@article {317, title = {A genome-wide association study for extremely high intelligence}, journal = {Molecular Psychiatry}, volume = {23}, year = {2018}, pages = {1226{\textendash}1232}, abstract = {

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.

}, doi = {https://doi.org/10.1038/mp.2017.121}, author = {Zabaneh, D and Krapohl, E and Gaspar, H A and Curtis, C and Lee, S H and Patel, H and Newhouse, S and Wu, H M and Simpson, M A and Putallaz, M and Lubinski, D and Plomin, R and Breen, G} } @article {291, title = {Multi-polygenic score approach to trait prediction}, volume = {23}, year = {2017}, month = {08/2017}, pages = {1368}, abstract = {

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9\% variance in educational achievement, 4.8\% in general cognitive ability and 5.4\% in BMI in an independent test set, predicting 1.1\%, 1.1\%, and 1.6\% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene\–environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

}, url = {https://doi.org/10.1038/mp.2017.163}, author = {Krapohl, E and Patel, H and Newhouse, S and Curtis, C J and von Stumm, S and Dale, P S and Zabaneh, D and Breen, G and O{\textquoteright}Reilly, P F and Plomin, R} }