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1.  LRP5 Regulates Human Body Fat Distribution by Modulating Adipose Progenitor Biology in a Dose- and Depot-Specific Fashion 
Cell Metabolism  2015;21(2):262-272.
Summary
Common variants in WNT pathway genes have been associated with bone mass and fat distribution, the latter predicting diabetes and cardiovascular disease risk. Rare mutations in the WNT co-receptors LRP5 and LRP6 are similarly associated with bone and cardiometabolic disorders. We investigated the role of LRP5 in human adipose tissue. Subjects with gain-of-function LRP5 mutations and high bone mass had enhanced lower-body fat accumulation. Reciprocally, a low bone mineral density-associated common LRP5 allele correlated with increased abdominal adiposity. Ex vivo LRP5 expression was higher in abdominal versus gluteal adipocyte progenitors. Equivalent knockdown of LRP5 in both progenitor types dose-dependently impaired β-catenin signaling and led to distinct biological outcomes: diminished gluteal and enhanced abdominal adipogenesis. These data highlight how depot differences in WNT/β-catenin pathway activity modulate human fat distribution via effects on adipocyte progenitor biology. They also identify LRP5 as a potential pharmacologic target for the treatment of cardiometabolic disorders.
Graphical Abstract
Highlights
•Carriers of LRP5 variants display altered body fat distribution•LRP5 is more highly expressed in abdominal versus gluteal fat progenitor cells•LRP5 knockdown in both progenitor types leads to different biological responses•LRP5 modulates fat progenitor biology by controlling β-catenin signaling dosage
Loh et al. identify the WNT co-receptor LRP5 as a regulator of human body fat distribution, an independent predictor of diabetes and cardiovascular disease risk. Studying LRP5 gene variant carriers and human fat progenitors, they show that LRP5 differentially modulates regional adipose progenitor biology by titrating WNT/β-catenin signaling dosage.
doi:10.1016/j.cmet.2015.01.009
PMCID: PMC4321886  PMID: 25651180
2.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
doi:10.1371/journal.pgen.1004876
PMCID: PMC4307976  PMID: 25625282
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.  Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independently of obesity 
Diabetes  2014;63(12):4378-4387.
We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterise their association with intermediate phenotypes, and to investigate their role in T2D risk among normal-weight, overweight and obese individuals.We investigated the association of genetic scores with euglycaemic-hyperinsulinaemic clamp- and OGTT-based measures of insulin resistance and secretion, and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs-per-allele [95%CI]:−0.03[−0.04,−0.01];p=0.004). This score was associated with lower BMI (−0.01[−0.01,−0.0;p=0.02) and gluteofemoral fat-mass (−0.03[−0.05,−0.02;p=1.4×10−6), and with higher ALT (0.02[0.01,0.03];p=0.002) and gamma-GT (0.02[0.01,0.03];p=0.001). While the secretion score had a stronger association with T2D in leaner individuals (pinteraction=0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI- or waist-strata(pinteraction>0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
doi:10.2337/db14-0319
PMCID: PMC4241116  PMID: 24947364
Genetics; type 2 diabetes; insulin resistance; insulin secretion; adipose expandability
5.  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
6.  The South Asian Genome 
PLoS ONE  2014;9(8):e102645.
The genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians.
doi:10.1371/journal.pone.0102645
PMCID: PMC4130493  PMID: 25115870
7.  Pancreatic islet enhancer clusters enriched in type 2 diabetes risk–associated variants 
Nature genetics  2014;46(2):136-143.
Type 2 diabetes affects over 300 million people, causing severe complications and premature death, yet the underlying molecular mechanisms are largely unknown. Pancreatic islet dysfunction is central for type 2 diabetes pathogenesis, and therefore understanding islet genome regulation could provide valuable mechanistic insights. We have now mapped and examined the function of human islet cis-regulatory networks. We identify genomic sequences that are targeted by islet transcription factors to drive islet-specific gene activity, and show that most such sequences reside in clusters of enhancers that form physical 3D chromatin domains. We find that sequence variants associated with type 2 diabetes and fasting glycemia are enriched in these clustered islet enhancers, and identify trait-associated variants that disrupt DNA-binding and islet enhancer activity. Our studies illustrate how islet transcription factors interact functionally with the epigenome, and provide systematic evidence that dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes.
doi:10.1038/ng.2870
PMCID: PMC3935450  PMID: 24413736
8.  Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population 
PLoS Genetics  2014;10(7):e1004494.
Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.
Author Summary
We explored the coding regions of 3,000 Finnish individuals with 3,000 non-Finnish Europeans (NFEs) using whole-exome sequence data, in order to understand how an individual from a bottlenecked population might differ from an individual from an out-bred population. We provide empirical evidence that there are more rare and low-frequency deleterious alleles in Finns compared to NFEs, such that an average Finn has almost twice as many low-frequency complete knockouts of a gene. As such, we hypothesized that some of these low-frequency loss-of-function variants might have important medical consequences in humans and genotyped 83 of these variants in 36,000 Finns. In doing so, we discovered that completely knocking out the TSFM gene might result in inviability or a very severe phenotype in humans and that knocking out the LPA gene might confer protection against coronary heart diseases, suggesting that LPA is likely to be a good potential therapeutic target.
doi:10.1371/journal.pgen.1004494
PMCID: PMC4117444  PMID: 25078778
9.  Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity 
Human Molecular Genetics  2013;22(13):2735-2747.
The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10−8) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits.
doi:10.1093/hmg/ddt104
PMCID: PMC3674797  PMID: 23449627
10.  Evaluation of Common Type 2 Diabetes Risk Variants in a South Asian Population of Sri Lankan Descent 
PLoS ONE  2014;9(6):e98608.
Introduction
Most studies seeking common variant associations with type 2 diabetes (T2D) have focused on individuals of European ancestry. These discoveries need to be evaluated in other major ancestral groups, to understand ethnic differences in predisposition, and establish whether these contribute to variation in T2D prevalence and presentation. This study aims to establish whether common variants conferring T2D-risk in Europeans contribute to T2D-susceptibility in the South Asian population of Sri Lanka.
Methodology
Lead single nucleotide polymorphism (SNPs) at 37 T2D-risk loci attaining genome-wide significance in Europeans were genotyped in 878 T2D cases and 1523 normoglycaemic controls from Sri Lanka. Association testing was performed by logistic regression adjusting for age and sex and by the Cochran-Mantel-Haenszel test after stratifying according to self-identified ethnolinguistic subgroup. A weighted genetic risk score was generated to examine the combined effect of these SNPs on T2D-risk in the Sri Lankan population.
Results
Of the 36 SNPs passing quality control, sixteen showed nominal (p<0.05) association in Sri Lankan samples, fifteen of those directionally-consistent with the original signal. Overall, these association findings were robust to analyses that accounted for membership of ethnolinguistic subgroups. Overall, the odds ratios for 31 of the 36 SNPs were directionally-consistent with those observed in Europeans (p = 3.2×10−6). Allelic odds ratios and risk allele frequencies in Sri Lankan subjects were not systematically different to those reported in Europeans. Genetic risk score and risk of T2D were strongly related in Sri Lankans (per allele OR 1.10 [95%CI 1.08–1.13], p = 1.2×10−17).
Conclusion
Our data indicate that most T2D-risk variants identified in Europeans have similar effects in South Asians from Sri Lanka, and that systematic difference in common variant associations are unlikely to explain inter-ethnic differences in prevalence or presentation of T2D.
doi:10.1371/journal.pone.0098608
PMCID: PMC4057178  PMID: 24926958
11.  Loss-of-function mutations in SLC30A8 protect against type 2 diabetes 
Flannick, Jason | Thorleifsson, Gudmar | Beer, Nicola L. | Jacobs, Suzanne B. R. | Grarup, Niels | Burtt, Noël P. | Mahajan, Anubha | Fuchsberger, Christian | Atzmon, Gil | Benediktsson, Rafn | Blangero, John | Bowden, Don W. | Brandslund, Ivan | Brosnan, Julia | Burslem, Frank | Chambers, John | Cho, Yoon Shin | Christensen, Cramer | Douglas, Desirée A. | Duggirala, Ravindranath | Dymek, Zachary | Farjoun, Yossi | Fennell, Timothy | Fontanillas, Pierre | Forsén, Tom | Gabriel, Stacey | Glaser, Benjamin | Gudbjartsson, Daniel F. | Hanis, Craig | Hansen, Torben | Hreidarsson, Astradur B. | Hveem, Kristian | Ingelsson, Erik | Isomaa, Bo | Johansson, Stefan | Jørgensen, Torben | Jørgensen, Marit Eika | Kathiresan, Sekar | Kong, Augustine | Kooner, Jaspal | Kravic, Jasmina | Laakso, Markku | Lee, Jong-Young | Lind, Lars | Lindgren, Cecilia M | Linneberg, Allan | Masson, Gisli | Meitinger, Thomas | Mohlke, Karen L | Molven, Anders | Morris, Andrew P. | Potluri, Shobha | Rauramaa, Rainer | Ribel-Madsen, Rasmus | Richard, Ann-Marie | Rolph, Tim | Salomaa, Veikko | Segrè, Ayellet V. | Skärstrand, Hanna | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Sulem, Patrick | Tai, E Shyong | Teo, Yik Ying | Teslovich, Tanya | Thorsteinsdottir, Unnur | Trimmer, Jeff K. | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Vaziri-Sani, Fariba | Voight, Benjamin F. | Wilson, James G. | Boehnke, Michael | McCarthy, Mark I. | Njølstad, Pål R. | Pedersen, Oluf | Groop, Leif | Cox, David R. | Stefansson, Kari | Altshuler, David
Nature genetics  2014;46(4):357-363.
Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets1,2,3, yet none are described for type 2 diabetes (T2D). Through sequencing or genotyping ~150,000 individuals across five ethnicities, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8)4 and harbors a common variant (p.Trp325Arg) associated with T2D risk, glucose, and proinsulin levels5–7. Collectively, protein-truncating variant carriers had 65% reduced T2D risk (p=1.7×10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34SerfsX50) demonstrated reduced glucose levels (−0.17 s.d., p=4.6×10−4). The two most common protein-truncating variants (p.Arg138X and p.Lys34SerfsX50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested reduced zinc transport increases T2D risk8,9, yet phenotypic heterogeneity was observed in rodent Slc30a8 knockouts10–15. Contrastingly, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, proposing ZnT8 inhibition as a therapeutic strategy in T2D prevention.
doi:10.1038/ng.2915
PMCID: PMC4051628  PMID: 24584071
12.  Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis 
Summary
Background
Metformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.
Methods
In this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.
Findings
5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1–68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.
Interpretation
Glycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.
Funding
Wellcome Trust.
doi:10.1016/S2213-8587(14)70050-6
PMCID: PMC4038749  PMID: 24731673
13.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Background
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
Conclusions
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001647.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001647
PMCID: PMC4028183  PMID: 24845081
14.  Genome-Wide Association Study Identifies a Novel Locus Contributing to Type 2 Diabetes Susceptibility in Sikhs of Punjabi Origin From India 
Diabetes  2013;62(5):1746-1755.
We performed a genome-wide association study (GWAS) and a multistage meta-analysis of type 2 diabetes (T2D) in Punjabi Sikhs from India. Our discovery GWAS in 1,616 individuals (842 case subjects) was followed by in silico replication of the top 513 independent single nucleotide polymorphisms (SNPs) (P < 10−3) in Punjabi Sikhs (n = 2,819; 801 case subjects). We further replicated 66 SNPs (P < 10−4) through genotyping in a Punjabi Sikh sample (n = 2,894; 1,711 case subjects). On combined meta-analysis in Sikh populations (n = 7,329; 3,354 case subjects), we identified a novel locus in association with T2D at 13q12 represented by a directly genotyped intronic SNP (rs9552911, P = 1.82 × 10−8) in the SGCG gene. Next, we undertook in silico replication (stage 2b) of the top 513 signals (P < 10−3) in 29,157 non-Sikh South Asians (10,971 case subjects) and de novo genotyping of up to 31 top signals (P < 10−4) in 10,817 South Asians (5,157 case subjects) (stage 3b). In combined South Asian meta-analysis, we observed six suggestive associations (P < 10−5 to < 10−7), including SNPs at HMG1L1/CTCFL, PLXNA4, SCAP, and chr5p11. Further evaluation of 31 top SNPs in 33,707 East Asians (16,746 case subjects) (stage 3c) and 47,117 Europeans (8,130 case subjects) (stage 3d), and joint meta-analysis of 128,127 individuals (44,358 case subjects) from 27 multiethnic studies, did not reveal any additional loci nor was there any evidence of replication for the new variant. Our findings provide new evidence on the presence of a population-specific signal in relation to T2D, which may provide additional insights into T2D pathogenesis.
doi:10.2337/db12-1077
PMCID: PMC3636649  PMID: 23300278
15.  Discovery and Refinement of Loci Associated with Lipid Levels 
Willer, Cristen J. | Schmidt, Ellen M. | Sengupta, Sebanti | Peloso, Gina M. | Gustafsson, Stefan | Kanoni, Stavroula | Ganna, Andrea | Chen, Jin | Buchkovich, Martin L. | Mora, Samia | Beckmann, Jacques S. | Bragg-Gresham, Jennifer L. | Chang, Hsing-Yi | Demirkan, Ayşe | Den Hertog, Heleen M. | Do, Ron | Donnelly, Louise A. | Ehret, Georg B. | Esko, Tõnu | Feitosa, Mary F. | Ferreira, Teresa | Fischer, Krista | Fontanillas, Pierre | Fraser, Ross M. | Freitag, Daniel F. | Gurdasani, Deepti | Heikkilä, Kauko | Hyppönen, Elina | Isaacs, Aaron | Jackson, Anne U. | Johansson, Åsa | Johnson, Toby | Kaakinen, Marika | Kettunen, Johannes | Kleber, Marcus E. | Li, Xiaohui | Luan, Jian’an | Lyytikäinen, Leo-Pekka | Magnusson, Patrik K.E. | Mangino, Massimo | Mihailov, Evelin | Montasser, May E. | Müller-Nurasyid, Martina | Nolte, Ilja M. | O’Connell, Jeffrey R. | Palmer, Cameron D. | Perola, Markus | Petersen, Ann-Kristin | Sanna, Serena | Saxena, Richa | Service, Susan K. | Shah, Sonia | Shungin, Dmitry | Sidore, Carlo | Song, Ci | Strawbridge, Rona J. | Surakka, Ida | Tanaka, Toshiko | Teslovich, Tanya M. | Thorleifsson, Gudmar | Van den Herik, Evita G. | Voight, Benjamin F. | Volcik, Kelly A. | Waite, Lindsay L. | Wong, Andrew | Wu, Ying | Zhang, Weihua | Absher, Devin | Asiki, Gershim | Barroso, Inês | Been, Latonya F. | Bolton, Jennifer L. | Bonnycastle, Lori L | Brambilla, Paolo | Burnett, Mary S. | Cesana, Giancarlo | Dimitriou, Maria | Doney, Alex S.F. | Döring, Angela | Elliott, Paul | Epstein, Stephen E. | Ingi Eyjolfsson, Gudmundur | Gigante, Bruna | Goodarzi, Mark O. | Grallert, Harald | Gravito, Martha L. | Groves, Christopher J. | Hallmans, Göran | Hartikainen, Anna-Liisa | Hayward, Caroline | Hernandez, Dena | Hicks, Andrew A. | Holm, Hilma | Hung, Yi-Jen | Illig, Thomas | Jones, Michelle R. | Kaleebu, Pontiano | Kastelein, John J.P. | Khaw, Kay-Tee | Kim, Eric | Klopp, Norman | Komulainen, Pirjo | Kumari, Meena | Langenberg, Claudia | Lehtimäki, Terho | Lin, Shih-Yi | Lindström, Jaana | Loos, Ruth J.F. | Mach, François | McArdle, Wendy L | Meisinger, Christa | Mitchell, Braxton D. | Müller, Gabrielle | Nagaraja, Ramaiah | Narisu, Narisu | Nieminen, Tuomo V.M. | Nsubuga, Rebecca N. | Olafsson, Isleifur | Ong, Ken K. | Palotie, Aarno | Papamarkou, Theodore | Pomilla, Cristina | Pouta, Anneli | Rader, Daniel J. | Reilly, Muredach P. | Ridker, Paul M. | Rivadeneira, Fernando | Rudan, Igor | Ruokonen, Aimo | Samani, Nilesh | Scharnagl, Hubert | Seeley, Janet | Silander, Kaisa | Stančáková, Alena | Stirrups, Kathleen | Swift, Amy J. | Tiret, Laurence | Uitterlinden, Andre G. | van Pelt, L. Joost | Vedantam, Sailaja | Wainwright, Nicholas | Wijmenga, Cisca | Wild, Sarah H. | Willemsen, Gonneke | Wilsgaard, Tom | Wilson, James F. | Young, Elizabeth H. | Zhao, Jing Hua | Adair, Linda S. | Arveiler, Dominique | Assimes, Themistocles L. | Bandinelli, Stefania | Bennett, Franklyn | Bochud, Murielle | Boehm, Bernhard O. | Boomsma, Dorret I. | Borecki, Ingrid B. | Bornstein, Stefan R. | Bovet, Pascal | Burnier, Michel | Campbell, Harry | Chakravarti, Aravinda | Chambers, John C. | Chen, Yii-Der Ida | Collins, Francis S. | Cooper, Richard S. | Danesh, John | Dedoussis, George | de Faire, Ulf | Feranil, Alan B. | Ferrières, Jean | Ferrucci, Luigi | Freimer, Nelson B. | Gieger, Christian | Groop, Leif C. | Gudnason, Vilmundur | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hingorani, Aroon | Hirschhorn, Joel N. | Hofman, Albert | Hovingh, G. Kees | Hsiung, Chao Agnes | Humphries, Steve E. | Hunt, Steven C. | Hveem, Kristian | Iribarren, Carlos | Järvelin, Marjo-Riitta | Jula, Antti | Kähönen, Mika | Kaprio, Jaakko | Kesäniemi, Antero | Kivimaki, Mika | Kooner, Jaspal S. | Koudstaal, Peter J. | Krauss, Ronald M. | Kuh, Diana | Kuusisto, Johanna | Kyvik, Kirsten O. | Laakso, Markku | Lakka, Timo A. | Lind, Lars | Lindgren, Cecilia M. | Martin, Nicholas G. | März, Winfried | McCarthy, Mark I. | McKenzie, Colin A. | Meneton, Pierre | Metspalu, Andres | Moilanen, Leena | Morris, Andrew D. | Munroe, Patricia B. | Njølstad, Inger | Pedersen, Nancy L. | Power, Chris | Pramstaller, Peter P. | Price, Jackie F. | Psaty, Bruce M. | Quertermous, Thomas | Rauramaa, Rainer | Saleheen, Danish | Salomaa, Veikko | Sanghera, Dharambir K. | Saramies, Jouko | Schwarz, Peter E.H. | Sheu, Wayne H-H | Shuldiner, Alan R. | Siegbahn, Agneta | Spector, Tim D. | Stefansson, Kari | Strachan, David P. | Tayo, Bamidele O. | Tremoli, Elena | Tuomilehto, Jaakko | Uusitupa, Matti | van Duijn, Cornelia M. | Vollenweider, Peter | Wallentin, Lars | Wareham, Nicholas J. | Whitfield, John B. | Wolffenbuttel, Bruce H.R. | Ordovas, Jose M. | Boerwinkle, Eric | Palmer, Colin N.A. | Thorsteinsdottir, Unnur | Chasman, Daniel I. | Rotter, Jerome I. | Franks, Paul W. | Ripatti, Samuli | Cupples, L. Adrienne | Sandhu, Manjinder S. | Rich, Stephen S. | Boehnke, Michael | Deloukas, Panos | Kathiresan, Sekar | Mohlke, Karen L. | Ingelsson, Erik | Abecasis, Gonçalo R.
Nature genetics  2013;45(11):10.1038/ng.2797.
Low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and total cholesterol are heritable, modifiable, risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,578 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5×10−8, including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian, and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipids are often associated with cardiovascular and metabolic traits including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio, and body mass index. Our results illustrate the value of genetic data from individuals of diverse ancestries and provide insights into biological mechanisms regulating blood lipids to guide future genetic, biological, and therapeutic research.
doi:10.1038/ng.2797
PMCID: PMC3838666  PMID: 24097068
16.  Common variants associated with plasma triglycerides and risk for coronary artery disease 
Do, Ron | Willer, Cristen J. | Schmidt, Ellen M. | Sengupta, Sebanti | Gao, Chi | Peloso, Gina M. | Gustafsson, Stefan | Kanoni, Stavroula | Ganna, Andrea | Chen, Jin | Buchkovich, Martin L. | Mora, Samia | Beckmann, Jacques S. | Bragg-Gresham, Jennifer L. | Chang, Hsing-Yi | Demirkan, Ayşe | Den Hertog, Heleen M. | Donnelly, Louise A. | Ehret, Georg B. | Esko, Tõnu | Feitosa, Mary F. | Ferreira, Teresa | Fischer, Krista | Fontanillas, Pierre | Fraser, Ross M. | Freitag, Daniel F. | Gurdasani, Deepti | Heikkilä, Kauko | Hyppönen, Elina | Isaacs, Aaron | Jackson, Anne U. | Johansson, Åsa | Johnson, Toby | Kaakinen, Marika | Kettunen, Johannes | Kleber, Marcus E. | Li, Xiaohui | Luan, Jian'an | Lyytikäinen, Leo-Pekka | Magnusson, Patrik K.E. | Mangino, Massimo | Mihailov, Evelin | Montasser, May E. | Müller-Nurasyid, Martina | Nolte, Ilja M. | O'Connell, Jeffrey R. | Palmer, Cameron D. | Perola, Markus | Petersen, Ann-Kristin | Sanna, Serena | Saxena, Richa | Service, Susan K. | Shah, Sonia | Shungin, Dmitry | Sidore, Carlo | Song, Ci | Strawbridge, Rona J. | Surakka, Ida | Tanaka, Toshiko | Teslovich, Tanya M. | Thorleifsson, Gudmar | Van den Herik, Evita G. | Voight, Benjamin F. | Volcik, Kelly A. | Waite, Lindsay L. | Wong, Andrew | Wu, Ying | Zhang, Weihua | Absher, Devin | Asiki, Gershim | Barroso, Inês | Been, Latonya F. | Bolton, Jennifer L. | Bonnycastle, Lori L | Brambilla, Paolo | Burnett, Mary S. | Cesana, Giancarlo | Dimitriou, Maria | Doney, Alex S.F. | Döring, Angela | Elliott, Paul | Epstein, Stephen E. | Eyjolfsson, Gudmundur Ingi | Gigante, Bruna | Goodarzi, Mark O. | Grallert, Harald | Gravito, Martha L. | Groves, Christopher J. | Hallmans, Göran | Hartikainen, Anna-Liisa | Hayward, Caroline | Hernandez, Dena | Hicks, Andrew A. | Holm, Hilma | Hung, Yi-Jen | Illig, Thomas | Jones, Michelle R. | Kaleebu, Pontiano | Kastelein, John J.P. | Khaw, Kay-Tee | Kim, Eric | Klopp, Norman | Komulainen, Pirjo | Kumari, Meena | Langenberg, Claudia | Lehtimäki, Terho | Lin, Shih-Yi | Lindström, Jaana | Loos, Ruth J.F. | Mach, François | McArdle, Wendy L | Meisinger, Christa | Mitchell, Braxton D. | Müller, Gabrielle | Nagaraja, Ramaiah | Narisu, Narisu | Nieminen, Tuomo V.M. | Nsubuga, Rebecca N. | Olafsson, Isleifur | Ong, Ken K. | Palotie, Aarno | Papamarkou, Theodore | Pomilla, Cristina | Pouta, Anneli | Rader, Daniel J. | Reilly, Muredach P. | Ridker, Paul M. | Rivadeneira, Fernando | Rudan, Igor | Ruokonen, Aimo | Samani, Nilesh | Scharnagl, Hubert | Seeley, Janet | Silander, Kaisa | Stančáková, Alena | Stirrups, Kathleen | Swift, Amy J. | Tiret, Laurence | Uitterlinden, Andre G. | van Pelt, L. Joost | Vedantam, Sailaja | Wainwright, Nicholas | Wijmenga, Cisca | Wild, Sarah H. | Willemsen, Gonneke | Wilsgaard, Tom | Wilson, James F. | Young, Elizabeth H. | Zhao, Jing Hua | Adair, Linda S. | Arveiler, Dominique | Assimes, Themistocles L. | Bandinelli, Stefania | Bennett, Franklyn | Bochud, Murielle | Boehm, Bernhard O. | Boomsma, Dorret I. | Borecki, Ingrid B. | Bornstein, Stefan R. | Bovet, Pascal | Burnier, Michel | Campbell, Harry | Chakravarti, Aravinda | Chambers, John C. | Chen, Yii-Der Ida | Collins, Francis S. | Cooper, Richard S. | Danesh, John | Dedoussis, George | de Faire, Ulf | Feranil, Alan B. | Ferrières, Jean | Ferrucci, Luigi | Freimer, Nelson B. | Gieger, Christian | Groop, Leif C. | Gudnason, Vilmundur | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hingorani, Aroon | Hirschhorn, Joel N. | Hofman, Albert | Hovingh, G. Kees | Hsiung, Chao Agnes | Humphries, Steve E. | Hunt, Steven C. | Hveem, Kristian | Iribarren, Carlos | Järvelin, Marjo-Riitta | Jula, Antti | Kähönen, Mika | Kaprio, Jaakko | Kesäniemi, Antero | Kivimaki, Mika | Kooner, Jaspal S. | Koudstaal, Peter J. | Krauss, Ronald M. | Kuh, Diana | Kuusisto, Johanna | Kyvik, Kirsten O. | Laakso, Markku | Lakka, Timo A. | Lind, Lars | Lindgren, Cecilia M. | Martin, Nicholas G. | März, Winfried | McCarthy, Mark I. | McKenzie, Colin A. | Meneton, Pierre | Metspalu, Andres | Moilanen, Leena | Morris, Andrew D. | Munroe, Patricia B. | Njølstad, Inger | Pedersen, Nancy L. | Power, Chris | Pramstaller, Peter P. | Price, Jackie F. | Psaty, Bruce M. | Quertermous, Thomas | Rauramaa, Rainer | Saleheen, Danish | Salomaa, Veikko | Sanghera, Dharambir K. | Saramies, Jouko | Schwarz, Peter E.H. | Sheu, Wayne H-H | Shuldiner, Alan R. | Siegbahn, Agneta | Spector, Tim D. | Stefansson, Kari | Strachan, David P. | Tayo, Bamidele O. | Tremoli, Elena | Tuomilehto, Jaakko | Uusitupa, Matti | van Duijn, Cornelia M. | Vollenweider, Peter | Wallentin, Lars | Wareham, Nicholas J. | Whitfield, John B. | Wolffenbuttel, Bruce H.R. | Altshuler, David | Ordovas, Jose M. | Boerwinkle, Eric | Palmer, Colin N.A. | Thorsteinsdottir, Unnur | Chasman, Daniel I. | Rotter, Jerome I. | Franks, Paul W. | Ripatti, Samuli | Cupples, L. Adrienne | Sandhu, Manjinder S. | Rich, Stephen S. | Boehnke, Michael | Deloukas, Panos | Mohlke, Karen L. | Ingelsson, Erik | Abecasis, Goncalo R. | Daly, Mark J. | Neale, Benjamin M. | Kathiresan, Sekar
Nature genetics  2013;45(11):1345-1352.
Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiologic studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P<5×10−8 for each) to examine the role of triglycerides on risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglycerides, and show that the direction and magnitude of both are factors in determining CAD risk. Second, we consider loci with only a strong magnitude of association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol, a polymorphism's strength of effect on triglycerides is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
doi:10.1038/ng.2795
PMCID: PMC3904346  PMID: 24097064
17.  Identification of seven loci affecting mean telomere length and their association with disease 
Codd, Veryan | Nelson, Christopher P. | Albrecht, Eva | Mangino, Massimo | Deelen, Joris | Buxton, Jessica L. | Jan Hottenga, Jouke | Fischer, Krista | Esko, Tõnu | Surakka, Ida | Broer, Linda | Nyholt, Dale R. | Mateo Leach, Irene | Salo, Perttu | Hägg, Sara | Matthews, Mary K. | Palmen, Jutta | Norata, Giuseppe D. | O’Reilly, Paul F. | Saleheen, Danish | Amin, Najaf | Balmforth, Anthony J. | Beekman, Marian | de Boer, Rudolf A. | Böhringer, Stefan | Braund, Peter S. | Burton, Paul R. | de Craen, Anton J. M. | Denniff, Matthew | Dong, Yanbin | Douroudis, Konstantinos | Dubinina, Elena | Eriksson, Johan G. | Garlaschelli, Katia | Guo, Dehuang | Hartikainen, Anna-Liisa | Henders, Anjali K. | Houwing-Duistermaat, Jeanine J. | Kananen, Laura | Karssen, Lennart C. | Kettunen, Johannes | Klopp, Norman | Lagou, Vasiliki | van Leeuwen, Elisabeth M. | Madden, Pamela A. | Mägi, Reedik | Magnusson, Patrik K.E. | Männistö, Satu | McCarthy, Mark I. | Medland, Sarah E. | Mihailov, Evelin | Montgomery, Grant W. | Oostra, Ben A. | Palotie, Aarno | Peters, Annette | Pollard, Helen | Pouta, Anneli | Prokopenko, Inga | Ripatti, Samuli | Salomaa, Veikko | Suchiman, H. Eka D. | Valdes, Ana M. | Verweij, Niek | Viñuela, Ana | Wang, Xiaoling | Wichmann, H.-Erich | Widen, Elisabeth | Willemsen, Gonneke | Wright, Margaret J. | Xia, Kai | Xiao, Xiangjun | van Veldhuisen, Dirk J. | Catapano, Alberico L. | Tobin, Martin D. | Hall, Alistair S. | Blakemore, Alexandra I.F. | van Gilst, Wiek H. | Zhu, Haidong | Erdmann, Jeanette | Reilly, Muredach P. | Kathiresan, Sekar | Schunkert, Heribert | Talmud, Philippa J. | Pedersen, Nancy L. | Perola, Markus | Ouwehand, Willem | Kaprio, Jaakko | Martin, Nicholas G. | van Duijn, Cornelia M. | Hovatta, Iiris | Gieger, Christian | Metspalu, Andres | Boomsma, Dorret I. | Jarvelin, Marjo-Riitta | Slagboom, P. Eline | Thompson, John R. | Spector, Tim D. | van der Harst, Pim | Samani, Nilesh J.
Nature genetics  2013;45(4):422-427e2.
Inter-individual variation in mean leukocyte telomere length (LTL) is associated with cancer and several age-associated diseases. Here, in a genome-wide meta-analysis of 37,684 individuals with replication of selected variants in a further 10,739 individuals, we identified seven loci, including five novel loci, associated with mean LTL (P<5x10−8). Five of the loci contain genes (TERC, TERT, NAF1, OBFC1, RTEL1) that are known to be involved in telomere biology. Lead SNPs at two loci (TERC and TERT) associate with several cancers and other diseases, including idiopathic pulmonary fibrosis. Moreover, a genetic risk score analysis combining lead variants at all seven loci in 22,233 coronary artery disease cases and 64,762 controls showed an association of the alleles associated with shorter LTL with increased risk of CAD (21% (95% CI: 5–35%) per standard deviation in LTL, p=0.014). Our findings support a causal role of telomere length variation in some age-related diseases.
doi:10.1038/ng.2528
PMCID: PMC4006270  PMID: 23535734
18.  A Central Role for GRB10 in Regulation of Islet Function in Man 
PLoS Genetics  2014;10(4):e1004235.
Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
Author Summary
In this paper, we report the first large genome-wide association study in man for glucose-stimulated insulin secretion (GSIS) indices during an oral glucose tolerance test. We identify seven genetic loci and provide effects on GSIS for all previously reported glycemic traits and obesity genetic loci in a large-scale sample. We observe paradoxical effects of genetic variants in the growth factor receptor-bound protein 10 (GRB10) gene yielding both reduced GSIS and reduced fasting plasma glucose concentrations, specifically showing a parent-of-origin effect of GRB10 on lower fasting plasma glucose and enhanced insulin sensitivity for maternal and elevated glucose and decreased insulin sensitivity for paternal transmissions of the risk allele. We also observe tissue-specific differences in DNA methylation and allelic imbalance in expression of GRB10 in human pancreatic islets. We further disrupt GRB10 by shRNA in human islets, showing reduction of both insulin and glucagon expression and secretion. In conclusion, we provide evidence for complex regulation of GRB10 in human islets. Our data suggest that tissue-specific methylation and imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
doi:10.1371/journal.pgen.1004235
PMCID: PMC3974640  PMID: 24699409
19.  Mutations in HNF1A Result in Marked Alterations of Plasma Glycan Profile 
Diabetes  2013;62(4):1329-1337.
A recent genome-wide association study identified hepatocyte nuclear factor 1-α (HNF1A) as a key regulator of fucosylation. We hypothesized that loss-of-function HNF1A mutations causal for maturity-onset diabetes of the young (MODY) would display altered fucosylation of N-linked glycans on plasma proteins and that glycan biomarkers could improve the efficiency of a diagnosis of HNF1A-MODY. In a pilot comparison of 33 subjects with HNF1A-MODY and 41 subjects with type 2 diabetes, 15 of 29 glycan measurements differed between the two groups. The DG9-glycan index, which is the ratio of fucosylated to nonfucosylated triantennary glycans, provided optimum discrimination in the pilot study and was examined further among additional subjects with HNF1A-MODY (n = 188), glucokinase (GCK)-MODY (n = 118), hepatocyte nuclear factor 4-α (HNF4A)-MODY (n = 40), type 1 diabetes (n = 98), type 2 diabetes (n = 167), and nondiabetic controls (n = 98). The DG9-glycan index was markedly lower in HNF1A-MODY than in controls or other diabetes subtypes, offered good discrimination between HNF1A-MODY and both type 1 and type 2 diabetes (C statistic ≥0.90), and enabled us to detect three previously undetected HNF1A mutations in patients with diabetes. In conclusion, glycan profiles are altered substantially in HNF1A-MODY, and the DG9-glycan index has potential clinical value as a diagnostic biomarker of HNF1A dysfunction.
doi:10.2337/db12-0880
PMCID: PMC3609552  PMID: 23274891
20.  Multiple type 2 diabetes susceptibility genes following genome-wide association scan in UK samples 
Science (New York, N.Y.)  2007;316(5829):1336-1341.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
doi:10.1126/science.1142364
PMCID: PMC3772310  PMID: 17463249
21.  Transcriptome and genome sequencing uncovers functional variation in humans 
Nature  2013;501(7468):506-511.
Summary
Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of mRNA and miRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project – the first uniformly processed RNA-seq data from multiple human populations with high-quality genome sequences. We discovered extremely widespread genetic variation affecting regulation of the majority of genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on cellular mechanisms of regulatory and loss-of-function variation, and allowed us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.
doi:10.1038/nature12531
PMCID: PMC3918453  PMID: 24037378
22.  Genome-Wide Association Study for Type 2 Diabetes in Indians Identifies a New Susceptibility Locus at 2q21 
Diabetes  2013;62(3):977-986.
Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes–associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10−9). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10−12) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D.
doi:10.2337/db12-0406
PMCID: PMC3581193  PMID: 23209189
23.  Insights Into the Molecular Mechanism for Type 2 Diabetes Susceptibility at the KCNQ1 Locus From Temporal Changes in Imprinting Status in Human Islets 
Diabetes  2013;62(3):987-992.
The molecular basis of type 2 diabetes predisposition at most established susceptibility loci remains poorly understood. KCNQ1 maps within the 11p15.5 imprinted domain, a region with an established role in congenital growth phenotypes. Variants intronic to KCNQ1 influence diabetes susceptibility when maternally inherited. By use of quantitative PCR and pyrosequencing of human adult islet and fetal pancreas samples, we investigated the imprinting status of regional transcripts and aimed to determine whether type 2 diabetes risk alleles influence regional DNA methylation and gene expression. The results demonstrate that gene expression patterns differ by developmental stage. CDKN1C showed monoallelic expression in both adult and fetal tissue, whereas PHLDA2, SLC22A18, and SLC22A18AS were biallelically expressed in both tissues. Temporal changes in imprinting were observed for KCNQ1 and KCNQ1OT1, with monoallelic expression in fetal tissues and biallelic expression in adult samples. Genotype at the type 2 diabetes risk variant rs2237895 influenced methylation levels of regulatory sequence in fetal pancreas but without demonstrable effects on gene expression. We demonstrate that CDKN1C, KCNQ1, and KCNQ1OT1 are most likely to mediate diabetes susceptibility at the KCNQ1 locus and identify temporal differences in imprinting status and methylation effects, suggesting that diabetes risk effects may be mediated in early development.
doi:10.2337/db12-0819
PMCID: PMC3581222  PMID: 23139357
24.  Data sharing in large research consortia: experiences and recommendations from ENGAGE 
Data sharing is essential for the conduct of cutting-edge research and is increasingly required by funders concerned with maximising the scientific yield from research data collections. International research consortia are encouraged to share data intra-consortia, inter-consortia and with the wider scientific community. Little is reported regarding the factors that hinder or facilitate data sharing in these different situations. This paper provides results from a survey conducted in the European Network for Genetic and Genomic Epidemiology (ENGAGE) that collected information from its participating institutions about their data-sharing experiences. The questionnaire queried about potential hurdles to data sharing, concerns about data sharing, lessons learned and recommendations for future collaborations. Overall, the survey results reveal that data sharing functioned well in ENGAGE and highlight areas that posed the most frequent hurdles for data sharing. Further challenges arise for international data sharing beyond the consortium. These challenges are described and steps to help address these are outlined.
doi:10.1038/ejhg.2013.131
PMCID: PMC3925260  PMID: 23778872
biobanks; data sharing; consortia; genetic research
25.  BMI-Associated Alleles Do Not Constitute Risk Alleles for Polycystic Ovary Syndrome Independently of BMI: A Case-Control Study 
PLoS ONE  2014;9(1):e87335.
Introduction
Polycystic Ovary Syndrome (PCOS) has a strong genetic background and the majority of patients with PCOS have elevated BMI levels. The aim of this study was to determine to which extent BMI-increasing alleles contribute to risk of PCOS when contemporaneous BMI is taken into consideration.
Methods
Patients with PCOS and controls were recruited from the United Kingdom (563 cases and 791 controls) and The Netherlands (510 cases and 2720 controls). Cases and controls were of similar BMI. SNPs mapping to 12 BMI-associated loci which have been extensively replicated across different ethnicities, i.e., BDNF, FAIM2, ETV5, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18, were studied in association with PCOS within each cohort using the additive genetic model followed by a combined analysis. A genetic allelic count risk score model was used to determine the risk of PCOS for individuals carrying increasing numbers of BMI-increasing alleles.
Results
None of the genetic variants, including FTO and MC4R, was associated with PCOS independently of BMI in the meta-analysis. Moreover, no differences were observed between cases and controls in the number of BMI-risk alleles present and no overall trend across the risk score groups was observed.
Conclusion
In this combined analysis of over 4,000 BMI-matched individuals from the United Kingdom and the Netherlands, we observed no association of BMI risk alleles with PCOS independent of BMI.
doi:10.1371/journal.pone.0087335
PMCID: PMC3909077  PMID: 24498077

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