Browsing by Author "Jiang, Yu"

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    Exome chip meta-analysis fine maps causal variants and elucidates the genetic architecture of rare coding variants in smoking and alcohol use
    (Elsevier, 2018) Brazel, David M.; Jiang, Yu; Hughey, Jordan M.; Turcot, Valérie; Zhan, Xiaowei; Gong, Jian; Batini, Chiara; Weissenkampen, J. Dylan; Liu, MengZhen; Barnes, Daniel R.; Bertelsen, Sarah; Chou, Yi-Ling; Erzurumluoglu, A. Mesut; Faul, Jessica D.; Haessler, Jeff; Hammerschlag, Anke R.; Hsu, Chris; Kapoor, Manav; Lai, Dongbing; Le, Nhung; de Leeuw, Christiaan A.; Loukola, Anu; Mangino, Massimo; Melbourne, Carl A.; Pistis, Giorgio; Qaiser, Beenish; Rohde, Rebecca; Shao, Yaming; Stringham, Heather; Wetherill, Leah; Zhao, Wei; Agrawal, Arpana; Bierut, Laura; Chen, Chu; Eaton, Charles B.; Goate, Alison; Haiman, Christopher; Heath, Andrew; Iacono, William G.; Martin, Nicholas G.; Polderman, Tinca J.; Reiner, Alex; Rice, John; Schlessinger, David; Scholte, H. Steven; Smith, Jennifer A.; Tardif, Jean-Claude; Tindle, Hilary A.; van der Leij, Andries R.; Boehnke, Michael; Chang-Claude, Jenny; Cucca, Francesco; David, Sean P.; Foroud, Tatiana; Howson, Joanna M. M.; Kardia, Sharon L. R.; Kooperberg, Charles; Laakso, Markku; Lettre, Guillaume; Madden, Pamela; McGue, Matt; North, Kari; Posthuma, Danielle; Spector, Timothy; Stram, Daniel; Tobin, Martin D.; Weir, David R.; Kaprio, Jaakko; Abecasis, Gonçalo R.; Liu, Dajiang J.; Vrieze, Scott; Medical and Molecular Genetics, School of Medicine
    Background Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences, and contribute to disease risk. Methods We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss of function coding variants. We performed a novel fine mapping analysis to winnow the number of putative causal variants within associated loci. Results Meta-analytic sample sizes ranged from 152,348-433,216, depending on the phenotype. Rare coding variation explained 1.1-2.2% of phenotypic variance, reflecting 11%-18% of the total SNP heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between 3 and 10 variants for 65 loci. 20 loci contained rare coding variants in the 95% credible intervals. Conclusions Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine mapping GWAS loci identifies specific variants contributing to the biological etiology of substance use behavior.
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    Genetic Factors Associated with Prostate Cancer Conversion from Active Surveillance to Treatment
    (Elsevier, 2022) Jiang, Yu; Meyers, Travis J.; Emeka, Adaeze A.; Folgosa Cooley, Lauren; Cooper, Phillip R.; Lancki, Nicola; Helenowski, Irene; Kachuri, Linda; Lin, Daniel W.; Stanford, Janet L.; Newcomb, Lisa F.; Kolb, Suzanne; Finelli, Antonio; Fleshner, Neil E.; Komisarenko, Maria; Eastham, James A.; Ehdaie, Behfar; Benfante, Nicole; Logothetis, Christopher J.; Gregg, Justin R.; Perez, Cherie A.; Garza, Sergio; Kim, Jeri; Marks, Leonard S.; Delfin, Merdie; Barsa, Danielle; Vesprini, Danny; Klotz, Laurence H.; Loblaw, Andrew; Mamedov, Alexandre; Goldenberg, S. Larry; Higano, Celestia S.; Spillane, Maria; Wu, Eugenia; Carter, H. Ballentine; Pavlovich, Christian P.; Mamawala, Mufaddal; Landis, Tricia; Carroll, Peter R.; Chan, June M.; Cooperberg, Matthew R.; Cowan, Janet E.; Morgan, Todd M.; Siddiqui, Javed; Martin, Rabia; Klein, Eric A.; Brittain, Karen; Gotwald, Paige; Barocas, Daniel A.; Dallmer, Jeremiah R.; Gordetsky, Jennifer B.; Steele, Pam; Kundu, Shilajit D.; Stockdale, Jazmine; Roobol, Monique J.; Venderbos, Lionne D.F.; Sanda, Martin G.; Arnold, Rebecca; Patil, Dattatraya; Evans, Christopher P.; Dall’Era, Marc A.; Vij, Anjali; Costello, Anthony J.; Chow, Ken; Corcoran, Niall M.; Rais-Bahrami, Soroush; Phares, Courtney; Scherr, Douglas S.; Flynn, Thomas; Karnes, R. Jeffrey; Koch, Michael; Dhondt, Courtney Rose; Nelson, Joel B.; McBride, Dawn; Cookson, Michael S.; Stratton, Kelly L.; Farriester, Stephen; Hemken, Erin; Stadler, Walter M.; Pera, Tuula; Banionyte, Deimante; Bianco, Fernando J., Jr.; Lopez, Isabel H.; Loeb, Stacy; Taneja, Samir S.; Byrne, Nataliya; Amling, Christopher L.; Martinez, Ann; Boileau, Luc; Gaylis, Franklin D.; Petkewicz, Jacqueline; Kirwen, Nicholas; Helfand, Brian T.; Xu, Jianfeng; Scholtens, Denise M.; Catalona, William J.; Witte, John S.; Urology, School of Medicine
    Men diagnosed with low-risk prostate cancer (PC) are increasingly electing active surveillance (AS) as their initial management strategy. While this may reduce the side effects of treatment for PC, many men on AS eventually convert to active treatment. PC is one of the most heritable cancers, and genetic factors that predispose to aggressive tumors may help distinguish men who are more likely to discontinue AS. To investigate this, we undertook a multi-institutional genome-wide association study (GWAS) of 5,222 PC patients and 1,139 other patients from replication cohorts, all of whom initially elected AS and were followed over time for the potential outcome of conversion from AS to active treatment. In the GWAS we detected 18 variants associated with conversion, 15 of which were not previously associated with PC risk. With a transcriptome-wide association study (TWAS), we found two genes associated with conversion (MAST3, p = 6.9 × 10−7 and GAB2, p = 2.0 × 10−6). Moreover, increasing values of a previously validated 269-variant genetic risk score (GRS) for PC was positively associated with conversion (e.g., comparing the highest to the two middle deciles gave a hazard ratio [HR] = 1.13; 95% confidence interval [CI] = 0.94–1.36); whereas decreasing values of a 36-variant GRS for prostate-specific antigen (PSA) levels were positively associated with conversion (e.g., comparing the lowest to the two middle deciles gave a HR = 1.25; 95% CI, 1.04–1.50). These results suggest that germline genetics may help inform and individualize the decision of AS—or the intensity of monitoring on AS—versus treatment for the initial management of patients with low-risk PC.
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    Interaction of TBC1D9B with Mammalian ATG8 Homologues Regulates Autophagic Flux
    (Springer Nature, 2018-09-10) Liao, Yong; Li, Min; Chen, Xiaoyun; Jiang, Yu; Yin, Xiao-Ming; Pathology and Laboratory Medicine, School of Medicine
    Autophagosomes are double-membraned vesicles with cytosolic components. Their destination is to fuse with the lysosome to degrade the enclosed cargo. However, autophagosomes may be fused with other membrane compartments and possibly misguided by the RAB molecules from these compartments. The mechanisms ensuring the proper trafficking are not well understood. Yeast ATG8 and its mammalian homologues are critically involved in the autophagosome formation and expansion. We hypothesized that they could be also involved in the regulation of autophagosome trafficking. Using the yeast two-hybrid system, we found that TBC1D9B, a GTPase activating protein for RAB11A, interacted with LC3B. TBC1D9B could also interact with other mammalian ATG8 homologues. This interaction was confirmed with purified proteins in vitro, and by co-immunoprecipitation in vivo. The interacting domain of TBC1D9B with LC3 was further determined, which is unique and different from the known LC3-interacting region previously defined in other LC3-interacting molecules. Functionally, TBC1D9B could be co-localized with LC3B on the autophagosome membranes. Inhibition of TBC1D9B suppressed the turnover of membrane-bound LC3B and the autophagic degradation of long-lived proteins. TBC1D9B can thus positively regulate autophagic flux, possibly through its GTPase activity to inactivate RAB11A, facilitating the proper destination of the autophagosomes to the degradation.
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    Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
    (Springer Nature, 2019-01-07) Erzurumluoglu, A. Mesut; Liu, Mengzhen; Jackson, Victoria E.; Barnes, Daniel R.; Datta, Gargi; Melbourne, Carl A.; Young, Robin; Batini, Chiara; Surendran, Praveen; Jiang, Tao; Adnan, Sheikh Daud; Afaq, Saima; Agrawal, Arpana; Altmaier, Elisabeth; Antoniou, Antonis C.; Asselbergs, Folkert W.; Baumbach, Clemens; Bierut, Laura; Bertelsen, Sarah; Boehnke, Michael; Bots, Michiel L.; Brazel, David M.; Chambers, John C.; Chang-Claude, Jenny; Chen, Chu; Corley, Janie; Chou, Yi-Ling; David, Sean P.; Boer, Rudolf A. de; Leeuw, Christiaan A. de; Dennis, Joe G.; Dominiczak, Anna F.; Dunning, Alison M.; Easton, Douglas F.; Eaton, Charles; Elliott, Paul; Evangelou, Evangelos; Faul, Jessica D.; Foroud, Tatiana; Goate, Alison; Gong, Jian; Grabe, Hans J.; Haessler, Jeff; Haiman, Christopher; Hallmans, Göran; Hammerschlag, Anke R.; Harris, Sarah E.; Hattersley, Andrew; Heath, Andrew; Hsu, Chris; Iacono, William G.; Kanoni, Stavroula; Kapoor, Manav; Kaprio, Jaakko; Kardia, Sharon L.; Karpe, Fredrik; Kontto, Jukka; Kooner, Jaspal S.; Kooperberg, Charles; Kuulasmaa, Kari; Laakso, Markku; Lai, Dongbing; Langenberg, Claudia; Le, Nhung; Lettre, Guillaume; Loukola, Anu; Luan, Jian’an; Madden, Pamela A. F.; Mangino, Massimo; Marioni, Riccardo E.; Marouli, Eirini; Marten, Jonathan; Martin, Nicholas G.; McGue, Matt; Michailidou, Kyriaki; Mihailov, Evelin; Moayyeri, Alireza; Moitry, Marie; Müller-Nurasyid, Martina; Naheed, Aliya; Nauck, Matthias; Neville, Matthew J.; Nielsen, Sune Fallgaard; North, Kari; Perola, Markus; Pharoah, Paul D. P.; Pistis, Giorgio; Polderman, Tinca J.; Posthuma, Danielle; Poulter, Neil; Qaiser, Beenish; Rasheed, Asif; Reiner, Alex; Renström, Frida; Rice, John; Rohde, Rebecca; Rolandsson, Olov; Samani, Nilesh J.; Samuel, Maria; Schlessinger, David; Scholte, Steven H.; Scott, Robert A.; Sever, Peter; Shao, Yaming; Shrine, Nick; Smith, Jennifer A.; Starr, John M.; Stirrups, Kathleen; Stram, Danielle; Stringham, Heather M.; Tachmazidou, Ioanna; Tardif, Jean-Claude; Thompson, Deborah J.; Tindle, Hilary A.; Tragante, Vinicius; Trompet, Stella; Turcot, Valerie; Tyrrell, Jessica; Vaartjes, Ilonca; Leij, Andries R. van der; Meer, Peter van der; Varga, Tibor V.; Verweij, Niek; Völzke, Henry; Wareham, Nicholas J.; Warren, Helen R.; Weir, David R.; Weiss, Stefan; Wetherill, Leah; Yaghootkar, Hanieh; Yavas, Ersin; Jiang, Yu; Chen, Fang; Zhan, Xiaowei; Zhang, Weihua; Zhao, Wei; Zhao, Wei; Zhou, Kaixin; Amouyel, Philippe; Blankenberg, Stefan; Caulfield, Mark J.; Chowdhury, Rajiv; Cucca, Francesco; Deary, Ian J.; Deloukas, Panos; Angelantonio, Emanuele Di; Ferrario, Marco; Ferrières, Jean; Franks, Paul W.; Frayling, Tim M.; Frossard, Philippe; Hall, Ian P.; Hayward, Caroline; Jansson, Jan-Håkan; Jukema, J. Wouter; Kee, Frank; Männistö, Satu; Metspalu, Andres; Munroe, Patricia B.; Nordestgaard, Børge Grønne; Palmer, Colin N. A.; Salomaa, Veikko; Sattar, Naveed; Spector, Timothy; Strachan, David Peter; Harst, Pim van der; Zeggini, Eleftheria; Saleheen, Danish; Butterworth, Adam S.; Wain, Louise V.; Abecasis, Goncalo R.; Danesh, John; Tobin, Martin D.; Vrieze, Scott; Liu, Dajiang J.; Howson, Joanna M. M.; Medical and Molecular Genetics, School of Medicine
    Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10−8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10−8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10−3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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    Robust Bayesian variable selection for gene-environment interactions
    (Wiley, 2022-06) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, School of Medicine
    Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.
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    Robust Bayesian variable selection for gene–environment interactions
    (Oxford University Press, 2023) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.
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    TBC1D9B functions as a GTPase-activating protein for Rab11a in polarized MDCK cells
    (American Society for Cell Biology, 2014-11-15) Gallo, Luciana I.; Ruiz, Wily G.; Clayton, Dennis R.; Liu, Yong-Jian; Jiang, Yu; Fukuda, Mitsunori; Apodaca, Gerard; Yin, Xiao-Ming; Department of Pathology & Laboratory Medicine, IU School of Medicine
    Rab11a is a key modulator of vesicular trafficking processes, but there is limited information about the guanine nucleotide-exchange factors and GTPase-activating proteins (GAPs) that regulate its GTP-GDP cycle. We observed that in the presence of Mg(2+) (2.5 mM), TBC1D9B interacted via its Tre2-Bub2-Cdc16 (TBC) domain with Rab11a, Rab11b, and Rab4a in a nucleotide-dependent manner. However, only Rab11a was a substrate for TBC1D9B-stimulated GTP hydrolysis. At limiting Mg(2+) concentrations (<0.5 mM), Rab8a was an additional substrate for this GAP. In polarized Madin-Darby canine kidney cells, endogenous TBC1D9B colocalized with Rab11a-positive recycling endosomes but less so with EEA1-positive early endosomes, transferrin-positive recycling endosomes, or late endosomes. Overexpression of TBC1D9B, but not an inactive mutant, decreased the rate of basolateral-to-apical IgA transcytosis--a Rab11a-dependent pathway--and shRNA-mediated depletion of TBC1D9B increased the rate of this process. In contrast, TBC1D9B had no effect on two Rab11a-independent pathways--basolateral recycling of the transferrin receptor or degradation of the epidermal growth factor receptor. Finally, expression of TBC1D9B decreased the amount of active Rab11a in the cell and concomitantly disrupted the interaction between Rab11a and its effector, Sec15A. We conclude that TBC1D9B is a Rab11a GAP that regulates basolateral-to-apical transcytosis in polarized MDCK cells.