Identification of novel loci associated with infant cognitive ability

Abstract

It is believed that genetic factors play a large role in the development of many cognitive and neurological processes; however, epidemiological evidence for the genetic basis of childhood neurodevelopment is very limited. Identification of the genetic polymorphisms associated with early-stage neurodevelopment will help elucidate biological mechanisms involved in neuro-behavior and provide a better understanding of the developing brain. To search for such variants, we performed a genome-wide association study (GWAS) for infant mental and motor ability at two years of age with mothers and children recruited from cohorts in Bangladesh and Mexico. Infant ability was assessed using mental and motor composite scores calculated with country-specific versions of the Bayley Scales of Infant Development. A missense variant (rs1055153) located in the gene WWTR1 reached genome-wide significance in association with mental composite score (meta-analysis effect size of minor allele βmeta = −6.04; 95% CI: −8.13 to −3.94; P = 1.56×10−8). Infants carrying the minor allele reported substantially lower cognitive scores in both cohorts, and this variant is predicted to be in the top 0.3% of most deleterious substitutions in the human genome. Fine mapping and region-based association testing provided additional suggestive evidence that both WWTR1 and a second gene, LRP1B, were associated with infant cognitive ability. Comparisons with recently conducted GWAS in intelligence and educational attainment indicate that our phenotypes do not possess a high genetic correlation with either adolescent or adult cognitive traits, suggesting that infant neurological assessments should be treated as an independent outcome of interest. Additional functional studies and replication efforts in other cohorts may help uncover new biological pathways and genetic architectures that are crucial to the developing brain.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1
Fig. 2

References

  1. 1.

    Krushkal J, Murphy LE, Palmer FB, Graff JC, Sutter TR, Mozhui K, et al. Epigenetic analysis of neurocognitive development at 1 year of age in a community-based pregnancy cohort. Behav Genet. 2014;44:113–25.

    Article  Google Scholar 

  2. 2.

    Deary IJ, Whalley LJ, Lemmon H, Crawford JR, Starr JM. The stability of individual differences in mental ability from childhood to old age: follow-up of the 1932 Scottish Mental Survey. Intelligence. 2000;28:49–55.

    Article  Google Scholar 

  3. 3.

    Plomin R, Deary IJ. Genetics and intelligence differences: five special findings. Mol Psychiatry. 2015;20:98–108.

    CAS  Article  Google Scholar 

  4. 4.

    Benyamin B, Pourcain B, Davis OS, Davies G, Hansell NK, Brion M-J, et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry. 2014;19:253.

    CAS  Article  Google Scholar 

  5. 5.

    Strenze T. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence. 2007;35:401–26.

    Article  Google Scholar 

  6. 6.

    Davis EE, Pitchford NJ, Limback E. The interrelation between cognitive and motor development in typically developing children aged 4–11 years is underpinned by visual processing and fine manual control. Br J Psychol. 2011;102:569–84.

    Article  Google Scholar 

  7. 7.

    Deary IJ, Johnson W, Houlihan LM. Genetic foundations of human intelligence. Hum Genet. 2009;126:215–32.

    Article  Google Scholar 

  8. 8.

    Desrivieres S, Lourdusamy A, Tao C, Toro R, Jia T, Loth E, et al. Single nucleotide polymorphism in the neuroplastin locus associates with cortical thickness and intellectual ability in adolescents. Mol Psychiatry. 2015;20:263–74.

    CAS  Article  Google Scholar 

  9. 9.

    Hayiou-Thomas ME. Genetic and environmental influences on early speech, language and literacy development. J Commun Disord. 2008;41:397–408.

    Article  Google Scholar 

  10. 10.

    Docherty SJ, Davis OS, Kovas Y, Meaburn EL, Dale PS, Petrill SA, et al. A genome-wide association study identifies multiple loci associated with mathematics ability and disability. Genes Brain Behav. 2010;9:234–47.

    CAS  Article  Google Scholar 

  11. 11.

    Davies G, Tenesa A, Payton A, Yang J, Harris SE, Liewald D, et al. Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry. 2011;16:996.

    CAS  Article  Google Scholar 

  12. 12.

    Davies G, Armstrong N, Bis JC, Bressler J, Chouraki V, Giddaluru S, et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53 949). Mol Psychiatry. 2015;20:183.

    CAS  Article  Google Scholar 

  13. 13.

    Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JR, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet. 2017;49:1107.

    CAS  Article  Google Scholar 

  14. 14.

    Rietveld CA, Esko T, Davies G, Pers TH, Turley P, Benyamin B, et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc Natl Acad Sci. 2014;111:13790–4.

    CAS  Article  Google Scholar 

  15. 15.

    Trampush J, Yang M, Yu J, Knowles E, Davies G, Liewald D, et al. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol Psychiatry. 2017;22:336.

    CAS  Article  Google Scholar 

  16. 16.

    Zabaneh D, Krapohl E, Gaspar HA, Curtis C, Lee SH, Patel H, et al. A genome-wide association study for extremely high intelligence. Mol Psychiatry. 2018;23:1226–32.

    CAS  Article  Google Scholar 

  17. 17.

    Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science. 2013;340:1467–71.

    CAS  Article  Google Scholar 

  18. 18.

    Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–42.

    CAS  Article  Google Scholar 

  19. 19.

    Davies G, Marioni RE, Liewald DC, Hill WD, Hagenaars SP, Harris SE, et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol Psychiatry. 2016;21:758–67.

    CAS  Article  Google Scholar 

  20. 20.

    Plomin R, von Stumm S. The new genetics of intelligence. Nat Rev Genet. 2018;19:148–59.

    CAS  Article  Google Scholar 

  21. 21.

    Davis OS, Haworth CM, Plomin R. Dramatic increase in heritability of cognitive development from early to middle childhood: an 8-year longitudinal study of 8,700 pairs of twins. Psychol Sci. 2009;20:1301–8.

    Article  Google Scholar 

  22. 22.

    Kile ML, Rodrigues EG, Mazumdar M, Dobson CB, Diao N, Golam M, et al. A prospective cohort study of the association between drinking water arsenic exposure and self-reported maternal health symptoms during pregnancy in Bangladesh. Environ Health. 2014;13:29.

    Article  Google Scholar 

  23. 23.

    Burris HH, Braun JM, Byun H-M, Tarantini L, Mercado A, Wright RJ, et al. Association between birth weight and DNA methylation of IGF2, glucocorticoid receptor and repetitive elements LINE-1 and Alu. Epigenomics. 2013;5:271–81.

    CAS  Article  Google Scholar 

  24. 24.

    Wang Z, Henn BC, Wang C, Wei Y, Su L, Sun R, et al. Genome-wide gene by lead exposure interaction analysis identifies UNC5D as a candidate gene for neurodevelopment. Environ Health. 2017;16:81.

    Article  Google Scholar 

  25. 25.

    Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    CAS  Article  Google Scholar 

  26. 26.

    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  Google Scholar 

  27. 27.

    Dawson E, Abecasis GR, Bumpstead S, Chen Y, Hunt S, Beare DM, et al. A first-generation linkage disequilibrium map of human chromosome 22. Nature. 2002;418:544–8.

    CAS  Article  Google Scholar 

  28. 28.

    Barnett I, Mukherjee R, Lin X. The generalized higher criticism for testing SNP-set effects in genetic association studies. J Am Stat Assoc. 2017;112:64–76.

    CAS  Article  Google Scholar 

  29. 29.

    Bellinger D, Leviton A, Waternaux C, Needleman H, Rabinowitz M. Longitudinal analyses of prenatal and postnatal lead exposure and early cognitive development. N Engl J Med. 1987;316:1037–43.

    CAS  Article  Google Scholar 

  30. 30.

    Claus Henn B, Ettinger AS, Schwartz J, Tellez-Rojo MM, Lamadrid-Figueroa H, Hernandez-Avila M, et al. Early postnatal blood manganese levels and children’s neurodevelopment. Epidemiology. 2010;21:433–9.

    Article  Google Scholar 

  31. 31.

    Sun R, Carroll RJ, Christiani DC, Lin X. Testing for gene-environment interaction under exposure misspecification. Biometrics. 2017;74:653–62.

    Article  Google Scholar 

  32. 32.

    Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    CAS  Article  Google Scholar 

  33. 33.

    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    CAS  Article  Google Scholar 

  34. 34.

    Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073.

    CAS  Article  Google Scholar 

  35. 35.

    Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010;6:e1001025.

    Article  Google Scholar 

  36. 36.

    Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15:1034–50.

    CAS  Article  Google Scholar 

  37. 37.

    Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010;20:110–21.

    CAS  Article  Google Scholar 

  38. 38.

    Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33:272–9.

    CAS  Article  Google Scholar 

  39. 39.

    Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Human Genet. 2011;88:76–82.

    CAS  Article  Google Scholar 

  40. 40.

    Genevet A, Tapon N. The Hippo pathway and apico-basal cell polarity. Biochem J. 2011;436:213–24.

    CAS  Article  Google Scholar 

  41. 41.

    Pan D. The hippo signaling pathway in development and cancer. Dev Cell. 2010;19:491–505.

    CAS  Article  Google Scholar 

  42. 42.

    Xie M, Zhang L, He CS, Hou JH, Lin SX, Hu ZH, et al. Prognostic significance of TAZ expression in resected non-small cell lung cancer. J Thorac Oncol. 2012;7:799–807.

    CAS  Article  Google Scholar 

  43. 43.

    Li Z, Wang Y, Zhu Y, Yuan C, Wang D, Zhang W, et al. The Hippo transducer TAZ promotes epithelial to mesenchymal transition and cancer stem cell maintenance in oral cancer. Mol Oncol. 2015;9:1091–105.

    CAS  Article  Google Scholar 

  44. 44.

    Moroishi T, Hansen CG, Guan KL. The emerging roles of YAP and TAZ in cancer. Nat Rev Cancer. 2015;15:73–79.

    CAS  Article  Google Scholar 

  45. 45.

    Sun Y, Yong KM, Villa-Diaz LG, Zhang X, Chen W, Philson R, et al. Hippo/YAP-mediated rigidity-dependent motor neuron differentiation of human pluripotent stem cells. Nat Mater. 2014;13:599–604.

    CAS  Article  Google Scholar 

  46. 46.

    Han D, Byun S-H, Park S, Kim J, Kim I, Ha S, et al. YAP/TAZ enhance mammalian embryonic neural stem cell characteristics in a Tead-dependent manner. Biochem Biophys Res Commun. 2015;458:110–6.

    CAS  Article  Google Scholar 

  47. 47.

    Cao X, Pfaff SL, Gage FH. YAP regulates neural progenitor cell number via the TEA domain transcription factor. Genes Dev. 2008;22:3320–34.

    CAS  Article  Google Scholar 

  48. 48.

    Ahmed AF, de Bock CE, Lincz LF, Pundavela J, Zouikr I, Sontag E, et al. FAT1 cadherin acts upstream of Hippo signalling through TAZ to regulate neuronal differentiation. Cell Mol Life Sci. 2015;72:4653–69.

    CAS  Article  Google Scholar 

  49. 49.

    Lavado A, He Y, Pare J, Neale G, Olson EN, Giovannini M, et al. Tumor suppressor Nf2 limits expansion of the neural progenitor pool by inhibiting Yap/Taz transcriptional coactivators. Development. 2013;140:3323–34.

    CAS  Article  Google Scholar 

  50. 50.

    Cappello S, Gray MJ, Badouel C, Lange S, Einsiedler M, Srour M, et al. Mutations in genes encoding the cadherin receptor-ligand pair DCHS1 and FAT4 disrupt cerebral cortical development. Nat Genet. 2013;45:1300–8.

    CAS  Article  Google Scholar 

  51. 51.

    Wang J, Xiao Y, Hsu CW, Martinez-Traverso IM, Zhang M, Bai Y, et al. Yap and Taz play a crucial role in neural crest-derived craniofacial development. Development. 2016;143:504–15.

    CAS  Article  Google Scholar 

  52. 52.

    Pfleger CM. The Hippo pathway: a master regulatory network important in development and dysregulated in disease. Curr Top Dev Biol. 2017;123:181–228.

    Article  Google Scholar 

  53. 53.

    Emoto K, Parrish JZ, Jan LY, Jan YN. The tumour suppressor Hippo acts with the NDR kinases in dendritic tiling and maintenance. Nature. 2006;443:210–3.

    CAS  Article  Google Scholar 

  54. 54.

    Huang X, Chen H, Miller WC, Mailman RB, Woodard JL, Chen PC, et al. Lower low‐density lipoprotein cholesterol levels are associated with Parkinson's disease. Mov Disord. 2007;22:377–81.

    Article  Google Scholar 

  55. 55.

    Poduslo SE, Huang R, Spiro A 3rd. A genome screen of successful aging without cognitive decline identifies LRP1B by haplotype analysis. Am J Med Genet B Neuropsychiatr Genet. 2010;153B:114–9.

    CAS  PubMed  Google Scholar 

  56. 56.

    John L, Jeffrey T, Mike S, Rebecca P, Edmund L, Saboor S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580.

    Article  Google Scholar 

  57. 57.

    Lenroot RK, Giedd JN. The changing impact of genes and environment on brain development during childhood and adolescence: initial findings from a neuroimaging study of pediatric twins. Dev Psychopathol. 2008;20:1161–75.

    Article  Google Scholar 

  58. 58.

    Wright IC, Sham P, Murray RM, Weinberger DR, Bullmore ET. Genetic contributions to regional variability in human brain structure: methods and preliminary results. Neuroimage. 2002;17:256–71.

    CAS  Article  Google Scholar 

  59. 59.

    Thompson PM, Cannon TD, Narr KL, van Erp T, Poutanen VP, Huttunen M, et al. Genetic influences on brain structure. Nat Neurosci. 2001;4:1253–8.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank Maegan Harden, PhD, Broad Institute of MIT and Harvard, Boston, MA for assistance in genotyping the Bangladesh cohort. We thank Hakon Hakonarson, MD, PhD and Cecelia Kim, PhD, from the Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA for assistance in genotyping the Mexico cohort. This work was supported in part by National Institute of Environmental Health Sciences grants R01-ES007821, R01-ES014930, R01-ES013744, R01-ES022986, R01-ES015533, P42-ES016454, and P30-ES00002. Funding was also provided by Science to Achieve Results Research Assistance Agreement No. FP-91690001, awarded by the U.S. Environmental Protection Agency (EPA). The EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ryan Sun.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sun, R., Wang, Z., Claus Henn, B. et al. Identification of novel loci associated with infant cognitive ability. Mol Psychiatry (2018). https://doi.org/10.1038/s41380-018-0205-3

Download citation

Further reading

Search