus on identifying risk factors for common diseases, to investigate diagnostic biomarkers, and to build models to identify individuals of high disease risk. My goal is to broaden our knowledge of disease pathophysiology, which is important for developing drugs to prevent, delay progression, or relief symptoms of disease. Another goal is to enable identification of patients during an early stage of disease, or even before the disease has developed, when patients are more receptive to preventive treatments. A third goal is to identify patients that are better suited for certain treatments, or for using certain medications.
One of the main focus in my research is the genetic contribution to common diseases. During last 15 yeas, we have identified thousands of genetic variants influencing the risk of common diseases using genome-wide association studies (GWAS). Despite this success, only a fraction of the genetic contribution to disease has been identified, and most of the genetic factors remain unknown. Our ongoing projects in genetic epidemiology/genomics medicine focus on:
- Gene-based tests to identify the phenotypic effects of rare generic variants form whole-genome sequencing data
- Using Machine Learning / Artificial Intelligence tools for capturing the phenotypic effect of genetic variants
- Polygenic risk prediction to identify high risk individuals that are suited for preventive treatments
Beside genomics, my research also focuses on lifestyle factors and their effects on disease risk. Here we use traditional epidemiological approaches combined with Mendelian randomization to identify causal effects of disease related traits, with a special interest also in gene-environment interactions and sex-specific effects. Some ongoing projects include:
- The effect of obesity-related traits on risk of cancer. Here we use traditional epidemiological approaches combined with Mendelian randomization to identify causal effects of disease related traits
- The effect of endogenous and exogenous hormones (hormonal contraceptives and hormone replacement therapy) on disease risk with special focus on stroke as well as ovarian, endometrial and breast cancer.
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