Genetic architecture of subcortical brain structures in 38,851 individuals.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
26
09
2017
accepted:
05
09
2019
pubmed:
23
10
2019
medline:
23
1
2020
entrez:
23
10
2019
Statut:
ppublish
Résumé
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
Identifiants
pubmed: 31636452
doi: 10.1038/s41588-019-0511-y
pii: 10.1038/s41588-019-0511-y
pmc: PMC7055269
mid: NIHMS1553652
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1624-1636Subventions
Organisme : NINDS NIH HHS
ID : R01 NS017950
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL130114
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS087541
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL120393
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG017917
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM104940
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG010161
Pays : United States
Organisme : NIMH NIH HHS
ID : R00 MH101367
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG052409
Pays : United States
Organisme : Medical Research Council
ID : MR/S015132/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : UH3 NS100605
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG054076
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL120393
Pays : United States
Organisme : Medical Research Council
ID : MR/M013111/1
Pays : United Kingdom
Organisme : NCATS NIH HHS
ID : UL1 TR000153
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00011/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States
Organisme : NIA NIH HHS
ID : T32 AG058507
Pays : United States
Organisme : Medical Research Council
ID : G1001245
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N027558/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : P30 AG010129
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG054936
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH119243
Pays : United States
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/F019394/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG015819
Pays : United States
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : U01 AG049505
Pays : United States
Références
Marsden, C. D. The mysterious motor function of the basal ganglia: the Robert Wartenberg Lecture. Neurology 32, 514–539 (1982).
pubmed: 7200209
doi: 10.1212/WNL.32.5.514
Yin, H. H. & Knowlton, B. J. The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7, 464–476 (2006).
pubmed: 16715055
doi: 10.1038/nrn1919
McDonald, A. J. & Mott, D. D. Functional neuroanatomy of amygdalohippocampal interconnections and their role in learning and memory. J. Neurosci. Res. 95, 797–820 (2016).
pubmed: 26876924
pmcid: 5094901
doi: 10.1002/jnr.23709
Hikosaka, O., Kim, H. F., Yasuda, M. & Yamamoto, S. Basal ganglia circuits for reward value-guided behavior. Annu. Rev. Neurosci. 37, 289–306 (2014).
pubmed: 25032497
pmcid: 4148825
doi: 10.1146/annurev-neuro-071013-013924
Salzman, C. D. & Fusi, S. Emotion, cognition, and mental state representation in amygdala and prefrontal cortex. Annu. Rev. Neurosci. 33, 173–202 (2010).
pubmed: 20331363
pmcid: 3108339
doi: 10.1146/annurev.neuro.051508.135256
Floresco, S. B. The nucleus accumbens: an interface between cognition, emotion, and action. Annu. Rev. Psychol. 66, 25–52 (2015).
pubmed: 25251489
doi: 10.1146/annurev-psych-010213-115159
Fabbro, F., Aglioti, S. M., Bergamasco, M., Clarici, A. & Panksepp, J. Evolutionary aspects of self- and world consciousness in vertebrates. Front. Hum. Neurosci. 9, 157 (2015).
pubmed: 25859205
pmcid: 4374625
doi: 10.3389/fnhum.2015.00157
Alexander, G. E., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986).
pubmed: 3085570
doi: 10.1146/annurev.ne.09.030186.002041
Jahanshahi, M., Obeso, I., Rothwell, J. C. & Obeso, J. A. A fronto–striato–subthalamic–pallidal network for goal-directed and habitual inhibition. Nat. Rev. Neurosci. 16, 719–732 (2015).
pubmed: 26530468
doi: 10.1038/nrn4038
Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).
pubmed: 23511908
pmcid: 4096337
doi: 10.1038/nrn3469
Stratmann, K. et al. Precortical phase of Alzheimer’s disease (AD)-related Tau cytoskeletal pathology. Brain Pathol. 26, 371–386 (2016).
pubmed: 26193084
doi: 10.1111/bpa.12289
Del Tredici, K., Rub, U., De Vos, R. A., Bohl, J. R. & Braak, H. Where does Parkinson disease pathology begin in the brain? J. Neuropathol. Exp. Neurol. 61, 413–426 (2002).
pubmed: 12030260
doi: 10.1093/jnen/61.5.413
Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015).
pubmed: 25607358
pmcid: 4393366
doi: 10.1038/nature14101
Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).
pubmed: 30305740
pmcid: 6786974
doi: 10.1038/s41586-018-0571-7
Renteria, M. E. et al. Genetic architecture of subcortical brain regions: common and region-specific genetic contributions. Genes Brain Behav. 13, 821–830 (2014).
pubmed: 25199620
pmcid: 4241157
doi: 10.1111/gbb.12177
Clarke, L. et al. The 1000 Genomes Project: data management and community access. Nat. Methods 9, 459–462 (2012).
pubmed: 22543379
pmcid: 3340611
doi: 10.1038/nmeth.1974
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
pubmed: 27548312
pmcid: 5388176
doi: 10.1038/ng.3643
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382
pmcid: 2922887
doi: 10.1093/bioinformatics/btq340
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
pubmed: 20634204
pmcid: 2935401
doi: 10.1093/bioinformatics/btq419
Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).
pubmed: 28098162
pmcid: 5253632
doi: 10.1038/ncomms13624
Adams, H. H. et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat. Neurosci. 19, 1569–1582 (2016).
pubmed: 27694991
pmcid: 5227112
doi: 10.1038/nn.4398
Verhaaren, B. F. et al. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. Circ. Cardiovasc. Genet. 8, 398–409 (2015).
pubmed: 25663218
pmcid: 4427240
doi: 10.1161/CIRCGENETICS.114.000858
Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).
pubmed: 29531354
pmcid: 5968830
doi: 10.1038/s41588-018-0058-3
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
pubmed: 30124842
pmcid: 6488973
doi: 10.1093/hmg/ddy271
Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9, 2098 (2018).
pubmed: 29844566
pmcid: 5974083
doi: 10.1038/s41467-018-04362-x
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
pubmed: 30820047
doi: 10.1038/s41588-019-0358-2
pmcid: 6463297
Simon-Sanchez, J. et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat. Genet. 41, 1308–1312 (2009).
pubmed: 19915575
pmcid: 2787725
doi: 10.1038/ng.487
Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715.e16 (2018).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
pubmed: 30478444
doi: 10.1038/s41588-018-0269-7
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678
pmcid: 4626285
doi: 10.1038/ng.3404
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
pubmed: 24119843
doi: 10.1016/j.cell.2013.09.053
Szklarczyk, D. et al. STRINGv10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).
pubmed: 25352553
doi: 10.1093/nar/gku1003
Deans, M. R. et al. Control of neuronal morphology by the atypical cadherin Fat3. Neuron 71, 820–832 (2011).
pubmed: 21903076
pmcid: 3521586
doi: 10.1016/j.neuron.2011.06.026
Takahashi, K. et al. Expression of FOXP2 in the developing monkey forebrain: comparison with the expression of the genes FOXP1, PBX3, and MEIS2. J. Comp. Neurol. 509, 180–189 (2008).
pubmed: 18461604
doi: 10.1002/cne.21740
Kjaer-Sorensen, K. et al. Pregnancy-associated plasma protein A (PAPP-A) modulates the early developmental rate in zebrafish independently of its proteolytic activity. J. Biol. Chem. 288, 9982–9992 (2013).
pubmed: 23430244
pmcid: 3617297
doi: 10.1074/jbc.M112.426304
Bayes-Genis, A. et al. Pregnancy-associated plasma protein A as a marker of acute coronary syndromes. N. Engl. J. Med. 345, 1022–1029 (2001).
pubmed: 11586954
doi: 10.1056/NEJMoa003147
Funayama, A. et al. Serum pregnancy-associated plasma protein A in patients with heart failure. J. Card. Fail. 17, 819–826 (2011).
pubmed: 21962420
doi: 10.1016/j.cardfail.2011.05.011
Desbuquois, B., Carre, N. & Burnol, A. F. Regulation of insulin and type 1 insulin-like growth factor signaling and action by the Grb10/14 and SH2B1/B2 adaptor proteins. FEBS J. 280, 794–816 (2013).
pubmed: 23190452
Li, J. et al. TXNDC5 contributes to rheumatoid arthritis by down-regulating IGFBP1 expression. Clin. Exp. Immunol. 192, 82–94 (2018).
pubmed: 29131315
doi: 10.1111/cei.13080
Matulka, K. et al. PTP1B is an effector of activin signaling and regulates neural specification of embryonic stem cells. Cell Stem Cell 13, 706–719 (2013).
pubmed: 24139759
doi: 10.1016/j.stem.2013.09.016
Krishnan, N. et al. PTP1B inhibition suggests a therapeutic strategy for Rett syndrome. J. Clin. Invest. 125, 3163–3177 (2015).
pubmed: 26214522
pmcid: 4563751
doi: 10.1172/JCI80323
Sebastian-Serrano, A. et al. Tissue-nonspecific alkaline phosphatase regulates purinergic transmission in the central nervous system during development and disease. Comput. Struct. Biotechnol. J. 13, 95–100 (2015).
pubmed: 25709758
doi: 10.1016/j.csbj.2014.12.004
Diaz-Hernandez, M. et al. Tissue-nonspecific alkaline phosphatase promotes the neurotoxicity effect of extracellular tau. J. Biol. Chem. 285, 32539–32548 (2010).
pubmed: 20634292
pmcid: 2952256
doi: 10.1074/jbc.M110.145003
Vardy, E. R., Kellett, K. A., Cocklin, S. L. & Hooper, N. M. Alkaline phosphatase is increased in both brain and plasma in Alzheimer’s disease. Neurodegener. Dis. 9, 31–37 (2012).
pubmed: 22024719
doi: 10.1159/000329722
Kellett, K. A., Williams, J., Vardy, E. R., Smith, A. D. & Hooper, N. M. Plasma alkaline phosphatase is elevated in Alzheimer’s disease and inversely correlates with cognitive function. Int. J. Mol. Epidemiol. Genet. 2, 114–121 (2011).
pubmed: 21686125
pmcid: 3110385
Searles Quick, V. B., Davis, J. M., Olincy, A. & Sikela, J. M. DUF1220 copy number is associated with schizophrenia risk and severity: implications for understanding autism and schizophrenia as related diseases. Transl. Psychiatry 5, e697 (2015).
pubmed: 26670282
pmcid: 5068589
doi: 10.1038/tp.2015.192
Hsu, S. C. et al. Mutations in SLC20A2 are a major cause of familial idiopathic basal ganglia calcification. Neurogenetics 14, 11–22 (2013).
pubmed: 23334463
pmcid: 4023541
doi: 10.1007/s10048-012-0349-2
Taglia, I., Bonifati, V., Mignarri, A., Dotti, M. T. & Federico, A. Primary familial brain calcification: update on molecular genetics. Neurol. Sci. 36, 787–794 (2015).
pubmed: 25686613
doi: 10.1007/s10072-015-2110-8
Figueiro-Silva, J. et al. Neuronal pentraxin 1 negatively regulates excitatory synapse density and synaptic plasticity. J. Neurosci. 35, 5504–5521 (2015).
pubmed: 25855168
pmcid: 6605318
doi: 10.1523/JNEUROSCI.2548-14.2015
Abad, M. A., Enguita, M., DeGregorio-Rocasolano, N., Ferrer, I. & Trullas, R. Neuronal pentraxin 1 contributes to the neuronal damage evoked by amyloid-β and is overexpressed in dystrophic neurites in Alzheimer’s brain. J. Neurosci. 26, 12735–12747 (2006).
pubmed: 17151277
pmcid: 6674827
doi: 10.1523/JNEUROSCI.0575-06.2006
Tobaben, S., Varoqueaux, F., Brose, N., Stahl, B. & Meyer, G. A brain-specific isoform of small glutamine-rich tetratricopeptide repeat-containing protein binds to Hsc70 and the cysteine string protein. J. Biol. Chem. 278, 38376–38383 (2003).
pubmed: 12878599
doi: 10.1074/jbc.M301558200
Fonte, V. et al. Interaction of intracellular β amyloid peptide with chaperone proteins. Proc. Natl Acad. Sci. USA 99, 9439–9444 (2002).
pubmed: 12089340
doi: 10.1073/pnas.152313999
pmcid: 123159
Mao, C. X. et al. Microtubule-severing protein katanin regulates neuromuscular junction development and dendritic elaboration in Drosophila. Development 141, 1064–1074 (2014).
pubmed: 24550114
doi: 10.1242/dev.097774
Yu, W. et al. The microtubule-severing proteins spastin and katanin participate differently in the formation of axonal branches. Mol. Biol. Cell 19, 1485–1498 (2008).
pubmed: 18234839
pmcid: 2291400
doi: 10.1091/mbc.e07-09-0878
Zhu, J., Shang, Y. & Zhang, M. Mechanistic basis of MAGUK-organized complexes in synaptic development and signalling. Nat. Rev. Neurosci. 17, 209–223 (2016).
pubmed: 26988743
doi: 10.1038/nrn.2016.18
Ingason, A. et al. Expression analysis in a rat psychosis model identifies novel candidate genes validated in a large case-control sample of schizophrenia. Transl. Psychiatry 5, e656 (2015).
pubmed: 26460480
pmcid: 4930128
doi: 10.1038/tp.2015.151
Nithianantharajah, J. et al. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nat. Neurosci. 16, 16–24 (2013).
pubmed: 23201973
doi: 10.1038/nn.3276
Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014).
pubmed: 25064009
pmcid: 4146673
doi: 10.1038/ng.3043
Guan, J. J. et al. DRAM1 regulates apoptosis through increasing protein levels and lysosomal localization of BAX. Cell Death Dis. 6, e1624 (2015).
pubmed: 25633293
pmcid: 4669745
doi: 10.1038/cddis.2014.546
Yu, M., Jiang, Y., Feng, Q., Ouyang, Y. & Gan, J. DRAM1 protects neuroblastoma cells from oxygen-glucose deprivation/reperfusion-induced injury via autophagy. Int. J. Mol. Sci. 15, 19253–19264 (2014).
pubmed: 25342320
pmcid: 4227272
doi: 10.3390/ijms151019253
Scarpa, J. R. et al. Systems genetic analyses highlight a TGFβ-FOXO3 dependent striatal astrocyte network conserved across species and associated with stress, sleep, and Huntington’s disease. PLoS Genet. 12, e1006137 (2016).
pubmed: 27390852
pmcid: 4938493
doi: 10.1371/journal.pgen.1006137
Donlon, T. A. et al. FOXO3 longevity interactome on chromosome 6. Aging Cell 16, 1016–1025 (2017).
pubmed: 28722347
pmcid: 5595686
doi: 10.1111/acel.12625
Sears, J. C. & Broihier, H. T. FoxO regulates microtubule dynamics and polarity to promote dendrite branching in Drosophila sensory neurons. Dev. Biol. 418, 40–54 (2016).
pubmed: 27546375
pmcid: 5045711
doi: 10.1016/j.ydbio.2016.08.018
Peng, K. et al. Knockdown of FoxO3a induces increased neuronal apoptosis during embryonic development in zebrafish. Neurosci. Lett. 484, 98–103 (2010).
pubmed: 20674670
doi: 10.1016/j.neulet.2010.07.068
Santama, N., Er, C. P., Ong, L. L. & Yu, H. Distribution and functions of kinectin isoforms. J. Cell Sci. 117, 4537–4549 (2004).
pubmed: 15316074
doi: 10.1242/jcs.01326
Liu, X. A., Rizzo, V. & Puthanveettil, S. V. Pathologies of axonal transport in neurodegenerative diseases. Transl. Neurosci. 3, 355–372 (2012).
pubmed: 23750323
doi: 10.2478/s13380-012-0044-7
Consortium, E. et al. Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32. Hum. Mol. Genet. 21, 5359–5372 (2012).
doi: 10.1093/hmg/dds373
Martins-de-Souza, D. et al. Proteomic analysis identifies dysfunction in cellular transport, energy, and protein metabolism in different brain regions of atypical frontotemporal lobar degeneration. J. Proteome Res. 11, 2533–2543 (2012).
pubmed: 22360420
doi: 10.1021/pr2012279
Shulman, J. M. et al. Functional screening in Drosophila identifies Alzheimer’s disease susceptibility genes and implicates Tau-mediated mechanisms. Hum. Mol. Genet. 23, 870–877 (2014).
pubmed: 24067533
doi: 10.1093/hmg/ddt478
Friede, R. L. & Samorajski, T. Axon caliber related to neurofilaments and microtubules in sciatic nerve fibers of rats and mice. Anat. Rec. 167, 379–387 (1970).
pubmed: 5454590
doi: 10.1002/ar.1091670402
Yuan, A., Rao, M. V., Veeranna & Nixon, R. A. Neurofilaments and neurofilament proteins in health and disease. Cold Spring Harb. Perspect. Biol. 9, a018309 (2017).
pubmed: 28373358
pmcid: 5378049
doi: 10.1101/cshperspect.a018309
Bis, J. C. et al. Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol Psychiatry https://doi.org/10.1038/s41380-018-0112-7 (2018).
Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018).
pubmed: 29777097
pmcid: 5959890
doi: 10.1038/s41398-018-0150-6
Psaty, B. M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ. Cardiovasc. Genet. 2, 73–80 (2009).
pubmed: 20031568
pmcid: 2875693
doi: 10.1161/CIRCGENETICS.108.829747
Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).
pubmed: 24399358
pmcid: 4008818
doi: 10.1007/s11682-013-9269-5
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).
pubmed: 25826379
pmcid: 4380465
doi: 10.1371/journal.pmed.1001779
Tsao, C. W. & Vasan, R. S. Cohort Profile: the Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. Int. J. Epidemiol. 44, 1800–1813 (2015).
pubmed: 26705418
pmcid: 5156338
doi: 10.1093/ije/dyv337
Schmidt, R. et al. Assessment of cerebrovascular risk profiles in healthy persons: definition of research goals and the Austrian Stroke Prevention Study (ASPS). Neuroepidemiology 13, 308–313 (1994).
pubmed: 7800110
doi: 10.1159/000110396
Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998).
pubmed: 9545414
pmcid: 1377101
doi: 10.1086/301844
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
pubmed: 21167468
pmcid: 3014363
doi: 10.1016/j.ajhg.2010.11.011
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630
pmcid: 4495769
doi: 10.1038/ng.3211
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
pubmed: 24762786
pmcid: 4083217
doi: 10.1038/nprot.2014.071
Bennett, D. A., Yu, L. & De Jager, P. L. Building a pipeline to discover and validate novel therapeutic targets and lead compounds for Alzheimer’s disease. Biochem. Pharm. 88, 617–630 (2014).
pubmed: 24508835
doi: 10.1016/j.bcp.2014.01.037
Chan, G. et al. CD33 modulates TREM2: convergence of Alzheimer loci. Nat. Neurosci. 18, 1556–1558 (2015).
pubmed: 26414614
pmcid: 4682915
doi: 10.1038/nn.4126
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
pubmed: 16632515
doi: 10.1093/biostatistics/kxj037
Roadmap Epigenomics Association et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
doi: 10.1038/nature14248
Eicher, J. D. et al. GRASPv2.0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes. Nucleic Acids Res. 43, D799–D804 (2015).
pubmed: 25428361
doi: 10.1093/nar/gku1202
Zhang, X. et al. Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs. BMC Genomics 15, 532 (2014).
pubmed: 24973796
pmcid: 4102726
doi: 10.1186/1471-2164-15-532
Zhang, W. et al. SCAN database: facilitating integrative analyses of cytosine modification and expression QTL. Database 2015, bav025 (2015).
pubmed: 25818895
pmcid: 4375357
doi: 10.1093/database/bav025
Consortium, G. T. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
doi: 10.1038/ng.2653
Veyrieras, J. B. et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 4, e1000214 (2008).
pubmed: 18846210
pmcid: 2556086
doi: 10.1371/journal.pgen.1000214
Bennett, D. A. et al. Overview and findings from the rush Memory and Aging Project. Curr. Alzheimer Res. 9, 646–663 (2012).
pubmed: 22471867
pmcid: 3439198
doi: 10.2174/156720512801322663
Bennett, D. A., Schneider, J. A., Arvanitakis, Z. & Wilson, R. S. Overview and findings from the religious orders study. Curr. Alzheimer Res. 9, 628–645 (2012).
pubmed: 22471860
pmcid: 3409291
doi: 10.2174/156720512801322573
Replogle, J. M. et al. A TREM1 variant alters the accumulation of Alzheimer-related amyloid pathology. Ann. Neurol. 77, 469–477 (2015).
pubmed: 25545807
pmcid: 4461024
doi: 10.1002/ana.24337
Barnes, L. L., Schneider, J. A., Boyle, P. A., Bienias, J. L. & Bennett, D. A. Memory complaints are related to Alzheimer disease pathology in older persons. Neurology 67, 1581–1585 (2006).
pubmed: 17101887
doi: 10.1212/01.wnl.0000242734.16663.09
McKeith, I. G. et al. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the Consortium on DLB International Workshop. Neurology 47, 1113–1124 (1996).
pubmed: 8909416
doi: 10.1212/WNL.47.5.1113
Schneider, J. A. et al. Substantia nigra tangles are related to gait impairment in older persons. Ann. Neurol. 59, 166–173 (2006).
pubmed: 16374822
doi: 10.1002/ana.20723
Zheng, J. 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 33, 272–279 (2017).
pubmed: 27663502
doi: 10.1093/bioinformatics/btw613
Wangler, M. F., Hu, Y. & Shulman, J. M. Drosophila and genome-wide association studies: a review and resource for the functional dissection of human complex traits. Dis. Model Mech. 10, 77–88 (2017).
pubmed: 28151408
pmcid: 5312009
doi: 10.1242/dmm.027680
Hu, Y. et al. An integrative approach to ortholog prediction for disease-focused and other functional studies. BMC Bioinformatics 12, 357 (2011).
pubmed: 21880147
pmcid: 3179972
doi: 10.1186/1471-2105-12-357
Marygold, S. J., Crosby, M. A., Goodman, J. L. & FlyBase, C. Using FlyBase, a database of Drosophila genes and genomes. Methods Mol. Biol. 1478, 1–31 (2016).
pubmed: 27730573
pmcid: 5107610
doi: 10.1007/978-1-4939-6371-3_1