Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment

Genetic factors and socioeconomic (SES) inequalities play a large role in educational attainment, and both have been associated with variations in brain structure and cognition. However, genetics and SES are correlated, and no prior study has assessed their neural effects independently. Here we used polygenic score for educational attainment (EduYears-PGS) as well as SES, in a longitudinal study of 551 adolescents, to tease apart genetic and environmental effects on brain development and cognition. Subjects received a structural MRI scan at ages 14 and 19. At both time-points, they performed three working memory (WM) tasks. SES and EduYears-PGS were correlated (r = 0.27) and had both common and independent effects on brain structure and cognition. Specifically, lower SES was related to less total cortical surface area and lower WM. EduYears-PGS was also related to total cortical surface area, but in addition had a regional effect on surface area in the right parietal lobe, a region related to non-verbal cognitive functions, including mathematics, problem solving and WM. SES, but not EduYears-PGS, affected the change in total cortical surface area from age 14 to 19. This is the first study demonstrating the regional effects of EduYears-PGS and the independent role of SES on cognitive function and brain development. It suggests that the SES effects are substantial, affect global aspects of cortical development, and exert a persistent influence on brain development during adolescence.

[1]  C. Chabris,et al.  The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. , 2019, Intelligence.

[2]  R. Plomin,et al.  Predicting educational achievement from genomic measures and socioeconomic status , 2019, bioRxiv.

[3]  E. Ferrer,et al.  Time-lagged associations between cognitive and cortical development from childhood to early adulthood. , 2019, Developmental psychology.

[4]  Cassidy L. McDermott,et al.  Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology , 2018, The Journal of Neuroscience.

[5]  C. Büchel,et al.  Epigenetic variance in dopamine D2 receptor: a marker of IQ malleability? , 2018, Translational Psychiatry.

[6]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[7]  M. Farah.  Socioeconomic status and the brain: prospects for neuroscience-informed policy , 2018, Nature Reviews Neuroscience.

[8]  Bruce Fischl,et al.  False positive rates in surface-based anatomical analysis , 2018, NeuroImage.

[9]  Annchen R. Knodt,et al.  A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains , 2018, bioRxiv.

[10]  Cayce J. Hook,et al.  A meta-analysis of the relationship between socioeconomic status and executive function performance among children. , 2018, Developmental science.

[11]  Bjarni V. Halldórsson,et al.  The nature of nurture: Effects of parental genotypes , 2017, Science.

[12]  Russell T. Shinohara,et al.  Harmonization of cortical thickness measurements across scanners and sites , 2017, NeuroImage.

[13]  Michael Moutoussis,et al.  Developmental cognitive neuroscience using latent change score models: A tutorial and applications , 2017, Developmental Cognitive Neuroscience.

[14]  S. Horvath,et al.  An epigenome-wide association study meta-analysis of educational attainment , 2017, Molecular Psychiatry.

[15]  M. Farah.  The Neuroscience of Socioeconomic Status: Correlates, Causes, and Consequences , 2017, Neuron.

[16]  Krzysztof J. Gorgolewski,et al.  MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites , 2016, bioRxiv.

[17]  C. Pantelis,et al.  Role of Positive Parenting in the Association Between Neighborhood Social Disadvantage and Brain Development Across Adolescence , 2017, JAMA psychiatry.

[18]  K. Mills,et al.  Structural brain development: A review of methodological approaches and best practices , 2017, Developmental Cognitive Neuroscience.

[19]  Torkel Klingberg,et al.  Specialization of the Right Intraparietal Sulcus for Processing Mathematics During Development , 2016, Cerebral cortex.

[20]  Daniel A. Hackman,et al.  Community Socioeconomic Disadvantage in Midlife Relates to Cortical Morphology via Neuroendocrine and Cardiometabolic Pathways , 2015, Cerebral cortex.

[21]  Laura Castro-Schilo,et al.  Preliminary Detection of Relations Among Dynamic Processes With Two-Occasion Data , 2016 .

[22]  Alan C. Evans,et al.  Trajectories of cortical thickness maturation in normal brain development — The importance of quality control procedures , 2016, NeuroImage.

[23]  Darren J. Yeo,et al.  The relation between 1st grade grey matter volume and 2nd grade math competence , 2016, NeuroImage.

[24]  R. Plomin,et al.  Genetic link between family socioeconomic status and children's educational achievement estimated from genome-wide SNPs , 2015, Molecular Psychiatry.

[25]  Martin Voracek,et al.  Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean? , 2015, Neuroscience & Biobehavioral Reviews.

[26]  Tanya M. Evans,et al.  Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities , 2015, The Journal of Neuroscience.

[27]  Alan C. Evans,et al.  Changes in thickness and surface area of the human cortex and their relationship with intelligence. , 2015, Cerebral cortex.

[28]  Thomas E. Nichols,et al.  Common genetic variants influence human subcortical brain structures , 2015, Nature.

[29]  B. J. Casey,et al.  Family Income, Parental Education and Brain Structure in Children and Adolescents , 2015, Nature Neuroscience.

[30]  M. Dylan Tisdall,et al.  Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.

[31]  Jack Euesden,et al.  PRSice: Polygenic Risk Score software , 2014, Bioinform..

[32]  Ian J. Deary,et al.  Common genetic variants associated with cognitive performance identified using the proxy-phenotype method , 2014, Proceedings of the National Academy of Sciences.

[33]  Lara M. Wierenga,et al.  Unique developmental trajectories of cortical thickness and surface area , 2014, NeuroImage.

[34]  R. Plomin,et al.  Genetic influence on family socioeconomic status and children's intelligence , 2014, Intelligence.

[35]  E. Iordache,et al.  The paired t-test as a simple latent change score model , 2013, Front. Psychol..

[36]  I. Deary,et al.  Life-course pathways to psychological distress: a cohort study , 2013, BMJ Open.

[37]  M. Posner,et al.  The impact of poverty on the development of brain networks , 2012, Front. Hum. Neurosci..

[38]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[39]  Verena D. Schmittmann,et al.  Qgraph: Network visualizations of relationships in psychometric data , 2012 .

[40]  K. Magnuson,et al.  Socioeconomic status and cognitive functioning: moving from correlation to causation. , 2012, Wiley interdisciplinary reviews. Cognitive science.

[41]  Iroise Dumontheil,et al.  Brain activity during a visuospatial working memory task predicts arithmetical performance 2 years later. , 2012, Cerebral cortex.

[42]  Stanislav Kolenikov,et al.  Testing Negative Error Variances , 2012 .

[43]  Armin Raznahan,et al.  How Does Your Cortex Grow? , 2011, The Journal of Neuroscience.

[44]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..

[45]  M. Rietschel,et al.  The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology , 2010, Molecular Psychiatry.

[46]  G. Abecasis,et al.  MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes , 2010, Genetic epidemiology.

[47]  Jason B. Mattingley,et al.  Spatial working memory and spatial attention rely on common neural processes in the intraparietal sulcus , 2010, NeuroImage.

[48]  Martha J. Farah,et al.  Socioeconomic status and the brain: mechanistic insights from human and animal research , 2010, Nature Reviews Neuroscience.

[49]  L. Palaniyappan.  Computing cortical surface measures in schizophrenia , 2010, British Journal of Psychiatry.

[50]  V. Michel,et al.  Recruitment of an Area Involved in Eye Movements During Mental Arithmetic , 2009, Science.

[51]  J. Morris,et al.  Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area , 2009, Neurobiology of Aging.

[52]  J. Mcardle.  Latent variable modeling of differences and changes with longitudinal data. , 2009, Annual review of psychology.

[53]  Stanislav Kolenikov,et al.  Constrained versus unconstrained estimation in structural equation modeling. , 2008, Psychological methods.

[54]  S. Wiebe,et al.  Short-Term Memory, Working Memory, and Executive Functioning in Preschoolers: Longitudinal Predictors of Mathematical Achievement at Age 7 Years , 2008, Developmental neuropsychology.

[55]  Alan C. Evans,et al.  Neurodevelopmental Trajectories of the Human Cerebral Cortex , 2008, The Journal of Neuroscience.

[56]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[57]  R. Lynn,et al.  National differences in intelligence and educational attainment , 2007 .

[58]  S. Dehaene,et al.  A Magnitude Code Common to Numerosities and Number Symbols in Human Intraparietal Cortex , 2007, Neuron.

[59]  Ayse Pinar Saygin,et al.  Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data , 2006, NeuroImage.

[60]  Avishai Henik,et al.  Are numbers special? The comparison systems of the human brain investigated by fMRI , 2005, Neuropsychologia.

[61]  Selcuk R. Sirin.  Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research , 2005 .

[62]  N. Lazar,et al.  Maturation of cognitive processes from late childhood to adulthood. , 2004, Child development.

[63]  John R. Anderson,et al.  The change of the brain activation patterns as children learn algebra equation solving. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Craig K. Enders,et al.  The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models , 2001 .

[65]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[66]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[67]  Paul Taubman,et al.  The Determinants of Earnings: Genetics, Family, and Other Environments; A Study of White Male Twins , 1976 .