MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

References

• Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012 July;13(7):484–15. issn: 1471-0064. doi:10.1038/nrg3230
   PubMed Web of Science ®Google Scholar
• Baselmans BML, Jansen R, Ip HF, et al. Multivariate genome-wide analyses of the well-being spectrum. Nat Genet. 2019;51(3):445–451. issn: 15461718. doi: 10.1038/s41588-018-0320-8
   PubMed Web of Science ®Google Scholar
• Barbu MC, Huider F, Campbell A, et al. Methylome-wide association study of antidepressant use in generation Scotland and the Netherlands Twin Register implicates the innate immune system. Mol Psychiatry. 2021. Oct;27(3):1647–1657. issn: 14765578. doi: 10.1038/s41380-021-01412-7
   PubMed Web of Science ®Google Scholar
• Wu L, Venters CC, Di C, et al. An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk. Nat Commun. 2020 Aug;11(1):1–11. issn: 2041-1723. doi: 10.1038/s41467-019-13993-7
   PubMed Web of Science ®Google Scholar
• Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016. 48(3):245–252. issn: 15461718. doi: 10.1038/ng.3506
   PubMed Web of Science ®Google Scholar
• Gamazon ER, Wheeler HE, Shah KP, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47(9):1091–1098. issn: 15461718. doi: 10.1038/ng.3367
   PubMed Web of Science ®Google Scholar
• Hu Y, Li M, Lu Q, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019 Feb;51(3):568–576. issn: 1546-1718. doi: 10.1038/s41588-019-0345-7
   PubMed Web of Science ®Google Scholar
• Zhang Z, Bae YE, Bradley JR, et al. SUMMIT: an integrative approach for better transcriptomic data imputation improves causal gene identification. Nat Commun. 2022 Oct 1;13(1):1–12. issn: 2041–1723. doi: 10.1038/s41467-022-34016-y
   PubMed Web of Science ®Google Scholar
• Melton HJ, Zhang Z, Deng, HW, et al. MIMOSA: a resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations (Preprint). medRxiv: Prepr Serv Health Sci. 2023 Oct.
   Google Scholar
• Min JL, Hemani G, Hannon E, et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nature Genet. 2021;53(9):1311–1321. issn: 15461718. doi: 10.1038/s41588-021-00923-x
   PubMed Web of Science ®Google Scholar
• Tibshirani R. Regression Shrinkage and Selection via the Lasso. J Royal Stat Soc Ser B (Methodological). 1996 Jan;58(1):267–288. issn: 25176161. doi: 10.1111/j.2517-6161.1996.tb02080.x
   Google Scholar
• Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005 Apr;67(2):301–320. issn: 1467-9868. doi: 10.1111/j.1467-9868.2005.00503.x
   Web of Science ®Google Scholar
• Zhang C-H. Nearly unbiased variable selection under minimax concave penalty. Ann Stat. 2010 2;38(2):894–942. doi: 10.1214/09-AOS729
   Web of Science ®Google Scholar
• Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001 Dec;96(456):1348–1360. issn: 0162-1459. doi: 10.1198/016214501753382273
   Web of Science ®Google Scholar
• Huang J, Breheny P, Lee S, et al. The Mnet method for variable selection. Stat Sinica. 2016;26:903–923. doi: 10.5705/ss.202014.0011
   Web of Science ®Google Scholar
• Auton A, Abecasis GR, Altshuler DM, et al. A global reference for human genetic variation. Nature. 2015 Sept;526(7571):68–74. issn: 1476-4687. doi: 10.1038/nature15393
   PubMed Web of Science ®Google Scholar
• Mak TSH, Porsch RM, Choi SW, et al. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol. 2017;41(6):469–480. doi: 10.1002/gepi.22050
   PubMed Web of Science ®Google Scholar
• Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1. issn: 15487660. doi: 10.18637/jss.v033.i01
   PubMed Web of Science ®Google Scholar
• Kannel WB, Feinleib M, McNamara PM, et al. An investigation of coronary heart disease in families: the Framingham offspring study. Am J Epidemiol. 1979 Sept;110(3):281–290. issn: 0002-9262. doi: 10.1093/oxfordjournals.aje.a112813
   PubMed Web of Science ®Google Scholar
• Zheng J, Haberland V, Baird D, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nature Genet. 2020;52(10):1122–1131. issn: 15461718. doi: 10.1038/s41588-020-0682-6
   PubMed Web of Science ®Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369. doi: 10.1093/bioinformatics/btu049
   PubMed Web of Science ®Google Scholar
• Liu X, Li YI, Pritchard JK. Trans effects on gene expression can drive omnigenic inheritance. Cell. 2019 May;177(4):1022–1034.e6. issn: 1097-4172. doi: 10.1016/j.cell.2019.04.014
   PubMed Web of Science ®Google Scholar
• Zhernakova DV, Deelen P, Vermaat M, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet. 2016 Dec 1;49(1):139–145. issn: 1546-1718. doi: 10.1038/ng.3737
   PubMed Web of Science ®Google Scholar
• Xue H, Shen X, Pan W. Causal inference in transcriptome-wide association studies with invalid instruments and GWAS summary data. J Am Stat Assoc. 2023 Mar;118(543):1–27. issn: 0162-1459.
   Web of Science ®Google Scholar
• Korotkevich G, Sukhov V, Budin N, et al. Fast gene set enrichment analysis. bioRxiv. 2021 Feb:060012.
   Google Scholar
• Zhou D, Jiang Y, Zhong X, et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet. 2020 Oct;52(11):1239–1246. issn: 1546-1718. doi: 10.1038/s41588-020-0706-2
   PubMed Web of Science ®Google Scholar
• Yao DW, O’Connor LJ, Price AL, et al. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet. 2020 May;52(6):626–633. issn: 15461718. doi: 10.1038/s41588-020-0625-2
   PubMed Web of Science ®Google Scholar
• Canton J, Neculai D, Grinstein S. Scavenger receptors in homeostasis and immunity. Nat Rev Immunol. 2013 Aug 9;13(9):621–634. issn: 1474-1741. doi: 10.1038/nri3515
   PubMed Web of Science ®Google Scholar
• Moore KJ, Freeman MW. Scavenger receptors in atherosclerosis. Arteriosclerosis Thrombosis Vasc Biol. 2006 Aug 8;26(8):1702–1711. issn: 10795642. doi: 10.1161/01.ATV.0000229218.97976.43
   PubMed Web of Science ®Google Scholar
• Van Berkel TJC, VAN Eck M, Herijgers N, et al. Scavenger receptor classes a and B: their roles in atherogenesis and the metabolism of modified LDL and HDL. Ann N Y Acad Sci. 2000 May 1;902(1):113–127. issn: 1749-6632. doi: 10.1111/j.1749-6632.2000.tb06306.x
   PubMedGoogle Scholar
• Redgrave TG. Chylomicron metabolism. Biochem Soc Trans. 2004 Feb 1;32(1):79–82. issn: 0300-5127. doi: 10.1042/bst0320079
   PubMedGoogle Scholar
• Roh JS, Hyun Sohn D. Damage-associated molecular patterns in inflammatory diseases. Immune Netw. 2018). issn: 20926685. Aug 4;18(4). doi: 10.4110/in.2018.18.e27
   Web of Science ®Google Scholar
• Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013 Oct;178(7):1177–1184. issn: 1476-6256. doi: 10.1093/aje/kwt084
   PubMed Web of Science ®Google Scholar
• Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003 Feb;32(1):1–22. issn: 0300-5771. doi: 10.1093/ije/dyg070
   PubMed Web of Science ®Google Scholar
• Belloni A, Chen D, Chernozhukov V, et al. Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica. 2012 Nov 6;80(6):2369–2429. issn: 1468-0262.
   Web of Science ®Google Scholar
• Verbanck M, Chen C-Y, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 Apr;50(5):693–698. issn: 1546-1718. doi: 10.1038/s41588-018-0099-7
   PubMed Web of Science ®Google Scholar
• Wahl S, Drong A, Lehne B, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2016 Dec;541(7635):81–86. issn: 1476-4687. doi: 10.1038/nature20784
   PubMed Web of Science ®Google Scholar
• Chambers JC, Loh M, Lehne B, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015 July;3(7):526. issn: 22138595. doi: 10.1016/S2213-8587(15)00127-8
   PubMed Web of Science ®Google Scholar
• Pidsley R, Zotenko E, Peters TJ, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Bio. 2016 Oct 1;17(1):1–17. issn: 1474760X. doi: 10.1186/s13059-016-1066-1
   PubMedGoogle Scholar