Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
30
11
2021
accepted:
18
07
2022
pubmed:
8
9
2022
medline:
16
9
2022
entrez:
7
9
2022
Statut:
ppublish
Résumé
Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type II
Identifiants
pubmed: 36071172
doi: 10.1038/s41588-022-01165-1
pii: 10.1038/s41588-022-01165-1
pmc: PMC9470530
doi:
Substances chimiques
ACTN3 protein, human
0
Actinin
11003-00-2
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1332-1344Subventions
Organisme : British Heart Foundation
ID : RG/13/13/30194
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : U01 AG070959
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00011
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/16/4/32697
Pays : United Kingdom
Organisme : NIEHS NIH HHS
ID : P30 ES007033
Pays : United States
Organisme : Department of Health
ID : BRC-1215-2001
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00006/4
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : P30 DK020572
Pays : United States
Organisme : British Heart Foundation
ID : RG/18/13/33946
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 217065/Z/19/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00006/1
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C18281/A19169
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : R01 DK062370
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS114045
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK020541
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00007/10
Pays : United Kingdom
Organisme : NIDDK NIH HHS
ID : U01 DK062370
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA164973
Pays : United States
Organisme : British Heart Foundation
ID : CH/12/2/29428
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 202802/Z/16/Z
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL149683
Pays : United States
Organisme : Department of Health
ID : BRC-1215-20010
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : UM1 CA182876
Pays : United States
Investigateurs
Behrooz Z Alizadeh
(BZ)
H Marike Boezen
(HM)
Lude Franke
(L)
Morris Swertz
(M)
Cisca Wijmenga
(C)
Pim van der Harst
(P)
Gerjan Navis
(G)
Marianne Rots
(M)
Bruce H R Wolffenbuttel
(BHR)
Informations de copyright
© 2022. The Author(s).
Références
Lee, I. M. et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380, 219–229 (2012).
pubmed: 22818936
pmcid: 3645500
doi: 10.1016/S0140-6736(12)61031-9
Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013–2020 (World Health Organization, 2013).
Guthold, R., Stevens, G. A., Riley, L. M. & Bull, F. C. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob. Health 6, e1077–e1086 (2018).
pubmed: 30193830
doi: 10.1016/S2214-109X(18)30357-7
Wang, Y. et al. Secular trends in sedentary behaviors and associations with weight indicators among Chinese reproductive-age women from 2004 to 2015: findings from the China Health and Nutrition Survey. Int J. Obes. (Lond.) 44, 2267–2278 (2020).
doi: 10.1038/s41366-020-00684-3
Wijndaele, K. et al. Television viewing time independently predicts all-cause and cardiovascular mortality: the EPIC Norfolk study. Int J. Epidemiol. 40, 150–159 (2011).
pubmed: 20576628
doi: 10.1093/ije/dyq105
Wijndaele, K., Sharp, S. J., Wareham, N. J. & Brage, S. Mortality risk reductions from substituting screen time by discretionary activities. Med Sci. Sports Exerc. 49, 1111–1119 (2017).
pubmed: 28106621
pmcid: 5402872
doi: 10.1249/MSS.0000000000001206
Bauman, A. E. et al. Correlates of physical activity: why are some people physically active and others not? Lancet 380, 258–271 (2012).
pubmed: 22818938
doi: 10.1016/S0140-6736(12)60735-1
den Hoed, M. et al. Heritability of objectively assessed daily physical activity and sedentary behavior. Am. J. Clin. Nutr. 98, 1317–1325 (2013).
doi: 10.3945/ajcn.113.069849
Stubbe, J. H. et al. Genetic influences on exercise participation in 37,051 twin pairs from seven countries. PLoS ONE 1, e22 (2006).
pubmed: 17183649
pmcid: 1762341
doi: 10.1371/journal.pone.0000022
Fan, W. et al. PPARδ promotes running endurance by preserving glucose. Cell Metab. 25, 1186–1193.e4 (2017).
pubmed: 28467934
pmcid: 5492977
doi: 10.1016/j.cmet.2017.04.006
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
Sarzynski, M. A. et al. Advances in exercise, fitness, and performance genomics in 2015. Med. Sci. Sports Exerc. 48, 1906–1916 (2016).
pubmed: 27183119
doi: 10.1249/MSS.0000000000000982
Klimentidis, Y. C. et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int. J. Obes. 42, 1161–1176 (2018).
doi: 10.1038/s41366-018-0120-3
Doherty, A. et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat. Commun. 9, 5257 (2018).
pubmed: 30531941
pmcid: 6288145
doi: 10.1038/s41467-018-07743-4
van de Vegte, Y. J., Said, M. A., Rienstra, M., van der Harst, P. & Verweij, N. Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours. Nat. Commun. 11, 1770 (2020).
pubmed: 32317632
pmcid: 7174427
doi: 10.1038/s41467-020-15553-w
Kilpeläinen, T. O. et al. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nat. Commun. 10, 376 (2019).
pubmed: 30670697
pmcid: 6342931
doi: 10.1038/s41467-018-08008-w
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
pubmed: 29292387
pmcid: 5805593
doi: 10.1038/s41588-017-0009-4
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 (2016).
pubmed: 27663502
pmcid: 5542030
doi: 10.1093/bioinformatics/btw613
Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).
pubmed: 25673412
pmcid: 4338562
doi: 10.1038/nature14132
Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).
pubmed: 30595370
doi: 10.1016/j.ajhg.2018.11.008
Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).
pubmed: 27863252
pmcid: 5300907
doi: 10.1016/j.cell.2016.10.042
Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).
pubmed: 30239722
doi: 10.1093/hmg/ddy327
Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11, e1005378 (2015).
pubmed: 26426971
pmcid: 4591371
doi: 10.1371/journal.pgen.1005378
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
pubmed: 25673413
pmcid: 4382211
doi: 10.1038/nature14177
Justice, A. E. et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat. Commun. 8, 14977 (2017).
pubmed: 28443625
pmcid: 5414044
doi: 10.1038/ncomms14977
Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M. & He, X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat. Genet. 52, 740–747 (2020).
pubmed: 32451458
pmcid: 7343608
doi: 10.1038/s41588-020-0631-4
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
pubmed: 29686387
pmcid: 6083837
doi: 10.1038/s41588-018-0099-7
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).
pubmed: 24114802
pmcid: 4377079
doi: 10.1002/gepi.21758
Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 46, 1985–1998 (2017).
pubmed: 29040600
pmcid: 5837715
doi: 10.1093/ije/dyx102
Hemani, G. et al. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. Preprint at bioRxiv https://doi.org/10.1101/173682 (2017).
Sanderson, E., Smith, G. D., Windmeijer, F. & Bowden, J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. 48, 713–727 (2019).
pubmed: 30535378
doi: 10.1093/ije/dyy262
Lightfoot, J. T. et al. Biological/genetic regulation of physical activity level: consensus from GenBioPAC. Med. Sci. Sports Exerc. 50, 863–873 (2018).
pubmed: 29166322
pmcid: 6481631
doi: 10.1249/MSS.0000000000001499
Pillon, N. J. et al. Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity. Nat. Commun. 11, 470 (2020).
pubmed: 31980607
pmcid: 6981202
doi: 10.1038/s41467-019-13869-w
Saul, M. C. et al. High motivation for exercise is associated with altered chromatin regulators of monoamine receptor gene expression in the striatum of selectively bred mice. Genes Brain Behav. 16, 328–341 (2017).
pubmed: 27749013
doi: 10.1111/gbb.12347
Threlfell, S., Sammut, S., Menniti, F. S., Schmidt, C. J. & West, A. R. Inhibition of phosphodiesterase 10A increases the responsiveness of striatal projection neurons to cortical stimulation. J. Pharmacol. Exp. Ther. 328, 785–795 (2009).
pubmed: 19056933
doi: 10.1124/jpet.108.146332
Harashima, A., Guettouche, T. & Barber, G. N. Phosphorylation of the NFAR proteins by the dsRNA-dependent protein kinase PKR constitutes a novel mechanism of translational regulation and cellular defense. Genes Dev. 24, 2640–2653 (2010).
pubmed: 21123651
pmcid: 2994038
doi: 10.1101/gad.1965010
Zhu, Y. et al. Identification of CD112R as a novel checkpoint for human T cells. J. Exp. Med. 213, 167–176 (2016).
pubmed: 26755705
pmcid: 4749091
doi: 10.1084/jem.20150785
Inoue, M., Chang, L., Hwang, J., Chiang, S. H. & Saltiel, A. R. The exocyst complex is required for targeting of Glut4 to the plasma membrane by insulin. Nature 422, 629–633 (2003).
pubmed: 12687004
doi: 10.1038/nature01533
Burri, L. et al. Mature DIABLO/Smac is produced by the IMP protease complex on the mitochondrial inner membrane. Mol. Biol. Cell 16, 2926–2933 (2005).
pubmed: 15814844
pmcid: 1142436
doi: 10.1091/mbc.e04-12-1086
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Muiños, M. & Ballesteros, S. Does physical exercise improve perceptual skills and visuospatial attention in older adults? A review. Eur. Rev. Aging Phys. Act. 15, 2 (2018).
Hillis, D. A. et al. Genetic basis of aerobically supported voluntary exercise: results from a selection experiment with house mice. Genetics 216, 781–804 (2020).
pubmed: 32978270
pmcid: 7648575
doi: 10.1534/genetics.120.303668
Timshel, P. N., Thompson, J. J. & Pers, T. H. Genetic mapping of etiologic brain cell types for obesity. eLife 9, e55851 (2020).
Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
pmcid: 6642641
doi: 10.1038/s41586-018-0590-4
Roberts, M. D., Ruegsegger, G. N., Brown, J. D. & Booth, F. W. Mechanisms associated with physical activity behavior: insights from rodent experiments. Exerc. Sport Sci. Rev. 45, 217–222 (2017).
pubmed: 28704221
doi: 10.1249/JES.0000000000000124
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481 (2016).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
pubmed: 26773131
pmcid: 4866522
doi: 10.1093/bioinformatics/btw018
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
pubmed: 24487276
pmcid: 3992975
doi: 10.1038/ng.2892
Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).
pubmed: 33828297
pmcid: 9153265
doi: 10.1038/s41586-021-03446-x
Bray, M. S. et al. The human gene map for performance and health-related fitness phenotypes: the 2006–2007 update. Med. Sci. Sports Exerc. 41, 35–73 (2009).
pubmed: 19123262
doi: 10.1249/MSS.0b013e3181844179
de Geus, E. J., Bartels, M., Kaprio, J., Lightfoot, J. T. & Thomis, M. Genetics of regular exercise and sedentary behaviors. Twin Res. Hum. Genet 17, 262–271 (2014).
pubmed: 25034445
doi: 10.1017/thg.2014.42
Weyerstraß, J., Stewart, K., Wesselius, A. & Zeegers, M. Nine genetic polymorphisms associated with power athlete status – a meta-analysis. J. Sci. Med. Sport 21, 213–220 (2018).
pubmed: 28666769
doi: 10.1016/j.jsams.2017.06.012
Moir, H. J. et al. Genes and elite marathon running performance: a systematic review. J. Sports Sci. Med. 18, 559–568 (2019).
pubmed: 31427879
pmcid: 6683622
Kim, D. S., Wheeler, M. T. & Ashley, E. A. The genetics of human performance. Nat. Rev. Genet. 23, 40–54 (2021).
pubmed: 34522035
doi: 10.1038/s41576-021-00400-5
Hagberg, J. M. et al. Apolipoprotein E genotype and exercise training-induced increases in plasma high-density lipoprotein (HDL)- and HDL2-cholesterol levels in overweight men. Metabolism 48, 943–945 (1999).
pubmed: 10459553
doi: 10.1016/S0026-0495(99)90185-3
Gielen, M. et al. Heritability and genetic etiology of habitual physical activity: a twin study with objective measures. Genes Nutr. 9, 415, 1–12 (2014).
Pickering, C. & Kiely, J. ACTN3: more than just a gene for speed. Front. Physiol. 8, 1080 (2017).
Vincent, B. et al. ACTN3 (R577X) genotype is associated with fiber type distribution. Physiol. Genomics 32, 58–63 (2007).
pubmed: 17848603
doi: 10.1152/physiolgenomics.00173.2007
Norman, B. et al. Strength, power, fiber types, and mRNA expression in trained men and women with different ACTN3 R577X genotypes. J. Appl. Physiol. (1985) 106, 959–965 (2009).
doi: 10.1152/japplphysiol.91435.2008
Broos, S. et al. Evidence for ACTN3 as a speed gene in isolated human muscle fibers. PLoS ONE 11, e0150594 (2016).
Broos, S. et al. The stiffness response of type IIa fibres after eccentric exercise-induced muscle damage is dependent on ACTN3 r577X polymorphism. Eur. J. Sport Sci. 19, 480–489 (2019).
pubmed: 30360698
doi: 10.1080/17461391.2018.1529200
Papadimitriou, N. et al. Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat. Commun. 11, 597 (2020).
Zhang, X. et al. Genetically predicted physical activity levels are associated with lower colorectal cancer risk: a Mendelian randomisation study. Br. J. Cancer 124, 1330–1338 (2021).
pubmed: 33510439
pmcid: 8007642
doi: 10.1038/s41416-020-01236-2
Choi, K. W. et al. Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample Mendelian randomization study. JAMA Psychiatry 76, 399–408 (2019).
pubmed: 30673066
pmcid: 6450288
doi: 10.1001/jamapsychiatry.2018.4175
Thompson, P. D. et al. Apolipoprotein E genotype and changes in serum lipids and maximal oxygen uptake with exercise training. Metabolism 53, 193–202 (2004).
pubmed: 14767871
doi: 10.1016/j.metabol.2003.09.010
de Frutos-Lucas, J. et al. Does APOE genotype moderate the relationship between physical activity, brain health and dementia risk? A systematic review. Ageing Res. Rev. 64, 101173 (2020).
Golji, J., Collins, R. & Mofrad, M. R. Molecular mechanics of the alpha-actinin rod domain: bending, torsional, and extensional behavior. PLoS Comput. Biol. 5, e1000389 (2009).
Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).
pubmed: 21407268
pmcid: 3137506
doi: 10.1038/ejhg.2011.39
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291 (2015).
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192 (2014).
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
Pulit, S. L., de With, S. A. & de Bakker, P. I. Resetting the bar: statistical significance in whole-genome sequencing-based association studies of global populations. Genet. Epidemiol. 41, 145–151 (2017).
pubmed: 27990689
doi: 10.1002/gepi.22032
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 5, 4851–4853 (2019).
doi: 10.1093/bioinformatics/btz469
Loh, P. R. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47, 1385–1392 (2015).
pubmed: 26523775
pmcid: 4666835
doi: 10.1038/ng.3431
Wainschtein, P. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
pubmed: 22426310
pmcid: 3593158
doi: 10.1038/ng.2213
Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–139 (2008).
pubmed: 18606306
pmcid: 2443852
doi: 10.1016/j.ajhg.2008.06.005
Choi, S. W. & O'Reilly, P. F. PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience 8, giz082 (2019).
Carroll, R. J., Bastarache, L. & Denny, J. C. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics 30, 2375–2376 (2014).
pubmed: 24733291
pmcid: 4133579
doi: 10.1093/bioinformatics/btu197
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).
Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).
pubmed: 27625185
pmcid: 5082560
doi: 10.1002/gepi.21998
Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I
pubmed: 27616674
pmcid: 5446088
doi: 10.1093/ije/dyw252
Lyon, M. S. et al. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol. 22, 32 (2021).
Koch, L. G. et al. Test of the principle of initial value in rat genetic models of exercise capacity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 288, R466–R472 (2005).
pubmed: 15528391
doi: 10.1152/ajpregu.00621.2004
Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
pubmed: 29022597
doi: 10.1038/nature24277
Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282–2282 (2018).
pubmed: 29891976
pmcid: 5995828
doi: 10.1038/s41467-018-04558-1
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
pubmed: 27668389
pmcid: 5083142
doi: 10.1038/nn.4399
Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).
pubmed: 28869584
pmcid: 5785926
doi: 10.1038/nn.4632
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
pubmed: 34475573
pmcid: 8432599
doi: 10.1038/s41588-021-00913-z
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).
pubmed: 22652625
doi: 10.1016/j.ymeth.2012.05.001
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).
pubmed: 25950237
pmcid: 5298202
doi: 10.1038/nprot.2015.053
Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).
doi: 10.1016/j.softx.2015.06.001
Huang, J. & MacKerell, A. D. Jr. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013).
pubmed: 23832629
pmcid: 3800559
doi: 10.1002/jcc.23354
Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.14.1 https://mc-stan.org/rstanarm (2016).
Pastore, M. & Calcagnì, A. Measuring distribution similarities between samples: a distribution-free overlapping index. Front. Psychol. 10, 1089 (2019).