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Is Population Structure in the genetic biobank era irrelevant, a challenge, or an opportunity?

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages19
JournalHuman Genetics
Early online date27 Apr 2019
DOIs
DateAccepted/In press - 12 Apr 2019
DateE-pub ahead of print (current) - 27 Apr 2019

Abstract

Replicable genetic association signals have consistently been found through genome wide association studies in recent years. The recent dramatic expansion of study sizes improves power of estimation of effect sizes, genomic prediction, causal inference, and polygenic selection, but it simultaneously increases susceptibility of these methods to bias due to subtle population structure. Standard methods using genetic principal components to correct for structure might not always be appropriate and we use a simulation study to illustrate when correction might be ineffective for avoiding biases. New methods such as trans-ethnic modeling and chromosome painting allow for a richer understanding of the relationship between traits and population structure. We illustrate the arguments using real examples (stroke and educational attainment) and provide a more nuanced understanding of population structure, which is set to be revisited as a critical aspect of future analyses in genetic epidemiology. We also make simple recommendations for how problems can be avoided in the future. Our results have particular importance for the implementation of GWAS meta-analysis, for prediction of traits, and for causal inference.

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Springer at https://link.springer.com/article/10.1007%2Fs00439-019-02014-8 . Please refer to any applicable terms of use of the publisher.

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