Understanding polygenic models, their development and the potential application of polygenic scores in healthcare
Open Access
- 1 November 2020
- journal article
- review article
- Published by BMJ in Journal of Medical Genetics
- Vol. 57 (11), 725-732
- https://doi.org/10.1136/jmedgenet-2019-106763
Abstract
The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.This publication has 79 references indexed in Scilit:
- Power and Predictive Accuracy of Polygenic Risk ScoresPLoS Genetics, 2013
- Genotype Imputation with Thousands of GenomesG3 Genes|Genomes|Genetics, 2011
- Genomics in the Post-GWAS EraSeminars in Liver Disease, 2011
- An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studiesBioinformatics, 2011
- A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analysesThe Lancet, 2010
- Data quality control in genetic case-control association studiesNature Protocols, 2010
- Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how?Human Genetics, 2009
- Prediction of individual genetic risk to disease from genome-wide association studiesGenome Research, 2007
- The evaluation of genetic testsJournal of Public Health, 2007
- Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetesNature Genetics, 2007