Decoding genetic associations of female reproductive health traits

Genetic variation, particularly single-nucleotide polymorphisms (SNPs), has shown to influence health and disease susceptibility for multiple complex diseases. This has been supported by many genome-wide association studies (GWAS) which have unravelled thousands of genetic variants in association with health traits. However, studies of genetic variation underlying female reproductive health traits remain limited, with only a small proportion of all GWAS focusing on this area. Currently, the availability of population-based biobanks, such as the Estonian Biobank, and population-based birth cohorts such as the Northern Finland Birth Cohort 1966, provide a valuable framework for studies in this field. Additionally, GWAS set the ground to move from genetic variations to potentially affected genes, proteins, biological pathways and tissues, serving as a foundation for forming hypotheses that can be validated through functional experiments. Understanding the genetic variation that is associated with a trait is important as it can provide insights into disease aetiology, prediction, and potential treatments. Another notable outcome of GWAS is the construction of polygenic risk scores (PRS), which provide a summary of an individual's genetic predisposition for a certain trait. PRS has the potential to predict disease susceptibility and serve as a tool to further explore disease biology, for example, relationships with a certain trait’s comorbidities. PRS have attracted massive attention in the pursuit of the so-called personalised medicine. This thesis aims to decode the genetic underpinnings of selected female reproductive health traits through GWAS and explores PRS as a tool for both risk stratification and for informing a trait’s biology. In conclusion, this research, placed at the intersection of genomics and female reproductive health, is poised to address a knowledge gap in both national and international research systems through the availability of large genomic datasets coupled with electronic health records (EHRs) and biological measurements.