Version 1
: Received: 27 June 2024 / Approved: 27 June 2024 / Online: 1 July 2024 (08:33:20 CEST)
How to cite:
Bouttle, K.; Ingold, N.; O’Mara, T. A. Using Genetics to Investigate Relationships Between Phenotypes: Application to Endometrial Cancer. Preprints2024, 2024061957. https://doi.org/10.20944/preprints202406.1957.v1
Bouttle, K.; Ingold, N.; O’Mara, T. A. Using Genetics to Investigate Relationships Between Phenotypes: Application to Endometrial Cancer. Preprints 2024, 2024061957. https://doi.org/10.20944/preprints202406.1957.v1
Bouttle, K.; Ingold, N.; O’Mara, T. A. Using Genetics to Investigate Relationships Between Phenotypes: Application to Endometrial Cancer. Preprints2024, 2024061957. https://doi.org/10.20944/preprints202406.1957.v1
APA Style
Bouttle, K., Ingold, N., & O’Mara, T. A. (2024). Using Genetics to Investigate Relationships Between Phenotypes: Application to Endometrial Cancer. Preprints. https://doi.org/10.20944/preprints202406.1957.v1
Chicago/Turabian Style
Bouttle, K., Nathan Ingold and Tracy A. O’Mara. 2024 "Using Genetics to Investigate Relationships Between Phenotypes: Application to Endometrial Cancer" Preprints. https://doi.org/10.20944/preprints202406.1957.v1
Abstract
Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetic that hold promise for advancing the understanding of endometrial cancer and other complex diseases.
Keywords
genome-wide association study; endometrial cancer; cross-phenotype; statistical genetics
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
