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1.  Heritability of Prevalent Vertebral Fracture and Volumetric Bone Mineral Density and Geometry at the Lumbar Spine in Three Generations of the Framingham Study 
Genetic factors likely contribute to the risk for vertebral fractures; however, there are few studies on the genetic contributions to vertebral fracture (VFrx), vertebral volumetric bone mineral density (vBMD) and geometry. Also the heritability (h2) for VFrx and its genetic correlation with phenotypes contributing to VFrx risk have not been established. This study aims to estimate the h2 of vertebral fracture, vBMD and cross-sectional-area (CSA) derived from quantitative computed tomography (QCT) scans, and to estimate the extent to which they share common genetic association in adults of European ancestry from three generations of Framingham Heart Study (FHS) families. Members of the FHS families were assessed for VFrx by lateral radiographs or QCT lateral scout views at 13 vertebral levels (T4-L4) using Genant’s semi-quantitative (SQ) scale (grades 0–3). Vertebral fracture was defined as having at least 25% reduction in height of any vertebra. We also analyzed QCT scans at the L3 level for integral (In.BMD) and trabecular (Tb.BMD) vBMD and cross-sectional area (CSA). Heritability estimates were calculated, and bivariate genetic correlation analysis was performed, adjusting for various covariates. For VFrx, we analyzed 4,099 individuals (148 VFrx cases) including 2,082 women and 2,017 men from 3 generations. Estimates of crude and multivariable-adjusted h2 were 0.43 to 0.69 (P< 1.1×10−2). 3,333 individuals including 1,737 men and 1,596 women from 2 generations had VFrx status and QCT-derived vBMD and CSA information. Estimates of crude and multivariable-adjusted h2 for vBMD and CSA ranged from 0.27 to 0.51. In a bivariate analysis, there was a moderate genetic correlation between VFrx and multivariable-adjusted In.BMD (−0.22) and Tb.BMD (−0.29). Our study suggests vertebral fracture, vertebral vBMD and CSA in adults of European ancestry are heritable, underscoring the importance of further work to identify the specific variants underlying genetic susceptibility to vertebral fracture, bone density and geometry.
doi:10.1002/jbmr.1537
PMCID: PMC3375687  PMID: 22222934
vertebral fracture; bone mineral density; heritability; QCT
2.  The distribution of circulating microRNA and their relation to coronary disease 
F1000Research  2012;1:50.
Background: MicroRNAs (miRNAs) are small RNAs that regulate gene expression by suppressing protein translation and may influence RNA expression. MicroRNAs are detected in extracellular locations such as plasma; however, the extent of miRNA expression in plasma its relation to cardiovascular disease is not clear and many clinical studies have utilized array-based platforms with poor reproducibility.
Methods and Results: Initially, to define distribution of miRNA in human blood; whole blood, platelets, mononuclear cells, plasma, and serum from 5 normal individuals were screened for 852 miRNAs using high-throughput micro-fluidic quantitative RT-PCR (qRT-PCR). In total; 609, 448, 658, 147, and 178 miRNAs were found to be expressed in moderate to high levels in whole blood, platelets, mononuclear cells, plasma, and serum, respectively, with some miRNAs uniquely expressed. To determine the cardiovascular relevance of blood miRNA expression, plasma miRNA (n=852) levels were measured in 83 patients presenting for cardiac catheterization. Eight plasma miRNAs were found to have over 2-fold increased expression in patients with significant coronary disease (≥70% stenosis) as compared to those with minimal coronary disease (less than 70% stenosis) or normal coronary arteries. Expression of miR-494, miR-490-3p, and miR-769-3p were found to have significantly different levels of expression. Using a multivariable regression model including cardiovascular risk factors and medications, hsa-miR-769-3p was found to be significantly correlated with the presence of significant coronary atherosclerosis.
Conclusions: This study utilized a superior high-throughput qRT-PCR based method and found that miRNAs are found to be widely expressed in human blood with differences expressed between cellular and extracellular fractions. Importantly, specific miRNAs from circulating plasma are associated with the presence of significant coronary disease.
doi:10.12688/f1000research.1-50.v1
PMCID: PMC3752638  PMID: 24358814
3.  Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins 
Postmus, Iris | Trompet, Stella | Deshmukh, Harshal A. | Barnes, Michael R. | Li, Xiaohui | Warren, Helen R. | Chasman, Daniel I. | Zhou, Kaixin | Arsenault, Benoit J. | Donnelly, Louise A. | Wiggins, Kerri L. | Avery, Christy L. | Griffin, Paula | Feng, QiPing | Taylor, Kent D. | Li, Guo | Evans, Daniel S. | Smith, Albert V. | de Keyser, Catherine E. | Johnson, Andrew D. | de Craen, Anton J. M. | Stott, David J. | Buckley, Brendan M. | Ford, Ian | Westendorp, Rudi G. J. | Eline Slagboom, P. | Sattar, Naveed | Munroe, Patricia B. | Sever, Peter | Poulter, Neil | Stanton, Alice | Shields, Denis C. | O’Brien, Eoin | Shaw-Hawkins, Sue | Ida Chen, Y.-D. | Nickerson, Deborah A. | Smith, Joshua D. | Pierre Dubé, Marie | Matthijs Boekholdt, S. | Kees Hovingh, G. | Kastelein, John J. P. | McKeigue, Paul M. | Betteridge, John | Neil, Andrew | Durrington, Paul N. | Doney, Alex | Carr, Fiona | Morris, Andrew | McCarthy, Mark I. | Groop, Leif | Ahlqvist, Emma | Bis, Joshua C. | Rice, Kenneth | Smith, Nicholas L. | Lumley, Thomas | Whitsel, Eric A. | Stürmer, Til | Boerwinkle, Eric | Ngwa, Julius S. | O’Donnell, Christopher J. | Vasan, Ramachandran S. | Wei, Wei-Qi | Wilke, Russell A. | Liu, Ching-Ti | Sun, Fangui | Guo, Xiuqing | Heckbert, Susan R | Post, Wendy | Sotoodehnia, Nona | Arnold, Alice M. | Stafford, Jeanette M. | Ding, Jingzhong | Herrington, David M. | Kritchevsky, Stephen B. | Eiriksdottir, Gudny | Launer, Leonore J. | Harris, Tamara B. | Chu, Audrey Y. | Giulianini, Franco | MacFadyen, Jean G. | Barratt, Bryan J. | Nyberg, Fredrik | Stricker, Bruno H. | Uitterlinden, André G. | Hofman, Albert | Rivadeneira, Fernando | Emilsson, Valur | Franco, Oscar H. | Ridker, Paul M. | Gudnason, Vilmundur | Liu, Yongmei | Denny, Joshua C. | Ballantyne, Christie M. | Rotter, Jerome I. | Adrienne Cupples, L. | Psaty, Bruce M. | Palmer, Colin N. A. | Tardif, Jean-Claude | Colhoun, Helen M. | Hitman, Graham | Krauss, Ronald M. | Wouter Jukema, J | Caulfield, Mark J.
Nature Communications  2014;5:5068.
Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.
Statins are effectively used to prevent and manage cardiovascular disease, but patient response to these drugs is highly variable. Here, the authors identify two new genes associated with the response of LDL cholesterol to statins and advance our understanding of the genetic basis of drug response.
doi:10.1038/ncomms6068
PMCID: PMC4220464  PMID: 25350695
4.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
doi:10.2337/db13-0128
PMCID: PMC3781444  PMID: 23835345
5.  Sequencing of SCN5A identifies rare and common variants associated with cardiac conduction 
Background
The cardiac sodium channel SCN5A regulates atrioventricular and ventricular conduction. Genetic variants in this gene are associated with PR and QRS intervals. We sought to further characterize the contribution of rare and common coding variation in SCN5A to cardiac conduction.
Methods and Results
In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study (CHARGE), we performed targeted exonic sequencing of SCN5A (n=3699, European-ancestry individuals) and identified 4 common (minor allele frequency >1%) and 157 rare variants. Common and rare SCN5A coding variants were examined for association with PR and QRS intervals through meta-analysis of European ancestry participants from CHARGE, NHLBI’s Exome Sequencing Project (ESP, n=607) and the UK10K (n=1275) and by examining ESP African-ancestry participants (N=972). Rare coding SCN5A variants in aggregate were associated with PR interval in European and African-ancestry participants (P=1.3×10−3). Three common variants were associated with PR and/or QRS interval duration among European-ancestry participants and one among African-ancestry participants. These included two well-known missense variants; rs1805124 (H558R) was associated with PR and QRS shortening in European-ancestry participants (P=6.25×10−4 and P=5.2×10−3 respectively) and rs7626962 (S1102Y) was associated with PR shortening in those of African ancestry (P=2.82×10−3). Among European-ancestry participants, two novel synonymous variants, rs1805126 and rs6599230, were associated with cardiac conduction. Our top signal, rs1805126 was associated with PR and QRS lengthening (P=3.35×10−7 and P=2.69×10−4 respectively), and rs6599230 was associated with PR shortening (P=2.67×10−5).
Conclusions
By sequencing SCN5A, we identified novel common and rare coding variants associated with cardiac conduction.
doi:10.1161/CIRCGENETICS.113.000098
PMCID: PMC4177904  PMID: 24951663
PR interval; QRS interval; genetics; sequencing; cohort
6.  Association of Levels of Fasting Glucose and Insulin with Rare Variants at the Chromosome 11p11.2-MADD Locus: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study 
Background
Common variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3 and SPI1, has been associated in genome-wide association studies with fasting glucose (FG) and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced five gene regions at 11p11.2 to identify rare, potentially functional variants influencing FG or FI levels.
Method & Results
Sequencing (mean depth 38×) across 16.1kb in 3,566 non-diabetic individuals identified 653 variants, 79.9% of which were rare (MAF <1%) and novel. We analyzed rare variants in five gene regions with FI or FG using the Sequence Kernel Association Test (SKAT). At NR1H3, 53 rare variants were jointly associated with FI (p=2.73 × 10−3); of these, seven were predicted to have regulatory function and showed association with FI (p=1.28 × 10−3). Conditioning on two previously associated variants at MADD (rs7944584, rs10838687) did not attenuate this association, suggesting that there are more than two independent signals at 11p11.2. One predicted regulatory variant, chr11:47227430 (hg18; MAF 0.00068), contributed 20.6% to the overall SKAT score at NR1H3, lies in intron 2 of NR1H3 and is a predicted binding site for FOXA1, a transcription factor associated with insulin regulation. In human HepG2 hepatoma cells, the rare chr11:47227430 A allele disrupted FOXA1 binding and reduced FOXA1-dependent transcriptional activity.
Conclusion
Sequencing at 11p11.2- NR1H3 identified rare variation associated with FI. One variant, chr11:47227430, appears to be functional, with the rare A allele reducing transcription factor FOXA1 binding and FOXA1-dependent transcriptional activity.
doi:10.1161/CIRCGENETICS.113.000169
PMCID: PMC4066205  PMID: 24951664
fasting glucose; fasting insulin; chr11p11.2; target sequencing; next-generation sequencing
7.  Genome-wide association study of age at menarche in African-American women 
Human Molecular Genetics  2013;22(16):3329-3346.
African-American (AA) women have earlier menarche on average than women of European ancestry (EA), and earlier menarche is a risk factor for obesity and type 2 diabetes among other chronic diseases. Identification of common genetic variants associated with age at menarche has a potential value in pointing to the genetic pathways underlying chronic disease risk, yet comprehensive genome-wide studies of age at menarche are lacking for AA women. In this study, we tested the genome-wide association of self-reported age at menarche with common single-nucleotide polymorphisms (SNPs) in a total of 18 089 AA women in 15 studies using an additive genetic linear regression model, adjusting for year of birth and population stratification, followed by inverse-variance weighted meta-analysis (Stage 1). Top meta-analysis results were then tested in an independent sample of 2850 women (Stage 2). First, while no SNP passed the pre-specified P < 5 × 10−8 threshold for significance in Stage 1, suggestive associations were found for variants near FLRT2 and PIK3R1, and conditional analysis identified two independent SNPs (rs339978 and rs980000) in or near RORA, strengthening the support for this suggestive locus identified in EA women. Secondly, an investigation of SNPs in 42 previously identified menarche loci in EA women demonstrated that 25 (60%) of them contained variants significantly associated with menarche in AA women. The findings provide the first evidence of cross-ethnic generalization of menarche loci identified to date, and suggest a number of novel biological links to menarche timing in AA women.
doi:10.1093/hmg/ddt181
PMCID: PMC3723312  PMID: 23599027
8.  Comparing baseline and longitudinal measures in association studies 
BMC Proceedings  2014;8(Suppl 1):S84.
In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × 10−7) compared to that in the baseline analysis (p = 2.48 × 10−5). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values.
doi:10.1186/1753-6561-8-S1-S84
PMCID: PMC4143666  PMID: 25519412
9.  Common variants in and near IRS1 and subclinical cardiovascular disease in the Framingham Heart Study 
Atherosclerosis  2013;229(1):149-154.
Objective
Common variants at the 2q36.3-IRS1 locus are associated with insulin resistance (IR), type 2 diabetes (T2D) and coronary artery disease (CAD) in large-scale association studies. We tested the hypothesis that variants at this locus are associated with subclinical atherosclerosis traits.
Methods
We studied 2740 Framingham Heart Study participants (54.9% women; mean age 57.8 years) with measures of coronary artery or abdominal aortic calcium, internal and common carotid intimamedia thickness, and ankle-brachial index (ABI). We tested 1) four SNPs previously shown to be associated with IR (rs2972146, rs2943650), T2D (rs2943641) or CAD (rs2943634) and 2) any SNP at 2q36.3-IRS1, for association with subclinical atherosclerosis traits, adjusting for atherosclerosis risk factors. We set type 1 error rate for test 1) as 0.05/5 traits = P < 0.01, and for test 2) as 0.05 divided by the effective number of independent tests, divided by 5 for the number of traits analyzed.
Results
We found no association between the four known SNPs and subclinical atherosclerosis, but identified one SNP (rs10167219, r2 with rs2943634 = 0.07) at 2q36.3 that was significantly associated with ABI (corrected P = 0.009). However, rs10167219 was not associated with ABI (P = 0.70) in 35,404 participants in a published ABI association study.
Conclusion
Common variants at the 2q36.3-IRS1 locus were not associated with subclinical atherosclerosis traits in this study which was adequately powered to find associations with moderate effect size. Although IR and T2D may be mechanistically linked to CAD via subclinical atherosclerosis, an alternate mechanism for the IR-T2D-CAD associations at 2q36.3-IRS1 must be postulated.
doi:10.1016/j.atherosclerosis.2013.03.037
PMCID: PMC4040123  PMID: 23659870
IRS1; 2q36.3; Genetic association; Subclinical atherosclerosis; Ankle-brachial index
10.  META-ANALYSIS OF GENOME-WIDE STUDIES IDENTIFIES WNT16 AND ESR1 SNPS ASSOCIATED WITH BONE MINERAL DENSITY IN PREMENOPAUSAL WOMEN 
Previous genome-wide association studies (GWAS) have identified common variants in genes associated with variation in bone mineral density (BMD), although most have been carried out in combined samples of older women and men. Meta-analyses of these results have identified numerous SNPs of modest effect at genome-wide significance levels in genes involved in both bone formation and resorption, as well as other pathways. We performed a meta-analysis restricted to premenopausal white women from four cohorts (n= 4,061 women, ages 20 to 45) to identify genes influencing peak bone mass at the lumbar spine and femoral neck. Following imputation, age- and weight-adjusted BMD values were tested for association with each SNP. Association of a SNP in the WNT16 gene (rs3801387; p=1.7 × 10−9) and multiple SNPs in the ESR1/C6orf97 (rs4870044; p=1.3 × 10−8) achieved genome-wide significance levels for lumbar spine BMD. These SNPs, along with others demonstrating suggestive evidence of association, were then tested for association in seven Replication cohorts that included premenopausal women of European, Hispanic-American, and African-American descent (combined n=5,597 for femoral neck; 4,744 for lumbar spine). When the data from the Discovery and Replication cohorts were analyzed jointly, the evidence was more significant (WNT16 joint p=1.3 × 10−11; ESR1/C6orf97 joint p= 1.4 × 10−10). Multiple independent association signals were observed with spine BMD at the ESR1 region after conditioning on the primary signal. Analyses of femoral neck BMD also supported association with SNPs in WNT16 and ESR1/C6orf97 (p< 1 × 10−5). Our results confirm that several of the genes contributing to BMD variation across a broad age range in both sexes have effects of similar magnitude on BMD of the spine in premenopausal women. These data support the hypothesis that variants in these genes of known skeletal function also affect BMD during the premenopausal period.
doi:10.1002/jbmr.1796
PMCID: PMC3691010  PMID: 23074152
Bone mineral density; GWAS; premenopausal; meta-analysis; genetics
11.  Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function 
Chasman, Daniel I. | Fuchsberger, Christian | Pattaro, Cristian | Teumer, Alexander | Böger, Carsten A. | Endlich, Karlhans | Olden, Matthias | Chen, Ming-Huei | Tin, Adrienne | Taliun, Daniel | Li, Man | Gao, Xiaoyi | Gorski, Mathias | Yang, Qiong | Hundertmark, Claudia | Foster, Meredith C. | O'Seaghdha, Conall M. | Glazer, Nicole | Isaacs, Aaron | Liu, Ching-Ti | Smith, Albert V. | O'Connell, Jeffrey R. | Struchalin, Maksim | Tanaka, Toshiko | Li, Guo | Johnson, Andrew D. | Gierman, Hinco J. | Feitosa, Mary F. | Hwang, Shih-Jen | Atkinson, Elizabeth J. | Lohman, Kurt | Cornelis, Marilyn C. | Johansson, Åsa | Tönjes, Anke | Dehghan, Abbas | Lambert, Jean-Charles | Holliday, Elizabeth G. | Sorice, Rossella | Kutalik, Zoltan | Lehtimäki, Terho | Esko, Tõnu | Deshmukh, Harshal | Ulivi, Sheila | Chu, Audrey Y. | Murgia, Federico | Trompet, Stella | Imboden, Medea | Coassin, Stefan | Pistis, Giorgio | Harris, Tamara B. | Launer, Lenore J. | Aspelund, Thor | Eiriksdottir, Gudny | Mitchell, Braxton D. | Boerwinkle, Eric | Schmidt, Helena | Cavalieri, Margherita | Rao, Madhumathi | Hu, Frank | Demirkan, Ayse | Oostra, Ben A. | de Andrade, Mariza | Turner, Stephen T. | Ding, Jingzhong | Andrews, Jeanette S. | Freedman, Barry I. | Giulianini, Franco | Koenig, Wolfgang | Illig, Thomas | Meisinger, Christa | Gieger, Christian | Zgaga, Lina | Zemunik, Tatijana | Boban, Mladen | Minelli, Cosetta | Wheeler, Heather E. | Igl, Wilmar | Zaboli, Ghazal | Wild, Sarah H. | Wright, Alan F. | Campbell, Harry | Ellinghaus, David | Nöthlings, Ute | Jacobs, Gunnar | Biffar, Reiner | Ernst, Florian | Homuth, Georg | Kroemer, Heyo K. | Nauck, Matthias | Stracke, Sylvia | Völker, Uwe | Völzke, Henry | Kovacs, Peter | Stumvoll, Michael | Mägi, Reedik | Hofman, Albert | Uitterlinden, Andre G. | Rivadeneira, Fernando | Aulchenko, Yurii S. | Polasek, Ozren | Hastie, Nick | Vitart, Veronique | Helmer, Catherine | Wang, Jie Jin | Stengel, Bénédicte | Ruggiero, Daniela | Bergmann, Sven | Kähönen, Mika | Viikari, Jorma | Nikopensius, Tiit | Province, Michael | Ketkar, Shamika | Colhoun, Helen | Doney, Alex | Robino, Antonietta | Krämer, Bernhard K. | Portas, Laura | Ford, Ian | Buckley, Brendan M. | Adam, Martin | Thun, Gian-Andri | Paulweber, Bernhard | Haun, Margot | Sala, Cinzia | Mitchell, Paul | Ciullo, Marina | Kim, Stuart K. | Vollenweider, Peter | Raitakari, Olli | Metspalu, Andres | Palmer, Colin | Gasparini, Paolo | Pirastu, Mario | Jukema, J. Wouter | Probst-Hensch, Nicole M. | Kronenberg, Florian | Toniolo, Daniela | Gudnason, Vilmundur | Shuldiner, Alan R. | Coresh, Josef | Schmidt, Reinhold | Ferrucci, Luigi | Siscovick, David S. | van Duijn, Cornelia M. | Borecki, Ingrid B. | Kardia, Sharon L.R. | Liu, Yongmei | Curhan, Gary C. | Rudan, Igor | Gyllensten, Ulf | Wilson, James F. | Franke, Andre | Pramstaller, Peter P. | Rettig, Rainer | Prokopenko, Inga | Witteman, Jacqueline | Hayward, Caroline | Ridker, Paul M | Parsa, Afshin | Bochud, Murielle | Heid, Iris M. | Kao, W.H. Linda | Fox, Caroline S. | Köttgen, Anna
Human Molecular Genetics  2012;21(24):5329-5343.
In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10−9) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10−4–2.2 × 10−7. Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.
doi:10.1093/hmg/dds369
PMCID: PMC3607468  PMID: 22962313
12.  Assessment of Gene-by-Sex Interaction Effect on Bone Mineral Density 
Liu, Ching-Ti | Estrada, Karol | Yerges-Armstrong, Laura M. | Amin, Najaf | Evangelou, Evangelos | Li, Guo | Minster, Ryan L. | Carless, Melanie A. | Kammerer, Candace M. | Oei, Ling | Zhou, Yanhua | Alonso, Nerea | Dailiana, Zoe | Eriksson, Joel | García-Giralt, Natalia | Giroux, Sylvie | Husted, Lise Bjerre | Khusainova, Rita I. | Koromila, Theodora | Kung, Annie WaiChee | Lewis, Joshua R. | Masi, Laura | Mencej-Bedrac, Simona | Nogues, Xavier | Patel, Millan S. | Prezelj, Janez | Richards, J Brent | Sham, Pak Chung | Spector, Timothy | Vandenput, Liesbeth | Xiao, Su-Mei | Zheng, Hou-Feng | Zhu, Kun | Balcells, Susana | Brandi, Maria Luisa | Frost, Morten | Goltzman, David | González-Macías, Jesús | Karlsson, Magnus | Khusnutdinova, Elza K. | Kollia, Panagoula | Langdahl, Bente Lomholt | Ljunggren, Östen | Lorentzon, Mattias | Marc, Janja | Mellström, Dan | Ohlsson, Claes | Olmos, José M. | Ralston, Stuart H. | Riancho, José A. | Rousseau, François | Urreizti, Roser | Van Hul, Wim | Zarrabeitia, María T. | Castano-Betancourt, Martha | Demissie, Serkalem | Grundberg, Elin | Herrera, Lizbeth | Kwan, Tony | Medina-Gómez, Carolina | Pastinen, Tomi | Sigurdsson, Gunnar | Thorleifsson, Gudmar | vanMeurs, Joyce B.J. | Blangero, John | Hofman, Albert | Liu, Yongmei | Mitchell, Braxton D. | O’Connell, Jeffrey R. | Oostra, Ben A. | Rotter, Jerome I | Stefansson, Kari | Streeten, Elizabeth A. | Styrkarsdottir, Unnur | Thorsteinsdottir, Unnur | Tylavsky, Frances A. | Uitterlinden, Andre | Cauley, Jane A. | Harris, Tamara B. | Ioannidis, John P.A. | Psaty, Bruce M. | Robbins, John A | Zillikens, M. Carola | vanDuijn, Cornelia M. | Prince, Richard L. | Karasik, David | Rivadeneira, Fernando | Kiel, Douglas P. | Cupples, L. Adrienne | Hsu, Yi-Hsiang
Background
Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed eQTL analysis and bioinformatics network analysis.
Methods
We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS-) and femoral neck (FN-) BMD, in 25,353 individuals from eight cohorts. In a second stage, we followed up the 12 top SNPs (P<1×10−5) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs.
Results
We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 & p-value = 3.0×10−5; female effect = −0.007 & p-value=3.3×10−2) and eleven suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (P<5×10−8) gene-by-sex interaction in the joint analysis of discovery and replication cohorts.
Conclusion
Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found influencing BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP.
doi:10.1002/jbmr.1679
PMCID: PMC3447125  PMID: 22692763
gene-by-sex; interaction; BMD; association; aging
13.  Genome-Wide Association of Body Fat Distribution in African Ancestry Populations Suggests New Loci 
Liu, Ching-Ti | Monda, Keri L. | Taylor, Kira C. | Lange, Leslie | Demerath, Ellen W. | Palmas, Walter | Wojczynski, Mary K. | Ellis, Jaclyn C. | Vitolins, Mara Z. | Liu, Simin | Papanicolaou, George J. | Irvin, Marguerite R. | Xue, Luting | Griffin, Paula J. | Nalls, Michael A. | Adeyemo, Adebowale | Liu, Jiankang | Li, Guo | Ruiz-Narvaez, Edward A. | Chen, Wei-Min | Chen, Fang | Henderson, Brian E. | Millikan, Robert C. | Ambrosone, Christine B. | Strom, Sara S. | Guo, Xiuqing | Andrews, Jeanette S. | Sun, Yan V. | Mosley, Thomas H. | Yanek, Lisa R. | Shriner, Daniel | Haritunians, Talin | Rotter, Jerome I. | Speliotes, Elizabeth K. | Smith, Megan | Rosenberg, Lynn | Mychaleckyj, Josyf | Nayak, Uma | Spruill, Ida | Garvey, W. Timothy | Pettaway, Curtis | Nyante, Sarah | Bandera, Elisa V. | Britton, Angela F. | Zonderman, Alan B. | Rasmussen-Torvik, Laura J. | Chen, Yii-Der Ida | Ding, Jingzhong | Lohman, Kurt | Kritchevsky, Stephen B. | Zhao, Wei | Peyser, Patricia A. | Kardia, Sharon L. R. | Kabagambe, Edmond | Broeckel, Ulrich | Chen, Guanjie | Zhou, Jie | Wassertheil-Smoller, Sylvia | Neuhouser, Marian L. | Rampersaud, Evadnie | Psaty, Bruce | Kooperberg, Charles | Manson, JoAnn E. | Kuller, Lewis H. | Ochs-Balcom, Heather M. | Johnson, Karen C. | Sucheston, Lara | Ordovas, Jose M. | Palmer, Julie R. | Haiman, Christopher A. | McKnight, Barbara | Howard, Barbara V. | Becker, Diane M. | Bielak, Lawrence F. | Liu, Yongmei | Allison, Matthew A. | Grant, Struan F. A. | Burke, Gregory L. | Patel, Sanjay R. | Schreiner, Pamela J. | Borecki, Ingrid B. | Evans, Michele K. | Taylor, Herman | Sale, Michele M. | Howard, Virginia | Carlson, Christopher S. | Rotimi, Charles N. | Cushman, Mary | Harris, Tamara B. | Reiner, Alexander P. | Cupples, L. Adrienne | North, Kari E. | Fox, Caroline S.
PLoS Genetics  2013;9(8):e1003681.
Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values<5.0×10−6 were followed-up (stage 2) in AA with WC and with WHR. Additionally, we interrogated genomic regions of previously identified European ancestry (EA) WHR loci among AA. In joint analysis of association results including both Stage 1 and 2 cohorts, 2 SNPs demonstrated association, rs2075064 at LHX2, p = 2.24×10−8 for WC-adjusted-for-BMI, and rs6931262 at RREB1, p = 2.48×10−8 for WHR-adjusted-for-BMI. However, neither signal was genome-wide significant after double GC-correction (LHX2: p = 6.5×10−8; RREB1: p = 5.7×10−8). Six of fourteen previously reported loci for waist in EA populations were significant (p<0.05 divided by the number of independent SNPs within the region) in AA studied here (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, ITPR2-SSPN). Further, we observed associations with metabolic traits: rs13389219 at GRB14 associated with HDL-cholesterol, triglycerides, and fasting insulin, and rs13060013 at ADAMTS9 with HDL-cholesterol and fasting insulin. Finally, we observed nominal evidence for sexual dimorphism, with stronger results in AA women at the GRB14 locus (p for interaction = 0.02). In conclusion, we identified two suggestive loci associated with fat distribution in AA populations in addition to confirming 6 loci previously identified in populations of EA. These findings reinforce the concept that there are fat distribution loci that are independent of generalized adiposity.
Author Summary
Central obesity is a marker of body fat distribution and is known to have a genetic underpinning. Few studies have reported genome-wide association study (GWAS) results among individuals of predominantly African ancestry (AA). We performed a collaborative meta-analysis in order to identify genetic loci associated with body fat distribution in AA individuals using waist circumference (WC) and waist to hip ratio (WHR) as measures of fat distribution, with and without adjustment for body mass index (BMI). We uncovered 2 genetic loci potentially associated with fat distribution: LHX2 in association with WC-adjusted-for-BMI and at RREB1 for WHR-adjusted-for-BMI. Six of fourteen previously reported loci for waist in EA populations were significant in AA studied here (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, ITPR2-SSPN). These findings reinforce the concept that there are loci for body fat distribution that are independent of generalized adiposity.
doi:10.1371/journal.pgen.1003681
PMCID: PMC3744443  PMID: 23966867
14.  Validated SNPs for eGFR and their associations with albuminuria 
Human Molecular Genetics  2012;21(14):3293-3298.
Albuminuria and reduced glomerular filtration rate are manifestations of chronic kidney disease (CKD) that predict end-stage renal disease, acute kidney injury, cardiovascular disease and death. We hypothesized that SNPs identified in association with the estimated glomerular filtration rate (eGFR) would also be associated with albuminuria. Within the CKDGen Consortium cohort (n= 31 580, European ancestry), we tested 16 eGFR-associated SNPs for association with the urinary albumin-to-creatinine ratio (UACR) and albuminuria [UACR >25 mg/g (women); 17 mg/g (men)]. In parallel, within the CARe Renal Consortium (n= 5569, African ancestry), we tested seven eGFR-associated SNPs for association with the UACR. We used a Bonferroni-corrected P-value of 0.003 (0.05/16) in CKDGen and 0.007 (0.05/7) in CARe. We also assessed whether the 16 eGFR SNPs were associated with the UACR in aggregate using a beta-weighted genotype score. In the CKDGen Consortium, the minor A allele of rs17319721 in the SHROOM3 gene, known to be associated with a lower eGFR, was associated with lower ln(UACR) levels (beta = −0.034, P-value = 0.0002). No additional eGFR-associated SNPs met the Bonferroni-corrected P-value threshold of 0.003 for either UACR or albuminuria. In the CARe Renal Consortium, there were no associations between SNPs and UACR with a P< 0.007. Although we found the genotype score to be associated with albuminuria (P= 0.0006), this result was driven almost entirely by the known SHROOM3 variant, rs17319721. Removal of rs17319721 resulted in a P-value 0.03, indicating a weak residual aggregate signal. No alleles, previously demonstrated to be associated with a lower eGFR, were associated with the UACR or albuminuria, suggesting that there may be distinct genetic components for these traits.
doi:10.1093/hmg/dds138
PMCID: PMC3491918  PMID: 22492995
15.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228
16.  Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways 
Scott, Robert A | Lagou, Vasiliki | Welch, Ryan P | Wheeler, Eleanor | Montasser, May E | Luan, Jian’an | Mägi, Reedik | Strawbridge, Rona J | Rehnberg, Emil | Gustafsson, Stefan | Kanoni, Stavroula | Rasmussen-Torvik, Laura J | Yengo, Loïc | Lecoeur, Cecile | Shungin, Dmitry | Sanna, Serena | Sidore, Carlo | Johnson, Paul C D | Jukema, J Wouter | Johnson, Toby | Mahajan, Anubha | Verweij, Niek | Thorleifsson, Gudmar | Hottenga, Jouke-Jan | Shah, Sonia | Smith, Albert V | Sennblad, Bengt | Gieger, Christian | Salo, Perttu | Perola, Markus | Timpson, Nicholas J | Evans, David M | Pourcain, Beate St | Wu, Ying | Andrews, Jeanette S | Hui, Jennie | Bielak, Lawrence F | Zhao, Wei | Horikoshi, Momoko | Navarro, Pau | Isaacs, Aaron | O’Connell, Jeffrey R | Stirrups, Kathleen | Vitart, Veronique | Hayward, Caroline | Esko, Tönu | Mihailov, Evelin | Fraser, Ross M | Fall, Tove | Voight, Benjamin F | Raychaudhuri, Soumya | Chen, Han | Lindgren, Cecilia M | Morris, Andrew P | Rayner, Nigel W | Robertson, Neil | Rybin, Denis | Liu, Ching-Ti | Beckmann, Jacques S | Willems, Sara M | Chines, Peter S | Jackson, Anne U | Kang, Hyun Min | Stringham, Heather M | Song, Kijoung | Tanaka, Toshiko | Peden, John F | Goel, Anuj | Hicks, Andrew A | An, Ping | Müller-Nurasyid, Martina | Franco-Cereceda, Anders | Folkersen, Lasse | Marullo, Letizia | Jansen, Hanneke | Oldehinkel, Albertine J | Bruinenberg, Marcel | Pankow, James S | North, Kari E | Forouhi, Nita G | Loos, Ruth J F | Edkins, Sarah | Varga, Tibor V | Hallmans, Göran | Oksa, Heikki | Antonella, Mulas | Nagaraja, Ramaiah | Trompet, Stella | Ford, Ian | Bakker, Stephan J L | Kong, Augustine | Kumari, Meena | Gigante, Bruna | Herder, Christian | Munroe, Patricia B | Caulfield, Mark | Antti, Jula | Mangino, Massimo | Small, Kerrin | Miljkovic, Iva | Liu, Yongmei | Atalay, Mustafa | Kiess, Wieland | James, Alan L | Rivadeneira, Fernando | Uitterlinden, Andre G | Palmer, Colin N A | Doney, Alex S F | Willemsen, Gonneke | Smit, Johannes H | Campbell, Susan | Polasek, Ozren | Bonnycastle, Lori L | Hercberg, Serge | Dimitriou, Maria | Bolton, Jennifer L | Fowkes, Gerard R | Kovacs, Peter | Lindström, Jaana | Zemunik, Tatijana | Bandinelli, Stefania | Wild, Sarah H | Basart, Hanneke V | Rathmann, Wolfgang | Grallert, Harald | Maerz, Winfried | Kleber, Marcus E | Boehm, Bernhard O | Peters, Annette | Pramstaller, Peter P | Province, Michael A | Borecki, Ingrid B | Hastie, Nicholas D | Rudan, Igor | Campbell, Harry | Watkins, Hugh | Farrall, Martin | Stumvoll, Michael | Ferrucci, Luigi | Waterworth, Dawn M | Bergman, Richard N | Collins, Francis S | Tuomilehto, Jaakko | Watanabe, Richard M | de Geus, Eco J C | Penninx, Brenda W | Hofman, Albert | Oostra, Ben A | Psaty, Bruce M | Vollenweider, Peter | Wilson, James F | Wright, Alan F | Hovingh, G Kees | Metspalu, Andres | Uusitupa, Matti | Magnusson, Patrik K E | Kyvik, Kirsten O | Kaprio, Jaakko | Price, Jackie F | Dedoussis, George V | Deloukas, Panos | Meneton, Pierre | Lind, Lars | Boehnke, Michael | Shuldiner, Alan R | van Duijn, Cornelia M | Morris, Andrew D | Toenjes, Anke | Peyser, Patricia A | Beilby, John P | Körner, Antje | Kuusisto, Johanna | Laakso, Markku | Bornstein, Stefan R | Schwarz, Peter E H | Lakka, Timo A | Rauramaa, Rainer | Adair, Linda S | Smith, George Davey | Spector, Tim D | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Gudnason, Vilmundur | Kivimaki, Mika | Hingorani, Aroon | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Boomsma, Dorret I | Stefansson, Kari | van der Harst, Pim | Dupuis, Josée | Pedersen, Nancy L | Sattar, Naveed | Harris, Tamara B | Cucca, Francesco | Ripatti, Samuli | Salomaa, Veikko | Mohlke, Karen L | Balkau, Beverley | Froguel, Philippe | Pouta, Anneli | Jarvelin, Marjo-Riitta | Wareham, Nicholas J | Bouatia-Naji, Nabila | McCarthy, Mark I | Franks, Paul W | Meigs, James B | Teslovich, Tanya M | Florez, Jose C | Langenberg, Claudia | Ingelsson, Erik | Prokopenko, Inga | Barroso, Inês
Nature genetics  2012;44(9):991-1005.
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.
doi:10.1038/ng.2385
PMCID: PMC3433394  PMID: 22885924
17.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
18.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
19.  Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture 
Estrada, Karol | Styrkarsdottir, Unnur | Evangelou, Evangelos | Hsu, Yi-Hsiang | Duncan, Emma L | Ntzani, Evangelia E | Oei, Ling | Albagha, Omar M E | Amin, Najaf | Kemp, John P | Koller, Daniel L | Li, Guo | Liu, Ching-Ti | Minster, Ryan L | Moayyeri, Alireza | Vandenput, Liesbeth | Willner, Dana | Xiao, Su-Mei | Yerges-Armstrong, Laura M | Zheng, Hou-Feng | Alonso, Nerea | Eriksson, Joel | Kammerer, Candace M | Kaptoge, Stephen K | Leo, Paul J | Thorleifsson, Gudmar | Wilson, Scott G | Wilson, James F | Aalto, Ville | Alen, Markku | Aragaki, Aaron K | Aspelund, Thor | Center, Jacqueline R | Dailiana, Zoe | Duggan, David J | Garcia, Melissa | Garcia-Giralt, Natàlia | Giroux, Sylvie | Hallmans, Göran | Hocking, Lynne J | Husted, Lise Bjerre | Jameson, Karen A | Khusainova, Rita | Kim, Ghi Su | Kooperberg, Charles | Koromila, Theodora | Kruk, Marcin | Laaksonen, Marika | Lacroix, Andrea Z | Lee, Seung Hun | Leung, Ping C | Lewis, Joshua R | Masi, Laura | Mencej-Bedrac, Simona | Nguyen, Tuan V | Nogues, Xavier | Patel, Millan S | Prezelj, Janez | Rose, Lynda M | Scollen, Serena | Siggeirsdottir, Kristin | Smith, Albert V | Svensson, Olle | Trompet, Stella | Trummer, Olivia | van Schoor, Natasja M | Woo, Jean | Zhu, Kun | Balcells, Susana | Brandi, Maria Luisa | Buckley, Brendan M | Cheng, Sulin | Christiansen, Claus | Cooper, Cyrus | Dedoussis, George | Ford, Ian | Frost, Morten | Goltzman, David | González-Macías, Jesús | Kähönen, Mika | Karlsson, Magnus | Khusnutdinova, Elza | Koh, Jung-Min | Kollia, Panagoula | Langdahl, Bente Lomholt | Leslie, William D | Lips, Paul | Ljunggren, Östen | Lorenc, Roman S | Marc, Janja | Mellström, Dan | Obermayer-Pietsch, Barbara | Olmos, José M | Pettersson-Kymmer, Ulrika | Reid, David M | Riancho, José A | Ridker, Paul M | Rousseau, François | Slagboom, P Eline | Tang, Nelson LS | Urreizti, Roser | Van Hul, Wim | Viikari, Jorma | Zarrabeitia, María T | Aulchenko, Yurii S | Castano-Betancourt, Martha | Grundberg, Elin | Herrera, Lizbeth | Ingvarsson, Thorvaldur | Johannsdottir, Hrefna | Kwan, Tony | Li, Rui | Luben, Robert | Medina-Gómez, Carolina | Palsson, Stefan Th | Reppe, Sjur | Rotter, Jerome I | Sigurdsson, Gunnar | van Meurs, Joyce B J | Verlaan, Dominique | Williams, Frances MK | Wood, Andrew R | Zhou, Yanhua | Gautvik, Kaare M | Pastinen, Tomi | Raychaudhuri, Soumya | Cauley, Jane A | Chasman, Daniel I | Clark, Graeme R | Cummings, Steven R | Danoy, Patrick | Dennison, Elaine M | Eastell, Richard | Eisman, John A | Gudnason, Vilmundur | Hofman, Albert | Jackson, Rebecca D | Jones, Graeme | Jukema, J Wouter | Khaw, Kay-Tee | Lehtimäki, Terho | Liu, Yongmei | Lorentzon, Mattias | McCloskey, Eugene | Mitchell, Braxton D | Nandakumar, Kannabiran | Nicholson, Geoffrey C | Oostra, Ben A | Peacock, Munro | Pols, Huibert A P | Prince, Richard L | Raitakari, Olli | Reid, Ian R | Robbins, John | Sambrook, Philip N | Sham, Pak Chung | Shuldiner, Alan R | Tylavsky, Frances A | van Duijn, Cornelia M | Wareham, Nick J | Cupples, L Adrienne | Econs, Michael J | Evans, David M | Harris, Tamara B | Kung, Annie Wai Chee | Psaty, Bruce M | Reeve, Jonathan | Spector, Timothy D | Streeten, Elizabeth A | Zillikens, M Carola | Thorsteinsdottir, Unnur | Ohlsson, Claes | Karasik, David | Richards, J Brent | Brown, Matthew A | Stefansson, Kari | Uitterlinden, André G | Ralston, Stuart H | Ioannidis, John P A | Kiel, Douglas P | Rivadeneira, Fernando
Nature genetics  2012;44(5):491-501.
Bone mineral density (BMD) is the most important predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and East Asian ancestry. We tested the top-associated BMD markers for replication in 50,933 independent subjects and for risk of low-trauma fracture in 31,016 cases and 102,444 controls. We identified 56 loci (32 novel)associated with BMD atgenome-wide significant level (P<5×10−8). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal-stem-cell differentiation, endochondral ossification and the Wnt signalling pathways. However, we also discovered loci containing genes not known to play a role in bone biology. Fourteen BMD loci were also associated with fracture risk (P<5×10−4, Bonferroni corrected), of which six reached P<5×10−8 including: 18p11.21 (C18orf19), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.
doi:10.1038/ng.2249
PMCID: PMC3338864  PMID: 22504420
20.  Genome-wide association study for serum urate concentrations and gout among African Americans identifies genomic risk loci and a novel URAT1 loss-of-function allele 
Human Molecular Genetics  2011;20(20):4056-4068.
Serum urate concentrations are highly heritable and elevated serum urate is a key risk factor for gout. Genome-wide association studies (GWAS) of serum urate in African American (AA) populations are lacking. We conducted a meta-analysis of GWAS of serum urate levels and gout among 5820 AA and a large candidate gene study among 6890 AA and 21 708 participants of European ancestry (EA) within the Candidate Gene Association Resource Consortium. Findings were tested for replication among 1996 independent AA individuals, and evaluated for their association among 28 283 EA participants of the CHARGE Consortium. Functional studies were conducted using 14C-urate transport assays in mammalian Chinese hamster ovary cells. In the discovery GWAS of serum urate, three loci achieved genome-wide significance (P< 5.0 × 10−8): a novel locus near SGK1/SLC2A12 on chromosome 6 (rs9321453, P= 1.0 × 10−9), and two loci previously identified in EA participants, SLC2A9 (P= 3.8 × 10−32) and SLC22A12 (P= 2.1 × 10−10). A novel rare non-synonymous variant of large effect size in SLC22A12, rs12800450 (minor allele frequency 0.01, G65W), was identified and replicated (beta −1.19 mg/dl, P= 2.7 × 10−16). 14C-urate transport assays showed reduced urate transport for the G65W URAT1 mutant. Finally, in analyses of 11 loci previously associated with serum urate in EA individuals, 10 of 11 lead single-nucleotide polymorphisms showed direction-consistent association with urate among AA. In summary, we identified and replicated one novel locus in association with serum urate levels and experimentally characterize the novel G65W variant in URAT1 as a functional allele. Our data support the importance of multi-ethnic GWAS in the identification of novel risk loci as well as functional variants.
doi:10.1093/hmg/ddr307
PMCID: PMC3177647  PMID: 21768215
21.  Bayesian Methods for Multivariate Modeling of Pleiotropic SNP Associations and Genetic Risk Prediction 
Frontiers in Genetics  2012;3:176.
Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The proposed method is robust, computationally efficient, and provides a powerful new approach for detecting and modeling pleiotropic disease loci.
doi:10.3389/fgene.2012.00176
PMCID: PMC3438684  PMID: 22973300
pleiotropy; SNP; GWAS; prediction; Bayesian
22.  Genome-Wide Association of Pericardial Fat Identifies a Unique Locus for Ectopic Fat 
PLoS Genetics  2012;8(5):e1002705.
Pericardial fat is a localized fat depot associated with coronary artery calcium and myocardial infarction. We hypothesized that genetic loci would be associated with pericardial fat independent of other body fat depots. Pericardial fat was quantified in 5,487 individuals of European ancestry from the Framingham Heart Study (FHS) and the Multi-Ethnic Study of Atherosclerosis (MESA). Genotyping was performed using standard arrays and imputed to ∼2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of pericardial fat adjusted for age, sex, weight, and height. A weighted z-score meta-analysis was conducted, and validation was obtained in an additional 3,602 multi-ethnic individuals from the MESA study. We identified a genome-wide significant signal in our primary meta-analysis at rs10198628 near TRIB2 (MAF 0.49, p = 2.7×10-08). This SNP was not associated with visceral fat (p = 0.17) or body mass index (p = 0.38), although we observed direction-consistent, nominal significance with visceral fat adjusted for BMI (p = 0.01) in the Framingham Heart Study. Our findings were robust among African ancestry (n = 1,442, p = 0.001), Hispanic (n = 1,399, p = 0.004), and Chinese (n = 761, p = 0.007) participants from the MESA study, with a combined p-value of 5.4E-14. We observed TRIB2 gene expression in the pericardial fat of mice. rs10198628 near TRIB2 is associated with pericardial fat but not measures of generalized or visceral adiposity, reinforcing the concept that there are unique genetic underpinnings to ectopic fat distribution.
Author Summary
Pericardial fat is a localized fat depot associated with coronary artery calcium and myocardial infarction. To test whether genetic loci are associated with pericardial fat independent of other body fat depots, we measured pericardial fat in 5,487 individuals of European ancestry. After performing an unbiased screen using genome-wide association, we identified a genome-wide significant signal in our primary meta-analysis at rs10198628 near TRIB2 (MAF 0.49, p = 2.7×10-08). This SNP was not associated with visceral fat (p = 0.17) or body mass index (p = 0.38). Our findings were robust among multi-ethnic participants from the MESA study, with a combined p-value of 5.4E-14. We observed TRIB2 gene expression in the pericardial fat of mice. rs10198628 near TRIB2 is associated with pericardial fat but not measures of generalized or visceral adiposity, reinforcing the concept that there are unique genetic underpinnings to ectopic fat distribution.
doi:10.1371/journal.pgen.1002705
PMCID: PMC3349742  PMID: 22589742
23.  Genome-Wide Association and Functional Follow-Up Reveals New Loci for Kidney Function 
Pattaro, Cristian | Köttgen, Anna | Teumer, Alexander | Garnaas, Maija | Böger, Carsten A. | Fuchsberger, Christian | Olden, Matthias | Chen, Ming-Huei | Tin, Adrienne | Taliun, Daniel | Li, Man | Gao, Xiaoyi | Gorski, Mathias | Yang, Qiong | Hundertmark, Claudia | Foster, Meredith C. | O'Seaghdha, Conall M. | Glazer, Nicole | Isaacs, Aaron | Liu, Ching-Ti | Smith, Albert V. | O'Connell, Jeffrey R. | Struchalin, Maksim | Tanaka, Toshiko | Li, Guo | Johnson, Andrew D. | Gierman, Hinco J. | Feitosa, Mary | Hwang, Shih-Jen | Atkinson, Elizabeth J. | Lohman, Kurt | Cornelis, Marilyn C. | Johansson, Åsa | Tönjes, Anke | Dehghan, Abbas | Chouraki, Vincent | Holliday, Elizabeth G. | Sorice, Rossella | Kutalik, Zoltan | Lehtimäki, Terho | Esko, Tõnu | Deshmukh, Harshal | Ulivi, Sheila | Chu, Audrey Y. | Murgia, Federico | Trompet, Stella | Imboden, Medea | Kollerits, Barbara | Pistis, Giorgio | Harris, Tamara B. | Launer, Lenore J. | Aspelund, Thor | Eiriksdottir, Gudny | Mitchell, Braxton D. | Boerwinkle, Eric | Schmidt, Helena | Cavalieri, Margherita | Rao, Madhumathi | Hu, Frank B. | Demirkan, Ayse | Oostra, Ben A. | de Andrade, Mariza | Turner, Stephen T. | Ding, Jingzhong | Andrews, Jeanette S. | Freedman, Barry I. | Koenig, Wolfgang | Illig, Thomas | Döring, Angela | Wichmann, H.-Erich | Kolcic, Ivana | Zemunik, Tatijana | Boban, Mladen | Minelli, Cosetta | Wheeler, Heather E. | Igl, Wilmar | Zaboli, Ghazal | Wild, Sarah H. | Wright, Alan F. | Campbell, Harry | Ellinghaus, David | Nöthlings, Ute | Jacobs, Gunnar | Biffar, Reiner | Endlich, Karlhans | Ernst, Florian | Homuth, Georg | Kroemer, Heyo K. | Nauck, Matthias | Stracke, Sylvia | Völker, Uwe | Völzke, Henry | Kovacs, Peter | Stumvoll, Michael | Mägi, Reedik | Hofman, Albert | Uitterlinden, Andre G. | Rivadeneira, Fernando | Aulchenko, Yurii S. | Polasek, Ozren | Hastie, Nick | Vitart, Veronique | Helmer, Catherine | Wang, Jie Jin | Ruggiero, Daniela | Bergmann, Sven | Kähönen, Mika | Viikari, Jorma | Nikopensius, Tiit | Province, Michael | Ketkar, Shamika | Colhoun, Helen | Doney, Alex | Robino, Antonietta | Giulianini, Franco | Krämer, Bernhard K. | Portas, Laura | Ford, Ian | Buckley, Brendan M. | Adam, Martin | Thun, Gian-Andri | Paulweber, Bernhard | Haun, Margot | Sala, Cinzia | Metzger, Marie | Mitchell, Paul | Ciullo, Marina | Kim, Stuart K. | Vollenweider, Peter | Raitakari, Olli | Metspalu, Andres | Palmer, Colin | Gasparini, Paolo | Pirastu, Mario | Jukema, J. Wouter | Probst-Hensch, Nicole M. | Kronenberg, Florian | Toniolo, Daniela | Gudnason, Vilmundur | Shuldiner, Alan R. | Coresh, Josef | Schmidt, Reinhold | Ferrucci, Luigi | Siscovick, David S. | van Duijn, Cornelia M. | Borecki, Ingrid | Kardia, Sharon L. R. | Liu, Yongmei | Curhan, Gary C. | Rudan, Igor | Gyllensten, Ulf | Wilson, James F. | Franke, Andre | Pramstaller, Peter P. | Rettig, Rainer | Prokopenko, Inga | Witteman, Jacqueline C. M. | Hayward, Caroline | Ridker, Paul | Parsa, Afshin | Bochud, Murielle | Heid, Iris M. | Goessling, Wolfram | Chasman, Daniel I. | Kao, W. H. Linda | Fox, Caroline S.
PLoS Genetics  2012;8(3):e1002584.
Chronic kidney disease (CKD) is an important public health problem with a genetic component. We performed genome-wide association studies in up to 130,600 European ancestry participants overall, and stratified for key CKD risk factors. We uncovered 6 new loci in association with estimated glomerular filtration rate (eGFR), the primary clinical measure of CKD, in or near MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. Morpholino knockdown of mpped2 and casp9 in zebrafish embryos revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. By providing new insights into genes that regulate renal function, these results could further our understanding of the pathogenesis of CKD.
Author Summary
Chronic kidney disease (CKD) is an important public health problem with a hereditary component. We performed a new genome-wide association study in up to 130,600 European ancestry individuals to identify genes that may influence kidney function, specifically genes that may influence kidney function differently depending on sex, age, hypertension, and diabetes status of individuals. We uncovered 6 new loci associated with estimated glomerular filtration rate (eGFR), the primary measure of renal function, in or near MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. CDK12 effect was stronger in younger and absent in older individuals. MPPED2, DDX1, SLC47A1, and CDK12 loci were associated with eGFR in African ancestry samples as well, highlighting the cross-ethnicity validity of our findings. Using the zebrafish model, we performed morpholino knockdown of mpped2 and casp9 in zebrafish embryos and revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. These results further our understanding of the pathogenesis of CKD and provide insights into potential novel mechanisms of disease.
doi:10.1371/journal.pgen.1002584
PMCID: PMC3315455  PMID: 22479191
24.  Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals 
Dastani, Zari | Hivert, Marie-France | Timpson, Nicholas | Perry, John R. B. | Yuan, Xin | Scott, Robert A. | Henneman, Peter | Heid, Iris M. | Kizer, Jorge R. | Lyytikäinen, Leo-Pekka | Fuchsberger, Christian | Tanaka, Toshiko | Morris, Andrew P. | Small, Kerrin | Isaacs, Aaron | Beekman, Marian | Coassin, Stefan | Lohman, Kurt | Qi, Lu | Kanoni, Stavroula | Pankow, James S. | Uh, Hae-Won | Wu, Ying | Bidulescu, Aurelian | Rasmussen-Torvik, Laura J. | Greenwood, Celia M. T. | Ladouceur, Martin | Grimsby, Jonna | Manning, Alisa K. | Liu, Ching-Ti | Kooner, Jaspal | Mooser, Vincent E. | Vollenweider, Peter | Kapur, Karen A. | Chambers, John | Wareham, Nicholas J. | Langenberg, Claudia | Frants, Rune | Willems-vanDijk, Ko | Oostra, Ben A. | Willems, Sara M. | Lamina, Claudia | Winkler, Thomas W. | Psaty, Bruce M. | Tracy, Russell P. | Brody, Jennifer | Chen, Ida | Viikari, Jorma | Kähönen, Mika | Pramstaller, Peter P. | Evans, David M. | St. Pourcain, Beate | Sattar, Naveed | Wood, Andrew R. | Bandinelli, Stefania | Carlson, Olga D. | Egan, Josephine M. | Böhringer, Stefan | van Heemst, Diana | Kedenko, Lyudmyla | Kristiansson, Kati | Nuotio, Marja-Liisa | Loo, Britt-Marie | Harris, Tamara | Garcia, Melissa | Kanaya, Alka | Haun, Margot | Klopp, Norman | Wichmann, H.-Erich | Deloukas, Panos | Katsareli, Efi | Couper, David J. | Duncan, Bruce B. | Kloppenburg, Margreet | Adair, Linda S. | Borja, Judith B. | Wilson, James G. | Musani, Solomon | Guo, Xiuqing | Johnson, Toby | Semple, Robert | Teslovich, Tanya M. | Allison, Matthew A. | Redline, Susan | Buxbaum, Sarah G. | Mohlke, Karen L. | Meulenbelt, Ingrid | Ballantyne, Christie M. | Dedoussis, George V. | Hu, Frank B. | Liu, Yongmei | Paulweber, Bernhard | Spector, Timothy D. | Slagboom, P. Eline | Ferrucci, Luigi | Jula, Antti | Perola, Markus | Raitakari, Olli | Florez, Jose C. | Salomaa, Veikko | Eriksson, Johan G. | Frayling, Timothy M. | Hicks, Andrew A. | Lehtimäki, Terho | Smith, George Davey | Siscovick, David S. | Kronenberg, Florian | van Duijn, Cornelia | Loos, Ruth J. F. | Waterworth, Dawn M. | Meigs, James B. | Dupuis, Josee | Richards, J. Brent
PLoS Genetics  2012;8(3):e1002607.
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8–1.2×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10−4). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10−3, n = 22,044), increased triglycerides (p = 2.6×10−14, n = 93,440), increased waist-to-hip ratio (p = 1.8×10−5, n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10−3, n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10−13, n = 96,748) and decreased BMI (p = 1.4×10−4, n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
Author Summary
Serum adiponectin levels are highly heritable and are inversely correlated with the risk of type 2 diabetes (T2D), coronary artery disease, stroke, and several metabolic traits. To identify common genetic variants associated with adiponectin levels and risk of T2D and metabolic traits, we conducted a meta-analysis of genome-wide association studies of 45,891 multi-ethnic individuals. In addition to confirming that variants at the ADIPOQ and CDH13 loci influence adiponectin levels, our analyses revealed that 10 new loci also affecting circulating adiponectin levels. We demonstrated that expression levels of several genes in these candidate regions are associated with serum adiponectin levels. Using a powerful novel method to assess the contribution of the identified variants with other traits using summary-level results from large-scale GWAS consortia, we provide evidence that the risk alleles for adiponectin are associated with deleterious changes in T2D risk and metabolic syndrome traits (triglycerides, HDL, post-prandial glucose, insulin, and waist-to-hip ratio), demonstrating that the identified loci, taken together, impact upon metabolic disease.
doi:10.1371/journal.pgen.1002607
PMCID: PMC3315470  PMID: 22479202
25.  Meta-analysis of Gene-Environment interaction: joint estimation of SNP and SNP×Environment regression coefficients 
Genetic Epidemiology  2011;35(1):11-18.
Introduction
Genetic discoveries are validated through the meta-analysis of genome-wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta-analysis of SNP and SNP by Environment (SNP×E) regression coefficients for use in gene-environment interaction studies.
Methods
In testing SNP×E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta-analyze the SNP and SNP×E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP×E terms, and a test of homogeneity across samples.
Results
We present a simulation study comparing this method to four other methods of meta-analysis and demonstrate that the joint meta-analysis performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta-analysis of the association between SNPs from the type 2 diabetes-associated gene PPARG and log-transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from 5 cohorts.
doi:10.1002/gepi.20546
PMCID: PMC3312394  PMID: 21181894
2 degree of freedom meta-analysis; joint meta-analysis; PPARG; Gene-environment interaction meta-analysis

Results 1-25 (30)