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5. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Author

Natarajan, P. (Pradeep), Pampana, A. (Akhil), Graham, S. E. (Sarah E.), Ruotsalainen, S. E. (Sanni E.), Perry, J. A. (James A.), de Vries, P. S. (Paul S.), Broome, J. G. (Jai G.), Pirruccello, J. P. (James P.), Honigbere, M. C. (Michael C.), Aragam, K. (Krishna), Wolford, B. (Brooke), Brody, J. A. (Jennifer A.), Antonacci-Fulton, L. (Lucinda), Arden, M. (Moscati), Aslibekyan, S. (Stella), Assimes, T. L. (Themistocles L.), Ballantyne, C. M. (Christie M.), Bielak, L. F. (Lawrence F.), Bisl, J. C. (Joshua C.), Cade, B. E. (Brian E.), Do, R. (Ron), Doddapaneni, H. (Harsha), Emery, L. S. (Leslie S.), Hung, Y.-J. (Yi-Jen), Irvin, M. R. (Marguerite R.), Khan, A. T. (Alyna T.), Lange, L. (Leslie), Lee, J. (Jiwon), Lemaitre, R. N. (Rozenn N.), Martin, L. W. (Lisa W.), Metcalf, G. (Ginger), Montasser, M. E. (May E.), Moon, J.-Y. (Jee-Young), Muzny, D. (Donna), Connell, J. R. (Jeffrey R. O.), Palmer, N. D. (Nicholette D.), Peralta, J. M. (Juan M.), Peyser, P. A. (Patricia A.), Stilp, A. M. (Adrienne M.), Tsai, M. (Michael), Wang, F. F. (Fei Fei), Weeks, D. E. (Daniel E.), Yanek, L. R. (Lisa R.), Wilson, J. G. (James G.), Abecasis, G. (Goncalo), Arnett, D. K. (Donna K.), Becker, L. C. (Lewis C.), Blangercy, J. (John), Boerwinkle, E. (Eric), Bowden, D. W. (Donald W.), Chang, Y.-C. (Yi-Cheng), Chen, Y. I. (Yii-Der, I), Choi, W. J. (Won Jung), Correa, A. (Adolfo), Curran, J. E. (Joanne E.), Daly, M. J. (Mark J.), DutcherE, S. K. (Susan K.), Ellinor, P. T. (Patrick T.), Fornage, M. (Myriam), Freedman, B. I. (Barry, I), Gabriel, S. (Stacey), Germer, S. (Soren), Gibbs, R. A. (Richard A.), He, J. (Jiang), Hveem, K. (Kristian), Jarvik, G. P. (Gail P.), Kaplan, R. C. (Robert C.), Kardia, S. L. (Sharon L. R.), Kennyn, E. (Eimear), Kim, R. W. (Ryan W.), Kooperberg, C. (Charles), Laurie, C. C. (Cathy C.), Lee, S. (Seonwook), Lloyd-Jones, D. M. (Don M.), Loos, R. J. (Ruth J. F.), Lubitz, S. A. (Steven A.), Mathias, R. A. (Rasika A.), Martinez, K. A. (Karine A. Viaud), McGarvey, S. T. (Stephen T.), Mitche, B. D. (Braxton D.), Nickerson, D. A. (Deborah A.), North, K. E. (Kari E.), Palotie, A. (Aarno), Park, C. J. (Cheol Joo), Psat, B. M. (Bruce M. Y.), Rao, D. C. (D. C.), Redline, S. (Susan), Reiner, A. P. (Alexander P.), Seo, D. (Daekwan), Seo, J.-S. (Jeong-Sun), Smith, A. V. (Albert, V), Tracy, R. P. (Russell P.), Kathiresan, S. (Sekar), Cupples, L. A. (L. Adrienne), Rotten, J. I. (Jerome, I), Morrison, A. C. (Alanna C.), Rich, S. S. (Stephen S.), Ripatti, S. (Samuli), Wilier, C. (Cristen), Peloso, G. M. (Gina M.), Vasan, R. S. (Ramachandran S.), Abe, N. (Namiko), Albert, C. (Christine), Almasy, L. (Laura), Alonso, A. (Alvaro), Ament, S. (Seth), Anderson, P. (Peter), Applebaum-Bowden, D. (Deborah), Arking, D. (Dan), Ashley-Koch, A. (Allison), Auer, P. (Paul), Avramopoulos, D. (Dimitrios), Barnard, J. (John), Barnes, K. (Kathleen), Barr, R. G. (R. Graham), Barron-Casella, E. (Emily), Beaty, T. (Terri), Becker, D. (Diane), Beer, R. (Rebecca), Begum, F. (Ferdouse), Beitelshees, A. (Amber), Benjamin, E. (Emelia), Bezerra, M. (Marcos), Bielak, L. (Larry), Blackwel, T. (Thomas), Bowler, R. (Russell), Broecke, U. (Ulrich), Bunting, K. (Karen), Burchard, E. (Esteban), Buth, E. (Erin), Cardwel, J. (Jonathan), Carty, C. (Cara), Casaburi, R. (Richard), Casella, J. (James), Chaffin, M. (Mark), Chang, C. (Christy), Chasman, D. (Daniel), Chavan, S. (Sameer), Chen, B.-J. (Bo-Juen), Chen, W.-M. (Wei-Min), Chol, M. (Michael), Choi, S. H. (Seung Hoan), Chuang, L.-M. (Lee-Ming), Chung, M. (Mina), Conomos, M. P. (Matthew P.), Cornell, E. (Elaine), Crapo, J. (James), Curtis, J. (Jeffrey), Custer, B. (Brian), Damcott, C. (Coleen), Darbar, D. (Dawood), Das, S. (Sayantan), David, S. (Sean), Davis, C. (Colleen), Daya, M. (Michelle), de Andrade, M. (Mariza), DeBaunuo, M. (Michael), Duan, Q. (Qing), Devine, R. D. (Ranjan Deka Dawn DeMeo Scott), Duggirala, Q. R. (Qing Ravi), Durda, J. P. (Jon Peter), Dutcher, S. (Susan), Eaton, C. (Charles), Ekunwe, L. (Lynette), Farber, C. (Charles), Farnaml, L. (Leanna), Fingerlin, T. (Tasha), Flickinger, M. (Matthew), Franceschini, N. (Nora), Fu, M. (Mao), Fullerton, S. M. (Stephanie M.), Fulton, L. (Lucinda), Gan, W. (Weiniu), Gao, Y. (Yan), Gass, M. (Margery), Ge, B. (Bruce), Geng, X. P. (Xiaoqi Priscilla), Gignoux, C. (Chris), Gladwin, M. (Mark), Glahn, D. (David), Gogarten, S. (Stephanie), Gong, D.-W. (Da-Wei), Goring, H. (Harald), Gu, C. C. (C. Charles), Guan, Y. (Yue), Guo, X. (Xiuqing), Haessler, J. (Jeff), Hall, M. (Michael), Harris, D. (Daniel), Hawle, N. Y. (Nicola Y.), Heavner, B. (Ben), Heckbert, S. (Susan), Hernandez, R. (Ryan), Herrington, D. (David), Hersh, C. (Craig), Hidalgo, B. (Bertha), Hixson, J. (James), Hokanson, J. (John), Hong, E. (Elliott), Hoth, K. (Karin), Hsiung, C. A. (Chao Agnes), Huston, H. (Haley), Hwu, C. M. (Chii Min), Jackson, R. (Rebecca), Jain, D. (Deepti), Jaquish, C. (Cashell), Jhun, M. A. (Min A.), Johnsen, J. (Jill), Johnson, A. (Andrew), Johnson, C. (Craig), Johnston, R. (Rich), Jones, K. (Kimberly), Kang, H. M. (Hyun Min), Kaufman, L. (Laura), Kell, S. Y. (Shannon Y.), Kessler, M. (Michael), Kinney, G. (Greg), Konkle, B. (Barbara), Kramer, H. (Holly), Krauter, S. (Stephanie), Lange, C. (Christoph), Lange, E. (Ethan), Laurie, C. (Cecelia), LeBoff, M. (Meryl), Lee, S. S. (Seunggeun Shawn), Lee, W.-J. (Wen-Jane), LeFaive, J. (Jonathon), Levine, D. (David), Levy, D. (Dan), Lewis, J. (Joshua), Li, Y. (Yun), Lin, H. (Honghuang), Lin, K. H. (Keng Han), Lin, X. (Xihong), Liu, S. (Simin), Liu, Y. (Yongmei), Lunetta, K. (Kathryn), Luo, J. (James), Mahaney, M. (Michael), Make, B. (Barry), Manichaikul, A. (Ani), Mansonl, J. (JoAnn), Margolin, L. (Lauren), Mathai, S. (Susan), McArdle, P. (Patrick), Mcdonald, M.-L. (Merry-Lynn), McFarland, S. (Sean), McHugh, C. (Caitlin), Mei, H. (Hao), Meyers, D. A. (Deborah A.), Mikulla, J. (Julie), Min, N. (Nancy), Minear, M. (Mollie), Minster, R. L. (Ryan L.), Musani, S. (Solomon), Mwasongwe, S. (Stanford), Mychaleckyj, J. C. (Josyf C.), Nadkarni, G. (Girish), Naik, R. (Rakhi), Naseri, T. (Take), Nekhai, S. (Sergei), Nelson, S. C. (Sarah C.), Nickerson, D. (Deborah), Connell, J. O. (Jeff O.), Connor, T. O. (Tim O.), Ochs-Balcom, H. (Heather), Pankow, J. (James), Papanicolaou, G. (George), Parkerl, M. (Margaret), Parsa, A. (Afshin), Penchey, S. (Sara), Perez, M. (Marco), Peters, U. (Ulrike), Phillips, L. S. (Lawrence S.), Phillips, S. (Sam), Pollin, T. (Toni), Post, W. (Wendy), Becker, J. P. (Julia Powers), Boorgula, M. P. (Meher Preethi), Preuss, M. (Michael), Prokopenko, D. (Dmitry), Qasba, P. (Pankaj), Qiao, D. (Dandi), Rafaels, N. (Nicholas), Raffield, L. (Laura), Rasmussen-Torvik, L. (Laura), Ratan, A. (Aakrosh), Reed, R. (Robert), Reganl, E. (Elizabeth), Reupena, M. S. (Muagututi Sefuiva), Rice, K. (Ken), Roden, D. (Dan), Roselli, C. (Carolina), Ruczinski, I. (Ingo), Russel, P. (Pamela), Ruuska, S. (Sarah), Ryan, K. (Kathleen), Sabino, E. C. (Ester Cerdeira), Sakornsakolpatl, P. (Phuwanat), Salzberg, S. (Steven), Sandow, K. (Kevin), Sankaran, V. G. (Vijay G.), Scheller, C. (Christopher), Schmidt, E. (Ellen), Schwander, K. (Karen), Schwartz, D. (David), Sciurba, F. (Frank), Seidman, C. (Christine), Seidman, J. (Jonathan), Sheehan, V. (Vivien), Shetty, A. (Amol), Shetty, A. (Aniket), Sheu, W. H. (Wayne Hui-Heng), Shoemaker, M. B. (M. Benjamin), Silver, B. (Brian), Silvermanl, E. (Edwin), Smith, J. (Jennifer), Smith, J. (Josh), Smith, N. (Nicholas), Smith, T. (Tanja), Smoller, S. (Sylvia), Snively, B. (Beverly), Soferlm, T. (Tamar), Streeten, E. (Elizabeth), Su, J. L. (Jessica Lasky), Sung, Y. J. (Yun Ju), Sylvia, J. (Jody), Sztalryd, C. (Carole), Taliun, D. (Daniel), Tang, H. (Hua), Taub, M. (Margaret), Taylor, K. D. (Kent D.), Taylor, S. (Simeon), Telen, M. (Marilyn), Thornton, T. A. (Timothy A.), Tinker, L. (Lesley), Tirschwel, D. (David), Tiwari, H. (Hemant), Vaidya, D. (Dhananjay), VandeHaar, P. (Peter), Vrieze, S. (Scott), Walker, T. (Tarik), Wallace, R. (Robert), Waits, A. (Avram), Wan, E. (Emily), Wang, H. (Heming), Watson, K. (Karol), Weir, B. (Bruce), Weiss, S. (Scott), Weng, L.-C. (Lu-Chen), Williams, K. (Kayleen), Williams, L. K. (L. Keoki), Wilson, C. (Carla), Wong, Q. (Quenna), Xu, H. (Huichun), Yang, I. (Ivana), Yang, R. (Rongze), Zaghlou, N. (Norann), Zekavat, M. (Maryam), Zhang, Y. (Yingze), Zhao, S. X. (Snow Xueyan), Zhao, W. (Wei), Zni, D. (Degui), Zhou, X. (Xiang), Zhu, X. (Xiaofeng), Zody, M. (Michael), Zoellner, S. (Sebastian), Daly, M. (Mark), Jacob, H. (Howard), Matakidou, A. (Athena), Runz, H. (Heiko), John, S. (Sally), Plenge, R. (Robert), McCarthy, M. (Mark), Hunkapiller, J. (Julie), Ehm, M. (Meg), Waterworth, D. (Dawn), Fox, C. (Caroline), Malarstig, A. (Anders), Klinger, K. (Kathy), Call, K. (Kathy), Mkel, T. (Tomi), Kaprio, J. (Jaakko), Virolainen, P. (Petri), Pulkki, K. (Kari), Kilpi, T. (Terhi), Perola, M. (Markus), Partanen, J. (Jukka), Pitkranta, A. (Anne), Kaarteenaho, R. (Riitta), Vainio, S. (Seppo), Savinainen, K. (Kimmo), Kosma, V.-M. (Veli-Matti), Kujala, U. (Urho), Tuovila, O. (Outi), Hendolin, M. (Minna), Pakkanen, R. (Raimo), Waring, J. (Jeff), Riley-Gillis, B. (Bridget), Liu, J. (Jimmy), Biswas, S. (Shameek), Diogo, D. (Dorothee), Marshall, C. (Catherine), Hu, X. (Xinli), Gossel, M. (Matthias), Schleutker, J. (Johanna), Arvas, M. (Mikko), Hinttala, R. (Reetta), Kettunen, J. (Johannes), Laaksonen, R. (Reijo), Mannermaa, A. (Arto), Paloneva, J. (Juha), Soininen, H. (Hilkka), Julkunen, V. (Valtteri), Remes, A. (Anne), Klviinen, R. (Reetta), Hiltunen, M. (Mikko), Peltola, J. (Jukka), Tienari, P. (Pentti), Rinne, J. (Juha), Ziemann, A. (Adam), Waring, J. (Jeffrey), Esmaeeli, S. (Sahar), Smaoui, N. (Nizar), Lehtonen, A. (Anne), Eaton, S. (Susan), Landenper, S. (Sanni), Michon, J. (John), Kerchner, G. (Geoff), Bowers, N. (Natalie), Teng, E. (Edmond), Eicher, J. (John), Mehta, V. (Vinay), Gormle, P. Y. (Padhraig Y.), Linden, K. (Kari), Whelan, C. (Christopher), Xu, F. (Fanli), Pulford, D. (David), Frkkil, M. (Martti), Pikkarainen, S. (Sampsa), Jussila, A. (Airi), Blomster, T. (Timo), Kiviniemi, M. (Mikko), Voutilainen, M. (Markku), Georgantas, B. (Bob), Heap, G. (Graham), Rahimov, F. (Fedik), Usiskin, K. (Keith), Maranville, J. (Joseph), Lu, T. (Tim), Oh, D. (Danny), Kalpala, K. (Kirsi), Miller, M. (Melissa), McCarthy, L. (Linda), Eklund, K. (Kari), Palomki, A. (Antti), Isomki, P. (Pia), Piri, L. (Laura), Kaipiainen-Seppnen, O. (Oili), Lertratanaku, A. (Apinya), Bing, D. C. (David Close Marla Hochfeld Nan), Gordillo, J. E. (Jorge Esparza), Mars, N. (Nina), Laitinen, T. (Tarja), Pelkonen, M. (Margit), Kauppi, P. (Paula), Kankaanranta, H. (Hannu), Harju, T. (Terttu), Greenberg, S. (Steven), Chen, H. (Hubert), Betts, J. (Jo), Ghosh, S. (Soumitra), Salomaa, V. (Veikko), Niiranen, T. (Teemu), Juonala, M. (Markus), Metsrinne, K. (Kaj), Khnen, M. (Mika), Junttila, J. (Juhani), Laakso, M. (Markku), Pihlajamki, J. (Jussi), Sinisalo, J. (Juha), Taskinen, M.-R. (Marja-Riitta), Tuomi, T. (Tiinamaija), Laukkanen, J. (Jari), Challis, B. (Ben), Peterson, A. (Andrew), Chu, A. (Audrey), Parkkinen, J. (Jaakko), Muslin, A. (Anthony), Joensuu, H. (Heikki), Meretoja, T. (Tuomo), Aaltonen, L. (Lauri), Auranen, A. (Annika), Karihtala, P. (Peeter), Kauppila, S. (Saila), Auvinen, P. (Pivi), Elenius, K. (Klaus), Popovic, R. (Relja), Schutzman, J. (Jennifer), Loboda, A. (Andrey), Chhibber, A. (Aparna), Lehtonen, H. (Heli), McDonough, S. (Stefan), Crohns, M. (Marika), Kulkarni, D. (Diptee), Kaarniranta, K. (Kai), Turunen, J. (Joni), Ollila, T. (Terhi), Seitsonen, S. (Sanna), Uusitalo, H. (Hannu), Aaltonen, V. (Vesa), Uusitalo-Jrvinen, H. (Hannele), Luodonp, M. (Marja), Hautala, N. (Nina), Strauss, E. (Erich), Chen, H. (Hao), Podgornaia, A. (Anna), Hoffman, J. (Joshua), Tasanen, K. (Kaisa), Huilaja, L. (Laura), Hannula-Jouppi, K. (Katariina), Salmi, T. (Teea), Peltonen, S. (Sirkku), Koulu, L. (Leena), Harvima, I. (Ilkka), Wu, Y. (Ying), Choy, D. (David), Jalanko, A. (Anu), Kajanne, R. (Risto), Lyhs, U. (Ulrike), Kaunisto, M. (Mari), Davis, J. W. (Justin Wade), Quarless, D. (Danjuma), Petrovski, S. (Slav), Chen, C.-Y. (Chia-Yen), Bronson, P. (Paola), Yang, R. (Robert), Chang, D. (Diana), Bhangale, T. (Tushar), Holzinger, E. (Emily), Wang, X. (Xulong), Chen, X. (Xing), Auro, K. (Kirsi), Wang, C. (Clarence), Xu, E. (Ethan), Auge, F. (Franck), Chatelain, C. (Clement), Kurki, M. (Mitja), Karjalainen, J. (Juha), Havulinna, A. (Aki), Palin, K. (Kimmo), Palta, P. (Priit), Parolo, P. D. (Pietro Della Briotta), Zhou, W. (Wei), Lemmel, S. (Susanna), Rivas, M. (Manuel), Harju, J. (Jarmo), Lehisto, A. (Arto), Ganna, A. (Andrea), Llorens, V. (Vincent), Karlsson, A. (Antti), Kristiansson, K. (Kati), Hyvrinen, K. (Kati), Ritari, J. (Jarmo), Wahlfors, T. (Tiina), Koskinen, M. (Miika), Pylkäs, K. (Katri), Kalaoja, M. (Marita), Karjalainen, M. (Minna), Mantere, T. (Tuomo), Kangasniemi, E. (Eeva), Heikkinen, S. (Sami), Laakkonen, E. (Eija), Kononen, J. (Juha), Loukola, A. (Anu), Laiho, P. (Pivi), Sistonen, T. (Tuuli), Kaiharju, E. (Essi), Laukkanen, M. (Markku), Jrvensivu, E. (Elina), Lhteenmki, S. (Sini), Mnnikk, L. (Lotta), Wong, R. (Regis), Mattsson, H. (Hannele), Hiekkalinna, T. (Tero), Jimnez, M. G. (Manuel Gonzlez), Donner, K. (Kati), Prn, K. (KaIle), Nunez-Fontarnau, J. (Javier), Kilpelinen, E. (Elina), Sipi, T. P. (Timo P.), Brein, G. (Georg), Dada, A. (Alexander), Awaisa, G. (Ghazal), Shcherban, A. (Anastasia), Sipil, T. (Tuomas), Laivuori, H. (Hannele), Kiiskinen, T. (Tuomo), Siirtola, H. (Harri), Tabuenca, J. G. (Javier Gracia), Kallio, L. (Lila), Soini, S. (Sirpa), Pitknen, K. (Kimmo), and Kuopio, T. (Teijo)

Subjects
Cardiovascular genetics, Genome-wide association studies
Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

Published
2021

8. MUC5B promoter variant and rheumatoid arthritis with interstitial lung disease

Author

Juge, P.-A. Lee, J.S. Ebstein, E. Furukawa, H. Dobrinskikh, E. Gazal, S. Kannengiesser, C. Ottaviani, S. Oka, S. Tohma, S. Tsuchiya, N. Rojas-Serrano, J. González-Pérez, M.I. Mejía, M. Buendía-Roldán, I. Falfán-Valencia, R. Ambrocio-Ortiz, E. Manali, E. Papiris, S.A. Karageorgas, T. Boumpas, D. Antoniou, K. Van Moorsel, C.H.M. Van Der Vis, J. De Man, Y.A. Grutters, J.C. Wang, Y. Borie, R. Wemeau-Stervinou, L. Wallaert, B. Flipo, R.-M. Nunes, H. Valeyre, D. Saidenberg-Kermanac'H, N. Boissier, M.-C. Marchand-Adam, S. Frazier, A. Richette, P. Allanore, Y. Sibilia, J. Dromer, C. Richez, C. Schaeverbeke, T. Lioté, H. Thabut, G. Nathan, N. Amselem, S. Soubrier, M. Cottin, V. Clément, A. Deane, K. Walts, A.D. Fingerlin, T. Fischer, A. Ryu, J.H. Matteson, E.L. Niewold, T.B. Assayag, D. Gross, A. Wolters, P. Schwarz, M.I. Holers, M. Solomon, J.J. Doyle, T. Rosas, I.O. Blauwendraat, C. Nalls, M.A. Debray, M.-P. Boileau, C. Crestani, B. Schwartz, D.A. Dieudé, P.

Abstract

BACKGROUND: Given the phenotypic similarities between rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) (hereafter, RA-ILD) and idiopathic pulmonary fibrosis, we hypothesized that the strongest risk factor for the development of idiopathic pulmonary fibrosis, the gain-of-function MUC5B promoter variant rs35705950, would also contribute to the risk of ILD among patients with RA. METHODS: Using a discovery population and multiple validation populations, we tested the association of the MUC5B promoter variant rs35705950 in 620 patients with RA-ILD, 614 patients with RA without ILD, and 5448 unaffected controls. RESULTS: Analysis of the discovery population revealed an association of the minor allele of the MUC5B promoter variant with RA-ILD when patients with RA-ILD were compared with unaffected controls (adjusted odds ratio, 3.8; 95% confidence interval [CI], 2.8 to 5.2; P = 9.7×10-17). The MUC5B promoter variant was also significantly overrepresented among patients with RA-ILD, as compared with unaffected controls, in an analysis of the multiethnic case series (adjusted odds ratio, 5.5; 95% CI, 4.2 to 7.3; P = 4.7×10-35) and in a combined analysis of the discovery population and the multiethnic case series (adjusted odds ratio, 4.7; 95% CI, 3.9 to 5.8; P = 1.3×10-49). In addition, the MUC5B promoter variant was associated with an increased risk of ILD among patients with RA (adjusted odds ratio in combined analysis, 3.1; 95% CI, 1.8 to 5.4; P = 7.4×10-5), particularly among those with evidence of usual interstitial pneumonia on high-resolution computed tomography (adjusted odds ratio in combined analysis, 6.1; 95% CI, 2.9 to 13.1; P = 2.5×10-6). However, no significant association with the MUC5B promoter variant was observed for the diagnosis of RA alone. CONCLUSIONS: We found that the MUC5B promoter variant was associated with RA-ILD and more specifically associated with evidence of usual interstitial pneumonia on imaging. Copyright © 2018 Massachusetts Medical Society.

Published
2018

9. MUC5B PROMOTER VARIANT RS35705950 IS A RISK FACTOR FOR RHEUMATOID ARTHRITIS - INTERSTITIAL LUNG DISEASE

Author

Juge, P. -A. Lee, J. S. Ebstein, E. Furukawa, H. and Dobrinskikh, E. Gazal, S. Kannengiesser, C. Ottaviani, S. and Tsuchiya, N. Oka, S. Tohma, S. Rojas-Serrano, J. and Gonzalez-Perez, M. -I. Mejia, M. Buendia-Roldan, I. and Falfan-Valencia, R. Manali, E. Papiris, S. A. Karageorgas, T. Boumpas, D. Antoniou, K. Van Moorsel, C. van der Vis, J. de Man, Y. Grutters, J. Wang, Y. Borie, R. and Wemeau-Stervinou, L. Wallaert, B. Flipo, R. -M. Nunes, H. and Valeyre, D. Saidenberg, N. Marchand-Adam, S. Deane, K. and Walts, A. Fingerlin, T. Matteson, E. Niewold, T. and Assayag, D. Gross, A. Wolters, P. Schwarz, M. Holers, M. and Solomon, J. Doyle, T. Debray, M. -P. Boileau, C. and Crestani, B. Schwartz, D. Dieude, P.

Published
2018

10. Association between telomere length and risk of cancer and non-neoplastic diseases: A Mendelian randomization study

Author

Collaboration, Telomeres Mendelian Randomization, Haycock, P, Burgess, S, Nounu, A, Zheng, J, Okoli, G, Bowden, J, Wade, K, Timpson, N, Evans, D, Willeit, P, Aviv, A, Gaunt, T, Hemani, G, Mangino, M, Ellis, H, Kurian, K, Pooley, K, Eeles, R, Lee, J, Fang, S, Chen, W, Law, M, Bowdler, L, Iles, M, Yang, Q, Worrall, B, Markus, H, Hung, R, Amos, C, Spurdle, A, Thompson, D, O'Mara, T, Wolpin, B, Amundadottir, L, Stolzenberg-Solomon, R, Trichopoulou, A, Onland-Moret, N, Lund, E, Duell, E, Canzian, F, Severi, G, Overvad, K, Gunter, M, Tumino, R, Svenson, U, van Rij, A, Baas, A, Bown, M, Samani, N, van t'Hof, F, Tromp, G, Jones, G, Kuivaniemi, H, Elmore, J, Johansson, M, Mckay, J, Scelo, G, Carreras-Torres, R, Gaborieau, V, Brennan, P, Bracci, P, Neale, R, Olson, S, Gallinger, S, Li, D, Petersen, G, Risch, H, Klein, A, Han, J, Abnet, C, Freedman, N, Taylor, P, Maris, J, Aben, K, Kiemeney, L, Vermeulen, S, Wiencke, J, Walsh, K, Wrensch, M, Rice, T, Turnbull, C, Litchfield, K, Paternoster, L, Standl, M, Abecasis, G, SanGiovanni, J, Li, Y, Mijatovic, V, Sapkota, Y, Low, S, Zondervan, K, Montgomery, G, Nyholt, D, van Heel, D, Hunt, K, Arking, D, Ashar, F, Sotoodehnia, N, Woo, D, Rosand, J, Comeau, M, Brown, W, Silverman, E, Hokanson, J, Cho, M, Hui, J, Ferreira, M, Thompson, P, Morrison, A, Felix, J, Smith, N, Christiano, A, Petukhova, L, Betz, R, Fan, X, Zhang, X, Zhu, C, Langefeld, C, Thompson, S, Wang, F, Lin, X, Schwartz, D, Fingerlin, T, Rotter, J, Cotch, M, Jensen, R, Munz, M, Dommisch, H, Schaefer, A, Han, F, Ollila, H, Hillary, R, Albagha, O, Ralston, S, Zeng, C, Zheng, W, Shu, X, Reis, A, Uebe, S, Hüffmeier, U, Kawamura, Y, Otowa, T, Sasaki, T, Hibberd, M, Davila, S, Xie, G, Siminovitch, K, Bei, J, Zeng, Y, Försti, A, Chen, B, Landi, S, Franke, A, Fischer, A, Ellinghaus, D, Flores, C, Noth, I, Ma, S, Foo, J, Liu, J, Kim, J, Cox, D, Delattre, O, Mirabeau, O, Skibola, C, Tang, C, Garcia-Barcelo, M, Chang, K, Su, W, Chang, Y, Martin, N, Gordon, S, Wade, T, Lee, C, Kubo, M, Cha, P, Nakamura, Y, Levy, D, Kimura, M, Hwang, S, Hunt, S, Spector, T, Soranzo, N, Manichaikul, A, Barr, R, Kahali, B, Speliotes, E, Yerges-Armstrong, L, Cheng, C, Jonas, J, Wong, T, Fogh, I, Lin, K, Powell, J, Rice, K, Relton, C, Martin, R, Davey Smith, G, Erasmus MC other, Epidemiology, and Pediatrics

Subjects
0301 basic medicine, Adult, Male, Cancer Research, Single-nucleotide polymorphism, Genome-wide association study, Disease, Bioinformatics, Polymorphism, Single Nucleotide, Risk Assessment, Article, 03 medical and health sciences, Telomere Homeostasis, SDG 3 - Good Health and Well-being, Neoplasms, Mendelian randomization, Journal Article, medicine, Humans, Genetic Predisposition to Disease, Càncer, Germ-Line Mutation, Aged, Cancer, Aged, 80 and over, business.industry, Nucleotides, Odds ratio, Mendelian Randomization Analysis, Middle Aged, Telomere, medicine.disease, Nucleòtids, 030104 developmental biology, Stem cell division, Oncology, Cardiovascular Diseases, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Female, ICEP, business, Genome-Wide Association Study, Bristol Population Health Science Institute
Abstract

Importance The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. Objective To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. Data Sources Genomewide association studies (GWAS) published up to January 15, 2015. Study Selection GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. Data Extraction and Synthesis Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. Main Outcomes and Measures Odds ratios (ORs) and 95%confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. Results Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95%CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95%CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95%CI, 0.49-0.81]), celiac disease (OR, 0.42 [95%CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95%CI, 0.05-0.15]). Conclusions and Relevance It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Published
2017

11. Candidate Loci for Insulin Sensitivity and Disposition Index from a Genome Wide Association Analysis of Hispanics in the IRAS Family Study

Author

Palmer, N. D., Langefeld, C. D., Ziegler, J. T., Hsu, F., Haffner, S. M., Fingerlin, T., Norris, J. M., Chen, Y. I., Rich, S. S., Haritunians, T., Taylor, K. D., Bergman, R. N., Rotter, J. I., and Bowden, D. W.

Subjects
Adult, Male, Genotype, Chromosome Mapping, Reproducibility of Results, DNA, Fasting, Hispanic or Latino, Middle Aged, Atherosclerosis, Polymorphism, Single Nucleotide, Article, United States, White People, Glucose, Diabetes Mellitus, Type 2, Humans, Insulin, Family, Female, Insulin Resistance, Minority Groups, Genome-Wide Association Study
Abstract

The majority of type 2 diabetes genome-wide association studies (GWAS) to date have been performed in European-derived populations and have identified few variants that mediate their effect through insulin resistance. The aim of this study was to evaluate two quantitative, directly assessed measures of insulin resistance, namely insulin sensitivity index (S(I)) and insulin disposition index (DI), in Hispanic-American participants using an agnostic, high-density single nucleotide polymorphism (SNP) scan, and to validate these findings in additional samples.A two-stage GWAS was performed in Hispanic-American samples from the Insulin Resistance Atherosclerosis Family Study. In Stage 1, 317,000 SNPs were assessed using 229 DNA samples. SNPs with evidence of association with glucose homeostasis and adiposity traits were then genotyped on the entire set of Hispanic-American samples (n = 1,190). This report focuses on the glucose homeostasis traits: S(I) and DI.Although evidence of association did not reach genome-wide significance (p = 5 x 10(-7)), in the combined analysis SNPs had admixture-adjusted p values of p (ADD) = 0.00010-0.0020 with 8 to 41% differences in genotypic means for S(I) and DI.Several candidate loci were identified that are nominally associated with S(I) and/or DI in Hispanic-American participants. Replication of these findings in independent cohorts and additional focused analysis of these loci is warranted.

Published
2009

12. Candidate loci for insulin sensitivity and disposition index from a genome-wide association analysis of Hispanic participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study

Author

Palmer, N. D., primary, Langefeld, C. D., additional, Ziegler, J. T., additional, Hsu, F., additional, Haffner, S. M., additional, Fingerlin, T., additional, Norris, J. M., additional, Chen, Y. I., additional, Rich, S. S., additional, Haritunians, T., additional, Taylor, K. D., additional, Bergman, R. N., additional, Rotter, J. I., additional, and Bowden, D. W., additional

Published
2009
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14. DUF1220-Domain Copy Number Implicated in Human Brain-Size Pathology and Evolution

Author

Jonathon G. Keeney, James R. Lupski, Tasha E. Fingerlin, Jay M. Jackson, Sandesh S.C. Nagamani, Judith L. Rapoport, Sau Wai Cheung, Jay N. Giedd, Rachel Sugalski, James M. Sikela, Jonathan M. Davis, C. Michael Dickens, Armin Raznahan, Majesta O'Bleness, Ayelet Erez, Laura Dumas, Megan Sikela, Nicola Brunetti-Pierri, Nathan Anderson, Dumas, Lj, O'Bleness, M, Davis, Jm, Dickens, Cm, Anderson, N, Keeney, Jg, Jackson, J, Sikela, M, Raznahan, A, Giedd, J, Rapoport, J, Nagamani, S, Erez, A, BRUNETTI PIERRI, Nicola, Sugalski, R, Lupski, Jr, Fingerlin, T, Cheung, Sw, and Sikela, J. M.

Subjects
Microcephaly, DNA Copy Number Variations, Population, Biology, Article, 03 medical and health sciences, 0302 clinical medicine, Molecular evolution, Gene Duplication, Genetics, medicine, Animals, Humans, Genetics(clinical), education, Genetics (clinical), 030304 developmental biology, Comparative Genomic Hybridization, 0303 health sciences, education.field_of_study, Base Sequence, Macrocephaly, Brain, Organ Size, medicine.disease, Biological Evolution, Megalencephaly, DUF1220, Chromosomes, Human, Pair 1, Brain size, Human genome, medicine.symptom, 030217 neurology & neurosurgery, Comparative genomic hybridization
Abstract

DUF1220 domains show the largest human-lineage-specific increase in copy number of any protein-coding region in the human genome and map primarily to 1q21, where deletions and reciprocal duplications have been associated with microcephaly and macrocephaly, respectively. Given these findings and the high correlation between DUF1220 copy number and brain size across primate lineages (R(2) = 0.98; p = 1.8 × 10(-6)), DUF1220 sequences represent plausible candidates for underlying 1q21-associated brain-size pathologies. To investigate this possibility, we used specialized bioinformatics tools developed for scoring highly duplicated DUF1220 sequences to implement targeted 1q21 array comparative genomic hybridization on individuals (n = 42) with 1q21-associated microcephaly and macrocephaly. We show that of all the 1q21 genes examined (n = 53), DUF1220 copy number shows the strongest association with brain size among individuals with 1q21-associated microcephaly, particularly with respect to the three evolutionarily conserved DUF1220 clades CON1(p = 0.0079), CON2 (p = 0.0134), and CON3 (p = 0.0116). Interestingly, all 1q21 DUF1220-encoding genes belonging to the NBPF family show significant correlations with frontal-occipital-circumference Z scores in the deletion group. In a similar survey of a nondisease population, we show that DUF1220 copy number exhibits the strongest correlation with brain gray-matter volume (CON1, p = 0.0246; and CON2, p = 0.0334). Notably, only DUF1220 sequences are consistently significant in both disease and nondisease populations. Taken together, these data strongly implicate the loss of DUF1220 copy number in the etiology of 1q21-associated microcephaly and support the view that DUF1220 domains function as general effectors of evolutionary, pathological, and normal variation in brain size.

Published
2012

15. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Author

Adolfo Correa, Kotaro Ogawa, Yukinori Okada, Paul J. McLaren, Philip E. Stuart, Kenichi Yamamoto, Peter K. Gregersen, Saori Sakaue, David W Haas, Tõnu Esko, Michael H. Cho, Albert V. Smith, Wanson Choi, Sebastian Schönherr, Yii-Der Ida Chen, James T. Elder, Soumya Raychaudhuri, Maria Gutierrez-Arcelus, Lukas Forer, Kent D. Taylor, Yang Luo, Xiuqing Guo, Jerome I. Rotter, Stephen S. Rich, Nicholette D. Palmer, Mary Carrington, Masahiro Kanai, Christian Fuchsberger, Buhm Han, Andres Metspalu, Xinyi Li, Sekar Kathiresan, James G. Wilson, Jacques Fellay, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Abe, N., Abecasis, G., Aguet, F., Albert, C., Almasy, L., Alonso, A., Ament, S., Anderson, P., Anugu, P., Applebaum-Bowden, D., Ardlie, K., Dan Arking, X., Arnett, D.K., Ashley-Koch, A., Aslibekyan, S., Assimes, T., Auer, P., Avramopoulos, D., Ayas, N., Balasubramanian, A., Barnard, J., Barnes, K., Barr, R.G., Barron-Casella, E., Barwick, L., Beaty, T., Beck, G., Becker, D., Becker, L., Beer, R., Beitelshees, A., Benjamin, E., Benos, T., Bezerra, M., Bielak, L., Bis, J., Blackwell, T., Blangero, J., Boerwinkle, E., Bowden, D.W., Bowler, R., Brody, J., Broeckel, U., Broome, J., Brown, D., Bunting, K., Burchard, E., Bustamante, C., Buth, E., Cade, B., Cardwell, J., Carey, V., Carrier, J., Carty, C., Casaburi, R., Romero, JPC, Casella, J., Castaldi, P., Chaffin, M., Chang, C., Chang, Y.C., Chasman, D., Chavan, S., Chen, B.J., Chen, W.M., Choi, S.H., Chuang, L.M., Chung, M., Chung, R.H., Clish, C., Comhair, S., Conomos, M., Cornell, E., Crandall, C., Crapo, J., Cupples, L.A., Curran, J., Curtis, J., Custer, B., Damcott, C., Darbar, D., David, S., Davis, C., Daya, M., de Andrade, M., Fuentes, L.L., de Vries, P., DeBaun, M., Deka, R., DeMeo, D., Devine, S., Dinh, H., Doddapaneni, H., Duan, Q., Dugan-Perez, S., Duggirala, R., Durda, J.P., Dutcher, S.K., Eaton, C., Ekunwe, L., Boueiz, A.E., Ellinor, P., Emery, L., Erzurum, S., Farber, C., Farek, J., Fingerlin, T., Flickinger, M., Fornage, M., Franceschini, N., Frazar, C., Fu, M., Fullerton, S.M., Fulton, L., Gabriel, S., Gan, W., Gao, S., Gao, Y., Gass, M., Geiger, H., Gelb, B., Geraci, M., Germer, S., Gerszten, R., Ghosh, A., Gibbs, R., Gignoux, C., Gladwin, M., Glahn, D., Gogarten, S., Gong, D.W., Goring, H., Graw, S., Gray, K.J., Grine, D., Gross, C., Gu, C.C., Guan, Y., Gupta, N., Haas, D.M., Haessler, J., Hall, M., Han, Y., Hanly, P., Harris, D., Hawley, N.L., He, J., Heavner, B., Heckbert, S., Hernandez, R., Herrington, D., Hersh, C., Hidalgo, B., Hixson, J., Hobbs, B., Hokanson, J., Hong, E., Hoth, K., Hsiung, C.A., Hu, J., Hung, Y.J., Huston, H., Hwu, C.M., Irvin, M.R., Jackson, R., Jain, D., Jaquish, C., Johnsen, J., Johnson, A., Johnson, C., Johnston, R., Jones, K., Kang, H.M., Kaplan, R., Kardia, S., Kelly, S., Kenny, E., Kessler, M., Khan, A., Khan, Z., Kim, W., Kimoff, J., Kinney, G., Konkle, B., Kooperberg, C., Kramer, H., Lange, C., Lange, E., Lange, L., Laurie, C., LeBoff, M., Lee, J., Lee, S., Lee, W.J., LeFaive, J., Levine, D., Dan Levy, X., Lewis, J., Li, X., Li, Y., Lin, H., Lin, X., Liu, S., Liu, Y., Loos, RJF, Lubitz, S., Lunetta, K., Luo, J., Magalang, U., Mahaney, M., Make, B., Manichaikul, A., Manning, A., Manson, J., Martin, L., Marton, M., Mathai, S., Mathias, R., May, S., McArdle, P., McDonald, M.L., McFarland, S., McGarvey, S., McGoldrick, D., McHugh, C., McNeil, B., Mei, H., Meigs, J., Menon, V., Mestroni, L., Metcalf, G., Meyers, D.A., Mignot, E., Mikulla, J., Min, N., Minear, M., Minster, R.L., Mitchell, B.D., Moll, M., Momin, Z., Montasser, M.E., Montgomery, C., Muzny, D., Mychaleckyj, J.C., Nadkarni, G., Naik, R., Naseri, T., Natarajan, P., Nekhai, S., Nelson, S.C., Neltner, B., Nessner, C., Nickerson, D., Nkechinyere, O., North, K., O'Connell, J., O'Connor, T., Ochs-Balcom, H., Okwuonu, G., Pack, A., Paik, D.T., Pankow, J., Papanicolaou, G., Parker, C., Peloso, G., Peralta, J.M., Perez, M., Perry, J., Peters, U., Peyser, P., Phillips, L.S., Pleiness, J., Pollin, T., Post, W., Becker, J.P., Boorgula, M.P., Preuss, M., Psaty, B., Qasba, P., Qiao, D., Qin, Z., Rafaels, N., Raffield, L., Rajendran, M., Ramachandran, V.S., Rao, D.C., Rasmussen-Torvik, L., Ratan, A., Redline, S., Reed, R., Reeves, C., Regan, E., Reiner, A., Reupena, M.S., Rice, K., Robillard, R., Robine, N., Dan Roden, X., Roselli, C., Ruczinski, I., Runnels, A., Russell, P., Ruuska, S., Ryan, K., Sabino, E.C., Saleheen, D., Salimi, S., Salvi, S., Salzberg, S., Sandow, K., Sankaran, V.G., Santibanez, J., Schwander, K., Schwartz, D., Sciurba, F., Seidman, C., Seidman, J., Sériès, F., Sheehan, V., Sherman, S.L., Shetty, A., Sheu, W.H., Shoemaker, M.B., Silver, B., Silverman, E., Skomro, R., Smith, J., Smith, N., Smith, T., Smoller, S., Snively, B., Snyder, M., Sofer, T., Sotoodehnia, N., Stilp, A.M., Storm, G., Streeten, E., Su, J.L., Sung, Y.J., Sylvia, J., Szpiro, A., Taliun, D., Tang, H., Taub, M., Taylor, M., Taylor, S., Telen, M., Thornton, T.A., Threlkeld, M., Tinker, L., Tirschwell, D., Tishkoff, S., Tiwari, H., Tong, C., Tracy, R., Tsai, M., Vaidya, D., Van Den Berg, D., VandeHaar, P., Vrieze, S., Walker, T., Wallace, R., Walts, A., Wang, F.F., Wang, H., Wang, J., Watson, K., Watt, J., Weeks, D.E., Weinstock, J., Weir, B., Weiss, S.T., Weng, L.C., Wessel, J., Willer, C., Williams, K., Williams, L.K., Wilson, C., Wilson, J., Winterkorn, L., Wong, Q., Wu, J., Xu, H., Yanek, L., Yang, I., Yu, K., Zekavat, S.M., Zhang, Y., Zhao, S.X., Zhao, W., Zhu, X., Zody, M., Zoellner, S., and Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed)

Subjects
haplotypes, Population genetics, Alleles, Amino Acids/genetics, Gene Frequency/genetics, Genetic Variation, Genetics, Population, HIV Infections/genetics, HIV-1/genetics, HLA Antigens/genetics, Haplotypes/genetics, Host-Pathogen Interactions/genetics, Humans, Linkage Disequilibrium/genetics, Physical Chromosome Mapping, Reference Standards, Selection, Genetic, Viral Load, HIV Infections, Immunogenetics, Human leukocyte antigen, Major histocompatibility complex, Linkage Disequilibrium, Article, Gene Frequency, HLA Antigens, Genetics, mhc, Amino Acids, Allele, biology, Haplotype, association, genetic-basis, micropolymorphism, polygenic risk scores, Evolutionary biology, Host-Pathogen Interactions, alleles, loci, HIV-1, biology.protein, amino-acid, identification, Viral load, Imputation (genetics)
Abstract

A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response., Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.

16. Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function.

Author

Vestal BE, Ghosh D, Estépar RSJ, Kechris K, Fingerlin T, and Carlson NE

Subjects
Humans, Benchmarking, Lung diagnostic imaging, Cluster Analysis, Pulmonary Emphysema diagnostic imaging, Emphysema diagnostic imaging
Abstract

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded., (© 2023. Springer Nature Limited.)

Published
2023
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17. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Author

Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian X, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu X, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo X, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin X, Lin KH, Liu C, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang W, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang Y, Zhao X, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan W, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, and Abecasis GR

Subjects
Cytochrome P-450 CYP2D6 genetics, Haplotypes genetics, Heterozygote, Humans, INDEL Mutation, Loss of Function Mutation, Mutagenesis, Phenotype, Polymorphism, Single Nucleotide, Population Density, Quality Control, Sample Size, United States, Whole Genome Sequencing standards, Genetic Variation genetics, Genome, Human genetics, Genomics, National Heart, Lung, and Blood Institute (U.S.), Precision Medicine standards
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Published
2021
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18. MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments.

Author

Vestal BE, Moore CM, Wynn E, Saba L, Fingerlin T, and Kechris K

Subjects
Bayes Theorem, Humans, Monte Carlo Method, RNA genetics, RNA metabolism, Tuberculosis genetics, Tuberculosis pathology, RNA chemistry, Sequence Analysis, RNA methods, User-Computer Interface
Abstract

Background: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference., Results: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements., Conclusions: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

Published
2020
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19. Longitudinal DNA methylation differences precede type 1 diabetes.

Author

Johnson RK, Vanderlinden LA, Dong F, Carry PM, Seifert J, Waugh K, Shorrosh H, Fingerlin T, Frohnert BI, Yang IV, Kechris K, Rewers M, and Norris JM

Subjects
Case-Control Studies, Child, Diabetes Mellitus, Type 1 metabolism, Epigenesis, Genetic, Female, Humans, Male, Prospective Studies, DNA Methylation, Diabetes Mellitus, Type 1 genetics
Abstract

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450 K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. Change in methylation with age differed between cases and controls in 10 regions. Average longitudinal methylation differed between cases and controls at two genomic positions and 28 regions. Some methylation differences were detectable and consistent as early as birth, including before and after the onset of preclinical islet autoimmunity. Results map to transcription factors, other protein coding genes, and non-coding regions of the genome with regulatory potential. The identification of methylation differences that predate islet autoimmunity and clinical diagnosis may suggest a role for epigenetics in T1D pathogenesis; however, functional validation is warranted.

Published
2020
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20. eQTL mapping of rare variant associations using RNA-seq data: An evaluation of approaches.

Author

Lutz SM, Thwing A, and Fingerlin T

Subjects
Algorithms, Evolution, Molecular, Humans, Models, Genetic, Models, Statistical, Chromosome Mapping, Genetic Variation, High-Throughput Nucleotide Sequencing, Quantitative Trait Loci, Sequence Analysis, RNA
Abstract

Expression quantitative trait loci (eQTL) provide insight on transcription regulation and illuminate the molecular basis of phenotypic outcomes. High-throughput RNA sequencing (RNA-seq) is becoming a popular technique to measure gene expression abundance. Traditional eQTL mapping methods for microarray expression data often assume the expression data follow a normal distribution. As a result, for RNA-seq data, total read count measurements can be normalized by normal quantile transformation in order to fit the data using a linear regression. Other approaches model the total read counts using a negative binomial regression. While these methods work well for common variants (minor allele frequencies > 5% or 1%), an extension of existing methodology is needed to accommodate a collection of rare variants in RNA-seq data. Here, we examine 2 approaches that are direct applications of existing methodology and apply these approaches to RNAseq studies: 1) collapsing the rare variants in the region and using either negative binomial regression or Poisson regression and 2) using the normalized read counts with the Sequence Kernel Association Test (SKAT), the burden test for SKAT (SKAT-Burden), or an optimal combination of these two tests (SKAT-O). We evaluated these approaches via simulation studies under numerous scenarios and applied these approaches to the 1,000 Genomes Project., Competing Interests: The authors have declared that no competing interests exist.

Published
2019
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21. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, and McCarthy MI

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018
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22. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

Author

Jun G, Manning A, Almeida M, Zawistowski M, Wood AR, Teslovich TM, Fuchsberger C, Feng S, Cingolani P, Gaulton KJ, Dyer T, Blackwell TW, Chen H, Chines PS, Choi S, Churchhouse C, Fontanillas P, King R, Lee S, Lincoln SE, Trubetskoy V, DePristo M, Fingerlin T, Grossman R, Grundstad J, Heath A, Kim J, Kim YJ, Laramie J, Lee J, Li H, Liu X, Livne O, Locke AE, Maller J, Mazur A, Morris AP, Pollin TI, Ragona D, Reich D, Rivas MA, Scott LJ, Sim X, Tearle RG, Teo YY, Williams AL, Zöllner S, Curran JE, Peralta J, Akolkar B, Bell GI, Burtt NP, Cox NJ, Florez JC, Hanis CL, McKeon C, Mohlke KL, Seielstad M, Wilson JG, Atzmon G, Below JE, Dupuis J, Nicolae DL, Lehman D, Park T, Won S, Sladek R, Altshuler D, McCarthy MI, Duggirala R, Boehnke M, Frayling TM, Abecasis GR, and Blangero J

Subjects
Diabetes Mellitus, Type 2 ethnology, Diabetes Mellitus, Type 2 pathology, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease ethnology, Genome-Wide Association Study methods, Genotype, Humans, Male, Pedigree, Phenotype, Quantitative Trait Loci genetics, Whole Genome Sequencing methods, Diabetes Mellitus, Type 2 genetics, Genetic Predisposition to Disease genetics, Genetic Variation, Mexican Americans genetics
Abstract

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants., Competing Interests: Conflict of interest statement: S.E.L., J. Laramie, and R.G.T. were employees of Complete Genomics during this study. T.M.T. is an employee of Regeneron Pharmaceuticals. D.A. is an employee of Vertex Pharmaceuticals.

Published
2018
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23. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JC, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, and McCarthy MI

Subjects
Humans, White People, Diabetes Mellitus, Type 2 genetics, Genetic Variation
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017
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24. A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk.

Author

Manning A, Highland HM, Gasser J, Sim X, Tukiainen T, Fontanillas P, Grarup N, Rivas MA, Mahajan A, Locke AE, Cingolani P, Pers TH, Viñuela A, Brown AA, Wu Y, Flannick J, Fuchsberger C, Gamazon ER, Gaulton KJ, Im HK, Teslovich TM, Blackwell TW, Bork-Jensen J, Burtt NP, Chen Y, Green T, Hartl C, Kang HM, Kumar A, Ladenvall C, Ma C, Moutsianas L, Pearson RD, Perry JRB, Rayner NW, Robertson NR, Scott LJ, van de Bunt M, Eriksson JG, Jula A, Koskinen S, Lehtimäki T, Palotie A, Raitakari OT, Jacobs SBR, Wessel J, Chu AY, Scott RA, Goodarzi MO, Blancher C, Buck G, Buck D, Chines PS, Gabriel S, Gjesing AP, Groves CJ, Hollensted M, Huyghe JR, Jackson AU, Jun G, Justesen JM, Mangino M, Murphy J, Neville M, Onofrio R, Small KS, Stringham HM, Trakalo J, Banks E, Carey J, Carneiro MO, DePristo M, Farjoun Y, Fennell T, Goldstein JI, Grant G, Hrabé de Angelis M, Maguire J, Neale BM, Poplin R, Purcell S, Schwarzmayr T, Shakir K, Smith JD, Strom TM, Wieland T, Lindstrom J, Brandslund I, Christensen C, Surdulescu GL, Lakka TA, Doney ASF, Nilsson P, Wareham NJ, Langenberg C, Varga TV, Franks PW, Rolandsson O, Rosengren AH, Farook VS, Thameem F, Puppala S, Kumar S, Lehman DM, Jenkinson CP, Curran JE, Hale DE, Fowler SP, Arya R, DeFronzo RA, Abboud HE, Syvänen AC, Hicks PJ, Palmer ND, Ng MCY, Bowden DW, Freedman BI, Esko T, Mägi R, Milani L, Mihailov E, Metspalu A, Narisu N, Kinnunen L, Bonnycastle LL, Swift A, Pasko D, Wood AR, Fadista J, Pollin TI, Barzilai N, Atzmon G, Glaser B, Thorand B, Strauch K, Peters A, Roden M, Müller-Nurasyid M, Liang L, Kriebel J, Illig T, Grallert H, Gieger C, Meisinger C, Lannfelt L, Musani SK, Griswold M, Taylor HA Jr, Wilson G Sr, Correa A, Oksa H, Scott WR, Afzal U, Tan ST, Loh M, Chambers JC, Sehmi J, Kooner JS, Lehne B, Cho YS, Lee JY, Han BG, Käräjämäki A, Qi Q, Qi L, Huang J, Hu FB, Melander O, Orho-Melander M, Below JE, Aguilar D, Wong TY, Liu J, Khor CC, Chia KS, Lim WY, Cheng CY, Chan E, Tai ES, Aung T, Linneberg A, Isomaa B, Meitinger T, Tuomi T, Hakaste L, Kravic J, Jørgensen ME, Lauritzen T, Deloukas P, Stirrups KE, Owen KR, Farmer AJ, Frayling TM, O'Rahilly SP, Walker M, Levy JC, Hodgkiss D, Hattersley AT, Kuulasmaa T, Stančáková A, Barroso I, Bharadwaj D, Chan J, Chandak GR, Daly MJ, Donnelly PJ, Ebrahim SB, Elliott P, Fingerlin T, Froguel P, Hu C, Jia W, Ma RCW, McVean G, Park T, Prabhakaran D, Sandhu M, Scott J, Sladek R, Tandon N, Teo YY, Zeggini E, Watanabe RM, Koistinen HA, Kesaniemi YA, Uusitupa M, Spector TD, Salomaa V, Rauramaa R, Palmer CNA, Prokopenko I, Morris AD, Bergman RN, Collins FS, Lind L, Ingelsson E, Tuomilehto J, Karpe F, Groop L, Jørgensen T, Hansen T, Pedersen O, Kuusisto J, Abecasis G, Bell GI, Blangero J, Cox NJ, Duggirala R, Seielstad M, Wilson JG, Dupuis J, Ripatti S, Hanis CL, Florez JC, Mohlke KL, Meigs JB, Laakso M, Morris AP, Boehnke M, Altshuler D, McCarthy MI, Gloyn AL, and Lindgren CM

Subjects
Black or African American genetics, Alleles, Asian People genetics, Case-Control Studies, Diabetes Mellitus, Type 2 metabolism, Finland, Gene Frequency, Genetic Predisposition to Disease, Genotype, Hispanic or Latino genetics, Humans, Odds Ratio, Diabetes Mellitus, Type 2 genetics, Fasting metabolism, Insulin metabolism, Insulin Resistance genetics, Proto-Oncogene Proteins c-akt genetics, White People genetics
Abstract

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2 ., (© 2017 by the American Diabetes Association.)

Published
2017
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25. The genetic architecture of type 2 diabetes.

Author

Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, Ma C, Fontanillas P, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, van der Schouw YT, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA Jr, Thameem F, Wilson G Sr, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Burtt NP, Mohlke KL, Meitinger T, Groop L, Abecasis G, Florez JC, Scott LJ, Morris AP, Kang HM, Boehnke M, Altshuler D, and McCarthy MI

Subjects
Alleles, DNA Mutational Analysis, Europe ethnology, Exome, Genome-Wide Association Study, Genotyping Techniques, Humans, Sample Size, Diabetes Mellitus, Type 2 genetics, Genetic Predisposition to Disease genetics, Genetic Variation genetics
Abstract

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Published
2016
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26. Improved Performance of Dynamic Measures of Insulin Response Over Surrogate Indices to Identify Genetic Contributors of Type 2 Diabetes: The GUARDIAN Consortium.

Author

Palmer ND, Wagenknecht LE, Langefeld CD, Wang N, Buchanan TA, Xiang AH, Allayee H, Bergman RN, Raffel LJ, Chen YD, Haritunians T, Fingerlin T, Goodarzi MO, Taylor KD, Rotter JI, Watanabe RM, and Bowden DW

Subjects
Adult, Aged, Diabetes Mellitus, Type 2 metabolism, Female, Glucose Tolerance Test, Homeostasis physiology, Humans, Male, Middle Aged, Blood Glucose metabolism, Diabetes Mellitus, Type 2 genetics, Insulin blood, Insulin Resistance physiology
Abstract

Type 2 diabetes (T2D) is a heterogeneous disorder with contributions from peripheral insulin resistance and β-cell dysfunction. For minimization of phenotypic heterogeneity, quantitative intermediate phenotypes characterizing basal glucose homeostasis (insulin resistance and HOMA of insulin resistance [HOMAIR] and of β-cell function [HOMAB]) have shown promise in relatively large samples. We investigated the utility of dynamic measures of glucose homeostasis (insulin sensitivity [SI] and acute insulin response [AIRg]) evaluating T2D-susceptibility variants (n = 57) in Hispanic Americans from the GUARDIAN Consortium (n = 2,560). Basal and dynamic measures were genetically correlated (HOMAB-AIRg: ρG = 0.28-0.73; HOMAIR-SI: ρG = -0.73 to -0.83) with increased heritability for the dynamic measure AIRg Significant association of variants with dynamic measures (P < 8.77 × 10(-4)) was observed. A pattern of superior performance of AIRg was observed for well-established loci including MTNR1B (P = 9.46 × 10(-12)), KCNQ1 (P = 1.35 × 10(-4)), and TCF7L2 (P = 5.10 × 10(-4)) with study-wise statistical significance. Notably, significant association of MTNR1B with AIRg (P < 1.38 × 10(-9)) was observed in a population one-fourteenth the size of the initial discovery cohort. These observations suggest that basal and dynamic measures provide different views and levels of sensitivity to discrete elements of glucose homeostasis. Although more costly to obtain, dynamic measures yield significant results that could be considered physiologically "closer" to causal pathways and provide insight into the discrete mechanisms of action., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Published
2016
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27. DUF1220-domain copy number implicated in human brain-size pathology and evolution.

Author

Dumas LJ, O'Bleness MS, Davis JM, Dickens CM, Anderson N, Keeney JG, Jackson J, Sikela M, Raznahan A, Giedd J, Rapoport J, Nagamani SS, Erez A, Brunetti-Pierri N, Sugalski R, Lupski JR, Fingerlin T, Cheung SW, and Sikela JM

Subjects
Animals, Base Sequence, Biological Evolution, Chromosomes, Human, Pair 1, Comparative Genomic Hybridization, Gene Duplication, Humans, Megalencephaly genetics, Microcephaly genetics, Brain pathology, DNA Copy Number Variations, Organ Size
Abstract

DUF1220 domains show the largest human-lineage-specific increase in copy number of any protein-coding region in the human genome and map primarily to 1q21, where deletions and reciprocal duplications have been associated with microcephaly and macrocephaly, respectively. Given these findings and the high correlation between DUF1220 copy number and brain size across primate lineages (R(2) = 0.98; p = 1.8 × 10(-6)), DUF1220 sequences represent plausible candidates for underlying 1q21-associated brain-size pathologies. To investigate this possibility, we used specialized bioinformatics tools developed for scoring highly duplicated DUF1220 sequences to implement targeted 1q21 array comparative genomic hybridization on individuals (n = 42) with 1q21-associated microcephaly and macrocephaly. We show that of all the 1q21 genes examined (n = 53), DUF1220 copy number shows the strongest association with brain size among individuals with 1q21-associated microcephaly, particularly with respect to the three evolutionarily conserved DUF1220 clades CON1(p = 0.0079), CON2 (p = 0.0134), and CON3 (p = 0.0116). Interestingly, all 1q21 DUF1220-encoding genes belonging to the NBPF family show significant correlations with frontal-occipital-circumference Z scores in the deletion group. In a similar survey of a nondisease population, we show that DUF1220 copy number exhibits the strongest correlation with brain gray-matter volume (CON1, p = 0.0246; and CON2, p = 0.0334). Notably, only DUF1220 sequences are consistently significant in both disease and nondisease populations. Taken together, these data strongly implicate the loss of DUF1220 copy number in the etiology of 1q21-associated microcephaly and support the view that DUF1220 domains function as general effectors of evolutionary, pathological, and normal variation in brain size., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Published
2012
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28. The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci.

Author

Watanabe RM, Ghosh S, Langefeld CD, Valle TT, Hauser ER, Magnuson VL, Mohlke KL, Silander K, Ally DS, Chines P, Blaschak-Harvan J, Douglas JA, Duren WL, Epstein MP, Fingerlin TE, Kaleta HS, Lange EM, Li C, McEachin RC, Stringham HM, Trager E, White PP, Balow J Jr, Birznieks G, Chang J, and Eldridge W

Subjects
Age Factors, Blood Glucose metabolism, Body Mass Index, Chromosomes, Human genetics, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 metabolism, Fasting, Female, Finland, Genetic Linkage genetics, Genetic Predisposition to Disease genetics, Humans, Insulin blood, Male, Matched-Pair Analysis, Middle Aged, Nuclear Family, Sex Factors, United States, Diabetes Mellitus, Type 2 genetics, Genetic Testing, Genome, Human, Quantitative Trait, Heritable
Abstract

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

Published
2000

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