Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
01
06
2021
accepted:
20
06
2022
pubmed:
27
9
2022
medline:
12
10
2022
entrez:
26
9
2022
Statut:
ppublish
Résumé
Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.
Identifiants
pubmed: 36163277
doi: 10.1038/s41588-022-01171-3
pii: 10.1038/s41588-022-01171-3
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1470-1478Subventions
Organisme : NIMH NIH HHS
ID : U01 MH109514
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH109536
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH125050
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH116442
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH105500
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH116037
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH109677
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH110427
Pays : United States
Organisme : NIAMS NIH HHS
ID : U01 AR076092
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH125246
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Dalsgaard, S. et al. Incidence rates and cumulative incidences of the full spectrum of diagnosed mental disorders in childhood and adolescence. JAMA Psychiatry 77, 155–164 (2020).
pubmed: 31746968
doi: 10.1001/jamapsychiatry.2019.3523
Faraone, S. V. & Larsson, H. Genetics of attention deficit hyperactivity disorder. Mol. Psychiatry 24, 562–575 (2019).
pubmed: 29892054
doi: 10.1038/s41380-018-0070-0
Pettersson, E. et al. Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls. Psychol. Med. 49, 1166–1173 (2019).
pubmed: 30221610
doi: 10.1017/S0033291718002039
Sandin, S. et al. The heritability of autism spectrum disorder. JAMA 318, 1182–1184 (2017).
pubmed: 28973605
pmcid: 5818813
doi: 10.1001/jama.2017.12141
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
pubmed: 30804558
pmcid: 6454898
doi: 10.1038/s41588-019-0344-8
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
Matoba, N. et al. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10, 265 (2020).
pubmed: 32747698
pmcid: 7400671
doi: 10.1038/s41398-020-00953-9
Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019).
pmcid: 7077032
doi: 10.1016/j.cell.2019.11.020
Martin, J. et al. Biological overlap of attention-deficit/hyperactivity disorder and autism spectrum disorder: evidence from copy number variants. J. Am. Acad. Child Adolesc. Psychiatry 53, 761–770.e26 (2014).
pubmed: 24954825
pmcid: 4074351
doi: 10.1016/j.jaac.2014.03.004
Satterstrom, F. K. et al. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22, 1961–1965 (2019).
pubmed: 31768057
pmcid: 6884695
doi: 10.1038/s41593-019-0527-8
Rommelse, N. N., Geurts, H. M., Franke, B., Buitelaar, J. K. & Hartman, C. A. A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit/hyperactivity disorder and facilitate the search for pleiotropic genes. Neurosci. Biobehav. Rev. 35, 1363–1396 (2011).
pubmed: 21382410
doi: 10.1016/j.neubiorev.2011.02.015
Zablotsky, B., Bramlett, M. D. & Blumberg, S. J. The co-occurrence of autism spectrum disorder in children with ADHD. J. Atten. Disord. 24, 94–103 (2020).
pubmed: 28614965
doi: 10.1177/1087054717713638
Lai, M. C. et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry 6, 819–829 (2019).
pubmed: 31447415
doi: 10.1016/S2215-0366(19)30289-5
Ottosen, C. et al. Sex differences in comorbidity patterns of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 58, 412–422.e3 (2019).
pubmed: 30768399
doi: 10.1016/j.jaac.2018.07.910
Ghirardi, L. et al. The familial co-aggregation of ASD and ADHD: a register-based cohort study. Mol. Psychiatry 23, 257–262 (2018).
pubmed: 28242872
doi: 10.1038/mp.2017.17
1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
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
Yang, Z. et al. Investigating shared genetic basis across Tourette syndrome and comorbid neurodevelopmental disorders along the impulsivity-compulsivity spectrum. Biol. Psychiatry 90, 317–327 (2021).
pubmed: 33714545
pmcid: 9152955
doi: 10.1016/j.biopsych.2020.12.028
Sabourdy, F. et al. A MANBA mutation resulting in residual beta-mannosidase activity associated with severe leukoencephalopathy: a possible pseudodeficiency variant. BMC Med. Genet. 10, 84 (2009).
pubmed: 19728872
pmcid: 2745377
doi: 10.1186/1471-2350-10-84
Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).
pubmed: 31444360
pmcid: 6707297
doi: 10.1038/s41467-019-11874-7
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
pubmed: 30545857
pmcid: 6413328
doi: 10.1126/science.aat8464
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
pubmed: 25885710
pmcid: 4401657
doi: 10.1371/journal.pcbi.1004219
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
pubmed: 29184056
pmcid: 5705698
doi: 10.1038/s41467-017-01261-5
Peyrot, W. J. & Price, A. L. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat. Genet. 53, 445–454 (2021).
pubmed: 33686288
pmcid: 8038973
doi: 10.1038/s41588-021-00787-1
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
pubmed: 30038396
pmcid: 6393768
doi: 10.1038/s41588-018-0147-3
Marzluff, W. F., Gongidi, P., Woods, K. R., Jin, J. & Maltais, L. J. The human and mouse replication-dependent histone genes. Genomics 80, 487–498 (2002).
pubmed: 12408966
doi: 10.1006/geno.2002.6850
Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).
pubmed: 31676860
pmcid: 6858580
doi: 10.1038/s41588-019-0516-6
Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).
pubmed: 30643256
doi: 10.1038/s41588-018-0320-8
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
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
pubmed: 26414676
pmcid: 4797329
doi: 10.1038/ng.3406
Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).
pubmed: 33106633
pmcid: 7606627
doi: 10.1038/s41588-020-00721-x
Graciarena, M., Seiffe, A., Nait-Oumesmar, B. & Depino, A. M. Hypomyelination and oligodendroglial alterations in a mouse model of autism spectrum disorder. Front. Cell. Neurosci. 12, 517 (2018).
pubmed: 30687009
doi: 10.3389/fncel.2018.00517
Wu, Z. M. et al. White matter microstructural alterations in children with ADHD: categorical and dimensional perspectives. Neuropsychopharmacology 42, 572–580 (2017).
pubmed: 27681441
doi: 10.1038/npp.2016.223
Aoki, Y. et al. Association of white matter structure with autism spectrum disorder and attention-deficit/hyperactivity disorder. JAMA Psychiatry 74, 1120–1128 (2017).
pubmed: 28877317
pmcid: 5710226
doi: 10.1001/jamapsychiatry.2017.2573
Neale, B. M. et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49, 884–897 (2010).
pubmed: 20732625
pmcid: 2928252
doi: 10.1016/j.jaac.2010.06.008
Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & van der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).
pubmed: 29500382
pmcid: 5834468
doi: 10.1038/s41467-018-03242-8
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
Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020).
pubmed: 31981491
pmcid: 7250485
doi: 10.1016/j.cell.2019.12.036
Duffney, L. J. et al. Epigenetics and autism spectrum disorder: a report of an autism case with mutation in H1 linker histone HIST1H1E and literature review. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 426–433 (2018).
pubmed: 29704315
pmcid: 5980735
doi: 10.1002/ajmg.b.32631
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).
pubmed: 25363760
pmcid: 4402723
doi: 10.1038/nature13772
Bryant, L. et al. Histone H3.3 beyond cancer: germline mutations in histone 3 family 3A and 3B cause a previously unidentified neurodegenerative disorder in 46 patients. Sci. Adv. 6, eabc9207 (2020).
pubmed: 33268356
pmcid: 7821880
doi: 10.1126/sciadv.abc9207
Subramanian, K. et al. Basal ganglia and autism - a translational perspective. Autism Res. 10, 1751–1775 (2017).
pubmed: 28730641
doi: 10.1002/aur.1837
Clarke, T. K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419–425 (2016).
pubmed: 25754080
doi: 10.1038/mp.2015.12
Traut, N. et al. Cerebellar volume in autism: literature meta-analysis and analysis of the Autism Brain Imaging Data Exchange Cohort. Biol. Psychiatry 83, 579–588 (2018).
pubmed: 29146048
doi: 10.1016/j.biopsych.2017.09.029
Hoogman, M. et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry 4, 310–319 (2017).
pubmed: 28219628
pmcid: 5933934
doi: 10.1016/S2215-0366(17)30049-4
Shaw, P. et al. A multicohort, longitudinal study of cerebellar development in attention deficit hyperactivity disorder. J. Child Psychol. Psychiatry 59, 1114–1123 (2018).
pubmed: 29693267
pmcid: 6158081
doi: 10.1111/jcpp.12920
Wolfers, T. et al. Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychol. Med. 50, 314–323 (2020).
pubmed: 30782224
doi: 10.1017/S0033291719000084
Fliers, E. et al. Motor coordination problems in children and adolescents with ADHD rated by parents and teachers: effects of age and gender. J. Neural Transm. 115, 211–220 (2008).
pubmed: 17994185
doi: 10.1007/s00702-007-0827-0
Franke, B. et al. Live fast, die young? A review on the developmental trajectories of ADHD across the lifespan. Eur. Neuropsychopharmacol. 28, 1059–1088 (2018).
pubmed: 30195575
pmcid: 6379245
doi: 10.1016/j.euroneuro.2018.08.001
Basile, G. A. et al. Red nucleus structure and function: from anatomy to clinical neurosciences. Brain Struct. Funct. 226, 69–91 (2021).
pubmed: 33180142
doi: 10.1007/s00429-020-02171-x
Dalsgaard, S., Nielsen, H. S. & Simonsen, M. Five-fold increase in national prevalence rates of attention-deficit/hyperactivity disorder medications for children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, and other psychiatric disorders: a Danish register-based study. J. Child Adolesc. Psychopharmacol. 23, 432–439 (2013).
pubmed: 24015896
pmcid: 3778945
doi: 10.1089/cap.2012.0111
Rosenberg, R. E. et al. Psychotropic medication use among children with autism spectrum disorders enrolled in a national registry, 2007-2008. J. Autism Dev. Disord. 40, 342–351 (2010).
pubmed: 19806445
doi: 10.1007/s10803-009-0878-1
Dalsgaard, S., Leckman, J. F., Mortensen, P. B., Nielsen, H. S. & Simonsen, M. Effect of drugs on the risk of injuries in children with attention deficit hyperactivity disorder: a prospective cohort study. Lancet Psychiatry 2, 702–709 (2015).
pubmed: 26249301
doi: 10.1016/S2215-0366(15)00271-0
Chang, Z., D’Onofrio, B. M., Quinn, P. D., Lichtenstein, P. & Larsson, H. Medication for attention-deficit/hyperactivity disorder and risk for depression: a nationwide longitudinal cohort study. Biol. Psychiatry 80, 916–922 (2016).
pubmed: 27086545
pmcid: 4995143
doi: 10.1016/j.biopsych.2016.02.018
Chang, Z. et al. Medication for attention-deficit/hyperactivity disorder and risk for suicide attempts. Biol. Psychiatry 88, 452–458 (2020).
pubmed: 31987492
doi: 10.1016/j.biopsych.2019.12.003
Keilow, M., Holm, A. & Fallesen, P. Medical treatment of attention deficit/hyperactivity disorder (ADHD) and children’s academic performance. PLoS ONE 13, e0207905 (2018).
pubmed: 30496240
pmcid: 6264851
doi: 10.1371/journal.pone.0207905
Brainstorm Consortiumet al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
doi: 10.1126/science.aap8757
Polderman, T. J., Hoekstra, R. A., Posthuma, D. & Larsson, H. The co-occurrence of autistic and ADHD dimensions in adults: an etiological study in 17,770 twins. Transl. Psychiatry 4, e435 (2014).
pubmed: 25180574
pmcid: 4203013
doi: 10.1038/tp.2014.84
Ronald, A., Larsson, H., Anckarsater, H. & Lichtenstein, P. Symptoms of autism and ADHD: a Swedish twin study examining their overlap. J. Abnorm Psychol. 123, 440–451 (2014).
pubmed: 24731073
doi: 10.1037/a0036088
Pedersen, C. B. et al. The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Mol. Psychiatry 23, 6–14 (2018).
pubmed: 28924187
doi: 10.1038/mp.2017.196
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
pubmed: 25722852
pmcid: 4342193
doi: 10.1186/s13742-015-0047-8
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
pubmed: 17194218
pmcid: 1713260
doi: 10.1371/journal.pgen.0020190
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
pubmed: 16862161
doi: 10.1038/ng1847
Lam, M. et al. RICOPILI: Rapid imputation for COnsortias PIpeLIne. Bioinformatics 36, 930–933 (2020).
pubmed: 31393554
doi: 10.1093/bioinformatics/btz633
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
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).
pmcid: 6432650
doi: 10.1016/j.cell.2018.05.046
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
pubmed: 31427789
doi: 10.1038/s41588-019-0481-0
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
pubmed: 30445434
doi: 10.1093/nar/gky1120
Byrne, E. M. et al. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders. Mol. Psychiatry 26, 2070–2081 (2021).
pubmed: 32398722
doi: 10.1038/s41380-020-0705-9
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
pubmed: 30545856
pmcid: 6443102
doi: 10.1126/science.aat8127
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
pubmed: 27571263
pmcid: 5157836
doi: 10.1038/ng.3656
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
Roadmap Epigenomics Consortiumet al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
pmcid: 4530010
doi: 10.1038/nature14248
Cao, C. et al. Power analysis of transcriptome-wide association study: implications for practical protocol choice. PLoS Genet. 17, e1009405 (2021).
pubmed: 33635859
pmcid: 7946362
doi: 10.1371/journal.pgen.1009405
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Liu, X. et al. Functional architectures of local and distal regulation of gene expression in multiple human tissues. Am. J. Hum. Genet. 100, 605–616 (2017).
pubmed: 28343628
pmcid: 5384099
doi: 10.1016/j.ajhg.2017.03.002
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
Watanabe, K., Umicevic Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).
pubmed: 31324783
pmcid: 6642112
doi: 10.1038/s41467-019-11181-1
Grotzinger, A. D. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat. Genet. 54, 548–559 (2022).
pubmed: 35513722
doi: 10.1038/s41588-022-01057-4
Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Mol. Psychiatry 21, 758–767 (2016).
pubmed: 27046643
pmcid: 4879186
doi: 10.1038/mp.2016.45
Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
pubmed: 27225129
pmcid: 4883595
doi: 10.1038/nature17671
Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19, 253–258 (2014).
pubmed: 23358156
doi: 10.1038/mp.2012.184
Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).
pubmed: 28530673
pmcid: 5665562
doi: 10.1038/ng.3869
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
pmcid: 4112379
doi: 10.1038/nature13595
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
pubmed: 29700475
pmcid: 5934326
doi: 10.1038/s41588-018-0090-3
Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
pubmed: 27089181
pmcid: 4884152
doi: 10.1038/ng.3552
Jones, S. E. et al. Genome-wide association analyses in 128,266 individuals identifies new morningness and sleep duration loci. PLoS Genet. 12, e1006125 (2016).
pubmed: 27494321
pmcid: 4975467
doi: 10.1371/journal.pgen.1006125
Deary, V. et al. Genetic contributions to self-reported tiredness. Mol. Psychiatry 23, 609–620 (2018).
pubmed: 28194004
doi: 10.1038/mp.2017.5
Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441–447 (2010).
doi: 10.1038/ng.571
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
pubmed: 31043756
pmcid: 6956732
doi: 10.1038/s41588-019-0397-8
Yang, J., Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc. Natl Acad. Sci. USA 113, E4579–E4580 (2016).
pubmed: 27457963
pmcid: 4987770
doi: 10.1073/pnas.1602743113
Altman, D. G. & Bland, J. M. How to obtain the confidence interval from a P value. BMJ 343, d2090 (2011).
pubmed: 21824904
doi: 10.1136/bmj.d2090