Browsing by Author "Le, Nhung"

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    Association of substance dependence phenotypes in the COGA sample
    (Wiley, 2015-05) Wetherill, Leah; Agrawal, Arpana; Kapoor, Manav; Bertelsen, Sarah; Bierut, Laura J.; Brooks, Andrew; Dick, Danielle; Hesselbrock, Michie; Hesselbrock, Victor; Koller, Daniel L.; Le, Nhung; Nurnberger Jr., John I.; Salvatore, Jessica E.; Schuckit, Marc; Tischfield, Jay A.; Wang, Jen-Chyong; Xuei, Xiaoling; Edenberg, Howard J.; Porjesz, Bernice; Bucholz, Kathleen; Goate, Alison M.; Foroud, Tatiana; Department of Medical & Molecular Genetics, IU School of Medicine
    Alcohol and drug use disorders are individually heritable (50%). Twin studies indicate that alcohol and substance use disorders share common genetic influences, and therefore may represent a more heritable form of addiction and thus be more powerful for genetic studies. This study utilized data from 2322 subjects from 118 European-American families in the Collaborative Study on the Genetics of Alcoholism sample to conduct genome-wide association analysis of a binary and a continuous index of general substance dependence liability. The binary phenotype (ANYDEP) was based on meeting lifetime criteria for any DSM-IV dependence on alcohol, cannabis, cocaine or opioids. The quantitative trait (QUANTDEP) was constructed from factor analysis based on endorsement across the seven DSM-IV criteria for each of the four substances. Heritability was estimated to be 54% for ANYDEP and 86% for QUANTDEP. One single-nucleotide polymorphism (SNP), rs2952621 in the uncharacterized gene LOC151121 on chromosome 2, was associated with ANYDEP (P = 1.8 × 10(-8) ), with support from surrounding imputed SNPs and replication in an independent sample [Study of Addiction: Genetics and Environment (SAGE); P = 0.02]. One SNP, rs2567261 in ARHGAP28 (Rho GTPase-activating protein 28), was associated with QUANTDEP (P = 3.8 × 10(-8) ), and supported by imputed SNPs in the region, but did not replicate in an independent sample (SAGE; P = 0.29). The results of this study provide evidence that there are common variants that contribute to the risk for a general liability to substance dependence.
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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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    Family-based association analysis of alcohol dependence criteria and severity
    (Wiley Blackwell (Blackwell Publishing), 2014-02) Wetherill, Leah; Kapoor, Manav; Agrawal, Arpana; Bucholz, Kathleen; Koller, Daniel; Bertelsen, Sarah E.; Le, Nhung; Wang, Jen-Chyong; Almasy, Laura; Hesselbrock, Victor; Kramer, John; Nurnberger, John I.; Schuckit, Marc; Tischfield, Jay A.; Xuei, Xiaoling; Porjesz, Bernice; Edenberg, Howard J.; Goate, Alison M.; Foroud, Tatiana; Department of Medical and Molecular Genetics, IU School of Medicine
    Background Despite the high heritability of alcohol dependence (AD), the genes found to be associated with it account for only a small proportion of its total variability. The goal of this study was to identify and analyze phenotypes based on homogeneous classes of individuals to increase the power to detect genetic risk factors contributing to the risk of AD. Methods The 7 individual DSM-IV criteria for AD were analyzed using latent class analysis (LCA) to identify classes defined by the pattern of endorsement of the criteria. A genome-wide association study was performed in 118 extended European American families (n = 2,322 individuals) densely affected with AD to identify genes associated with AD, with each of the seven DSM-IV criteria, and with the probability of belonging to two of three latent classes. Results Heritability for DSM-IV AD was 61%, and ranged from 17-60% for the other phenotypes. A SNP in the olfactory receptor OR51L1 was significantly associated (7.3 × 10−8) with the DSM-IV criterion of persistent desire to, or inability to, cut down on drinking. LCA revealed a three-class model: the “low risk” class (50%) rarely endorsed any criteria, and none met criteria for AD; the “moderate risk” class (33) endorsed primarily 4 DSM-IV criteria, and 48% met criteria for AD; the “high risk” class (17%) manifested high endorsement probabilities for most criteria and nearly all (99%) met criteria for AD One single nucleotide polymorphism (SNP) in a sodium leak channel NALCN demonstrated genome-wide significance with the high risk class (p=4.1 × 10−8). Analyses in an independent sample did not replicate these associations. Conclusion We explored the genetic contribution to several phenotypes derived from the DSM-IV alcohol dependence criteria. The strongest evidence of association was with SNPs in NALCN and OR51L1.
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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.