Browsing by Author "Lim, E."

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Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci
(Frontiers Research Foundation, 2023) de Las, F.L.; Schwande, K.L.; Brown, M.R.; Bentley, A.R.; Winkler, T.W.; Sung, Y.J.; Munroe, P.B.; Miller, C.L.; Aschard, H.; Aslibekyan, S.; Bartz, T.M.; Bielak, L.F.; Chai, J.F.; Cheng, C.Y.; Dorajoo, R.; Feitosa, M.F.; Guo, X.; Hartwig, F.P.; Horimoto, A.; Kolčić, I.; Lim, E.; Liu, Y.; Manning, A.K.; Marten, J.; Musani, S.K.; Noordam, R.; Padmanabhan, S.; Rankinen, T.; Richard, M.A.; Ridker, P.M.; Smith, A.V.; Vojinovic, D.; Zonderman, A.B.; Alver, M.; Boissel, M.; Christensen, K.; Freedman, B.I.; Gao, C.; Giulianini, F.; Harris, S.E.; He, M.; Hsu, F.C.; Kühnel, B.; Laguzzi, F.; Li, X.; Lyytikäinen, L.P.; Nolte, I.M.; Poveda, A.; Rauramaa, R.; Riaz, M.; Robino, A.; Sofer, T.; Takeuchi, F.; Tayo, B.O.; van der, M.P.J.; Verweij, N.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhan, Y.; Amin, N.; Arking, D.E.; Ballantyne, C.; Boerwinkle, E.; Brody, J.A.; Broeckel, U.; Campbell, A.; Canouil, M.; Chai, X.; Chen, Y.I.; Chen, X.; Chitrala, K.N.; Concas, M.P.; de Faire, U.; de Mutsert, R.; de Silva, H.J.; de Vries, P.S.; Do, A.; Faul, J.D.; Fisher, V.; Floyd, J.S.; Forrester, T.; Friedlander, Y.; Girotto, G.; Gu, C.C.; Hallmans, G.; Heikkinen, S.; Heng, C.K.; Homuth, G.; Hunt, S.; Ikram, M.A.; Jacobs, D.R.J.R.; Kavousi, M.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W.P.; Komulainen, P.; Langefeld, C.D.; Liang, J.; Liu, K.; Liu, J.; Lohman, K.; Mägi, R.; Manichaikul, A.W.; McKenzie, C.A.; Meitinger, T.; Milaneschi, Y.; Nauck, M.; Nelson, C.P.; O'Connell, J.R.; Palmer, N.D.; Pereira, A.C.; Perls, T.; Peters, A.; Polašek, O.; Raitakari, O.T.; Rice, K.; Rice, T.K.; Rich, S.S.; Sabanayagam, C.; Schreiner, P.J.; Shu, X.; Sidney, S.; Sims, M.; Smith, J.A.; Starr, J.M.; Strauch, K.; Tai, E.S.; Taylor, K.D.; Tsai, M.Y.; Uitterlinden, A.G.; Heemst, D.V.; Waldenberger, M.; Wang, Y.; Wei, W.; Wilson, G.; Xuan, D.; Yao, J.; Yu, C.; Yuan, J.; Zhao, W.; Becker, D.M.; Bonnefond, A.; Bowden, D.W.; Cooper, R.S.; Deary, I.J.; Divers, J.; Esko, T.; Franks, P.W.; Froguel, P.; Gieger, C.; Jonas, J.B.; Kato, N.; Lakka, T.A.; Leander, K.; Lehtimäki, T.; Magnusson, P.K.E.; North, K.E.; Ntalla, I.; Penninx, B.; Samani, N.J.; Snieder, H.; Spedicati, B.; Harst, P.V.D.; Völzke, H.; Wagenknecht, L.E.; Weir, D.R.; Wojczynski, M.K.; Wu, T.; Zheng, W.; Zhu, X.; Bouchard, C.; Chasman, D.I.; Evans, M.K.; Fox, E.R.; Gudnason, V.; Hayward, C.; Horta, B.L.; Kardia, S.L.R.; Krieger, J.E.; Mook-Kanamori, D.O.; Peyser, P.A.; Province, M.M.; Psaty, B.M.; Rudan, I.; Sim, X.; Smith, B.H.; Dam, R.M.V.; Duijn, C.M.V.; Wong, T.Y.; Arnett, D.K.; Rao, D.C.; Gauderman, J.; Liu, C.; Morrison, A.C.; Rotter, J.I.; Fornage, M.
INTRODUCTION: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. METHODS: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). RESULTS: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. DISCUSSION: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.
Investigating the benefits of molecular profiling of advanced non-small cell lung cancer tumors to guide treatments
(Impact Journals, 2018) Alifrangis, C.; Carter, P.; Cereser, B.; Chandrasinghe, P.; Belluz, L.D.B.; Lim, E.; Moderau, N.; Poyia, F.; Tabassum, N.; Zhang, H.; Krell, J.; Stebbing, J.
In this study we utilized data on patient responses to guided treatments, and we evaluated their benefit for a non-small cell lung cancer cohort. The recommended therapies used were predicted using tumor molecular profiles that involved a range of biomarkers but primarily used immunohistochemistry markers. A dataset describing 91 lung non-small cell lung cancer patients was retrospectively split into two. The first group's drugs were consistent with a treatment plan whereby all drugs received agreed with their tumor's molecular profile. The second group each received one or more drug that was expected to lack benefit. We found that there was no significant difference in overall survival or mortality between the two groups. Patients whose treatments were predicted to be of benefit survived for an average of 402 days, compared to 382 days for those that did not (P = 0.7934). In the matched treatment group, 48% of patients were deceased by the time monitoring had finished compared to 53% in the unmatched group (P = 0.6094). The immunohistochemistry biomarker for the ERCC1 receptor was found to be a marker that could be used to predict future survival; ERCC1 loss was found to be predictive of poor survival.
Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids.
(Nature Publishing Group, 2019) Bentley, A.R.; Chasman, D. I.; Schwander, K.; Ntalla, I.; Kraja, A.T.; Winkler, T.W.; Brown, M. R.; Sung, Y. J.; Lim, E.; Huffman, J.E.; Vojinovic, D.; Sim, X.; Cheng, C.Y.; Lu, Y.; Liu, J.; Guo, X.; Deng, X.; Musani, S.K.; Li, C.; Feitosa, M.F.; Richard, M.A.; Noordam, R.; Baker, J.; Chen, G.; Aschard, H.; Bartz, T.M.; Ding, J.; Dorajoo, R.; Manning, A.K.; Rankinen, T.; Smith, A. V.; Tajuddin, S.M.; Zhao, W.; Graff, M.; Alver, M.; Boissel, M.; Chai, J. F.; Chen, X.; Divers, J.; Evangelou, E.; Gao, C.; Goel, A.; Hagemeijer, Y.; Harris, S. E.; Hartwig, F. P.; He, M.; Horimoto, A.R.V. R.; Hsu, F.C.; Hung, Y. J.; Jackson, A. U.; Kasturiratne, A.; Komulainen, P.; Kühnel, B.; Leander, K.; Lin, K. H.; Luan, J.; Lyytikäinen, L.P.; Matoba, N.; Nolte, I. M.; Pietzner, M.; Prins, B.; Riaz, M.; Robino, A.; Said, M. A.; Schupf, N.; Scott, R. A.; Sofer, T.; Stancáková, A.; Takeuchi, F.; Tayo, B. O.; van der Most, P. J.; Varga, T. V.; Wang, T. D.; Wang, Y.; Ware, E. B.; Wen, W.; Xiang, Y. B.; Yanek, L. R.; Zhang, W.; Zhao, J. H.; Adeyemo, A.; Afaq, S.; Amin, N.; Amini, M.; Arking, D.E.; Arzumanyan, Z.; Aung, T.; Ballantyne, C.; Barr, R. G.; Bielak, L. F.; Boerwinkle, E.; Bottinger, E.P.; Broeckel, U.; Chen, Y. I.; Charumathi, S.; Canouil, M.; Campbell, A.; Cade, B. E.; Brown, M.; Christensen, K.; de Las Fuentes, L.; Connell, J. M.; Concas, M. P.; COGENT-Kidney Consortium; de Silva, H.J.; de Vries, P. S.; Doumatey, A.; Duan, Q.; Eaton, C. B.; Eppinga, R.N.; Faul, J. D.; Floyd, J.S.; Gigante, B.; Gharib, S. A.; Forouhi, N.G.; Ghanbari, M.; Gao, H.; Gandin, I.; Friedlander, Y.; Forrester, T.; Hixson, J. E.; Hirata, M.; Justice, A. E.; Jonas, J. B.; Johnson, C.; Joehanes, R.; Jia, Y.; EPIC-InterAct Consortium; Ikram, M.A.; Katsuya, T.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W. P.; Kolcic, I.; Kooperberg, C.; Krieger, J.E.; Kritchevsky, S.B.; Kubo, M.; Kuusisto, J.; Lakka, T. A.; Langefeld, C.D.; Langenberg, C.; Launer, L. J.; Lehne, B.; Lewis, C. E.; Li, Y.; Liang, J.; Lin, S.; Liu, C.T.; Liu, J.; Liu, K.; Loh, M.; Lohman, K.K.; Louie, T.; Luzzi, A.; Mägi, R.; Mahajan, A.; Manichaikul, A.W.; McKenzie, C.A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; Mohlke, K. L.; Momozawa, Y.; Morris, A. P.; Murray, A. D.; Nalls, M. A.; Nauck, M.; Nelson, C. P.; North, K. E.; O'Connell, J.R.; Palmer, N.D.; Papanicolau, G.J.; Pedersen, N. L.; Peters, A.; Peyser, P. A.; Polasek, O.; Poulter, N.; Raitakari, O.T.; Reiner, A. P.; Renström, F.; Rice, T.K.; Rich, S.S.; Robinson, J.G.; Rose, L. M.; Rosendaal, F. R.; Rudan, I.; Schmidt, C.O.; Schreiner, P. J.; Scott, W.R.; Sever, P.; Shi, Y.; Sidney, S.; Sims, M.; Smith, J. A.; Snieder, H.; Starr, J. M.; Strauch, K.; Stringham, H. M.; Tan, N. Y. Q.; Tang, H.; Taylor, K. D.; Teo, Y. Y.; Tham, Y. C.; Tiemeier, H.; Turner, S. T.; Uitterlinden, A. G.; Understanding Society Scientific Group; van Heemst, D.; Waldenberger, M.; Wang, H.; Wang, L.; Wang, L.; Wei, W. B.; Williams, C. A.; Wilson, G. Sr.; Wojczynski, M. K.; Yao, J.; Young, K.; Yu, C.; Yuan, J. M.; Zhou, J.; Zonderman, A. B.; Becker, D. M.; Boehnke, M.; Bowden, D. W.; Chambers, J. C.; Cooper, R. S.; de Faire, U.; Deary, I. J.; Elliott, P.; Esko, T.; Farrall, M.; Franks, P. W.; Freedman, B. I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B. L.; Juang, J. J.; Kamatani, Y.; Kammerer, C. M.; Kato, N.; Kooner, J. S.; Laakso, M.; Laurie, C. C.; Lee, I. T.; Lehtimäki, T.; Lifelines Cohort; Magnusson, P. K. E.; Oldehinkel, A. J.; Penninx, B. W. J. H.; Pereira, A. C.; Rauramaa, R.; Redline, S.; Samani, N. J.; Scott, J.; Shu, X. O.; van der Harst, P.; Wagenknecht, L. E.; Wang, J. S.; Wang, Y. X.; Wareham, N. J.; Watkins, H.; Weir, D. R.; Wickremasinghe, A.R.; Wu, T.; Zeggini, E.; Zheng, W.; Bouchard, C.; Evans, M. K.; Gudnason, V.; Kardia, S. L. R.; Liu, Y.; Psaty, B. M.; Ridker, P. M.; van Dam, R. M.; Mook-Kanamori, D. O.; Fornage, M.; Province, M. A.; Kelly, T. N.; Fox, E. R.; Hayward, C.; van Duijn, C. M.; Tai, E. S.; Wong, T. Y.; Loos, R. J. F.; Franceschini, N.; Rotter, J. I.; Zhu, X.; Bierut, L. J.; Gauderman, W. J.; Rice, K.; Munroe, P. B.; Morrison, A. C.; Rao, D. C.; Cupples, L. A.; Rotimi, C. N.
The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.