Genome-wide association studies of brain imaging phenotypes in UK Biobank

Nature

Volumn 562, Issue 7726, 2018, Pages 210-216

  Elliott, Lloyd T ^a   Sharp, Kevin ^a   Alfaro Almagro, Fidel ^b   Shi, Sinan ^a   Miller, Karla L ^b   Douaud, Gwenaëlle ^b   Marchini, Jonathan ^a,c   Smith, Stephen M ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDERMAL GROWTH FACTOR; IRON;

BRAIN; DATABASE; DEVELOPMENTAL BIOLOGY; GENE EXPRESSION; GENETIC ANALYSIS; GENETIC MARKER; GENOME; NERVOUS SYSTEM DISORDER; NEUROLOGY; PHENOTYPE;

AGING; ARTICLE; BIOBANK; BRAIN DAMAGE; BRAIN DEVELOPMENT; BRAIN RADIOGRAPHY; BRAIN TISSUE; CLUSTER ANALYSIS; EPIDERMAL GROWTH FACTOR GENE; EXTRACELLULAR MATRIX; GENE; GENE FUNCTION; GENE STRUCTURE; GENETIC ASSOCIATION; GENOME-WIDE ASSOCIATION STUDY; HERITABILITY; HUMAN; IRON TRANSPORT; MAGNETISM; MAJOR CLINICAL STUDY; MENTAL DISEASE; NERVE GROWTH; NEUROANATOMY; NEUROLOGIC DISEASE; PHENOTYPE; PONTINE CROSSING TRACT; PRIORITY JOURNAL; REGULATOR GENE; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM; STORAGE GENE; SUBCORTEX; UNITED KINGDOM; WHITE MATTER; ANATOMY AND HISTOLOGY; BRAIN; DIAGNOSTIC IMAGING; FEMALE; GENETICS; GROWTH, DEVELOPMENT AND AGING; HEREDITY; INFORMATION PROCESSING; MALE; METABOLISM; NERVE CELL PLASTICITY; NEUROIMAGING; PATHOLOGY; PUTAMEN;

AGING; BIOLOGICAL SPECIMEN BANKS; BRAIN; DATASETS AS TOPIC; EPIDERMAL GROWTH FACTOR; EXTRACELLULAR MATRIX; FEMALE; GENOME-WIDE ASSOCIATION STUDY; HEREDITY; HUMANS; IRON; MALE; NEUROIMAGING; NEURONAL PLASTICITY; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PUTAMEN; SIGNAL TRANSDUCTION; UNITED KINGDOM; WHITE MATTER;

EID: 85054699117     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/s41586-018-0571-7     Document Type: Article

References (51)

1. Toga, A. W. Brain Mapping: An Encyclopedic Reference (Academic, Amsterdam, 2015)
2. Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015)
3. Allen, N. et al. UK Biobank: Current status and what it means for epidemiology. Health Policy Technol. 1, 123–126 (2012)
4. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016)
5. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018)
6. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 10.1038/s41586-018-0579-z (2018)
7. Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015)
8. Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017)
9. Shen, L. et al. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage 53, 1051–1063 (2010)
10. Koran, M. E. et al. Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using sequential oligogenic linkage analysis routines. J. Med. Imaging (Bellingham) 1, 014005 (2014)
11. Colclough, G. L. et al. The heritability of multi-modal connectivity in human brain activity. eLife 6, e20178 (2017)
12. Roalf, D. R. et al. Heritability of subcortical and limbic brain volume and shape in multiplex-multigenerational families with schizophrenia. Biol. Psychiatry 77, 137–146 (2015)
13. den Braber, A. et al. Heritability of subcortical brain measures: a perspective for future genome-wide association studies. Neuroimage 83, 98–102 (2013)
14. Kremen, W. S. et al. Genetic and environmental influences on the size of specific brain regions in midlife: the VETSA MRI study. Neuroimage 49, 1213–1223 (2010)
15. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015)
16. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012)
17. Purcell, S. Variance components models for gene-environment interaction in twin analysis. Twin Res. 5, 554–571 (2002)
18. Fornage, M. et al. Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium. Ann. Neurol. 69, 928–939 (2011)
19. Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017)
20. Duyn, J. MR susceptibility imaging. J. Magn. Reson. 229, 198–207 (2013)
21. Dusi, S. et al. Exome sequence reveals mutations in CoA synthase as a cause of neurodegeneration with brain iron accumulation. Am. J. Hum. Genet. 94, 11–22 (2014)
22. Vul, E., Harris, C., Winkielman, P. & Pashler, H. Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4, 274–290 (2009)
23. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018)
24. Goes, F. S. et al. Genome-wide association study of schizophrenia in Ashkenazi Jews. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 168, 649–659 (2015)
25. International Consortium for Blood Pressure Genome-Wide Association Studies Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011)
26. Johansson, Å. et al. Genome-wide association and Mendelian randomization study of NT-proBNP in patients with acute coronary syndrome. Hum. Mol. Genet. 25, 1447–1456 (2016)
27. Duncan, J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14, 172–179 (2010)
28. Dolan, R. J. et al. Dopaminergic modulation of impaired cognitive activation in the anterior cingulate cortex in schizophrenia. Nature 378, 180–182 (1995)
29. Critchley, H. D. et al. Human cingulate cortex and autonomic control: converging neuroimaging and clinical evidence. Brain 126, 2139–2152 (2003)
30. Deak, K. L. et al. Analysis of ALDH1A2, CYP26A1, CYP26B1, CRABP1, and CRABP2 in human neural tube defects suggests a possible association with alleles in ALDH1A2. Birth Defects Res. A Clin. Mol. Teratol. 73, 868–875 (2005)
31. Jen, J. C. et al. Mutations in a human ROBO gene disrupt hindbrain axon pathway crossing and morphogenesis. Science 304, 1509–1513 (2004)
32. Douaud, G. et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage 55, 880–890 (2011)
33. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014)
34. Haijma, S. V. et al. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr. Bull. 39, 1129–1138 (2013)
35. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015)
36. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012)
37. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999)
38. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999)
39. Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Front. Neurosci. 6, 171 (2012)
40. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010)
41. Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999)
42. Duff, E. P. et al. Learning to identify CNS drug action and efficacy using multistudy fMRI data. Sci. Transl. Med. 7, 274ra16 (2015)
43. The Haplotype Reference Consortium A reference panel of 64,976 haplotypes for genotype imputation. Nat. Med. 48, 1279–1283 (2015)
44. Marchini, J., Cardon, L. R., Phillips, M. S. & Donnelly, P. The effects of human population structure on large genetic association studies. Nat. Genet. 36, 512–517 (2004)
45. Smith, S. M. & Nichols, T. E. Statistical challenges in “big data” human neuroimaging. Neuron 97, 263–268 (2018)
46. Cai, J.-F., Candès, E. J. & Shen, Z. A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20, 1956–1982 (2010)
47. Smith, S. M. et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17, 479–489 (2002)
48. Dahl, A. et al. A multiple-phenotype imputation method for genetic studies. Nat. Genet. 48, 466–472 (2016)
49. Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2017)
50. Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018)
51. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015)