Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Siri Ranlund; Maria Joao Rosa; Simone de Jong; James H Cole; Marinos Kyriakopoulos; Cynthia H Y Fu; Mitul A Mehta; Danai Dima

doi:10.1016/j.nicl.2018.10.008

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Siri Ranlund, Maria Joao Rosa, Simone de Jong, James H Cole, Marinos Kyriakopoulos, Cynthia H Y Fu, Mitul A Mehta, Danai Dima

Neuroimaging Department
Health Psychology Section, Psychology Department, Institute of Psychology, Psychiatry and Neuroscience, King's College London, United Kingdom.
1Department of Hepatology, Imperial College London, London, UK. 2Institute of Liver Studies at King's College School of Medicine at King's College Hospital, London, UK. 3Institute of Human Health and Performance, University College London, London, UK. 4Department of Asthma Allergy and Lung Biology, King's College London, London, UK. 5Division of Critical Care Medicine, University of Alberta, Edmonton, Canada.
Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ
NIHR BRC SLaM BioResource for Mental Health
Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust (SLaM) and King's College London, London, United Kingdom.
MRC Social
Social Genetic and Developmental Psychiatry Centre
National and Specialist Acorn Lodge Inpatient Children Unit
South London and Maudsley NHS Foundation Trust, London, UK.
Department of Child and Adolescent Psychiatry/Psychology, Department of Child and Adolescent Psychiatry, PO Box 2060, Rotterdam 3000 CB, The Netherlands.
Research Department of Epidemiology and Public Health, University College London, UK; School of Psychology, University of East London, London, UK.
Centre for Pain Research, University of Bath, Department of Experimental Psychology, University of Oxford, Department of Anaesthesiology & Pain Medicine, University of Washington, Clinical Health Psychology, University College London, and University College London Hospitals, and Psychology Department, Kings College London.
School of Education, Faculty of Arts and Social Sciences, UNSW Australia, Sydney NSW 2052, Australia.
Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei City

Research output: Contribution to journal › Article › peer-review

Abstract

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Original language	English
Pages (from-to)	1026-1036
Number of pages	11
Journal	NeuroImage. Clinical
Volume	20
DOIs	https://doi.org/10.1016/j.nicl.2018.10.008
Publication status	Published - 2018

Keywords

Adult
Aged
Autistic Disorder/genetics
Bipolar Disorder/genetics
Brain/pathology
Female
Genetic Predisposition to Disease
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Multifactorial Inheritance/genetics
Neuroimaging
Risk Factors
Schizophrenia/genetics

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10.1016/j.nicl.2018.10.008

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@article{4b5b8c5d7081492faa8bfd3c2da05c78,

title = "Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition",

abstract = "Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.",

keywords = "Adult, Aged, Autistic Disorder/genetics, Bipolar Disorder/genetics, Brain/pathology, Female, Genetic Predisposition to Disease, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multifactorial Inheritance/genetics, Neuroimaging, Risk Factors, Schizophrenia/genetics",

author = "Siri Ranlund and Rosa, {Maria Joao} and {de Jong}, Simone and Cole, {James H} and Marinos Kyriakopoulos and Fu, {Cynthia H Y} and Mehta, {Mitul A} and Danai Dima",

year = "2018",

doi = "10.1016/j.nicl.2018.10.008",

language = "English",

volume = "20",

pages = "1026--1036",

journal = "NeuroImage. Clinical",

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T1 - Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

AU - Ranlund, Siri

AU - Rosa, Maria Joao

AU - de Jong, Simone

AU - Cole, James H

AU - Kyriakopoulos, Marinos

AU - Fu, Cynthia H Y

AU - Mehta, Mitul A

AU - Dima, Danai

PY - 2018

Y1 - 2018

N2 - Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

AB - Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

KW - Adult

KW - Aged

KW - Autistic Disorder/genetics

KW - Bipolar Disorder/genetics

KW - Brain/pathology

KW - Female

KW - Genetic Predisposition to Disease

KW - Humans

KW - Magnetic Resonance Imaging

KW - Male

KW - Middle Aged

KW - Multifactorial Inheritance/genetics

KW - Neuroimaging

KW - Risk Factors

KW - Schizophrenia/genetics

U2 - 10.1016/j.nicl.2018.10.008

DO - 10.1016/j.nicl.2018.10.008

M3 - Article

C2 - 30340201

SN - 2213-1582

VL - 20

SP - 1026

EP - 1036

JO - NeuroImage. Clinical

JF - NeuroImage. Clinical

ER -

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Abstract

Keywords

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