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 and psychiatric traits. 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
University roles and responsibilities
- Academic Integrity Officer
- Ethics Committee member
- Personal tutor
- PTY tutor
- Research supervisor
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, Igors Pupko, University of Surrey
2019-present, Liudmila Zudina, University of Surrey
2021-present, Yuwei Jiao, Imperial College London
2021-present, Wenjie Li, Imperial College London
Completed postgraduate research projects I have supervised
2015-2021, Mila Desi Anasanti, Imperial College London
2020, Suruthi Shasheetharan, Imperial College London
2019, Jared Maina, Imperial College London
2018, Laurie Prelot, Imperial College London
2018, Edita Pileckyte, Imperial College London
2017, Kelsey Gibbs, Imperial College London
2016, Longda Jiang, Imperial College London
2015, Annique Claringbould, 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
We assessed the predictive ability of a combined genetic variant panel for the risk of recurrent pregnancy loss (RPL) through a case-control study. Our study sample was from Ukraine and included 114 cases with idiopathic RPL and 106 controls without any pregnancy losses/complications and with at least one healthy child. We genotyped variants within 12 genetic loci reflecting the main biological pathways involved in pregnancy maintenance: blood coagulation (F2, F5, F7, GP1A), hormonal regulation (ESR1, ADRB2), endometrium and placental function (ENOS, ACE), folate metabolism (MTHFR) and inflammatory response (IL6, IL8, IL10). We showed that a genetic risk score (GRS) calculated from the 12 variants was associated with an increased risk of RPL (odds ratio 1.56, 95% CI: 1.21, 2.04, p = 8.7 × 10−4). The receiver operator characteristic (ROC) analysis resulted in an area under the curve (AUC) of 0.64 (95% CI: 0.57, 0.72), indicating an improved ability of the GRS to classify women with and without RPL. Ιmplementation of the GRS approach can help define women at higher risk of complex multifactorial conditions such as RPL. Future well-powered genome-wide association studies will help in dissecting biological pathways previously unknown for RPL and further improve the identification of women with RPL susceptibility.
Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
Introduction The role of TOMM40-APOE 19q13.3 region variants is well documented in Alzheimer's disease (AD) but remains contentious in dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). Methods We dissected genetic profiles within the TOMM40-APOE region in 451 individuals from four European brain banks, including DLB and PDD cases with/without neuropathological evidence of AD-related pathology and healthy controls. Results TOMM40-L/APOE-ε4 alleles were associated with DLB (ORTOMM40-L = 3.61; P value = 3.23 × 10−9; ORAPOE-ε4 = 3.75; P value = 4.90 × 10−10) and earlier age at onset of DLB (HRTOMM40-L = 1.33, P value = .031; HRAPOE-ε4 = 1.46, P value = .004), but not with PDD. The TOMM40-L/APOE-ε4 effect was most pronounced in DLB individuals with concomitant AD pathology (ORTOMM40-L = 4.40, P value = 1.15 × 10−6; ORAPOE-ε4 = 5.65, P value = 2.97 × 10−8) but was not significant in DLB without AD. Meta-analyses combining all APOE-ε4 data in DLB confirmed our findings (ORDLB = 2.93, P value = 3.78 × 10−99; ORDLB+AD = 5.36, P value = 1.56 × 10−47). Discussion APOE-ε4/TOMM40-L alleles increase susceptibility and risk of earlier DLB onset, an effect explained by concomitant AD-related pathology. These findings have important implications in future drug discovery and development efforts in DLB.
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