Skip to content

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

Research output: Contribution to journalArticle (Academic Journal)

Standard

Is Population Structure in the genetic biobank era irrelevant, a challenge, or an opportunity? / Lawson, Daniel; Davies, Neil; Haworth, Simon; Ashraf, Bilal; Howe, Laurence; Crawford, Andrew; Hemani, Gibran; Davey Smith, George; Timpson, Nicholas.

In: Human Genetics, 27.04.2019.

Research output: Contribution to journalArticle (Academic Journal)

Harvard

APA

Vancouver

Author

Bibtex

@article{288622946bff4682b360df8604515ea1,
title = "Is Population Structure in the genetic biobank era irrelevant, a challenge, or an opportunity?",
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.",
author = "Daniel Lawson and Neil Davies and Simon Haworth and Bilal Ashraf and Laurence Howe and Andrew Crawford and Gibran Hemani and {Davey Smith}, George and Nicholas Timpson",
year = "2019",
month = apr,
day = "27",
doi = "10.1007/s00439-019-02014-8",
language = "English",
journal = "Human Genetics",
issn = "0340-6717",
publisher = "Springer Berlin Heidelberg",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Is Population Structure in the genetic biobank era irrelevant, a challenge, or an opportunity?

AU - Lawson, Daniel

AU - Davies, Neil

AU - Haworth, Simon

AU - Ashraf, Bilal

AU - Howe, Laurence

AU - Crawford, Andrew

AU - Hemani, Gibran

AU - Davey Smith, George

AU - Timpson, Nicholas

PY - 2019/4/27

Y1 - 2019/4/27

N2 - 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.

AB - 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.

U2 - 10.1007/s00439-019-02014-8

DO - 10.1007/s00439-019-02014-8

M3 - Article (Academic Journal)

C2 - 31030318

JO - Human Genetics

JF - Human Genetics

SN - 0340-6717

ER -