Genetic architecture of glycomic and lipidomic phenotypes in isolated populations

This dataset contains the extended supplementary tables from the PhD thesis entitled "Genetic architecture of glycomic and lipidomic phenotypes in isolated populations" by Arianna Landini. Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as ‘proxies’ for traits or diseases of interest. They are regarded as “intermediate” traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of the related research is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health