Childhood intelligence is heritable, highly polygenic and associated with FNBP1L

B. Benyamin, Bst Pourcain, O. S. Davis, G. Davies, N. K. Hansell, M. Ja Brion, R. M. Kirkpatrick, R. A M Cents, S. Franić, M. B. Miller, C. M A Haworth, E. Meaburn, T. S. Price, D. M. Evans, N. Timpson, J. Kemp, S. Ring, W. McArdle, S. E. Medland, J. YangS. E. Harris, D. C. Liewald, P. Scheet, X. Xiao, J. J. Hudziak, E. J C De Geus, V. W V Jaddoe, J. M. Starr, F. C. Verhulst, C. Pennell, H. Tiemeier, W. G. Iacono, L. J. Palmer, G. W. Montgomery, N. G. Martin, D. I. Boomsma, D. Posthuma, M. McGue, M. J. Wright, G. Davey Smith, I. J. Deary, R. Plomin, P. M. Visscher

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145 Scopus citations

Abstract

Intelligence in childhood, as measured by psychometric cognitive tests, is a strong predictor of many important life outcomes, including educational attainment, income, health and lifespan. Results from twin, family and adoption studies are consistent with general intelligence being highly heritable and genetically stable throughout the life course. No robustly associated genetic loci or variants for childhood intelligence have been reported. Here, we report the first genome-wide association study (GWAS) on childhood intelligence (age range 6-18 years) from 17 989 individuals in six discovery and three replication samples. Although no individual single-nucleotide polymorphisms (SNPs) were detected with genome-wide significance, we show that the aggregate effects of common SNPs explain 22-46% of phenotypic variation in childhood intelligence in the three largest cohorts (P=3.9 × 10 -15, 0.014 and 0.028). FNBP1L, previously reported to be the most significantly associated gene for adult intelligence, was also significantly associated with childhood intelligence (P=0.003). Polygenic prediction analyses resulted in a significant correlation between predictor and outcome in all replication cohorts. The proportion of childhood intelligence explained by the predictor reached 1.2% (P=6 × 10 -5), 3.5% (P=10 -3) and 0.5% (P=6 × 10 -5) in three independent validation cohorts. Given the sample sizes, these genetic prediction results are consistent with expectations if the genetic architecture of childhood intelligence is like that of body mass index or height. Our study provides molecular support for the heritability and polygenic nature of childhood intelligence. Larger sample sizes will be required to detect individual variants with genome-wide significance.

Original languageEnglish (US)
Pages (from-to)253-258
Number of pages6
JournalMolecular psychiatry
Volume19
Issue number2
DOIs
StatePublished - Feb 2014

Bibliographical note

Funding Information:
We acknowledge funding from the Australian National Health and Medical Research Council (Grants 552498, 613672, 613601 and 1011506) and the Australian Research Council (Grant DP1093502). Funding support for the GWAS of Gene and Environment Initiatives in Type 2 Diabetes was provided through the NIH Genes, Environment and Health Initiative [GEI] (U01HG004399). The human subjects participating in the GWAS derive from The Nurses’ Health Study and Health Professionals’ Follow-up Study and these studies are supported by National Institutes of Health Grants CA87969, CA55075 and DK58845. Assistance with phenotype harmonisation and genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies, GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH GEI (U01HG004424). The data sets used for the analyses described in this manuscript were obtained from dbGaP at [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=gap] through dbGaP accession number [phs000091]. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN26820 1100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL 086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. BB and PMV are the recipients of the Australian National Health and Medical Research Council (NHMRC) fellowships. Acknowledgements for individual study cohorts are presented in the Supplementary Note.

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