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1.  Correction: A whole genome association study of mother-to-child transmission of HIV in Malawi 
Genome Medicine  2010;2(10):76.
A correction to: Bonnie R Joubert, Ethan M Lange, Nora Franceschini, Victor Mwapasa, Kari E North, Steven R Meshnick andthe NIAID Center for HIV/AIDS Vaccine Immunology. A whole genome association study of mother-to-child transmission of HIV in Malawi. Genome Medicine 2010, 2:17.
doi:10.1186/gm197
PMCID: PMC3092107
2.  Association of Cardiometabolic Genes with Arsenic Metabolism Biomarkers in American Indian Communities: The Strong Heart Family Study (SHFS) 
Background:
Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants.
Objectives:
We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS).
Methods:
We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography–inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing.
Results:
Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10–5).
Conclusions:
This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24.
Citation:
Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15–22; http://dx.doi.org/10.1289/EHP251
doi:10.1289/EHP251
PMCID: PMC5226702  PMID: 27352405
3.  Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling 
High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.
doi:10.1080/01621459.2015.1008099
PMCID: PMC4565625  PMID: 26366025
Association studies; Gene-level tests; Linear regression; Quantitative traits; Rare variants; Sequencing studies
4.  Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array 
PLoS ONE  2016;11(12):e0167758.
Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.
doi:10.1371/journal.pone.0167758
PMCID: PMC5156387  PMID: 27973554
5.  Integrative pathway genomics of lung function and airflow obstruction 
Human Molecular Genetics  2015;24(23):6836-6848.
Chronic respiratory disorders are important contributors to the global burden of disease. Genome-wide association studies (GWASs) of lung function measures have identified several trait-associated loci, but explain only a modest portion of the phenotypic variability. We postulated that integrating pathway-based methods with GWASs of pulmonary function and airflow obstruction would identify a broader repertoire of genes and processes influencing these traits. We performed two independent GWASs of lung function and applied gene set enrichment analysis to one of the studies and validated the results using the second GWAS. We identified 131 significantly enriched gene sets associated with lung function and clustered them into larger biological modules involved in diverse processes including development, immunity, cell signaling, proliferation and arachidonic acid. We found that enrichment of gene sets was not driven by GWAS-significant variants or loci, but instead by those with less stringent association P-values. Next, we applied pathway enrichment analysis to a meta-analyzed GWAS of airflow obstruction. We identified several biologic modules that functionally overlapped with those associated with pulmonary function. However, differences were also noted, including enrichment of extracellular matrix (ECM) processes specifically in the airflow obstruction study. Network analysis of the ECM module implicated a candidate gene, matrix metalloproteinase 10 (MMP10), as a putative disease target. We used a knockout mouse model to functionally validate MMP10's role in influencing lung's susceptibility to cigarette smoke-induced emphysema. By integrating pathway analysis with population-based genomics, we unraveled biologic processes underlying pulmonary function traits and identified a candidate gene for obstructive lung disease.
doi:10.1093/hmg/ddv378
PMCID: PMC4643644  PMID: 26395457
6.  Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity 
Human Molecular Genetics  2015;24(23):6849-6860.
To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the ‘VErsatile Gene-based Association Study’ (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10−6 for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.
doi:10.1093/hmg/ddv379
PMCID: PMC4643645  PMID: 26376864
7.  Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for 13 Cancer Types 
Sampson, Joshua N. | Wheeler, William A. | Yeager, Meredith | Panagiotou, Orestis | Wang, Zhaoming | Berndt, Sonja I. | Lan, Qing | Abnet, Christian C. | Amundadottir, Laufey T. | Figueroa, Jonine D. | Landi, Maria Teresa | Mirabello, Lisa | Savage, Sharon A. | Taylor, Philip R. | Vivo, Immaculata De | McGlynn, Katherine A. | Purdue, Mark P. | Rajaraman, Preetha | Adami, Hans-Olov | Ahlbom, Anders | Albanes, Demetrius | Amary, Maria Fernanda | An, She-Juan | Andersson, Ulrika | Andriole, Gerald | Andrulis, Irene L. | Angelucci, Emanuele | Ansell, Stephen M. | Arici, Cecilia | Armstrong, Bruce K. | Arslan, Alan A. | Austin, Melissa A. | Baris, Dalsu | Barkauskas, Donald A. | Bassig, Bryan A. | Becker, Nikolaus | Benavente, Yolanda | Benhamou, Simone | Berg, Christine | Van Den Berg, David | Bernstein, Leslie | Bertrand, Kimberly A. | Birmann, Brenda M. | Black, Amanda | Boeing, Heiner | Boffetta, Paolo | Boutron-Ruault, Marie-Christine | Bracci, Paige M. | Brinton, Louise | Brooks-Wilson, Angela R. | Bueno-de-Mesquita, H. Bas | Burdett, Laurie | Buring, Julie | Butler, Mary Ann | Cai, Qiuyin | Cancel-Tassin, Geraldine | Canzian, Federico | Carrato, Alfredo | Carreon, Tania | Carta, Angela | Chan, John K. C. | Chang, Ellen T. | Chang, Gee-Chen | Chang, I-Shou | Chang, Jiang | Chang-Claude, Jenny | Chen, Chien-Jen | Chen, Chih-Yi | Chen, Chu | Chen, Chung-Hsing | Chen, Constance | Chen, Hongyan | Chen, Kexin | Chen, Kuan-Yu | Chen, Kun-Chieh | Chen, Ying | Chen, Ying-Hsiang | Chen, Yi-Song | Chen, Yuh-Min | Chien, Li-Hsin | Chirlaque, María-Dolores | Choi, Jin Eun | Choi, Yi Young | Chow, Wong-Ho | Chung, Charles C. | Clavel, Jacqueline | Clavel-Chapelon, Françoise | Cocco, Pierluigi | Colt, Joanne S. | Comperat, Eva | Conde, Lucia | Connors, Joseph M. | Conti, David | Cortessis, Victoria K. | Cotterchio, Michelle | Cozen, Wendy | Crouch, Simon | Crous-Bou, Marta | Cussenot, Olivier | Davis, Faith G. | Ding, Ti | Diver, W. Ryan | Dorronsoro, Miren | Dossus, Laure | Duell, Eric J. | Ennas, Maria Grazia | Erickson, Ralph L. | Feychting, Maria | Flanagan, Adrienne M. | Foretova, Lenka | Fraumeni, Joseph F. | Freedman, Neal D. | Beane Freeman, Laura E. | Fuchs, Charles | Gago-Dominguez, Manuela | Gallinger, Steven | Gao, Yu-Tang | Gapstur, Susan M. | Garcia-Closas, Montserrat | García-Closas, Reina | Gascoyne, Randy D. | Gastier-Foster, Julie | Gaudet, Mia M. | Gaziano, J. Michael | Giffen, Carol | Giles, Graham G. | Giovannucci, Edward | Glimelius, Bengt | Goggins, Michael | Gokgoz, Nalan | Goldstein, Alisa M. | Gorlick, Richard | Gross, Myron | Grubb, Robert | Gu, Jian | Guan, Peng | Gunter, Marc | Guo, Huan | Habermann, Thomas M. | Haiman, Christopher A. | Halai, Dina | Hallmans, Goran | Hassan, Manal | Hattinger, Claudia | He, Qincheng | He, Xingzhou | Helzlsouer, Kathy | Henderson, Brian | Henriksson, Roger | Hjalgrim, Henrik | Hoffman-Bolton, Judith | Hohensee, Chancellor | Holford, Theodore R. | Holly, Elizabeth A. | Hong, Yun-Chul | Hoover, Robert N. | Horn-Ross, Pamela L. | Hosain, G. M. Monawar | Hosgood, H. Dean | Hsiao, Chin-Fu | Hu, Nan | Hu, Wei | Hu, Zhibin | Huang, Ming-Shyan | Huerta, Jose-Maria | Hung, Jen-Yu | Hutchinson, Amy | Inskip, Peter D. | Jackson, Rebecca D. | Jacobs, Eric J. | Jenab, Mazda | Jeon, Hyo-Sung | Ji, Bu-Tian | Jin, Guangfu | Jin, Li | Johansen, Christoffer | Johnson, Alison | Jung, Yoo Jin | Kaaks, Rudolph | Kamineni, Aruna | Kane, Eleanor | Kang, Chang Hyun | Karagas, Margaret R. | Kelly, Rachel S. | Khaw, Kay-Tee | Kim, Christopher | Kim, Hee Nam | Kim, Jin Hee | Kim, Jun Suk | Kim, Yeul Hong | Kim, Young Tae | Kim, Young-Chul | Kitahara, Cari M. | Klein, Alison P. | Klein, Robert J. | Kogevinas, Manolis | Kohno, Takashi | Kolonel, Laurence N. | Kooperberg, Charles | Kricker, Anne | Krogh, Vittorio | Kunitoh, Hideo | Kurtz, Robert C. | Kweon, Sun-Seog | LaCroix, Andrea | Lawrence, Charles | Lecanda, Fernando | Lee, Victor Ho Fun | Li, Donghui | Li, Haixin | Li, Jihua | Li, Yao-Jen | Li, Yuqing | Liao, Linda M. | Liebow, Mark | Lightfoot, Tracy | Lim, Wei-Yen | Lin, Chien-Chung | Lin, Dongxin | Lindstrom, Sara | Linet, Martha S. | Link, Brian K. | Liu, Chenwei | Liu, Jianjun | Liu, Li | Ljungberg, Börje | Lloreta, Josep | Lollo, Simonetta Di | Lu, Daru | Lund, Eiluv | Malats, Nuria | Mannisto, Satu | Marchand, Loic Le | Marina, Neyssa | Masala, Giovanna | Mastrangelo, Giuseppe | Matsuo, Keitaro | Maynadie, Marc | McKay, James | McKean-Cowdin, Roberta | Melbye, Mads | Melin, Beatrice S. | Michaud, Dominique S. | Mitsudomi, Tetsuya | Monnereau, Alain | Montalvan, Rebecca | Moore, Lee E. | Mortensen, Lotte Maxild | Nieters, Alexandra | North, Kari E. | Novak, Anne J. | Oberg, Ann L. | Offit, Kenneth | Oh, In-Jae | Olson, Sara H. | Palli, Domenico | Pao, William | Park, In Kyu | Park, Jae Yong | Park, Kyong Hwa | Patiño-Garcia, Ana | Pavanello, Sofia | Peeters, Petra H. M. | Perng, Reury-Perng | Peters, Ulrike | Petersen, Gloria M. | Picci, Piero | Pike, Malcolm C. | Porru, Stefano | Prescott, Jennifer | Prokunina-Olsson, Ludmila | Qian, Biyun | Qiao, You-Lin | Rais, Marco | Riboli, Elio | Riby, Jacques | Risch, Harvey A. | Rizzato, Cosmeri | Rodabough, Rebecca | Roman, Eve | Roupret, Morgan | Ruder, Avima M. | de Sanjose, Silvia | Scelo, Ghislaine | Schned, Alan | Schumacher, Fredrick | Schwartz, Kendra | Schwenn, Molly | Scotlandi, Katia | Seow, Adeline | Serra, Consol | Serra, Massimo | Sesso, Howard D. | Setiawan, Veronica Wendy | Severi, Gianluca | Severson, Richard K. | Shanafelt, Tait D. | Shen, Hongbing | Shen, Wei | Shin, Min-Ho | Shiraishi, Kouya | Shu, Xiao-Ou | Siddiq, Afshan | Sierrasesúmaga, Luis | Sihoe, Alan Dart Loon | Skibola, Christine F. | Smith, Alex | Smith, Martyn T. | Southey, Melissa C. | Spinelli, John J. | Staines, Anthony | Stampfer, Meir | Stern, Marianna C. | Stevens, Victoria L. | Stolzenberg-Solomon, Rachael S. | Su, Jian | Su, Wu-Chou | Sund, Malin | Sung, Jae Sook | Sung, Sook Whan | Tan, Wen | Tang, Wei | Tardón, Adonina | Thomas, David | Thompson, Carrie A. | Tinker, Lesley F. | Tirabosco, Roberto | Tjønneland, Anne | Travis, Ruth C. | Trichopoulos, Dimitrios | Tsai, Fang-Yu | Tsai, Ying-Huang | Tucker, Margaret | Turner, Jenny | Vajdic, Claire M. | Vermeulen, Roel C. H. | Villano, Danylo J. | Vineis, Paolo | Virtamo, Jarmo | Visvanathan, Kala | Wactawski-Wende, Jean | Wang, Chaoyu | Wang, Chih-Liang | Wang, Jiu-Cun | Wang, Junwen | Wei, Fusheng | Weiderpass, Elisabete | Weiner, George J. | Weinstein, Stephanie | Wentzensen, Nicolas | White, Emily | Witzig, Thomas E. | Wolpin, Brian M. | Wong, Maria Pik | Wu, Chen | Wu, Guoping | Wu, Junjie | Wu, Tangchun | Wu, Wei | Wu, Xifeng | Wu, Yi-Long | Wunder, Jay S. | Xiang, Yong-Bing | Xu, Jun | Xu, Ping | Yang, Pan-Chyr | Yang, Tsung-Ying | Ye, Yuanqing | Yin, Zhihua | Yokota, Jun | Yoon, Ho-Il | Yu, Chong-Jen | Yu, Herbert | Yu, Kai | Yuan, Jian-Min | Zelenetz, Andrew | Zeleniuch-Jacquotte, Anne | Zhang, Xu-Chao | Zhang, Yawei | Zhao, Xueying | Zhao, Zhenhong | Zheng, Hong | Zheng, Tongzhang | Zheng, Wei | Zhou, Baosen | Zhu, Meng | Zucca, Mariagrazia | Boca, Simina M. | Cerhan, James R. | Ferri, Giovanni M. | Hartge, Patricia | Hsiung, Chao Agnes | Magnani, Corrado | Miligi, Lucia | Morton, Lindsay M. | Smedby, Karin E. | Teras, Lauren R. | Vijai, Joseph | Wang, Sophia S. | Brennan, Paul | Caporaso, Neil E. | Hunter, David J. | Kraft, Peter | Rothman, Nathaniel | Silverman, Debra T. | Slager, Susan L. | Chanock, Stephen J. | Chatterjee, Nilanjan
Background:
Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites.
Methods:
Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers.
Results:
GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl 2, on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures.
Conclusion:
Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.
doi:10.1093/jnci/djv279
PMCID: PMC4806328  PMID: 26464424
8.  A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape 
Ried, Janina S. | Jeff M., Janina | Chu, Audrey Y. | Bragg-Gresham, Jennifer L. | van Dongen, Jenny | Huffman, Jennifer E. | Ahluwalia, Tarunveer S. | Cadby, Gemma | Eklund, Niina | Eriksson, Joel | Esko, Tõnu | Feitosa, Mary F. | Goel, Anuj | Gorski, Mathias | Hayward, Caroline | Heard-Costa, Nancy L. | Jackson, Anne U. | Jokinen, Eero | Kanoni, Stavroula | Kristiansson, Kati | Kutalik, Zoltán | Lahti, Jari | Luan, Jian'an | Mägi, Reedik | Mahajan, Anubha | Mangino, Massimo | Medina-Gomez, Carolina | Monda, Keri L. | Nolte, Ilja M. | Pérusse, Louis | Prokopenko, Inga | Qi, Lu | Rose, Lynda M. | Salvi, Erika | Smith, Megan T. | Snieder, Harold | Stančáková, Alena | Ju Sung, Yun | Tachmazidou, Ioanna | Teumer, Alexander | Thorleifsson, Gudmar | van der Harst, Pim | Walker, Ryan W. | Wang, Sophie R. | Wild, Sarah H. | Willems, Sara M. | Wong, Andrew | Zhang, Weihua | Albrecht, Eva | Couto Alves, Alexessander | Bakker, Stephan J. L. | Barlassina, Cristina | Bartz, Traci M. | Beilby, John | Bellis, Claire | Bergman, Richard N. | Bergmann, Sven | Blangero, John | Blüher, Matthias | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Bruinenberg, Marcel | Campbell, Harry | Chen, Yii-Der Ida | Chiang, Charleston W. K. | Chines, Peter S. | Collins, Francis S | Cucca, Fracensco | Cupples, L Adrienne | D'Avila, Francesca | de Geus, Eco J .C. | Dedoussis, George | Dimitriou, Maria | Döring, Angela | Eriksson, Johan G. | Farmaki, Aliki-Eleni | Farrall, Martin | Ferreira, Teresa | Fischer, Krista | Forouhi, Nita G. | Friedrich, Nele | Gjesing, Anette Prior | Glorioso, Nicola | Graff, Mariaelisa | Grallert, Harald | Grarup, Niels | Gräßler, Jürgen | Grewal, Jagvir | Hamsten, Anders | Harder, Marie Neergaard | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew Tym | Havulinna, Aki S. | Heliövaara, Markku | Hillege, Hans | Hofman, Albert | Holmen, Oddgeir | Homuth, Georg | Hottenga, Jouke-Jan | Hui, Jennie | Husemoen, Lise Lotte | Hysi, Pirro G. | Isaacs, Aaron | Ittermann, Till | Jalilzadeh, Shapour | James, Alan L. | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Marie Justesen, Johanne | Justice, Anne E. | Kähönen, Mika | Karaleftheri, Maria | Tee Khaw, Kay | Keinanen-Kiukaanniemi, Sirkka M. | Kinnunen, Leena | Knekt, Paul B. | Koistinen, Heikki A. | Kolcic, Ivana | Kooner, Ishminder K. | Koskinen, Seppo | Kovacs, Peter | Kyriakou, Theodosios | Laitinen, Tomi | Langenberg, Claudia | Lewin, Alexandra M. | Lichtner, Peter | Lindgren, Cecilia M. | Lindström, Jaana | Linneberg, Allan | Lorbeer, Roberto | Lorentzon, Mattias | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Manunta, Paolo | Leach, Irene Mateo | McArdle, Wendy L. | Mcknight, Barbara | Mohlke, Karen L. | Mihailov, Evelin | Milani, Lili | Mills, Rebecca | Montasser, May E. | Morris, Andrew P. | Müller, Gabriele | Musk, Arthur W. | Narisu, Narisu | Ong, Ken K. | Oostra, Ben A. | Osmond, Clive | Palotie, Aarno | Pankow, James S. | Paternoster, Lavinia | Penninx, Brenda W. | Pichler, Irene | Pilia, Maria G. | Polašek, Ozren | Pramstaller, Peter P. | Raitakari, Olli T | Rankinen, Tuomo | Rao, D. C. | Rayner, Nigel W. | Ribel-Madsen, Rasmus | Rice, Treva K. | Richards, Marcus | Ridker, Paul M. | Rivadeneira, Fernando | Ryan, Kathy A. | Sanna, Serena | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Sebert, Sylvain | Southam, Lorraine | Sparsø, Thomas Hempel | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stolk, Ronald P. | Strauch, Konstantin | Stringham, Heather M. | Swertz, Morris A. | Swift, Amy J. | Tönjes, Anke | Tsafantakis, Emmanouil | van der Most, Peter J. | Van Vliet-Ostaptchouk, Jana V. | Vandenput, Liesbeth | Vartiainen, Erkki | Venturini, Cristina | Verweij, Niek | Viikari, Jorma S. | Vitart, Veronique | Vohl, Marie-Claude | Vonk, Judith M. | Waeber, Gérard | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Winkler, Thomas W. | Wright, Alan F. | Yerges-Armstrong, Laura M. | Hua Zhao, Jing | Carola Zillikens, M. | Boomsma, Dorret I. | Bouchard, Claude | Chambers, John C. | Chasman, Daniel I. | Cusi, Daniele | Gansevoort, Ron T. | Gieger, Christian | Hansen, Torben | Hicks, Andrew A. | Hu, Frank | Hveem, Kristian | Jarvelin, Marjo-Riitta | Kajantie, Eero | Kooner, Jaspal S. | Kuh, Diana | Kuusisto, Johanna | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Metspalu, Andres | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Palmer, Lyle J. | Pedersen, Oluf | Perola, Markus | Peters, Annette | Psaty, Bruce M. | Puolijoki, Hannu | Rauramaa, Rainer | Rudan, Igor | Salomaa, Veikko | Schwarz, Peter E. H. | Shudiner, Alan R. | Smit, Jan H. | Sørensen, Thorkild I. A. | Spector, Timothy D. | Stefansson, Kari | Stumvoll, Michael | Tremblay, Angelo | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Völker, Uwe | Vollenweider, Peter | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Zeggini, Eleftheria | Abecasis, Goncalo R. | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | van Duijn, Cornelia M. | Fox, Caroline | Groop, Leif C. | Heid, Iris M. | Hunter, David J. | Kaplan, Robert C. | McCarthy, Mark I. | North, Kari E. | O'Connell, Jeffrey R. | Schlessinger, David | Thorsteinsdottir, Unnur | Strachan, David P. | Frayling, Timothy | Hirschhorn, Joel N. | Müller-Nurasyid, Martina | Loos, Ruth J. F.
Nature Communications  2016;7:13357.
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
Past genome-wide associate studies have identified hundreds of genetic loci that influence body size and shape when examined one trait at a time. Here, Jeff and colleagues develop an aggregate score of various body traits, and use meta-analysis to find new loci linked to body shape.
doi:10.1038/ncomms13357
PMCID: PMC5114527  PMID: 27876822
9.  Linkage Analysis of Urine Arsenic Species Patterns in the Strong Heart Family Study 
Toxicological Sciences  2015;148(1):89-100.
Arsenic toxicokinetics are important for disease risks in exposed populations, but genetic determinants are not fully understood. We examined urine arsenic species patterns measured by HPLC-ICPMS among 2189 Strong Heart Study participants 18 years of age and older with data on ∼400 genome-wide microsatellite markers spaced ∼10 cM and arsenic speciation (683 participants from Arizona, 684 from Oklahoma, and 822 from North and South Dakota). We logit-transformed % arsenic species (% inorganic arsenic, %MMA, and %DMA) and also conducted principal component analyses of the logit % arsenic species. We used inverse-normalized residuals from multivariable-adjusted polygenic heritability analysis for multipoint variance components linkage analysis. We also examined the contribution of polymorphisms in the arsenic metabolism gene AS3MT via conditional linkage analysis. We localized a quantitative trait locus (QTL) on chromosome 10 (LOD 4.12 for %MMA, 4.65 for %DMA, and 4.84 for the first principal component of logit % arsenic species). This peak was partially but not fully explained by measured AS3MT variants. We also localized a QTL for the second principal component of logit % arsenic species on chromosome 5 (LOD 4.21) that was not evident from considering % arsenic species individually. Some other loci were suggestive or significant for 1 geographical area but not overall across all areas, indicating possible locus heterogeneity. This genome-wide linkage scan suggests genetic determinants of arsenic toxicokinetics to be identified by future fine-mapping, and illustrates the utility of principal component analysis as a novel approach that considers % arsenic species jointly.
doi:10.1093/toxsci/kfv164
PMCID: PMC4731407  PMID: 26209557
toxicogenetics;  toxicokinetics;  arsenic metabolism;  arsenic species;  linkage analysis;  Strong Heart Family Study
10.  Population genetic differentiation of height and body mass index across Europe 
Nature genetics  2015;47(11):1357-1362.
Across-nation differences in the mean of complex traits such as obesity and stature are common1–8, but the reasons for these differences are not known. Here, we find evidence that many independent loci of small effect combine to create population genetic differences in height and body mass index (BMI) in a sample of 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased estimates of effect sizes from 17,500 sib pairs, we estimate that 24% (95% CI: 9%, 41%) and 8% (95% CI: 4%, 16%) of the captured additive genetic variance for height and BMI across Europe are attributed to among-population genetic differences. Population genetic divergence differed significantly from that expected under a null model (P <3.94e−08 for height and P<5.95e−04 for BMI), and we find an among-population genetic correlation for tall and slender nations (r = −0.80 (95% CI: −0.95, −0.60), contrasting no genetic correlation between height and BMI within populations (r = −0.016, 95% CI: −0.041, 0.001), consistent with selection on height genes that also act to reduce BMI. Observations of mean height across nations correlated with the predicted genetic means for height (r = 0.51, P<0.001), so that a proportion of observed differences in height within Europe reflect genetic factors. In contrast, observed mean BMI did not correlate with the genetic estimates (P<0.58), implying that genetic differentiation in BMI is masked by environmental differences across Europe.
doi:10.1038/ng.3401
PMCID: PMC4984852  PMID: 26366552
11.  Comparison of 2 models for gene–environment interactions: an example of simulated gene–medication interactions on systolic blood pressure in family-based data 
BMC Proceedings  2016;10(Suppl 7):371-377.
Background
Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene–environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. The first approach uses a gene–environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the “interaction model”). The second approach tests for effect-size differences between strata of an environmental exposure (the “med-diff” approach). However, no studies have quantitatively compared how these methods perform with respect to 1 or 2 degree of freedom (DF) tests or in family-based data sets. We evaluated these 2 approaches using simulated genotype–medication response interactions at 3 single nucleotide polymorphisms (SNPs) across a range of minor allele frequencies (MAFs 0.1–5.4 %) using the Genetic Analysis Workshop 19 family sample.
Results
The estimated interaction effect sizes were on average larger in the interaction model approach compared to the med-diff approach. The true positive proportion was higher for the med-diff approach for SNPs less than 1 % MAF, but higher for the interaction model when common variants were evaluated (MAF >5 %). The interaction model produced lower false-positive proportions than expected (5 %) across a range of MAFs for both the 1DF and 2DF tests. In contrast, the med-diff approach produced higher but stable false-positive proportions around 5 % across MAFs for both tests.
Conclusions
Although the 1DF tests both performed similarly for common variants, the interaction model estimated true interaction effects with less bias and higher true positive proportions than the med-diff approach. However, if rare variation (MAF <5 %) is of interest, our findings suggest that when convergence is achieved, the med-diff approach may estimate true interaction effects more conservatively and with less variability.
doi:10.1186/s12919-016-0058-1
PMCID: PMC5133512  PMID: 27980664
12.  Genome-wide association of trajectories of systolic blood pressure change 
BMC Proceedings  2016;10(Suppl 7):321-327.
Background
There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The majority of genetic studies have examined SBP measured cross-sectionally; however, SBP changes over time, and not necessarily in a linear fashion. Therefore, this study conducted a genome-wide association (GWA) study of SBP change trajectories using data available through the Genetic Analysis Workshop 19 (GAW19) of 959 individuals from 20 extended Mexican American families from the San Antonio Family Studies with up to 4 measures of SBP. We performed structural equation modeling (SEM) while taking into account potential genetic effects to identify how, if at all, to include covariates in estimating the SBP change trajectories using a mixture model based latent class growth modeling (LCGM) approach for use in the GWA analyses.
Results
The semiparametric LCGM approach identified 5 trajectory classes that captured SBP changes across age. Each LCGM identified trajectory group was ranked based on the average number of cumulative years as hypertensive. Using a pairwise comparison of these classes the heritability estimates range from 12 to 94 % (SE = 17 to 40 %).
Conclusion
These identified trajectories are significantly heritable, and we identified a total of 8 promising loci that influence one’s trajectory in SBP change across age. Our results demonstrate the potential utility of capitalizing on extant genetic data and longitudinal SBP assessments available through GAW19 to explore novel analytical methods with promising results.
doi:10.1186/s12919-016-0050-9
PMCID: PMC5133524  PMID: 27980656
13.  Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in Hispanics and African Americans 
PLoS ONE  2016;11(10):e0164132.
Despite the substantial burden of hypertension in US minority populations, few genetic studies of blood pressure have been conducted in Hispanics and African Americans, and it is unclear whether many of the established loci identified in European-descent populations contribute to blood pressure variation in non-European descent populations. Using the Metabochip array, we sought to characterize the genetic architecture of previously identified blood pressure loci, and identify novel cardiometabolic variants related to systolic and diastolic blood pressure in a multi-ethnic US population including Hispanics (n = 19,706) and African Americans (n = 18,744). Several known blood pressure loci replicated in African Americans and Hispanics. Fourteen variants in three loci (KCNK3, FGF5, ATXN2-SH2B3) were significantly associated with blood pressure in Hispanics. The most significant diastolic blood pressure variant identified in our analysis, rs2586886/KCNK3 (P = 5.2 x 10−9), also replicated in independent Hispanic and European-descent samples. African American and trans-ethnic meta-analysis data identified novel variants in the FGF5, ULK4 and HOXA-EVX1 loci, which have not been previously associated with blood pressure traits. Our identification and independent replication of variants in KCNK3, a gene implicated in primary hyperaldosteronism, as well as a variant in HOTTIP (HOXA-EVX1) suggest that further work to clarify the roles of these genes may be warranted. Overall, our findings suggest that loci identified in European descent populations also contribute to blood pressure variation in diverse populations including Hispanics and African Americans—populations that are understudied for hypertension genetic risk factors.
doi:10.1371/journal.pone.0164132
PMCID: PMC5063457  PMID: 27736895
14.  Molecular mechanisms underlying variations in lung function: a systems genetics analysis 
The Lancet. Respiratory medicine  2015;3(10):782-795.
Summary
Background
Lung function measures reflect the physiological state of the lung, and are essential to the diagnosis of chronic obstructive pulmonary disease (COPD). The SpiroMeta-CHARGE consortium undertook the largest genome-wide association study (GWAS) so far (n=48 201) for forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in the general population. The lung expression quantitative trait loci (eQTLs) study mapped the genetic architecture of gene expression in lung tissue from 1111 individuals. We used a systems genetics approach to identify single nucleotide polymorphisms (SNPs) associated with lung function that act as eQTLs and change the level of expression of their target genes in lung tissue; termed eSNPs.
Methods
The SpiroMeta-CHARGE GWAS results were integrated with lung eQTLs to map eSNPs and the genes and pathways underlying the associations in lung tissue. For comparison, a similar analysis was done in peripheral blood. The lung mRNA expression levels of the eSNP-regulated genes were tested for associations with lung function measures in 727 individuals. Additional analyses identified the pleiotropic effects of eSNPs from the published GWAS catalogue, and mapped enrichment in regulatory regions from the ENCODE project. Finally, the Connectivity Map database was used to identify potential therapeutics in silico that could reverse the COPD lung tissue gene signature.
Findings
SNPs associated with lung function measures were more likely to be eQTLs and vice versa. The integration mapped the specific genes underlying the GWAS signals in lung tissue. The eSNP-regulated genes were enriched for developmental and inflammatory pathways; by comparison, SNPs associated with lung function that were eQTLs in blood, but not in lung, were only involved in inflammatory pathways. Lung function eSNPs were enriched for regulatory elements and were over-represented among genes showing differential expression during fetal lung development. An mRNA gene expression signature for COPD was identified in lung tissue and compared with the Connectivity Map. This in-silico drug repurposing approach suggested several compounds that reverse the COPD gene expression signature, including a nicotine receptor antagonist. These findings represent novel therapeutic pathways for COPD.
Interpretation
The system genetics approach identified lung tissue genes driving the variation in lung function and susceptibility to COPD. The identification of these genes and the pathways in which they are enriched is essential to understand the pathophysiology of airway obstruction and to identify novel therapeutic targets and biomarkers for COPD, including drugs that reverse the COPD gene signature in silico.
Funding
The research reported in this article was not specifically funded by any agency. See Acknowledgments for a full list of funders of the lung eQTL study and the Spiro-Meta CHARGE GWAS.
doi:10.1016/S2213-2600(15)00380-X
PMCID: PMC5021067  PMID: 26404118
15.  Association of Apolipoprotein E (ApoE) Polymorphism with the Prevalence of Metabolic Syndrome: The National Heart, Lung and Blood Institute Family Heart Study 
Objective
Metabolic syndrome (MetS), characterized by abdominal obesity, atherogenic dyslipidemia, elevated blood pressure, and insulin resistance is a major public health concern in the United States. The effects of Apolipoprotein E (Apo E) polymorphism on MetS are not well established.
Methods
We conducted a cross-sectional study consisting of 1,551 participants from the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study to assess the relation of Apo E polymorphism with the prevalence of MetS. MetS was defined according to the AHA-NHLBI-IDF-WHO Harmonized Criteria. We used generalized estimating equations to estimate adjusted odds ratios for prevalent MetS and the Bonferroni correction to account for multiple testing in the secondary analysis.
Results
Our study population had a mean age (SD) of 56.5 (11.0) years and 49.7% had MetS. There was no association between the Apo E genotypes and MetS. The multivariable adjusted ORs (95% CI) were 1.00 (reference), 1.26 (0.31-5.21), 0.89 (0.62-1.29), 1.13 (0.61-2.10), 1.13 (0.88-1.47), and 1.87 (0.91-3.85) for the ε3/ε3, ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε4, and ε4/ε4 genotypes, respectively. In a secondary analysis, ε2/ε3 genotype was associated with 41% lower prevalence odds of low HDL [multivariable adjusted ORs (95% CI) = 0.59 (0.36-0.95)] compared to ε3/ε3 genotype.
Conclusions
Our findings do not support an association between Apo E polymorphism and MetS in a multi-center population based study of predominantly white US men and women.
doi:10.1002/dmrr.2638
PMCID: PMC4720970  PMID: 25656378
Apolipoprotein E (Apo E) polymorphism; metabolic syndrome; blood pressure; glucose; dyslipidemia; high-density lipoprotein cholesterol
16.  Angiotensin II type 1 receptor polymorphisms and susceptibility to hypertension: A HuGE review 
The angiotensin II type 1 receptor (AGTR1) plays an integral role in blood pressure control, and is implicated in the pathogenesis of hypertension. Polymorphisms within this gene have been extensively studied in association with hypertension; however, findings are conflicting. To clarify these data, we conducted a systematic review of association studies of AGTR1 polymorphisms and hypertension, and performed a meta-analysis of the rs5186 variant. Results show that the currently available literature is too heterogeneous to draw meaningful conclusions. The definition of hypertension and gender composition of individual studies helps to explain this heterogeneity. Although the structure and splicing pattern of AGTR1 would suggest a likely effect of polymorphisms within the promoter region on gene function, few studies have been conducted thus far. In conclusion, there is insufficient evidence that polymorphisms in the AGTR1 gene are risk factors for hypertension. However, most studies are inadequately powered, and larger well-designed studies of haplotypes are warranted.
PMCID: PMC4993203  PMID: 18641512
17.  Evidence for Three Novel QTLs for Adiposity on Chromosome 2 With Epistatic Interactions: The NHLBI Family Heart Study 
Obesity (Silver Spring, Md.)  2009;17(12):2190-2195.
We sought to identify quantitative trait loci (QTLs) by genome-wide linkage analysis for BMI and waist circumference (WC) exploring various strategies to address heterogeneity including covariate adjustments and complex models based on epistatic components of variance. Because cholesterol-lowering drugs and diabetes medications may affect adiposity and risk of coronary heart disease, we excluded subjects medicated for hypercholesterolemia and hyperglycemia. The evidence of linkage increased on 2p25 (BMI: lod = 1.59 vs. 2.43, WC: lod = 1.32 vs. 2.26). Because environmental and/or genetic components could mask the effect of a specific locus, we investigated further whether a QTL could influence adiposity independently of lipid pathway and dietary habits. Strong evidence of linkage on 2p25 (BMI: lod = 4.31; WC: lod = 4.23) was found using Willet’s dietary factors and lipid profile together with age and sex in adjustment. It suggests that lipid profile and dietary habits are confounding factors for detecting a 2p25 QTL for adiposity. Because evidence of linkage has been previously detected for BMI on 7q34 and 13q14 in National Heart, Lung, and Blood Institute Family Heart Study (NHLBI FHS), and for diabetes on 15q13, we investigated epistasis between chromosome 2 and these loci. Significant epistatic interactions were found between QTLs 2p25 and 7q34, 2q37 and 7q34, 2q31 and 13q14, and 2q31–q36 and 15q13. These results suggest multiple pathways and factors involving genetic and environmental effects influencing adiposity. By taking some of these known factors into account, we clarified our linkage evidence of a QTL on 2p25 influencing BMI and WC. The 2p25, 2q24–q31, and 2q36–q37 showed evidence of epistatic interaction with 7q34, 13q14, and 15q13.
doi:10.1038/oby.2009.181
PMCID: PMC4976636  PMID: 19521348
18.  Evidence of Heterogeneity by Race/Ethnicity in Genetic Determinants of QT Interval 
Epidemiology (Cambridge, Mass.)  2014;25(6):790-798.
Background
QT-interval (QT) prolongation is an established risk factor for ventricular tachyarrhythmia and sudden cardiac death. Previous genome-wide association studies in populations of the European descent have identified multiple genetic loci that influence QT, but few have examined these loci in ethnically diverse populations.
Methods
Here, we examine the direction, magnitude, and precision of effect sizes for 21 previously reported SNPs from 12 QT loci, in populations of European (n=16,398), African (n=5,437), American Indian (n=5,032), Hispanic (n=1,143), and Asian (n=932) descent as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. Estimates obtained from linear regression models stratified by race/ethnicity were combined using inverse-variance weighted meta-analysis. Heterogeneity was evaluated using Cochran's Q test.
Results
Of 21 SNPs, seven showed consistent direction of effect across all five populations, and an additional nine had estimated effects that were consistent across four populations. Despite consistent direction of effect, nine of 16 SNPs had evidence (P < 0.05) of heterogeneity by race/ethnicity. For these 9 SNPs, linkage disequilibrium plots often indicated substantial variation in linkage disequilibrium patterns among the various racial/ethnic groups, as well as possible allelic heterogeneity.
Conclusions
These results emphasize the importance of analyzing racial/ethnic groups separately in genetic studies. Furthermore, they underscore the possible utility of trans-ethnic studies to pinpoint underlying casual variants influencing heritable traits such as QT.
doi:10.1097/EDE.0000000000000168
PMCID: PMC4380285  PMID: 25166880
19.  A QTL on 12q Influencing an Inflammation Marker and Obesity in White Women: The NHLBI Family Heart Study 
Obesity (Silver Spring, Md.)  2008;17(3):525-531.
It has been recognized that obese individuals are intrinsically in a state of chronic inflammation, as indicated by positive correlations between serum levels of C-reactive protein (CRP) and various anthropometric measures of obesity. To explore the hypothesis that a gene(s) may underlie this relationship, we conducted bivariate linkage analyses of BMI and CRP in white and African-American (AA) families of the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study (FHS). Variance components linkage analysis as implemented in SOLAR was performed in 1,825 whites (840 men and 985 women) and 548 AAs (199 men and 351 women). CRP exhibited significant genetic correlations with BMI in women (0.54 ± 0.10 for white and 0.53 ± 0.14 for AA) and the combined samples (0.37 ± 0.09 for white and 0.56 ± 0.13 for AA), but not in men. We detected a maximum bivariate lod score of 3.86 on chromosome 12q24.2–24.3 at 139 cM and a suggestive linkage signal (lod = 2.19) on chromosome 19p13.1 (44 cM) in white women. Both bivariate peaks were substantially higher than their respective univariate lods at the same locus for each trait. No significant lod scores were detected in AAs. Our results indicate that chromosome 12q may harbor quantitative trait loci (QTLs) jointly regulating BMI and CRP in white women.
doi:10.1038/oby.2008.556
PMCID: PMC4962615  PMID: 19238141
20.  Maternal Genotype and Gestational Diabetes 
American journal of perinatology  2013;31(1):10.1055/s-0033-1334451.
OBJECTIVE
To determine whether genetic variants associated with glucose homeostasis are associated with gestational diabetes (GDM).
STUDY DESIGN
We genotyped 899 self-identified Caucasian women and 386 self-identified African-American women in the Pregnancy, Infection and Nutrition (PIN) Studies cohorts for 36 single-nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2DM) and/or glucose homeostasis in European populations.
RESULTS
GDM was diagnosed in 56 of 899 (6.2%) Caucasian and 24 of 386 (6.2%) African-American women. Among Caucasian women, GDM was associated with carriage of TCF7L2 rs7901695, MTNR1B rs10830963 and GCKR rs780094 alleles associated with T2DM and fasting glucose in non-pregnant populations. Among African-American participants, we found an increased risk among TSPAN8 rs7961581 C allele homozygotes and reduced risk among carriers of the JAZF1 rs864745 T allele.
CONCLUSION
We found several SNPs that are associated with GDM risk in the PIN cohorts. Maternal genotyping may identify women at risk for impaired gestational glucose tolerance.
doi:10.1055/s-0033-1334451
PMCID: PMC3884679  PMID: 23456907
diabetes; gestational diabetes; genetics; single nucleotide polymorphisms
21.  Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study 
Winkler, Thomas W. | Justice, Anne E. | Graff, Mariaelisa | Barata, Llilda | Feitosa, Mary F. | Chu, Su | Czajkowski, Jacek | Esko, Tõnu | Fall, Tove | Kilpeläinen, Tuomas O. | Lu, Yingchang | Mägi, Reedik | Mihailov, Evelin | Pers, Tune H. | Rüeger, Sina | Teumer, Alexander | Ehret, Georg B. | Ferreira, Teresa | Heard-Costa, Nancy L. | Karjalainen, Juha | Lagou, Vasiliki | Mahajan, Anubha | Neinast, Michael D. | Prokopenko, Inga | Simino, Jeannette | Teslovich, Tanya M. | Jansen, Rick | Westra, Harm-Jan | White, Charles C. | Absher, Devin | Ahluwalia, Tarunveer S. | Ahmad, Shafqat | Albrecht, Eva | Alves, Alexessander Couto | Bragg-Gresham, Jennifer L. | de Craen, Anton J. M. | Bis, Joshua C. | Bonnefond, Amélie | Boucher, Gabrielle | Cadby, Gemma | Cheng, Yu-Ching | Chiang, Charleston W. K. | Delgado, Graciela | Demirkan, Ayse | Dueker, Nicole | Eklund, Niina | Eiriksdottir, Gudny | Eriksson, Joel | Feenstra, Bjarke | Fischer, Krista | Frau, Francesca | Galesloot, Tessel E. | Geller, Frank | Goel, Anuj | Gorski, Mathias | Grammer, Tanja B. | Gustafsson, Stefan | Haitjema, Saskia | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jackson, Anne U. | Jacobs, Kevin B. | Johansson, Åsa | Kaakinen, Marika | Kleber, Marcus E. | Lahti, Jari | Mateo Leach, Irene | Lehne, Benjamin | Liu, Youfang | Lo, Ken Sin | Lorentzon, Mattias | Luan, Jian'an | Madden, Pamela A. 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Eline | Smit, Jan H. | Sparsø, Thomas H. | Stirrups, Kathleen | Stolk, Ronald P. | Stringham, Heather M. | Swertz, Morris A | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Thorand, Barbara | Tönjes, Anke | Tremblay, Angelo | Tsafantakis, Emmanouil | van der Most, Peter J. | Völker, Uwe | Vohl, Marie-Claude | Vonk, Judith M. | Waldenberger, Melanie | Walker, Ryan W. | Wennauer, Roman | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Wright, Alan F. | Zillikens, M. Carola | van Dijk, Suzanne C. | van Schoor, Natasja M. | Asselbergs, Folkert W. | de Bakker, Paul I. 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M. | Kivimaki, Mika | Knekt, Paul B. | Koistinen, Heikki A. | Kooner, Jaspal S. | Koskinen, Seppo | Kuusisto, Johanna | Maerz, Winfried | Martin, Nicholas G | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Lettre, Guillaume | Levinson, Douglas F. | Lind, Lars | Lokki, Marja-Liisa | Mäntyselkä, Pekka | Melbye, Mads | Metspalu, Andres | Mitchell, Braxton D. | Moll, Frans L. | Murray, Jeffrey C. | Musk, Arthur W. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Oostra, Ben A. | Palmer, Lyle J | Pankow, James S. | Pasterkamp, Gerard | Pedersen, Nancy L. | Pedersen, Oluf | Penninx, Brenda W. | Perola, Markus | Peters, Annette | Polašek, Ozren | Pramstaller, Peter P. | Psaty, Bruce M. | Qi, Lu | Quertermous, Thomas | Raitakari, Olli T. | Rankinen, Tuomo | Rauramaa, Rainer | Ridker, Paul M. | Rioux, John D. | Rivadeneira, Fernando | Rotter, Jerome I. | Rudan, Igor | den Ruijter, Hester M. | Saltevo, Juha | Sattar, Naveed | Schunkert, Heribert | Schwarz, Peter E. H. | Shuldiner, Alan R. | Sinisalo, Juha | Snieder, Harold | Sørensen, Thorkild I. A. | Spector, Tim D. | Staessen, Jan A. | Stefania, Bandinelli | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tardif, Jean-Claude | Tremoli, Elena | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Verbeek, André L. M. | Vermeulen, Sita H. | Viikari, Jorma S. | Vitart, Veronique | Völzke, Henry | Vollenweider, Peter | Waeber, Gérard | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Zeggini, Eleftheria | Chakravarti, Aravinda | Clegg, Deborah J. | Cupples, L. Adrienne | Gordon-Larsen, Penny | Jaquish, Cashell E. | Rao, D. C. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Berndt, Sonja I. | Boehnke, Michael | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Hunter, David J. | Ingelsson, Erik | Kaplan, Robert C. | McCarthy, Mark I. | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Heid, Iris M. | North, Kari E. | Borecki, Ingrid B. | Kutalik, Zoltán | Loos, Ruth J. F.
PLoS Genetics  2016;12(6):e1006166.
doi:10.1371/journal.pgen.1006166
PMCID: PMC4927064  PMID: 27355579
22.  The Influence of Obesity-Related Single Nucleotide Polymorphisms on BMI Across the Life Course 
Diabetes  2013;62(5):1763-1767.
Evidence is limited as to whether heritable risk of obesity varies throughout adulthood. Among >34,000 European Americans, aged 18–100 years, from multiple U.S. studies in the Population Architecture using Genomics and Epidemiology (PAGE) Consortium, we examined evidence for heterogeneity in the associations of five established obesity risk variants (near FTO, GNPDA2, MTCH2, TMEM18, and NEGR1) with BMI across four distinct epochs of adulthood: 1) young adulthood (ages 18–25 years), adulthood (ages 26–49 years), middle-age adulthood (ages 50–69 years), and older adulthood (ages ≥70 years); or 2) by menopausal status in women and stratification by age 50 years in men. Summary-effect estimates from each meta-analysis were compared for heterogeneity across the life epochs. We found heterogeneity in the association of the FTO (rs8050136) variant with BMI across the four adulthood epochs (P = 0.0006), with larger effects in young adults relative to older adults (β [SE] = 1.17 [0.45] vs. 0.09 [0.09] kg/m2, respectively, per A allele) and smaller intermediate effects. We found no evidence for heterogeneity in the association of GNPDA2, MTCH2, TMEM18, and NEGR1 with BMI across adulthood. Genetic predisposition to obesity may have greater effects on body weight in young compared with older adulthood for FTO, suggesting changes by age, generation, or secular trends. Future research should compare and contrast our findings with results using longitudinal data.
doi:10.2337/db12-0863
PMCID: PMC3636619  PMID: 23300277
23.  Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption 
Cornelis, Marilyn C | Byrne, Enda M | Esko, Tõnu | Nalls, Michael A | Ganna, Andrea | Paynter, Nina | Monda, Keri L | Amin, Najaf | Fischer, Krista | Renstrom, Frida | Ngwa, Julius S | Huikari, Ville | Cavadino, Alana | Nolte, Ilja M | Teumer, Alexander | Yu, Kai | Marques-Vidal, Pedro | Rawal, Rajesh | Manichaikul, Ani | Wojczynski, Mary K | Vink, Jacqueline M | Zhao, Jing Hua | Burlutsky, George | Lahti, Jari | Mikkilä, Vera | Lemaitre, Rozenn N | Eriksson, Joel | Musani, Solomon K | Tanaka, Toshiko | Geller, Frank | Luan, Jian’an | Hui, Jennie | Mägi, Reedik | Dimitriou, Maria | Garcia, Melissa E | Ho, Weang-Kee | Wright, Margaret J | Rose, Lynda M | Magnusson, Patrik KE | Pedersen, Nancy L | Couper, David | Oostra, Ben A | Hofman, Albert | Ikram, Mohammad Arfan | Tiemeier, Henning W | Uitterlinden, Andre G | van Rooij, Frank JA | Barroso, Inês | Johansson, Ingegerd | Xue, Luting | Kaakinen, Marika | Milani, Lili | Power, Chris | Snieder, Harold | Stolk, Ronald P | Baumeister, Sebastian E | Biffar, Reiner | Gu, Fangyi | Bastardot, François | Kutalik, Zoltán | Jacobs, David R | Forouhi, Nita G | Mihailov, Evelin | Lind, Lars | Lindgren, Cecilia | Michaëlsson, Karl | Morris, Andrew | Jensen, Majken | Khaw, Kay-Tee | Luben, Robert N | Wang, Jie Jin | Männistö, Satu | Perälä, Mia-Maria | Kähönen, Mika | Lehtimäki, Terho | Viikari, Jorma | Mozaffarian, Dariush | Mukamal, Kenneth | Psaty, Bruce M | Döring, Angela | Heath, Andrew C | Montgomery, Grant W | Dahmen, Norbert | Carithers, Teresa | Tucker, Katherine L | Ferrucci, Luigi | Boyd, Heather A | Melbye, Mads | Treur, Jorien L | Mellström, Dan | Hottenga, Jouke Jan | Prokopenko, Inga | Tönjes, Anke | Deloukas, Panos | Kanoni, Stavroula | Lorentzon, Mattias | Houston, Denise K | Liu, Yongmei | Danesh, John | Rasheed, Asif | Mason, Marc A | Zonderman, Alan B | Franke, Lude | Kristal, Bruce S | Karjalainen, Juha | Reed, Danielle R | Westra, Harm-Jan | Evans, Michele K | Saleheen, Danish | Harris, Tamara B | Dedoussis, George | Curhan, Gary | Stumvoll, Michael | Beilby, John | Pasquale, Louis R | Feenstra, Bjarke | Bandinelli, Stefania | Ordovas, Jose M | Chan, Andrew T | Peters, Ulrike | Ohlsson, Claes | Gieger, Christian | Martin, Nicholas G | Waldenberger, Melanie | Siscovick, David S | Raitakari, Olli | Eriksson, Johan G | Mitchell, Paul | Hunter, David J | Kraft, Peter | Rimm, Eric B | Boomsma, Dorret I | Borecki, Ingrid B | Loos, Ruth JF | Wareham, Nicholas J | Vollenweider, Peter | Caporaso, Neil | Grabe, Hans Jörgen | Neuhouser, Marian L | Wolffenbuttel, Bruce HR | Hu, Frank B | Hyppönen, Elina | Järvelin, Marjo-Riitta | Cupples, L Adrienne | Franks, Paul W | Ridker, Paul M | van Duijn, Cornelia M | Heiss, Gerardo | Metspalu, Andres | North, Kari E | Ingelsson, Erik | Nettleton, Jennifer A | van Dam, Rob M | Chasman, Daniel I
Molecular psychiatry  2014;20(5):647-656.
doi:10.1038/mp.2014.107
PMCID: PMC4388784  PMID: 25288136
24.  Using genetics to test the causal relationship of total adiposity and periodontitis: Mendelian randomization analyses in the Gene-Lifestyle Interactions and Dental Endpoints (GLIDE) Consortium 
Background: The observational relationship between obesity and periodontitis is widely known, yet causal evidence is lacking. Our objective was to investigate causal associations between periodontitis and body mass index (BMI).
Methods: We performed Mendelian randomization analyses with BMI-associated loci combined in a genetic risk score (GRS) as the instrument for BMI. All analyses were conducted within the Gene-Lifestyle Interactions and Dental Endpoints (GLIDE) Consortium in 13 studies from Europe and the USA, including 49 066 participants with clinically assessed (seven studies, 42.1% of participants) and self-reported (six studies, 57.9% of participants) periodontitis and genotype data (17 672/31 394 with/without periodontitis); 68 761 participants with BMI and genotype data; and 57 871 participants (18 881/38 990 with/without periodontitis) with data on BMI and periodontitis.
Results: In the observational meta-analysis of all participants, the pooled crude observational odds ratio (OR) for periodontitis was 1.13 [95% confidence interval (CI): 1.03, 1.24] per standard deviation increase of BMI. Controlling for potential confounders attenuated this estimate (OR = 1.08; 95% CI:1.03, 1.12). For clinically assessed periodontitis, corresponding ORs were 1.25 (95% CI: 1.10, 1.42) and 1.13 (95% CI: 1.10, 1.17), respectively. In the genetic association meta-analysis, the OR for periodontitis was 1.01 (95% CI: 0.99, 1.03) per GRS unit (per one effect allele) in all participants and 1.00 (95% CI: 0.97, 1.03) in participants with clinically assessed periodontitis. The instrumental variable meta-analysis of all participants yielded an OR of 1.05 (95% CI: 0.80, 1.38) per BMI standard deviation, and 0.90 (95% CI: 0.56, 1.46) in participants with clinical data.
Conclusions: Our study does not support total adiposity as a causal risk factor for periodontitis, as the point estimate is very close to the null in the causal inference analysis, with wide confidence intervals.
doi:10.1093/ije/dyv075
PMCID: PMC4817600  PMID: 26050256
Mendelian randomization; BMI; periodontitis; casual inference; confounding
25.  Meta-analysis of genome-wide association studies discovers multiple loci for chronic lymphocytic leukemia 
Berndt, Sonja I. | Camp, Nicola J. | Skibola, Christine F. | Vijai, Joseph | Wang, Zhaoming | Gu, Jian | Nieters, Alexandra | Kelly, Rachel S. | Smedby, Karin E. | Monnereau, Alain | Cozen, Wendy | Cox, Angela | Wang, Sophia S. | Lan, Qing | Teras, Lauren R. | Machado, Moara | Yeager, Meredith | Brooks-Wilson, Angela R. | Hartge, Patricia | Purdue, Mark P. | Birmann, Brenda M. | Vajdic, Claire M. | Cocco, Pierluigi | Zhang, Yawei | Giles, Graham G. | Zeleniuch-Jacquotte, Anne | Lawrence, Charles | Montalvan, Rebecca | Burdett, Laurie | Hutchinson, Amy | Ye, Yuanqing | Call, Timothy G. | Shanafelt, Tait D. | Novak, Anne J. | Kay, Neil E. | Liebow, Mark | Cunningham, Julie M. | Allmer, Cristine | Hjalgrim, Henrik | Adami, Hans-Olov | Melbye, Mads | Glimelius, Bengt | Chang, Ellen T. | Glenn, Martha | Curtin, Karen | Cannon-Albright, Lisa A. | Diver, W Ryan | Link, Brian K. | Weiner, George J. | Conde, Lucia | Bracci, Paige M. | Riby, Jacques | Arnett, Donna K. | Zhi, Degui | Leach, Justin M. | Holly, Elizabeth A. | Jackson, Rebecca D. | Tinker, Lesley F. | Benavente, Yolanda | Sala, Núria | Casabonne, Delphine | Becker, Nikolaus | Boffetta, Paolo | Brennan, Paul | Foretova, Lenka | Maynadie, Marc | McKay, James | Staines, Anthony | Chaffee, Kari G. | Achenbach, Sara J. | Vachon, Celine M. | Goldin, Lynn R. | Strom, Sara S. | Leis, Jose F. | Weinberg, J. Brice | Caporaso, Neil E. | Norman, Aaron D. | De Roos, Anneclaire J. | Morton, Lindsay M. | Severson, Richard K. | Riboli, Elio | Vineis, Paolo | Kaaks, Rudolph | Masala, Giovanna | Weiderpass, Elisabete | Chirlaque, María- Dolores | Vermeulen, Roel C. H. | Travis, Ruth C. | Southey, Melissa C. | Milne, Roger L. | Albanes, Demetrius | Virtamo, Jarmo | Weinstein, Stephanie | Clavel, Jacqueline | Zheng, Tongzhang | Holford, Theodore R. | Villano, Danylo J. | Maria, Ann | Spinelli, John J. | Gascoyne, Randy D. | Connors, Joseph M. | Bertrand, Kimberly A. | Giovannucci, Edward | Kraft, Peter | Kricker, Anne | Turner, Jenny | Ennas, Maria Grazia | Ferri, Giovanni M. | Miligi, Lucia | Liang, Liming | Ma, Baoshan | Huang, Jinyan | Crouch, Simon | Park, Ju-Hyun | Chatterjee, Nilanjan | North, Kari E. | Snowden, John A. | Wright, Josh | Fraumeni, Joseph F. | Offit, Kenneth | Wu, Xifeng | de Sanjose, Silvia | Cerhan, James R. | Chanock, Stephen J. | Rothman, Nathaniel | Slager, Susan L.
Nature Communications  2016;7:10933.
Chronic lymphocytic leukemia (CLL) is a common lymphoid malignancy with strong heritability. To further understand the genetic susceptibility for CLL and identify common loci associated with risk, we conducted a meta-analysis of four genome-wide association studies (GWAS) composed of 3,100 cases and 7,667 controls with follow-up replication in 1,958 cases and 5,530 controls. Here we report three new loci at 3p24.1 (rs9880772, EOMES, P=2.55 × 10−11), 6p25.2 (rs73718779, SERPINB6, P=1.97 × 10−8) and 3q28 (rs9815073, LPP, P=3.62 × 10−8), as well as a new independent SNP at the known 2q13 locus (rs9308731, BCL2L11, P=1.00 × 10−11) in the combined analysis. We find suggestive evidence (P<5 × 10−7) for two additional new loci at 4q24 (rs10028805, BANK1, P=7.19 × 10−8) and 3p22.2 (rs1274963, CSRNP1, P=2.12 × 10−7). Pathway analyses of new and known CLL loci consistently show a strong role for apoptosis, providing further evidence for the importance of this biological pathway in CLL susceptibility.
Chronic lymphocytic leukemia is a highly inheritable cancer. Here the authors conduct a metaanalysis of four genome-wide association studies and identify three novel loci located near EOMES, SERPINB6 and LPP associated with risk of this disease.
doi:10.1038/ncomms10933
PMCID: PMC4786871  PMID: 26956414

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