scholarly journals Negative selection on complex traits limits genetic risk prediction accuracy between populations

2019 ◽  
Author(s):  
Arun Durvasula ◽  
Kirk E. Lohmueller

Accurate genetic risk prediction is a key goal for medical genetics and great progress has been made toward identifying individuals with extreme risk across several traits and diseases (Collins and Varmus, 2015). However, many of these studies are done in predominantly European populations (Bustamante et al., 2011; Popejoy and Fullerton, 2016). Although GWAS effect sizes correlate across ancestries (Wojcik et al., 2019), risk scores show substantial reductions in accuracy when applied to non-European populations (Kim et al., 2018; Martin et al., 2019; Scutari et al., 2016). We use simulations to show that human demographic history and negative selection on complex traits result in population specific genetic architectures. For traits under moderate negative selection, ~50% of the heritability can be accounted for by variants in Europe that are absent from Africa. We show that this directly leads to poor performance in risk prediction when using variants discovered in Europe to predict risk in African populations, especially in the tails of the risk distribution. To evaluate the impact of this effect in genomic data, we built a Bayesian model to stratify heritability between European-specific and shared variants and applied it to 43 traits and diseases in the UK Biobank. Across these phenotypes, we find ~50% of the heritability comes from European-specific variants, setting an upper bound on the accuracy of genetic risk prediction in non-European populations using effect sizes discovered in European populations. We conclude that genetic association studies need to include more diverse populations to enable to utility of genetic risk prediction in all populations.

2016 ◽  
Author(s):  
Yiming Hu ◽  
Qiongshi Lu ◽  
Ryan Powles ◽  
Xinwei Yao ◽  
Fang Fang ◽  
...  

AbstractGenome wide association studies have identified numerous regions in the genome associated with hundreds of human diseases. Building accurate genetic risk prediction models from these data will have great impacts on disease prevention and treatment strategies. However, prediction accuracy remains moderate for most diseases, which is largely due to the challenges in identifying all the disease-associated variants and accurately estimating their effect sizes. We introduce AnnoPred, a principled framework that incorporates diverse functional annotation data to improve risk prediction accuracy, and demonstrate its performance on multiple human complex diseases.


2020 ◽  
Vol 107 (4) ◽  
pp. 788-789
Author(s):  
Alicia R. Martin ◽  
Christopher R. Gignoux ◽  
Raymond K. Walters ◽  
Genevieve L. Wojcik ◽  
Benjamin M. Neale ◽  
...  

2019 ◽  
Vol 29 (1) ◽  
pp. 44-56
Author(s):  
Changshuai Wei ◽  
Ming Li ◽  
Yalu Wen ◽  
Chengyin Ye ◽  
Qing Lu

Genetic association studies using high-throughput genotyping and sequencing technologies have identified a large number of genetic variants associated with complex human diseases. These findings have provided an unprecedented opportunity to identify individuals in the population at high risk for disease who carry causal genetic mutations and hold great promise for early intervention and individualized medicine. While interest is high in building risk prediction models based on recent genetic findings, it is crucial to have appropriate statistical measurements to assess the performance of a genetic risk prediction model. Predictiveness curves were recently proposed as a graphic tool for evaluating a risk prediction model on the basis of a single continuous biomarker. The curve evaluates a risk prediction model for classification performance as well as its usefulness when applied to a population. In this article, we extend the predictiveness curve to measure the collective contribution of multiple genetic variants. We further propose a nonparametric, U-statistics-based measurement, referred to as the U-Index, to quantify the performance of a multi-locus predictiveness curve. In particular, a global U-Index and a partial U-Index can be used in the general population and a subpopulation of particular clinical interest, respectively. Through simulation studies, we demonstrate that the proposed U-Index has advantages over several existing summary statistics under various disease models. We also show that the partial U-Index can have its own uniqueness when rare variants have a substantial contribution to disease risk. Finally, we use the proposed predictiveness curve and its corresponding U-Index to evaluate the performance of a genetic risk prediction model for nicotine dependence.


2017 ◽  
Vol 100 (4) ◽  
pp. 635-649 ◽  
Author(s):  
Alicia R. Martin ◽  
Christopher R. Gignoux ◽  
Raymond K. Walters ◽  
Genevieve L. Wojcik ◽  
Benjamin M. Neale ◽  
...  

2011 ◽  
Vol 4 (2) ◽  
pp. 206-209 ◽  
Author(s):  
A. Cecile J.W. Janssens ◽  
John P.A. Ioannidis ◽  
Cornelia M. van Duijn ◽  
Julian Little ◽  
Muin J. Khoury

Sign in / Sign up

Export Citation Format

Share Document