González Manteiga, WenceslaoCostas Costas, JavierCastro Prado, Fernando2024-09-302024http://hdl.handle.net/10347/34959Nowadays, genetics studies large amounts of very diverse variables. Mathematical statistics has evolved in parallel to its applications, with much recent interest high-dimensional settings. In the genetics of human common disease, a number of relevant problems can be formulated as tests of independence. We show how defining adequate premetric structures on the support spaces of the genetic data allows for novel approaches to such testing. This yields a solid theoretical framework, which reflects the underlying biology, and allows for computationally-efficient implementations. For each problem, we provide mathematical results, simulations and the application to real data.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Xenómicaestatística en alta dimensiónespazos métricosmétodos non paramétricos120914 Técnicas de predicción estadística241007 Genética humanadoctoral thesisopen access