MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity
Autoři:
Anqi Zhu aff001; Nana Matoba aff002; Emma P. Wilson aff002; Amanda L. Tapia aff001; Yun Li aff001; Joseph G. Ibrahim aff001; Jason L. Stein aff002; Michael I. Love aff001
Působiště autorů:
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff001; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff002; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff003; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
aff004
Vyšlo v časopise:
MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 17(4): e1009455. doi:10.1371/journal.pgen.1009455
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009455
Souhrn
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
Klíčová slova:
Arteries – Blood – Gene expression – Genetic loci – Genome-wide association studies – Heredity – Quantitative trait loci – Simulation and modeling
Zdroje
1. Yao DW, O’Connor LJ, Price AL, Gusev A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet. 2020;52: 626–633. doi: 10.1038/s41588-020-0625-2 32424349
2. Plagnol V, Smyth DJ, Todd JA, Clayton DG. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics. 2009;10: 327–334. doi: 10.1093/biostatistics/kxn039 19039033
3. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10: e1004383. doi: 10.1371/journal.pgen.1004383 24830394
4. Wallace C, Rotival M, Cooper JD, Rice CM, Yang JHM, McNeill M, et al. Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum Mol Genet. 2012;21: 2815–2824. doi: 10.1093/hmg/dds098 22403184
5. Hormozdiari F, van de Bunt M, Segrè AV, Li X, Joo JWJ, Bilow M, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99: 1245–1260. doi: 10.1016/j.ajhg.2016.10.003 27866706
6. Wen X, Pique-Regi R, Luca F. Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLoS Genet. 2017;13: e1006646. doi: 10.1371/journal.pgen.1006646 28278150
7. Ongen H, Brown AA, Delaneau O, Panousis NI, Nica AC, Dermitzakis ET. Estimating the causal tissues for complex traits and diseases. Nat Genet. 2017;49: 1676–1683. doi: 10.1038/ng.3981 29058715
8. Gleason KJ, Yang F, Pierce BL, He X, Chen LS. Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits. Genome Biol. 2020;21: 236. doi: 10.1186/s13059-020-02125-w 32912334
9. 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: 1091–1098. doi: 10.1038/ng.3367 26258848
10. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48: 245–252. doi: 10.1038/ng.3506 26854917
11. Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019;51: 675–682. doi: 10.1038/s41588-019-0367-1 30926970
12. Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32: 1–22. doi: 10.1093/ije/dyg070 12689998
13. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23: R89–98. doi: 10.1093/hmg/ddu328 25064373
14. Richardson TG, Hemani G, Gaunt TR, Relton CL, Davey Smith G. A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. Nat Commun. 2020;11: 185. doi: 10.1038/s41467-019-13921-9 31924771
15. Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y, Kim-Hellmuth S, et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 2021;22: 49. doi: 10.1186/s13059-020-02252-4 33499903
16. Broekema RV, Bakker OB, Jonkers IH. A practical view of fine-mapping and gene prioritization in the post-genome-wide association era. Open Biol. 2020;10: 190221. doi: 10.1098/rsob.190221 31937202
17. Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009;10: 23. doi: 10.1186/1471-2156-10-23 19473544
18. Zhong W, Spracklen CN, Mohlke KL, Zheng X, Fine J, Li Y. Multi-SNP mediation intersection-union test. Bioinformatics. 2019;35: 4724–4729. doi: 10.1093/bioinformatics/btz285 31099385
19. van der Graaf A, Claringbould A, Rimbert A, BIOS consortium, Westra H-J, Li Y, et al. A novel Mendelian randomization method identifies causal relationships between gene expression and low-density lipoprotein cholesterol levels. Nat Commun. 2020;11: 4930.
20. Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet. 2017;18: 117–127. doi: 10.1038/nrg.2016.142 27840428
21. Park Y, Sarkar AK, He L, Davila-Velderrain J, De Jager PL, Kellis M. A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease. bioRxiv [Preprint]. 2017 bioRxiv 219428 [posted 2017 Nov 14; revised 2017 Nov 18; revised 2017 Dec 1; cited 2021 Mar 9]: [23 p.]. Available from: https://www.biorxiv.org/content/10.1101/219428v3
22. Porcu E, Rüeger S, Lepik K, eQTLGen Consortium, BIOS Consortium, Santoni FA, et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun. 2019;10: 3300. doi: 10.1038/s41467-019-10936-0 31341166
23. Barfield R, Feng H, Gusev A, Wu L, Zheng W, Pasaniuc B, et al. Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet Epidemiol. 2018;42: 418–433. doi: 10.1002/gepi.22131 29808603
24. 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: 3861. doi: 10.1038/s41467-020-17668-6 32737316
25. Zhang Y, Quick C, Yu K, Barbeira A, The GTEx Consortium, Luca F, et al. Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis. Genome Biol. 2020;21: 232 doi: 10.1186/s13059-020-02026-y 32912253
26. Gleason KJ, Yang F, Chen LS. A robust two-sample Mendelian Randomization method integrating GWAS with multi-tissue eQTL summary statistics. bioRxiv [Preprint]. 2020 bioRxiv 135541 [posted 2020 Jun 5; cited 2021 Mar 9]: [31 p.]. Available from: https://www.biorxiv.org/content/10.1101/2020.06.04.135541v1
27. Brown CD, Mangravite LM, Engelhardt BE. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 2013;9: e1003649. doi: 10.1371/journal.pgen.1003649 23935528
28. Jansen R, Hottenga J-J, Nivard MG, Abdellaoui A, Laport B, de Geus EJ, et al. Conditional eQTL analysis reveals allelic heterogeneity of gene expression. Hum Mol Genet. 2017;26: 1444–1451. doi: 10.1093/hmg/ddx043 28165122
29. Consortium GTEx. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369: 1318–1330. doi: 10.1126/science.aaz1776 32913098
30. Huang QQ, Tang HHF, Teo SM, Mok D, Ritchie SC, Nath AP, et al. Neonatal genetics of gene expression reveal potential origins of autoimmune and allergic disease risk. Nat Commun. 2020;11: 3761. doi: 10.1038/s41467-020-17477-x 32724101
31. Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A Probabilistic Programming Language. J Stat Softw. 2017;76. doi: 10.18637/jss.v076.i01
32. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81: 559–575. doi: 10.1086/519795 17701901
33. Wen X, Lee Y, Luca F, Pique-Regi R. Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors. Am J Hum Genet. 2016;98: 1114–1129. doi: 10.1016/j.ajhg.2016.03.029 27236919
34. Wallace C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 2020;16: e1008720. doi: 10.1371/journal.pgen.1008720 32310995
35. Carvalho CM, Polson NG, Scott JG. The horseshoe estimator for sparse signals. Biometrika. 2010. pp. 465–480. doi: 10.1093/biomet/asq017
36. Berzuini C, Guo H, Burgess S, Bernardinelli L. A Bayesian approach to Mendelian randomization with multiple pleiotropic variants. Biostatistics. 2020;21: 86–101. doi: 10.1093/biostatistics/kxy027 30084873
37. Fazia T, Egidi L, Ayoglu B, Beecham A, Bitti PP, Ticca A, et al. Bayesian Mendelian Randomization identifies disease causing proteins via pedigree data, partially observed exposures and correlated instruments. 2019. Available: http://arxiv.org/abs/1903.00682
38. Uche-Ikonne OO, Dondelinger F, Palmer T. Bayesian estimation of IVW and MR-Egger models for two-sample Mendelian randomization studies. Epidemiology. medRxiv; 2019.
39. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine. 2002. pp. 1539–1558. doi: 10.1002/sim.1186 12111919
40. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48: 481–487. doi: 10.1038/ng.3538 27019110
41. Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9: 918. doi: 10.1038/s41467-018-03371-0 29500431
42. Mancuso, N. twas_sim repository; 2021 [cited 2021 Mar 9]. Database: GitHub [Internet] Available from: https://github.com/mancusolab/twas_sim
43. Koster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics. 2012. pp. 2520–2522. doi: 10.1093/bioinformatics/bts480 22908215
44. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37: 658–665. doi: 10.1002/gepi.21758 24114802
45. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7. doi: 10.7554/eLife.34408 29846171
46. Consortium GTEx, Laboratory Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204–213. doi: 10.1038/nature24277 29022597
47. Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv [Preprint]. 2018 bioRxiv 447367 [posted 2018 Oct 19; cited 2021 Mar 9]: [57 p.]. Available from: https://www.biorxiv.org/content/10.1101/447367v1
48. Strunz T, Grassmann F, Gayán J, Nahkuri S, Souza-Costa D, Maugeais C, et al. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci Rep. 2018;8: 5865. doi: 10.1038/s41598-018-24219-z 29650998
49. Nikpay M, Goel A, Won H-H, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1000 Genomes—based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47: 1121. doi: 10.1038/ng.3396 26343387
50. Bacanu SA, Devlin B, Roeder K. The power of genomic control. Am J Hum Genet. 2000;66: 1933–1944. doi: 10.1086/302929 10801388
51. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39: 1217–1224. doi: 10.1038/ng2142 17873874
52. Wright FA, Sullivan PF, Brooks AI, Zou F, Sun W, Xia K, et al. Heritability and genomics of gene expression in peripheral blood. Nat Genet. 2014;46: 430–437. doi: 10.1038/ng.2951 24728292
53. Lloyd-Jones LR, Holloway A, McRae A, Yang J, Small K, Zhao J, et al. The Genetic Architecture of Gene Expression in Peripheral Blood. Am J Hum Genet. 2017;100: 228–237. doi: 10.1016/j.ajhg.2016.12.008 28065468
54. Ouwens KG, Jansen R, Nivard MG, van Dongen J, Frieser MJ, Hottenga J-J, et al. A characterization of cis- and trans-heritability of RNA-Seq-based gene expression. Eur J Hum Genet. 2020;28: 253–263. doi: 10.1038/s41431-019-0511-5 31558840
55. Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, GTEx Consortium, et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genet. 2016;12: e1006423. doi: 10.1371/journal.pgen.1006423 27835642
56. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26: 2336–2337. doi: 10.1093/bioinformatics/btq419 20634204
57. Hahne F, Ivanek R. Visualizing Genomic Data Using Gviz and Bioconductor. In: Mathé E, Davis S, editors. Statistical Genomics. New York, NY: Springer New York; 2016. pp. 335–351.
58. Strong A, Patel K, Rader DJ. Sortilin and lipoprotein metabolism: making sense out of complexity. Curr Opin Lipidol. 2014;25: 350–357. doi: 10.1097/MOL.0000000000000110 25101658
59. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 2010;466: 714–719. doi: 10.1038/nature09266 20686566
60. Alshahid M, Wakil SM, Al-Najai M, Muiya NP, Elhawari S, Gueco D, et al. New susceptibility locus for obesity and dyslipidaemia on chromosome 3q22.3. Hum Genomics. 2013;7: 15. doi: 10.1186/1479-7364-7-15 23738802
61. Song Y, Ma R, Zhang H. The influence of MRAS gene variants on ischemic stroke and serum lipid levels in Chinese Han population. Medicine. 2019;98: e18065. doi: 10.1097/MD.0000000000018065 31770223
62. Wu J, Yin R-X, Guo T, Lin Q-Z, Shi G-Y, Sun J-Q, et al. Association between the MARS rs6782181 polymorphism and serum lipid levels. Int J Clin Exp Pathol. 2015;8: 1855–1866. 25973078
63. Codina-Fauteux V-A, Beaudoin M, Lalonde S, Lo KS, Lettre G. PHACTR1 splicing isoforms and eQTLs in atherosclerosis-relevant human cells. BMC Med Genet. 2018;19: 97. doi: 10.1186/s12881-018-0616-7 29884117
64. Chen L, Qian H, Luo Z, Li D, Xu H, Chen J, et al. PHACTR1 gene polymorphism with the risk of coronary artery disease in Chinese Han population. Postgrad Med J. 2019;95: 67–71. doi: 10.1136/postgradmedj-2018-136298 30777881
65. Tall AR. Functions of cholesterol ester transfer protein and relationship to coronary artery disease risk. J Clin Lipidol. 2010;4: 389–393. doi: 10.1016/j.jacl.2010.08.006 21076631
66. Guerra R, Wang J, Grundy SM, Cohen JC. A hepatic lipase (LIPC) allele associated with high plasma concentrations of high density lipoprotein cholesterol. Proc Natl Acad Sci U S A. 1997;94: 4532–4537. doi: 10.1073/pnas.94.9.4532 9114024
67. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101: 5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856
68. Qin Y, Meric G, Long T, Watrous J, Burgess S, Havulinna A, et al. Genome-wide association and Mendelian randomization analysis prioritizes bioactive metabolites with putative causal effects on common diseases. Genetic and Genomic Medicine. medRxiv; 2020.
69. He B, Shi J, Wang X, Jiang H, Zhu H-J. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol. 2020;18: 97. doi: 10.1186/s12915-020-00830-3 32778093
70. Folkersen L, Gustafsson S, Wang Q, Hansen DH, Hedman ÅK, Schork A, et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab. 2020;2: 1135–1148. doi: 10.1038/s42255-020-00287-2 33067605
71. Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet. 2020. doi: 10.1038/s41576-020-0258-4 32709985
72. Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, et al. Genomic variation. Impact of regulatory variation from RNA to protein. Science. 2015;347: 664–667. doi: 10.1126/science.1260793 25657249
73. Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA, Golan D, et al. RNA splicing is a primary link between genetic variation and disease. Science. 2016;352: 600–604. doi: 10.1126/science.aad9417 27126046
74. Chick JM, Munger SC, Simecek P, Huttlin EL, Choi K, Gatti DM, et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature. 2016;534: 500–505. doi: 10.1038/nature18270 27309819
75. Sinnott-Armstrong N, Naqvi S, Rivas M, Pritchard JK. GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background. eLife. 2021;10: e58615. doi: 10.7554/eLife.58615 33587031
76. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581: 434–443. doi: 10.1038/s41586-020-2308-7 32461654
77. Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL, Shan Y, et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 2019;15: e1008500. doi: 10.1371/journal.pgen.1008500 31869403
78. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, 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. 2015;12: e1001779. doi: 10.1371/journal.pmed.1001779 25826379
79. Servin B, Stephens M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genetics. 2005. p. e114. doi: 10.1371/journal.pgen.0030114.eor
80. Wellcome Trust Case Control Consortium, Maller JB, McVean G, Byrnes J, Vukcevic D, Palin K, et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet. 2012;44: 1294–1301. doi: 10.1038/ng.2435 23104008
81. Kichaev G, Yang W-Y, Lindstrom S, Hormozdiari F, Eskin E, Price AL, et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 2014;10: e1004722. doi: 10.1371/journal.pgen.1004722 25357204
82. Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E. Identifying causal variants at loci with multiple signals of association. Genetics. 2014;198: 497–508. doi: 10.1534/genetics.114.167908 25104515
83. Hutchinson A, Watson H, Wallace C. Improving the coverage of credible sets in Bayesian genetic fine-mapping. PLoS Comput Biol. 2020;16: e1007829. doi: 10.1371/journal.pcbi.1007829 32282791
84. Wang G, Sarkar A, Carbonetto P, Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Series B Stat Methodol. 2020;25: 1.
85. Valdar W, Sabourin J, Nobel A, Holmes CC. Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging. Genet Epidemiol. 2012;36: 451–462. doi: 10.1002/gepi.21639 22549815
86. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44: 512–525. doi: 10.1093/ije/dyv080 26050253
87. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28: 30–42. doi: 10.1097/EDE.0000000000000559 27749700
88. Martin JS, Xu Z, Reiner AP, Mohlke KL, Sullivan P, Ren B, et al. HUGIn: Hi-C Unifying Genomic Interrogator. Bioinformatics. 2017;33: 3793–3795. doi: 10.1093/bioinformatics/btx359 28582503
89. Sey NYA, Hu B, Mah W, Fauni H, McAfee JC, Rajarajan P, et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat Neurosci. 2020;23: 583–593. doi: 10.1038/s41593-020-0603-0 32152537
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