Publications

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Author Title [ Type(Desc)] Year
Filters: Author is Kobor, Michael S  [Clear All Filters]
Journal Article
Forest M, O'Donnell K, Voisin G, Gaudreau H, MacIsaac JL, McEwen LM, Silveira PP, Steiner M, Kobor MS, Meaney MJ et al..  2018.  Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time.. Epigenetics. 13(1):19-32.
Dass SAHari, McCracken K, Pokhvisneva I, Chen LM, Garg E, Nguyen TTT, Wang Z, Barth B, Yaqubi M, McEwen LM et al..  2019.  A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions.. EBioMedicine. 42:188-202.
Chen L, Pan H, Tuan TAnh, Teh ALing, MacIsaac JL, Mah SM, McEwen LM, Li Y, Chen H, Broekman BFP et al..  2015.  Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism influences the association of the methylome with maternal anxiety and neonatal brain volumes.. Dev Psychopathol. 27(1):137-50.
Lin X, Teh ALing, Chen L, Lim IYubin, Tan PFang, MacIsaac JL, Morin AM, Yap F, Tan KHian, Saw SMei et al..  2017.  Choice of surrogate tissue influences neonatal EWAS findings.. BMC Med. 15(1):211.
Solomon O, MacIsaac J, Quach H, Tindula G, Kobor MS, Huen K, Meaney MJ, Eskenazi B, Barcellos LF, Holland N.  2018.  Comparison of DNA methylation measured by Illumina 450K and EPIC BeadChips in blood of newborns and 14-year-old children.. Epigenetics. :1-10.
Teh ALing, Pan H, Lin X, Lim YIves, Patro CPawan Kuma, Cheong CYujing, Gong M, MacIsaac JL, Kwoh C-K, Meaney MJ et al..  2016.  Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples.. Epigenetics. 11(1):36-48.
Farré P, Jones MJ, Meaney MJ, Emberly E, Turecki G, Kobor MS.  2015.  Concordant and discordant DNA methylation signatures of aging in human blood and brain.. Epigenetics Chromatin. 8:19.
Lin X, Lim IYubin, Wu Y, Teh ALing, Chen L, Aris IM, Soh SE, Tint MThway, MacIsaac JL, Morin AM et al..  2017.  Developmental pathways to adiposity begin before birth and are influenced by genotype, prenatal environment and epigenome.. BMC Med. 15(1):50.
O'Donnell K, Chen L, MacIsaac JL, McEwen LM, Nguyen T, Beckmann K, Zhu Y, Chen LMing, Brooks-Gunn J, Goldman D et al..  2018.  DNA methylome variation in a perinatal nurse-visitation program that reduces child maltreatment: a 27-year follow-up.. Transl Psychiatry. 8(1):15.
Teh ALing, Pan H, Chen L, Ong M-L, Dogra S, Wong J, MacIsaac JL, Mah SM, McEwen LM, Saw S-M et al..  2014.  The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes.. Genome Res. 24(7):1064-74.
Klein KOros, Grinek S, Bernatsky S, Bouchard L, Ciampi A, Colmegna I, Fortin J-P, Gao L, Hivert M-F, Hudson M et al..  2016.  funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.. Bioinformatics. 32(4):593-5.
Pan H, Lin X, Wu Y, Chen L, Teh ALing, Soh SE, Lee YSeng, Tint MThway, MacIsaac JL, Morin AM et al..  2015.  HIF3A association with adiposity: the story begins before birth.. Epigenomics. 7(6):937-50.
Ong M-L, Tan PYean, MacIsaac JL, Mah SM, Buschdorf JPaul, Cheong CY, Stünkel W, Chan L, Gluckman PD, Chng K et al..  2014.  Infinium monkeys: Infinium 450K array for the Cynomolgus macaque (Macaca fascicularis).. G3 (Bethesda). 4(7):1227-34.
Czamara D, Eraslan G, Page CM, Lahti J, Lahti-Pulkkinen M, Hämäläinen E, Kajantie E, Laivuori H, Villa PM, Reynolds RM et al..  2019.  Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns.. Nat Commun. 10(1):2548.
Ong M-L, Tuan TA, Poh J, Teh AL, Chen L, Pan H, MacIsaac JL, Kobor MS, Chong YS, Kwek K et al..  2019.  Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome.. Genes Brain Behav. :e12576.
Chen LM, Yao N, Garg E, Zhu Y, Nguyen TTT, Pokhvisneva I, Dass SAHari, Unternaehrer E, Gaudreau H, Forest M et al..  2018.  PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores.. BMC Bioinformatics. 19(1):295.

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