%0 Journal Article %J Molecular Psychiatry %D 2018 %T A genome-wide association study for extremely high intelligence %A Zabaneh, D %A Krapohl, E %A Gaspar, H A %A Curtis, C %A Lee, S H %A Patel, H %A Newhouse, S %A Wu, H M %A Simpson, M A %A Putallaz, M %A Lubinski, D %A Plomin, R %A Breen, G %K Cognitive Ability %K Education %K Genome %K Hereditary %K Humans %X We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population. %B Molecular Psychiatry %V 23 %P 1226 - 1232 %8 Apr-05-2018 %G eng %U http://www.nature.com/doifinder/10.1038/mp.2017.121http://www.nature.com/doifinder/10.1038/mp.2017.121 %N 5 %! Mol Psychiatry %R 10.1038/mp.2017.121 %0 Journal Article %J Mol Psychiatry %D 2015 %T The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects. %A Wouter J Peyrot %A Lee, S H %A Milaneschi, Y %A Abdel Abdellaoui %A Byrne, E M %A Tõnu Esko %A Eco J. C. de Geus %A Hemani, G %A Jouke-Jan Hottenga %A Kloiber, S %A Douglas F Levinson %A Lucae, S %A Nicholas G Martin %A Sarah E Medland %A Andres Metspalu %A Lili Milani %A Markus M Nöthen %A Potash, J B %A Rietschel, M %A Cornelius A Rietveld %A Ripke, S %A Jianxin Shi %A Gonneke Willemsen %A Zhihong Zhu %A Dorret I Boomsma %A Naomi R. Wray %A Brenda W J H Penninx %K Adult %K Aged %K Cohort Studies %K Depressive Disorder, Major %K Educational Status %K Estonia %K Female %K Gene-Environment Interaction %K Genetic Association Studies %K Genotype %K Humans %K Likelihood Functions %K Male %K Middle Aged %K Netherlands %K Odds Ratio %K Polymorphism, Single Nucleotide %K Psychiatric Status Rating Scales %K Regression Analysis %X

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.

%B Mol Psychiatry %V 20 %P 735-43 %8 2015 Jun %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/25917368?dopt=Abstract %R 10.1038/mp.2015.50