RT Dissertation/Thesis
T1 Kriging: applying geostatistical techniques to the genetic study of complex diseases
A1 Martínez Calvo, Laura
K1 geostatistics
K1 kriging
K1 molecular genetics
K1 genetic imputation
K1 multiple sclerosis
AB Complex diseases often display geographic distribution patterns.Therefore, the integration of genetic and environmental factors usinggeographic information systems (GIS) and specific statistical analysesthat consider the spatial dimension of data greatly assist in the researchof their gene-environment interactions (GxE). The objectives of thepresent work were to assess the application of a geostatisticalinterpolation technique (kriging) in the study of complex diseases witha distinct heterogeneous geographic distribution and to test itsperformance as an alternative to conventional genetic imputationmethods. Using multiple sclerosis as a case study, kriging demonstratedto be a flexible and valuable tool for integrating information fromvarious sources and at a different spatial resolution into a model thateasily allowed to visualize its heterogeneous geographic distribution inEurope and to explore the intertwined interactions between severalknown genetic and environmental risk factors. Even though theperformance of kriging did not surpass the results obtained with currentimputation techniques, this pilot study revealed a worse performance ofthe latter for rare variants in chromosomal regions with a low densityof markers.
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23569
UL http://hdl.handle.net/10347/23569
LA eng
DS Minerva
RD 4 may 2026