Developing and evaluating polygenic risk prediction models for stratified disease prevention

By N. Chatterjee, Jianxin Shi, M. García-Closas. Nature reviews genetics, 2016

677 citations recorded by the source corpus.

Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/870d0421232ab5c1e8d6e394612e1d3dada22635

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Cancer Progress and Priorities: Lung Cancer (2019, background): “Data from these large GWA studies could be leveraged toward development of risk models based on polygenic risk scores defined by the combination of SNPs that yield the best predictive model (140).”
Polygenic prediction of weight and obesity trajectories from birth to adulthood (2019, positive): “Predictive power will likely continue to improve in coming years as a function of larger discovery GWAS studies and improved computational algorithms that integrate functional genomics annotation, variant-variant interactions, and rare large-effect variants into the predictive model (Chatterjee et a”
Improved polygenic prediction by Bayesian multiple regression on summary statistics (2019, neutral): “L(β; b, D, B) := N (b; D− 1 2 BD (1)2 β, D− 1 2 BD− (1)2 ), (4)”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “To maximize the translational potential of PRS, statistical and computational methods are needed that can (1) jointly model genetic markers across the genome to make full use of the available information while accounting for local linkage disequilibrium (LD) structures; (2) accommodate varying effec”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, methodology): “Second, fine-mapping results (posterior mean effect sizes) can be used to compute transethnic polygenic risk scores 48 which may be less sensitive to LD differences between populations than existing methods 49,50 .”
Large-scale whole-genome sequencing of three diverse Asian populations in Singapore (2018, neutral): “Population genetic information has become the cornerstone of precision medicine, with wide applications in the diagnosis of Mendelian diseases,(10,11) optimization of therapeutic treatments,(12) drug development,(13) and disease risk prediction and stratification.(14)”
Improving reporting standards for polygenic scores in risk prediction studies (2020, positive): “polygenic scores in risk prediction studies Hannah Wand1,2,31, Samuel A. Lambert3,4,5,6,7,31, Cecelia Tamburro8, Michael A. Iacocca1, Jack W. O’Sullivan1,2, Catherine Sillari8, Iftikhar J. Kullo9, Robb Rowley8, Jacqueline S. Dron10,11, Deanna Brockman10, Eric Venner12, Mark I. McCarthy13,14, Antonis”