Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia
Autoři:
Shizhen Tang aff001; Aron S. Buchman aff003; Philip L. De Jager aff004; David A. Bennett aff003; Michael P. Epstein aff001; Jingjing Yang aff001
Působiště autorů:
Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, United States of America
aff001; Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, Georgia, United States of America
aff002; Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America
aff003; Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, New York, United States of America
aff004
Vyšlo v časopise:
Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia. PLoS Genet 17(4): e1009482. doi:10.1371/journal.pgen.1009482
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009482
Souhrn
Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.
Klíčová slova:
Alzheimer's disease – Gene expression – Genome-wide association studies – Medical risk factors – Phenotypes – Simulation and modeling – Single nucleotide polymorphisms – Transcriptome analysis
Zdroje
1. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews Genetics. 2008;9(5):356–69. Epub 2008/04/10. doi: 10.1038/nrg2344 18398418.
2. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44(3):269–76. Epub 2012/01/31. doi: 10.1038/ng.1073 22286219; PubMed Central PMCID: PMC3605033.
3. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature genetics. 2013;45(12):1452–8. Epub 2013/10/29. doi: 10.1038/ng.2802 24162737; PubMed Central PMCID: PMC3896259.
4. Cannon ME, Mohlke KL. Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci. Am J Hum Genet. 2018;103(5):637–53. Epub 2018/11/06. doi: 10.1016/j.ajhg.2018.10.001 30388398; PubMed Central PMCID: PMC6218604.
5. He X, Fuller CK, Song Y, Meng Q, Zhang B, Yang X, et al. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. American journal of human genetics. 2013;92(5):667–80. Epub 2013/05/07. doi: 10.1016/j.ajhg.2013.03.022 23643380; PubMed Central PMCID: PMC3644637.
6. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWASdoi: 10.1371/journal.pgen.1000888 20369019 PLoS genetics. 2010;6(4):e1000888. Epub 2010/04/07. PubMed Central PMCID: PMC2848547.
7. Gamazon ER, Huang RS, Cox NJ, Dolan ME. Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci. Proc Natl Acad Sci U S A. 2010;107(20):9287–92. Epub 2010/05/06. doi: 10.1073/pnas.1001827107 20442332; PubMed Central PMCID: PMC2889115.
8. Nagpal S, Meng X, Epstein MP, Tsoi LC, Patrick M, Gibson G, et al. TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. The American Journal of Human Genetics. 2019;105(2):258–66. doi: 10.1016/j.ajhg.2019.05.018 31230719
9. Consortium GT, Laboratory DA, Coordinating Center -Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing Gg, Fund NIHC, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–13. Epub 2017/10/13. doi: 10.1038/nature24277 29022597; PubMed Central PMCID: PMC5776756.
10. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47(9):1091–8. Epub 2015/08/11. doi: 10.1038/ng.3367 26258848; PubMed Central PMCID: PMC4552594.
11. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature genetics. 2016;48(3):245–52. Epub 2016/02/09. doi: 10.1038/ng.3506 26854917; PubMed Central PMCID: PMC4767558.
12. Zou H, Hastie T. Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society Series B (Statistical Methodology). 2005;67(2):301–20.
13. Zeng P, Zhou X. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. Nat Commun. 2017;8(1):456. Epub 2017/09/08. doi: 10.1038/s41467-017-00470-2 28878256; PubMed Central PMCID: PMC5587666.
14. Li YI, Wong G, Humphrey J, Raj T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat Commun. 2019;10(1):994. Epub 2019/03/03. doi: 10.1038/s41467-019-08912-9 30824768; PubMed Central PMCID: PMC6397174.
15. Keys KL, Mak ACY, White MJ, Eckalbar WL, Dahl AW, Mefford J, et al. On the cross-population generalizability of gene expression prediction models. bioRxiv. 2019:552042. doi: 10.1101/552042
16. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. American journal of human genetics. 2011;89(1):82–93. Epub 2011/07/09. doi: 10.1016/j.ajhg.2011.05.029 21737059; PubMed Central PMCID: PMC3135811.
17. Kwee LC, Liu D, Lin X, Ghosh D, Epstein MP. A powerful and flexible multilocus association test for quantitative traits. Am J Hum Genet. 2008;82(2):386–97. Epub 2008/02/07. doi: 10.1016/j.ajhg.2007.10.010 18252219; PubMed Central PMCID: PMC2664991.
18. Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, et al. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010;86(6):929–42. Epub 2010/06/22. doi: 10.1016/j.ajhg.2010.05.002 20560208; PubMed Central PMCID: PMC3032061.
19. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X. Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. 2013;92(6):841–53. Epub 2013/05/21. doi: 10.1016/j.ajhg.2013.04.015 23684009; PubMed Central PMCID: PMC3675243.
20. Yeung K-F, Yang Y, Yang C, Liu J. CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits. Bioinformatics and Biology Insights. 2019;13:117793221988143. doi: 10.1177/1177932219881435 31662603
21. Yang Y, Shi X, Jiao Y, Huang J, Chen M, Zhou X, et al. CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics. 2019;36(7):2009–16. doi: 10.1093/bioinformatics/btz880 31755899
22. Yuan Z, Zhu H, Zeng P, Yang S, Sun S, Yang C, et al. Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies. Nat Commun. 2020;11(1):3861. Epub 2020/08/02. doi: 10.1038/s41467-020-17668-6 32737316; PubMed Central PMCID: PMC7395774.
23. Liu L, Zeng P, Xue F, Yuan Z, Zhou X. Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. American journal of human genetics. 2021;108(2):240–56. Epub 2021/01/13. doi: 10.1016/j.ajhg.2020.12.006 33434493.
24. Bennett DA, Schneider JA, Arvanitakis Z, Wilson RS. Overview and findings from the religious orders study. Curr Alzheimer Res. 2012;9(6):628–45. Epub 2012/04/05. doi: 10.2174/156720512801322573 22471860; PubMed Central PMCID: PMC3409291.
25. Bennett DA, Schneider JA, Buchman AS, Barnes LL, Boyle PA, Wilson RS. Overview and findings from the rush Memory and Aging Project. Curr Alzheimer Res. 2012;9(6):646–63. Epub 2012/04/05. doi: 10.2174/156720512801322663 22471867; PubMed Central PMCID: PMC3439198.
26. Ng B, White CC, Klein HU, Sieberts SK, McCabe C, Patrick E, et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci. 2017;20(10):1418–26. Epub 2017/09/05. doi: 10.1038/nn.4632 28869584; PubMed Central PMCID: PMC5785926.
27. Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. J Alzheimers Dis. 2018;64(s1):S161–S89. Epub 2018/06/06. doi: 10.3233/JAD-179939 29865057; PubMed Central PMCID: PMC6380522.
28. Carrasquillo MM, Zou F, Pankratz VS, Wilcox SL, Ma L, Walker LP, et al. Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer’s disease. Nature genetics. 2009;41(2):192–8. Epub 2009/01/13. doi: 10.1038/ng.305 19136949; PubMed Central PMCID: PMC2873177.
29. Zou F, Chai HS, Younkin CS, Allen M, Crook J, Pankratz VS, et al. Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants. PLOS Genetics. 2012;8(6):e1002707. doi: 10.1371/journal.pgen.1002707 22685416
30. Luningham JM, Chen J, Tang S, De Jager PL, Bennett DA, Buchman AS, et al. Bayesian Genome-wide TWAS method to leverage both cis- and trans- eQTL information through summary statistics. bioRxiv. 2020:2020.03.05.979187. doi: 10.1016/j.ajhg.2020.08.022 32961112
31. Liu D, Lin X, Ghosh D. Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics. 2007;63(4):1079–88. Epub 2007/12/15. doi: 10.1111/j.1541-0420.2007.00799.x 18078480; PubMed Central PMCID: PMC2665800.
32. Liu D, Ghosh D, Lin X. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC Bioinformatics. 2008;9:292. Epub 2008/06/26. doi: 10.1186/1471-2105-9-292 18577223; PubMed Central PMCID: PMC2483287.
33. Moschopoulos PG, Canada WB. The distribution function of a linear combination of chi-squares. Computers & Mathematics with Applications. 1984;10(4):383–6. https://doi.org/10.1016/0898-1221(84)90066-X.
34. Lee S, Teslovich TM, Boehnke M, Lin X. General framework for meta-analysis of rare variants in sequencing association studies. American journal of human genetics. 2013;93(1):42–53. Epub 2013/06/19. doi: 10.1016/j.ajhg.2013.05.010 23768515; PubMed Central PMCID: PMC3710762.
35. Feng S, Liu D, Zhan X, Wing MK, Abecasis GR. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics. 2014;30(19):2828–9. Epub 2014/06/05. doi: 10.1093/bioinformatics/btu367 24894501; PubMed Central PMCID: PMC4173011.
36. Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of ATC, Replication DIG, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature genetics. 2012;44(4):369–75, S1-3. Epub 2012/03/20. doi: 10.1038/ng.2213 22426310; PubMed Central PMCID: PMC3593158.
37. Buchanan CC, Torstenson ES, Bush WS, Ritchie MD. A comparison of cataloged variation between International HapMap Consortium and 1000 Genomes Project data. J Am Med Inform Assoc. 2012;19(2):289–94. Epub 2012/02/10. doi: 10.1136/amiajnl-2011-000652 22319179; PubMed Central PMCID: PMC3277631.
38. Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nature genetics. 2016;48(10):1284–7. Epub 2016/08/30. doi: 10.1038/ng.3656 27571263; PubMed Central PMCID: PMC5157836.
39. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17(9):1156–63. Epub 2014/08/19. doi: 10.1038/nn.3786 25129075; PubMed Central PMCID: PMC4292795.
40. De Jager PL, Shulman JM, Chibnik LB, Keenan BT, Raj T, Wilson RS, et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol Aging. 2012;33(5):1017 e1–15. Epub 2011/11/08. doi: 10.1016/j.neurobiolaging.2011.09.033 22054870; PubMed Central PMCID: PMC3307898.
41. Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J, et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nature genetics. 2018;50(7):968–78. Epub 2018/06/20. doi: 10.1038/s41588-018-0132-x 29915430; PubMed Central PMCID: PMC6314198.
42. Rödel E. Fisher, R. A.: Statistical Methods for Research Workers, 14. Aufl., Oliver & Boyd, Edinburgh, London 1970. XIII, 362 S., 12 Abb., 74 Tab., 40 s. Biometrische Zeitschrift. 1971;13(6):429–30. doi: 10.1002/bimj.19710130623
43. Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature genetics. 2019;51(3):404–13. Epub 2019/01/09. doi: 10.1038/s41588-018-0311-9 30617256.
44. Marioni RE, Harris SE, Zhang Q, McRae AF, Hagenaars SP, Hill WD, et al. GWAS on family history of Alzheimer’s disease. doi: 10.1038/s41398-018-0150-6 29777097. 2018;8(1):99. Epub 2018/05/20. PubMed Central PMCID: PMC5959890.
45. Andaleon A, Mogil LS, Wheeler HE. Genetically regulated gene expression underlies lipid traits in Hispanic cohorts. PLoS One. 2019;14(8):e0220827. Epub 2019/08/09. doi: 10.1371/journal.pone.0220827 31393916; PubMed Central PMCID: PMC6687110.
46. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9(1):224. Epub 2018/01/18. doi: 10.1038/s41467-017-02317-2 29335400; PubMed Central PMCID: PMC5768719.
47. Tripathi S, Christie KR, Balakrishnan R, Huntley R, Hill DP, Thommesen L, et al. Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort. Database (Oxford). 2013;2013:bat062. Epub 2013/08/29. doi: 10.1093/database/bat062 23981286; PubMed Central PMCID: PMC3753819.
48. Chiba-Falek O, Gottschalk WK, Lutz MW. The effects of the TOMM40 poly-T alleles on Alzheimer’s disease phenotypes. Alzheimers Dement. 2018;14(5):692–8. Epub 2018/03/11. doi: 10.1016/j.jalz.2018.01.015 29524426; PubMed Central PMCID: PMC5938113.
49. Xi ZQ, Sun JJ, Wang XF, Li MW, Liu XZ, Wang LY, et al. HSPBAP1 is found extensively in the anterior temporal neocortex of patients with intractable epilepsy. Synapse. 2007;61(9):741–7. Epub 2007/06/15. doi: 10.1002/syn.20417 17568411.
50. Albers MW, Gilmore GC, Kaye J, Murphy C, Wingfield A, Bennett DA, et al. At the interface of sensory and motor dysfunctions and Alzheimer’s disease. Alzheimers Dement. 2015;11(1):70–98. Epub 2014/07/16. doi: 10.1016/j.jalz.2014.04.514 25022540; PubMed Central PMCID: PMC4287457.
51. Saha P, Sen N. Tauopathy: A common mechanism for neurodegeneration and brain aging. Mech Ageing Dev. 2019;178:72–9. Epub 2019/01/23. doi: 10.1016/j.mad.2019.01.007 30668956; PubMed Central PMCID: PMC6377302.
52. Sherva R, Tripodis Y, Bennett DA, Chibnik LB, Crane PK, de Jager PL, et al. Genome-wide association study of the rate of cognitive decline in Alzheimer’s disease. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2014;10(1):45–52. Epub 2013/03/25. doi: 10.1016/j.jalz.2013.01.008 23535033.
53. Kamboh MI, Fan KH, Yan Q, Beer JC, Snitz BE, Wang X, et al. Population-based genome-wide association study of cognitive decline in older adults free of dementia: identification of a novel locus for the attention domain. Neurobiol Aging. 2019;84:239.e15–.e24. Epub 2019/04/08. doi: 10.1016/j.neurobiolaging.2019.02.024 30954325; PubMed Central PMCID: PMC6739197.
54. Beecham GW, Hamilton K, Naj AC, Martin ER, Huentelman M, Myers AJ, et al. Genome-wide association meta-analysis of neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet. 2014;10(9):e1004606. Epub 2014/09/05. doi: 10.1371/journal.pgen.1004606 25188341; PubMed Central PMCID: PMC4154667.
55. Hu X, Pickering EH, Hall SK, Naik S, Liu YC, Soares H, et al. Genome-wide association study identifies multiple novel loci associated with disease progression in subjects with mild cognitive impairment. Transl Psychiatry. 2011;1(11):e54. Epub 2011/01/01. doi: 10.1038/tp.2011.50 22833209; PubMed Central PMCID: PMC3309471.
56. Sandí M-J, Marshall CB, Balan M, Coyaud É, Zhou M, Monson DM, et al. MARK3-mediated phosphorylation of ARHGEF2 couples microtubules to the actin cytoskeleton to establish cell polarity. Science Signaling. 2017;10(503):eaan3286. doi: 10.1126/scisignal.aan3286 29089450
57. Sun D, Yu Z, Fang X, Liu M, Pu Y, Shao Q, et al. LncRNA GAS5 inhibits microglial M2 polarization and exacerbates demyelination. EMBO Rep. 2017;18(10):1801–16. Epub 2017/08/14. doi: 10.15252/embr.201643668 28808113.
58. Sun Z, Xu Y. Nuclear Receptor Coactivators (NCOAs) and Corepressors (NCORs) in the Brain. Endocrinology. 2020;161(8). doi: 10.1210/endocr/bqaa083 32449767
59. Yang R, Zhan M, Guo M, Yuan H, Wang Y, Zhang Y, et al. Yolk sac-derived Pdcd11-positive cells modulate zebrafish microglia differentiation through the NF-κB-Tgfβ1 pathway. Cell Death & Differentiation. 2020. doi: 10.1038/s41418-020-0591-3 32709934
60. Lucatelli JF, Barros AC, Silva VK, Machado Fda S, Constantin PC, Dias AA, et al. Genetic influences on Alzheimer’s disease: evidence of interactions between the genes APOE, APOC1 and ACE in a sample population from the South of Brazil. Neurochem Res. 2011;36(8):1533–9. Epub 2011/05/03. doi: 10.1007/s11064-011-0481-7 21533863.
61. García-Ayllón M-S, Small DH, Avila J, Sáez-Valero J. Revisiting the Role of Acetylcholinesterase in Alzheimer’s Disease: Cross-Talk with P-tau and β-Amyloid. Front Mol Neurosci. 2011;4:22–. doi: 10.3389/fnmol.2011.00022 21949503.
62. Sheffield LG, Miskiewicz HB, Tannenbaum LB, Mirra SS. Nuclear pore complex proteins in Alzheimer disease. J Neuropathol Exp Neurol. 2006;65(1):45–54. Epub 2006/01/18. doi: 10.1097/01.jnen.0000195939.40410.08 16410748.
63. Reinikainen KJ, Riekkinen PJ, Halonen T, Laakso M. Decreased muscarinic receptor binding in cerebral cortex and hippocampus in alzheimer’s disease. Life Sciences. 1987;41(4):453–61. doi: 10.1016/0024-3205(87)90221-9 3600187
64. Ouellette AR, Neuner SM, Dumitrescu L, Anderson LC, Gatti DM, Mahoney ER, et al. Cross-Species Analyses Identify Dlgap2 as a Regulator of Age-Related Cognitive Decline and Alzheimer’s Dementia. Cell Reports. 2020;32(9):108091. doi: 10.1016/j.celrep.2020.108091 32877673
65. Ramos-García NA, Orozco-Ibarra M, Estudillo E, Elizondo G, Gómez Apo E, Chávez Macías LG, et al. Aryl Hydrocarbon Receptor in Post-Mortem Hippocampus and in Serum from Young, Elder, and Alzheimer’s Patients. Int J Mol Sci. 2020;21(6):1983. doi: 10.3390/ijms21061983 32183254.
66. Gong CX, Singh TJ, Grundke-Iqbal I, Iqbal K. Phosphoprotein phosphatase activities in Alzheimer disease brain. J Neurochem. 1993;61(3):921–7. Epub 1993/09/01. doi: 10.1111/j.1471-4159.1993.tb03603.x 8395566.
67. Remnestål J. Expression and distribution of transcription factors NPAS3 och RFX3 in Alzheimer’s disease [Student thesis]2015.
68. Colton CA, Vitek MP, Wink DA, Xu Q, Cantillana V, Previti ML, et al. NO synthase 2 (NOS2) deletion promotes multiple pathologies in a mouse model of Alzheimer’s disease. Proceedings of the National Academy of Sciences. 2006;103(34):12867–72. doi: 10.1073/pnas.0601075103 16908860
69. Rusilowicz-Jones E, Jardine J, Kallinos A, Pinto-Fernandez A, Guenther F, Giurrandino M, et al. A novel USP30 inhibitor recapitulates genetic loss of USP30 and sets the trigger for PINK1-PARKIN amplification of mitochondrial ubiquitylation. bioRxiv. 2020:2020.04.16.044206. doi: 10.1101/2020.04.16.044206
70. Myers S, Yuan H, Kang J, Tan F, Traynelis S, Low C. Distinct roles of GRIN2A and GRIN2B variants in neurological conditions [version 1; peer review: 2 approved]. F1000Research. 2019;8(1940). doi: 10.12688/f1000research.18949.1 31807283
71. Babenko VN, Smagin DA, Galyamina AG, Kovalenko IL, Kudryavtseva NN. Altered Slc25 family gene expression as markers of mitochondrial dysfunction in brain regions under experimental mixed anxiety/depression-like disorder. BMC Neurosci. 2018;19(1):79. Epub 2018/12/13. doi: 10.1186/s12868-018-0480-6 30537945; PubMed Central PMCID: PMC6288882.
72. Yokoyama JS, Wang Y, Schork AJ, Thompson WK, Karch CM, Cruchaga C, et al. Association Between Genetic Traits for Immune-Mediated Diseases and Alzheimer Disease. JAMA Neurol. 2016;73(6):691–7. doi: 10.1001/jamaneurol.2016.0150 27088644.
73. Russ J. Systematic interaction mapping reveals novel modifiers of neurodegenerative disease processes 2012.
74. Wostyn P, Audenaert K, De Deyn PP. Choroidal Proteins Involved in Cerebrospinal Fluid Production may be Potential Drug Targets for Alzheimer’s Disease Therapy. Perspect Medicin Chem. 2011;5:11–7. doi: 10.4137/PMC.S6509 21487536
75. La Piana R, Weraarpachai W, Ospina LH, Tetreault M, Majewski J, Bruce Pike G, et al. Identification and functional characterization of a novel MTFMT mutation associated with selective vulnerability of the visual pathway and a mild neurological phenotype. neurogenetics. 2017;18(2):97–103. doi: 10.1007/s10048-016-0506-0 28058511
76. Jeromin A, Lasseter HC, Provost AC, Daskalakis NP, Etkin A, Gehrman P, et al. Driving Progress in Posttraumatic Stress Disorder Biomarkers. Biol Psychiatry. 2020;87(6):e13–e4. Epub 2019/10/19. doi: 10.1016/j.biopsych.2019.07.036 31623824.
77. Mogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, et al. Genetic architecture of gene expression traits across diverse populations. PLOS Genetics. 2018;14(8):e1007586. doi: 10.1371/journal.pgen.1007586 30096133
78. Chen H, Wang C, Conomos MP, Stilp AM, Li Z, Sofer T, et al. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. American journal of human genetics. 2016;98(4):653–66. Epub 2016/03/29. doi: 10.1016/j.ajhg.2016.02.012 27018471; PubMed Central PMCID: PMC4833218.
Článek vyšel v časopise
PLOS Genetics
2021 Číslo 4
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- „Jednohubky“ z klinického výzkumu – 2024/44
Nejčtenější v tomto čísle
- Aicardi-Goutières syndrome-associated gene SAMHD1 preserves genome integrity by preventing R-loop formation at transcription–replication conflict regions
- Functional assessment of the “two-hit” model for neurodevelopmental defects in Drosophila and X. laevis
- Pathways and signatures of mutagenesis at targeted DNA nicks
- Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study
