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. 2016 May 26;533(7604):539-42.
doi: 10.1038/nature17671. Epub 2016 May 11.

Genome-wide Association Study Identifies 74 Loci Associated With Educational Attainment

Aysu Okbay  1   2   3 Jonathan P Beauchamp  4 Mark Alan Fontana  5 James J Lee  6 Tune H Pers  7   8   9   10 Cornelius A Rietveld  1   2   3 Patrick Turley  4 Guo-Bo Chen  11 Valur Emilsson  12   13 S Fleur W Meddens  3   14   15 Sven Oskarsson  16 Joseph K Pickrell  17 Kevin Thom  18 Pascal Timshel  8   19 Ronald de Vlaming  1   2   3 Abdel Abdellaoui  20 Tarunveer S Ahluwalia  9   21   22 Jonas Bacelis  23 Clemens Baumbach  24   25 Gyda Bjornsdottir  26 Johannes H Brandsma  27 Maria Pina Concas  28 Jaime Derringer  29 Nicholas A Furlotte  30 Tessel E Galesloot  31 Giorgia Girotto  32 Richa Gupta  33 Leanne M Hall  34   35 Sarah E Harris  36   37 Edith Hofer  38   39 Momoko Horikoshi  40   41 Jennifer E Huffman  42 Kadri Kaasik  43 Ioanna P Kalafati  44 Robert Karlsson  45 Augustine Kong  26 Jari Lahti  43   46 Sven J van der Lee  2 Christiaan deLeeuw  14   47 Penelope A Lind  48 Karl-Oskar Lindgren  16 Tian Liu  49 Massimo Mangino  50   51 Jonathan Marten  42 Evelin Mihailov  52 Michael B Miller  6 Peter J van der Most  53 Christopher Oldmeadow  54   55 Antony Payton  56   57 Natalia Pervjakova  52   58 Wouter J Peyrot  59 Yong Qian  60 Olli Raitakari  61 Rico Rueedi  62   63 Erika Salvi  64 Börge Schmidt  65 Katharina E Schraut  66 Jianxin Shi  67 Albert V Smith  12   68 Raymond A Poot  27 Beate St Pourcain  69   70 Alexander Teumer  71 Gudmar Thorleifsson  26 Niek Verweij  72 Dragana Vuckovic  32 Juergen Wellmann  73 Harm-Jan Westra  8   74   75 Jingyun Yang  76   77 Wei Zhao  78 Zhihong Zhu  11 Behrooz Z Alizadeh  53   79 Najaf Amin  2 Andrew Bakshi  11 Sebastian E Baumeister  71   80 Ginevra Biino  81 Klaus Bønnelykke  21 Patricia A Boyle  76   82 Harry Campbell  66 Francesco P Cappuccio  83 Gail Davies  36   84 Jan-Emmanuel De Neve  85 Panos Deloukas  86   87 Ilja Demuth  88   89 Jun Ding  60 Peter Eibich  90   91 Lewin Eisele  65 Niina Eklund  58 David M Evans  69   92 Jessica D Faul  93 Mary F Feitosa  94 Andreas J Forstner  95   96 Ilaria Gandin  32 Bjarni Gunnarsson  26 Bjarni V Halldórsson  26   97 Tamara B Harris  98 Andrew C Heath  99 Lynne J Hocking  100 Elizabeth G Holliday  54   55 Georg Homuth  101 Michael A Horan  102 Jouke-Jan Hottenga  20 Philip L de Jager  8   103   104 Peter K Joshi  66 Astanand Jugessur  105 Marika A Kaakinen  106 Mika Kähönen  107   108 Stavroula Kanoni  86 Liisa Keltigangas-Järvinen  43 Lambertus A L M Kiemeney  31 Ivana Kolcic  109 Seppo Koskinen  58 Aldi T Kraja  94 Martin Kroh  90 Zoltan Kutalik  62   63   110 Antti Latvala  33 Lenore J Launer  111 Maël P Lebreton  15   112 Douglas F Levinson  113 Paul Lichtenstein  45 Peter Lichtner  114 David C M Liewald  36   84 LifeLines Cohort StudyAnu Loukola  33 Pamela A Madden  99 Reedik Mägi  52 Tomi Mäki-Opas  58 Riccardo E Marioni  11   36   115 Pedro Marques-Vidal  116 Gerardus A Meddens  117 George McMahon  69 Christa Meisinger  25 Thomas Meitinger  114 Yusplitri Milaneschi  59 Lili Milani  52 Grant W Montgomery  118 Ronny Myhre  105 Christopher P Nelson  34   35 Dale R Nyholt  118   119 William E R Ollier  56 Aarno Palotie  8   120   121   122   123   124 Lavinia Paternoster  69 Nancy L Pedersen  45 Katja E Petrovic  38 David J Porteous  37 Katri Räikkönen  43   46 Susan M Ring  69 Antonietta Robino  125 Olga Rostapshova  4   126 Igor Rudan  66 Aldo Rustichini  127 Veikko Salomaa  58 Alan R Sanders  128   129 Antti-Pekka Sarin  123   130 Helena Schmidt  38   131 Rodney J Scott  55   132 Blair H Smith  133 Jennifer A Smith  78 Jan A Staessen  134   135 Elisabeth Steinhagen-Thiessen  88 Konstantin Strauch  136   137 Antonio Terracciano  138 Martin D Tobin  139 Sheila Ulivi  125 Simona Vaccargiu  28 Lydia Quaye  50 Frank J A van Rooij  2   140 Cristina Venturini  50   51 Anna A E Vinkhuyzen  11 Uwe Völker  101 Henry Völzke  71 Judith M Vonk  53 Diego Vozzi  126 Johannes Waage  21   22 Erin B Ware  78   141 Gonneke Willemsen  20 John R Attia  54   55 David A Bennett  76   77 Klaus Berger  72 Lars Bertram  142   143 Hans Bisgaard  21 Dorret I Boomsma  20 Ingrid B Borecki  94 Ute Bültmann  144 Christopher F Chabris  145 Francesco Cucca  146 Daniele Cusi  64   147 Ian J Deary  36   84 George V Dedoussis  44 Cornelia M van Duijn  2 Johan G Eriksson  46   148 Barbara Franke  149 Lude Franke  150 Paolo Gasparini  32   125   151 Pablo V Gejman  128   129 Christian Gieger  24 Hans-Jörgen Grabe  152   153 Jacob Gratten  11 Patrick J F Groenen  154 Vilmundur Gudnason  12   68 Pim van der Harst  72   150   155 Caroline Hayward  42   156 David A Hinds  30 Wolfgang Hoffmann  71 Elina Hyppönen  157   158   159 William G Iacono  6 Bo Jacobsson  23   105 Marjo-Riitta Järvelin  160   161   162   163 Karl-Heinz Jöckel  65 Jaakko Kaprio  33   58   123 Sharon L R Kardia  78 Terho Lehtimäki  164   165 Steven F Lehrer  166   167 Patrik K E Magnusson  45 Nicholas G Martin  168 Matt McGue  6 Andres Metspalu  52   169 Neil Pendleton  170   171 Brenda W J H Penninx  59 Markus Perola  52   58 Nicola Pirastu  32 Mario Pirastu  28 Ozren Polasek  66   172 Danielle Posthuma  14   173 Christine Power  159 Michael A Province  94 Nilesh J Samani  34   35 David Schlessinger  60 Reinhold Schmidt  38 Thorkild I A Sørensen  9   69   174 Tim D Spector  50 Kari Stefansson  26   68 Unnur Thorsteinsdottir  26   68 A Roy Thurik  1   3   175   176 Nicholas J Timpson  69 Henning Tiemeier  2   177   178 Joyce Y Tung  30 André G Uitterlinden  2   140 Veronique Vitart  42 Peter Vollenweider  116 David R Weir  93 James F Wilson  42   66 Alan F Wright  42 Dalton C Conley  179   180 Robert F Krueger  6 George Davey Smith  69 Albert Hofman  2 David I Laibson  4 Sarah E Medland  48 Michelle N Meyer  181 Jian Yang  11   92 Magnus Johannesson  182 Peter M Visscher  11   92 Tõnu Esko  7   8   52   183 Philipp D Koellinger  3   14   15 David Cesarini  18   184 Daniel J Benjamin  5
Collaborators, Affiliations
Free PMC article

Genome-wide Association Study Identifies 74 Loci Associated With Educational Attainment

Aysu Okbay et al. Nature. .
Free PMC article

Abstract

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Figures

Extended Data Figure 1
Extended Data Figure 1. Quantile-quantile plot of the genome-wide association meta-analysis of 64 EduYears results files
Observed and expected P-values are on a –log10 scale. The grey region depicts the 95% confidence interval under the null hypothesis of a uniform P-value distribution. The observed λGC is 1.28. (As reported in Supplementary Information section 1.5.4, the unweighted mean λGC is 1.02, the unweighted median is 1.01, and the range across cohorts is 0.95–1.15.)
Extended Data Figure 2
Extended Data Figure 2. The distribution of effect sizes of the 74 lead SNPs
a, SNPs ordered by absolute value of the standardized effect of one more copy of the education-increasing allele, with 95% confidence intervals. b, SNPs ordered by R2. Effects on EduYears are benchmarked against the top 74 genome-wide significant hits identified in the largest GWAS conducted to date of height and body mass index (BMI), and the 48 associations reported for waist-to-hip ratio adjusted for BMI (WHR). These results are based on the GIANT consortium's publicly available results for pooled analyses restricted to European-ancestry individuals: https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium.
Extended Data Figure 3
Extended Data Figure 3. Assessing the extent to which population stratification affects the estimates from the GWAS
a, LD Score regression plot with the summary statistics from the GWAS. Each point represents an LD Score quantile for a chromosome (the x and y coordinates of the point are the mean LD Score and the mean χ2 statistic of variants in that quantile). The facts that the intercept is close to one and that the χ2 statistics increase linearly with the LD Scores suggest that the bulk of the inflation in the χ2 statistics is due to true polygenic signal and not to population stratification. b, Estimates and 95% confidence intervals from individual-level and WF regressions of EduYears on polygenic scores, for scores constructed with sets of SNPs meeting different P-value thresholds. In addition to the analyses shown here, we conduct a sign concordance test, and we decompose the variance of the polygenic score. Overall, these analyses suggest that population stratification is unlikely to be a major concern for our 74 lead SNPs. See Supplementary Information section 3 for additional details.
Extended Data Figure 4
Extended Data Figure 4. Replication of 74 lead SNPs in the UK Biobank data
Estimated effect sizes (in years of schooling) and 95% confidence intervals of the 74 lead SNPs in the meta-analysis sample (N = 293,723) and the UK Biobank replication sample (N = 111,349). The reference allele is the allele associated with higher values of EduYears in the meta-analysis sample. SNPs are in descending order of R2 in the meta-analysis sample. Of the 74 lead SNPs, 72 have the anticipated sign in the replication sample, 52 replicate at the 0.05 significance level, and 7 replicate at the 5×10−8 significance level.
Extended Data Figure 5
Extended Data Figure 5. Q-Q plots for the 74 lead EduYears SNPs (or LD proxies) in published GWAS of other phenotypes
SNPs with concordant effects on both phenotypes are pink, and SNPs with discordant effects are blue. SNPs outside the gray area pass Bonferroni-corrected significance thresholds that correct for the total number of SNPs we tested (P < 0.05/74 = 6.8×10-4) and are labeled with their rs numbers. Observed and expected P-values are on a –log10 scale. For the sign concordance test: * P < 0.05, ** P < 0.01, and *** P < 0.001.
Extended Data Figure 6
Extended Data Figure 6. Regional association plots for four of the ten prioritized SNPs for MHBA phenotypes identified using EduYears as a proxy phenotype
a, cognitive performance; b, hippocampus; c, intracranial volume; d, neuroticism. The four were selected because very few genome-wide significant SNPs have been previously reported for these traits. Data sources and methods are described in Supplementary Information section 3. The R2 values are from the hg19 / 1000 Genomes Nov 2014 EUR references samples. The figures were created with LocusZoom (http://csg.sph.umich.edu/locuszoom/). Mb, megabases.
Extended Data Figure 7
Extended Data Figure 7. Application of fgwas to EduYears. See Supplementary Information section 4.2 for further details
a, The results of single-annotation models. “Enrichment” refers to the factor by which the prior odds of association at an LD-defined region must be multiplied if the region bears the given annotation; this factor is estimated using an empirical Bayes method applied to all SNPs in the GWAS meta-analysis regardless of statistical significance. Annotations were derived from ENCODE and a number of other data sources. Plotted are the base-2 logarithms of the enrichments and their 95% confidence intervals. Multiple instances of the same annotation correspond to independent replicates of the same experiment. b, The results of combining multiple annotations and applying model selection and cross-validation. Although the maximum-likelihood estimates are plotted, model selection was performed with penalized likelihood. c, Reweighting of GWAS loci. Each point represents an LD-defined region of the genome, and shown are the regional posterior probabilities of association (PPAs). The x-axis give the PPA calculated from the GWAS summary statistics alone, whereas the y-axis gives the PPA upon reweighting on the basis of the annotations in b. The orange points represent genomic regions where the PPA is equivalent to the standard GWAS significance threshold only upon reweighting.
Extended Data Figure 8
Extended Data Figure 8. Tissue-level biological annotation
a, The enrichment factor for a given tissue type is the ratio of variance explained by SNPs in that group to the overall fraction of SNPs in that group. To benchmark the estimates for EduYears, we compare the enrichment factors to those obtained when we use the largest GWAS conducted to date on body mass index, height, and waist-to-hip ratio adjusted for BMI. The estimates were produced with the LDSC python software, using the LD Scores and functional annotations introduced in Finucane et al. (2015) and the HapMap3 SNPs with MAF > 0.05. Each of the 10 enrichment calculations for a particular cell type is performed independently, while each controlling for the 52 functional annotation categories in the full baseline model. The error bars show the 95% confidence intervals. b, We took measurements of gene expression by the Genotype-Tissue Expression (GTEx) Consortium and determined whether the genes overlapping EduYears-associated loci are significantly overexpressed (relative to genes in random sets of loci matched by gene density) in each of 37 tissue types. These types are grouped in the panel by organ. The colored bars corresponding to tissues where there is significant overexpression. The y-axis is the significance on a –log10 scale.
Extended Data Figure 9
Extended Data Figure 9. Gene-level biological annotation
a, The DEPICT-prioritized genes for EduYears measured in the BrainSpan Developmental Transcriptome data (red curve) are more strongly expressed in the brain prenatally rather than postnatally. The DEPICT-prioritized genes exhibit similar gene-expression levels across different brain regions (gray lines). Analyses were based on log2-transformed RNA-Seq data. Error bars represent 95% confidence intervals. b, For each phenotype and disorder, we calculated the overlap between the phenotype's DEPICT-prioritized genes and genes believed to harbor de novo mutations causing the disorder. The bars correspond to odds ratios. EduYears, years of education; BMI, body mass index; WHR, waist-to-hip ratio adjusted for BMI. c, DEPICT-prioritized genes in EduYears-associated loci exhibit substantial overlap with genes previously reported to harbor sites where mutations increase risk of intellectual disability and autism spectrum disorder (Supplementary Table 4.6.1).
Extended Figure 10
Extended Figure 10. The predictive power of a polygenic score (PGS) varies in Sweden by birth cohort
Five-year rolling regressions of years of education on the PGS (left axis in all four panels), share of individuals not affected by the comprehensive school reform (a, right axis), and average distance to nearest junior high school (b, right axis), nearest high school (c, right axis) and nearest college/university (d, right axis). The shaded area displays the 95% confidence intervals for the PGS effect.
Figure 1
Figure 1. Manhattan plot for EduYears associations (N = 293,723)
The x-axis is chromosomal position, and the y-axis is the significance on a –log10 scale. The black line shows the genome-wide significance level (5×10-8). The red x's are the 74 approximately independent genome-wide significant associations (“lead SNPs”). The black dots labeled with rs numbers are the 3 Rietveld et al. SNPs.
Figure 2
Figure 2. Genetic correlations between EduYears and other traits
Results from bivariate Linkage-Disequilibrium (LD) Score regressions: estimates of genetic correlation with brain volume, neuropsychiatric, behavioral, and anthropometric phenotypes using published GWAS summary statistics. The error bars show the 95% confidence intervals.
Figure 3
Figure 3. Overview of biological annotation
34 clusters of significantly enriched gene sets. Each cluster is named after one of its member gene sets. The color represents the P-value of the member set exhibiting the most statistically significant enrichment. Overlap between pairs of clusters is represented by an edge. Edge width represents the Pearson correlation ρ between the two vectors of gene membership scores (ρ < 0.3, no edge; 0.3 ≤ ρ < 0.5, thin edge; 0.5 ≤ ρ < 0.7, intermediate edge; ρ ≥ 0.7, thick edge), where each cluster's vector is the vector for the gene set after which the cluster is named.

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