Imperial College London
Dr Weihua Zhang

DrWeihuaZhang

Faculty of Medicine, School of Public Health

Honorary Research Associate
 
 
 
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Contact

 

+44 (0)20 7594 1612weihua.zhang

 
 
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Location

 

165Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Márquez-Luna:2017:10.1002/gepi.22083,
author = {Márquez-Luna, C and Loh, P-R and South, Asian Type 2 Diabetes SAT2D Consortium and SIGMA, Type 2 Diabetes Consortium and Price, AL},
doi = {10.1002/gepi.22083},
journal = {Genet Epidemiol},
pages = {811--823},
title = {Multiethnic polygenic risk scores improve risk prediction in diverse populations.},
url = {http://dx.doi.org/10.1002/gepi.22083},
volume = {41},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff  = 40k) and Latino training data in small sample size (Neff  = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2  = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff  = 40k) and South Asian (Neff  = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
AU  - Márquez-Luna,C
AU  - Loh,P-R
AU  - South,Asian Type 2 Diabetes SAT2D Consortium
AU  - SIGMA,Type 2 Diabetes Consortium
AU  - Price,AL
DO  - 10.1002/gepi.22083
EP  - 823
PY  - 2017///
SP  - 811
TI  - Multiethnic polygenic risk scores improve risk prediction in diverse populations.
T2  - Genet Epidemiol
UR  - http://dx.doi.org/10.1002/gepi.22083
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29110330
VL  - 41
ER  -
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