Marika Kaakinen holds an MSc degree in statistics and a PhD in genetic and life-course epidemiology from the University of Oulu, Finland. Before joining the University of Surrey in April 2019, she worked as a Marie Curie Fellow, followed by a post as a Research Associate at Imperial College London, UK.
She develops and applies statistical analysis methods for genomic/omics research of complex human traits, including type 2 diabetes. She works with various types of omics data, including metabolomics, proteomics, gut microbiome and whole-genome sequencing data. She has developed/contributed to the following software tools: MARV and SCOPA.She has also contributed to numerous GWAS within several consortia, including DIAGRAM (DIAbetes Genetics Replication And Meta-analysis), MAGIC (Meta-Analyses of Glucose and Insulin related traits), ENGAGE (European Network of Genomic and Genetic Epidemiology), EGG (Early Growth Genetics) and SSGAC (Social Science Genetic Association Consortium).
Areas of specialism
Affiliations and memberships
Dr Kaakinen's overall research interest is to develop and apply statistical methodology to better understand complex human traits in order to improve prevention and treatment of diseases. She has contributed to numerous genome-wide association studies (GWAS) of several complex traits, leading to the discovery of hundreds of genetic variants associated with these traits. More recently, she has developed software for multi-phenotype GWAS to improve power for the analysis as well as to discover potential pleiotropic and other multi-phenotype effects. She is keen on finding new ways to utilise the huge amounts of data that are generated constantly, by applying methods, such as machine learning or methods based on already published summary statistics.
Northern Finland Birth Cohorts, University of Oulu, Finland.
Estonian Genome Center, University of Tartu, Estonia.
University of Lausanne, Switzerland.
Pondicherry University, Puducherry, India.
Stremble Ventures, AVVA Pharmaceuticals and Europan University Cyprus, Cyprus.
Imperial College London, UK.
Postgraduate research supervision
2019-present, Mr Igors Pupko, PhD project, University of Surrey
2019-present, Ms Liudmila Zudina, PhD project, University of Surrey
2021-present, Ms Yuwei Jiao, MSc project, Imperial College London
2021-present, Mr Wenjie Li, MSc project, Imperial College London
Completed postgraduate research projects I have supervised
2015-2021, Ms Mila Desi Anasanti, PhD project, Imperial College London
2020, Ms Suruthi Shasheetharan, MSc thesis, Imperial College London
2019, Mr Jared Maina, MSc thesis, Imperial College London
2018, Ms Laurie Prelot, MSc thesis, Imperial College London
2018, Ms Edita Pileckyte, MSc thesis, Imperial College London
2017, Ms Kelsey Gibbs, MSc thesis, Imperial College London
2016, Mr Longda Jiang, MSc thesis, Imperial College London
2015, Ms Annique Claringbould, MSc thesis, Imperial College London
Courses I teach on
CPD - Introduction to the statistical analysis of genome-wide association studies (Course organiser and lecturer)
BMS2043 - Analytical and Clinical Biochemistry (Lecturer in Statistics and Data-Analysis)
BMS3048 - BSc in Biomedical Sciences dissertation project (Student supervision)
2016-present, Omics module for the MSc in Genomic Medicine, Imperial College London, London, UK (Lecturer)
Courses I teach on
Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.
A full list of my publications can be found under my Google Scholar profile https://scholar.google.com/citations?user=MBCp0McAAAAJ&hl=fi