Research Review: How to interpret associations between polygenic scores, environmental risks, and phenotypes

TY - JOUR

T1 - Research Review

T2 - How to interpret associations between polygenic scores, environmental risks, and phenotypes

AU - Pingault, Jean Baptiste

AU - Allegrini, Andrea G.

AU - Odigie, Tracy

AU - Frach, Leonard

AU - Baldwin, Jessie R.

AU - Rijsdijk, Frühling

AU - Dudbridge, Frank

N1 - Funding Information: J‐B.P., A.G.A., and L.F. are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 863981). J‐B.P. is supported by the Medical Research Foundation 2018 Emerging Leaders 1st Prize in Adolescent Mental Health (MRF‐160‐0002‐ELP‐PINGA). J.R.B. is funded by a Wellcome Trust Sir Henry Wellcome fellowship (grant 215917/Z/19/Z). The authors have declared that they have no competing or potential conflicts of interest. Key points Funding Information: J-B.P., A.G.A., and L.F. are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 863981). J-B.P. is supported by the Medical Research Foundation 2018 Emerging Leaders 1st Prize in Adolescent Mental Health (MRF-160-0002-ELP-PINGA). J.R.B. is funded by a Wellcome Trust Sir Henry Wellcome fellowship (grant 215917/Z/19/Z). The authors have declared that they have no competing or potential conflicts of interest.Key points Many studies have now uncovered associations between polygenic scores and a vast array of phenotypes and environmental exposures. Such associations are much more complex to interpret than initially thought due to biases present in genetic and epidemiological studies. Here, we show how those biases can profoundly affect the results of analyses commonly implemented in developmental research, such as mediation or adjustment for confounding. Awareness of such complexities is essential to ensure that polygenic score research actually contributes to our understanding of child psychology and psychiatry. Many studies have now uncovered associations between polygenic scores and a vast array of phenotypes and environmental exposures. Such associations are much more complex to interpret than initially thought due to biases present in genetic and epidemiological studies. Here, we show how those biases can profoundly affect the results of analyses commonly implemented in developmental research, such as mediation or adjustment for confounding. Awareness of such complexities is essential to ensure that polygenic score research actually contributes to our understanding of child psychology and psychiatry. Publisher Copyright: © 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

PY - 2022/10

Y1 - 2022/10

N2 - Background: Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases. Methods: Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases. Results: Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter-intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene–environment correlations; and (c) adjusting an exposure-outcome association for a polygenic score can increase rather than decrease bias. Conclusions: Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

AB - Background: Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases. Methods: Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases. Results: Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter-intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene–environment correlations; and (c) adjusting an exposure-outcome association for a polygenic score can increase rather than decrease bias. Conclusions: Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

KW - biases

KW - environment

KW - epidemiology

KW - phenotypes

KW - Polygenic scores

UR - http://www.scopus.com/inward/record.url?scp=85127220923&partnerID=8YFLogxK

U2 - 10.1111/jcpp.13607

DO - 10.1111/jcpp.13607

M3 - Review article

AN - SCOPUS:85127220923

SN - 0021-9630

VL - 63

SP - 1125

EP - 1139

JO - Journal of Child Psychology and Psychiatry and Allied Disciplines

JF - Journal of Child Psychology and Psychiatry and Allied Disciplines

IS - 10

ER -