In Search of Complex Disease Risk through Genome Wide Association Studies

Lorena Alonso, Ignasi Morán, Cecilia Salvoro and David Torrents
Additional contact information
Lorena Alonso: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
Ignasi Morán: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
Cecilia Salvoro: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
David Torrents: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain

Mathematics, 2021, vol. 9, issue 23, 1-26

Abstract: The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

Keywords: bioinformatics; genomics; GWAS; chi-square; logistic regression; generalized linear models; Markov models; imputation; machine learning; polygenic risk scores (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/23/3083/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/23/3083/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:23:p:3083-:d:691634

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().