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1.  Effect of Serotonin Transporter 5HTTLPR Polymorphism on Gastrointestinal Intolerance to Metformin: A GoDARTS Study 
Diabetes care  2016;39(11):1896-1901.
Objective
The mechanism causing gastrointestinal intolerance to metformin treatment is unknown. We have previously shown that reduced-function alleles of organic cation transporter 1 (OCT1) are associated with increased intolerance to metformin. Considering recent findings that serotonin transporter (SERT) might also be involved in metformin intestinal absorption, and serotonin role in gastrointestinal physiology, in this study we investigated the association between a common polymorphism in SERT gene and metformin gastrointestinal intolerance.
Research Design and Methods
We explored the effect of composite SERT 5-HTTLPR/rs25531 genotypes, L*L* (LALA), L*S*(LALG, LAS), and S*S* (SS, SLG, LGLG), in 1,356 fully tolerant and 164 extreme metformin-intolerant patients by using logistic regression model, adjusted for age, sex, weight, OCT1 genotype, and concomitant use of medications known to inhibit OCT1 activity.
Results
The number of low-expressing SERT S* alleles increased the odds of metformin intolerance (OR=1.31, 95% CI 1.02-1.67, P=0.031). Moreover, a multiplicative interaction between the OCT1 and SERT genotypes was observed (P=0.003). In the analyses stratified by SERT genotype, the presence of two deficient OCT1 alleles was associated with over a nine-fold higher odds of metformin intolerance in patients carrying L*L* genotype (OR=9.25, 95% CI 3.18-27.0, P<10-4), however, it showed much smaller effect in L*S* carriers, and no effect in S*S* carriers.
Conclusions
Our results indicate that interaction between OCT1 and SERT genes might play an important role in metformin intolerance. Further studies are needed to replicate these findings and to substantiate the hypothesis that metformin gastrointestinal side-effects could be related to the reduced intestinal serotonin uptake.
doi:10.2337/dc16-0706
PMCID: PMC5122449  PMID: 27493135
2.  How can we optimise inhaled beta2 agonist dose as ‘reliever’ medicine for wheezy pre-school children? Study protocol for a randomised controlled trial 
Trials  2016;17:541.
Background
Asthma is a common problem in children and, if inadequately controlled, may seriously diminish their quality of life. Inhaled short-acting beta2 agonists such as salbutamol are usually prescribed as ‘reliever’ medication to help control day-to-day symptoms such as wheeze. As with many medications currently prescribed for younger children (defined as those aged 2 years 6 months to 6 years 11 months), there has been no pre-licensing age-specific pharmacological testing; consequently, the doses currently prescribed (200–1000 μg) may be ineffective or likely to induce unnecessary side effects. We plan to use the interrupter technique to measure airway resistance in this age group, allowing us for the first time to correlate inhaled salbutamol dose with changes in clinical response. We will measure urinary salbutamol levels 30 min after dosing as an estimate of salbutamol doses in the lungs, and also look for genetic polymorphisms linked to poor responses to inhaled salbutamol.
Methods
This is a phase IV, randomised, controlled, observer-blinded, single-centre trial with four parallel groups (based on a sparse sampling approach) and a primary endpoint of the immediate bronchodilator response to salbutamol so that we can determine the most appropriate dose for an individual younger child. Simple randomisation will be used with a 1:1:1:1 allocation.
Discussion
The proposed research will exploit simple, non-invasive and inexpensive tests that can mostly be performed in an outpatient setting in order to help develop the evidence for the correct dose of salbutamol in younger children with recurrent wheeze who have been prescribed salbutamol by their doctor.
Trial registration
EudraCT2014-001978-33, ISRCTN15513131. Registered on 8 April 2015.
doi:10.1186/s13063-016-1437-7
PMCID: PMC5106800  PMID: 27836009
Asthma; Wheeze; Children; Paediatric; Salbutamol; Dose finding
3.  A Genome-Wide Association Study Provides New Evidence That CACNA1C Gene is Associated With Diabetic Cataract 
Purpose
Diabetic cataract is one of the major eye complications of diabetes. It was reported that cataract occurs two to five times more frequently in patients with diabetes compared with those with no diabetes. The purpose of this study was to identify genetic contributors of diabetic cataract based on a genome-wide association approach using a well-defined Scottish diabetic cohort.
Methods
We adapted linked e-health records to define diabetic cataract. A diabetic cataract case in this study was defined as a type 2 diabetic patient who has ever been recorded in the linked e-health records to have cataracts in both eyes or who had previous cataract extraction surgeries in at least one eye. A control in this study was defined as a type 2 diabetic individual who has never been diagnosed as cataract in the linked e-health records and had no history of cataract surgeries. A standard genome-wide association approach was applied.
Results
Overall, we have 2341 diabetic cataract cases and 2878 controls in the genetics of diabetes audit and research in Tayside Scotland (GoDARTS) dataset. We found that the P value of rs2283290 in the CACNA1C gene was 8.81 × 10−10, which has reached genome-wide significance. We also identified that the blood calcium level was statistically different between diabetic cataract cases and controls.
Conclusions
We identified supporting evidence that CACNA1C gene is associated with diabetic cataract. The role of calcium in the cataractogenesis needs to be reevaluated in future studies.
doi:10.1167/iovs.16-19332
PMCID: PMC4855826  PMID: 27124316
genome-wide association study; cataract; diabetes; genetics
4.  The common p.R114W HNF4A mutation causes a distinct clinical subtype of monogenic diabetes 
Diabetes  2016;65(10):3212-3217.
HNF4A mutations cause increased birth weight, transient neonatal hypoglycaemia and maturity onset diabetes of the young (MODY). The most frequently reported HNF4A mutation is p.R114W (previously p.R127W) but functional studies have shown inconsistent results, there is lack of co-segregation in some pedigrees and an unexpectedly high frequency in public variant databases. We confirm that p.R114W is a pathogenic mutation with an odds ratio of 30.4 (95% CI: 9.79 – 125, P=2x10-21) for diabetes in our MODY cohort compared to controls. p.R114W heterozygotes do not have the increased birth weight of patients with other HNF4A mutations (3476g vs. 4147g, P=0.0004) and fewer patients responded to sulfonylurea treatment (48% vs. 73%, P=0.038). p.R114W has reduced penetrance; only 54% of heterozygotes developed diabetes by age 30 compared to 71% for other HNF4A mutations. We re-define p.R114W as a pathogenic mutation causing a distinct clinical subtype of HNF4A MODY with reduced penetrance, reduced sensitivity to sulfonylurea treatment and no effect on birth weight. This has implications for diabetes treatment, management of pregnancy and predictive testing of at-risk relatives. The increasing availability of large-scale sequence data is likely to reveal similar examples of rare, low-penetrance MODY mutations.
doi:10.2337/db16-0628
PMCID: PMC5035684  PMID: 27486234
5.  Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin 
Nature genetics  2016;48(9):1055-1059.
Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6×10−14) greater metformin-induced in haemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 is the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.
doi:10.1038/ng.3632
PMCID: PMC5007158  PMID: 27500523
6.  PPARα Is Required for PPARδ Action in Regulation of Body Weight and Hepatic Steatosis in Mice 
PPAR Research  2015;2015:927057.
Peroxisome proliferator activated receptors alpha (PPARα) and delta (PPARδ) belong to the nuclear receptor superfamily. PPARα is a target of well established lipid-lowering drugs. PPARδ (also known as PPARβ/δ) has been investigated as a promising antidiabetic drug target; however, the evidence in the literature on PPARδ effect on hepatic lipid metabolism is inconsistent. Mice conditionally expressing human PPARδ demonstrated pronounced weight loss and promoted hepatic steatosis when treated with GW501516 (PPARδ-agonist) when compared to wild type mice. This effect was completely absent in mice with either a dominant negative form of PPARδ or deletion of the DNA binding domain of PPARδ. This confirmed the absolute requirement for PPARδ in the physiological actions of GW501516 and confirmed the potential utility against the human form of this receptor. Surprisingly the genetic deletion of PPARα also abrogated the effect of GW501516 in terms of both weight loss and hepatic lipid accumulation. Also the levels of the PPARα endogenous agonist 16:0/18:1-GPC were shown to be modulated by PPARδ in wild type mice. Our results show that both PPARδ and PPARα receptors are essential for GW501516-driven adipose tissue reduction and subsequently hepatic steatosis, with PPARα working downstream of PPARδ.
doi:10.1155/2015/927057
PMCID: PMC4641930  PMID: 26604919
7.  Exome-wide analysis of rare coding variation identifies novel associations with COPD and airflow limitation in MOCS3, IFIT3 and SERPINA12 
Thorax  2016;71(6):501-509.
Background
Several regions of the genome have shown to be associated with COPD in genome-wide association studies of common variants.
Objective
To determine rare and potentially functional single nucleotide polymorphisms (SNPs) associated with the risk of COPD and severity of airflow limitation.
Methods
3226 current or former smokers of European ancestry with lung function measures indicative of Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 COPD or worse were genotyped using an exome array. An analysis of risk of COPD was carried out using ever smoking controls (n=4784). Associations with %predicted FEV1 were tested in cases. We followed-up signals of interest (p<10−5) in independent samples from a subset of the UK Biobank population and also undertook a more powerful discovery study by meta-analysing the exome array data and UK Biobank data for variants represented on both arrays.
Results
Among the associated variants were two in regions previously unreported for COPD; a low frequency non-synonymous SNP in MOCS3 (rs7269297, pdiscovery=3.08×10−6, preplication=0.019) and a rare SNP in IFIT3, which emerged in the meta-analysis (rs140549288, pmeta=8.56×10−6). In the meta-analysis of % predicted FEV1 in cases, the strongest association was shown for a splice variant in a previously unreported region, SERPINA12 (rs140198372, pmeta=5.72×10−6). We also confirmed previously reported associations with COPD risk at MMP12, HHIP, GPR126 and CHRNA5. No associations in novel regions reached a stringent exome-wide significance threshold (p<3.7×10−7).
Conclusions
This study identified several associations with the risk of COPD and severity of airflow limitation, including novel regions MOCS3, IFIT3 and SERPINA12, which warrant further study.
doi:10.1136/thoraxjnl-2015-207876
PMCID: PMC4893124  PMID: 26917578
COPD epidemiology; Tobacco and the lung
8.  ST13 polymorphisms and their effect on exacerbations in steroid-treated asthmatic children and young adults 
Background
The clinical response to inhaled corticosteroids (ICS) is associated with single nucleotide polymorphisms (SNPs) in various genes. This study aimed to relate variations in genes in the steroid pathway and asthma susceptibility genes to exacerbations in children and young adults treated with ICS.
Methods
We performed a meta-analysis of three cohort studies: PACMAN (n=357, age: 4-12 years, the Netherlands), BREATHE (n=820, age: 3-22 years, UK) and PAGES (n=391, age: 2-16 years, UK). Seventeen genes were selected based on a role in the glucocorticoid signaling pathway or a reported association with asthma. Two outcome parameters were used to reflect exacerbations: hospital visits and oral corticosteroid (OCS) use in the previous year. The most significant associations were tested in three independent validation cohorts; the CAMP (clinical trial, n=172, age:5-12 years, USA), GALA II (n=745, age:8-21, USA) and PASS cohorts (n=391, age:5-18, UK) to test the robustness of the findings. Finally, all results were meta-analyzed.
Results
Two SNPs in ST13 (rs138335 and rs138337), but not in the other genes, were associated at a nominal level with an increased risk of exacerbations in asthmatics using ICS in the three cohorts studied. In a meta-analysis of all six studies, ST13 rs138335 remained associated with an increased risk of asthma-related hospital visits and OCS use in the previous year,; OR=1.22 (p=0.013) and OR=1.22 (p=0.0017) respectively.
Conclusion and clinical relevance
A novel susceptibility gene, ST13, coding for a co-chaperone of the glucocorticoid receptor, is associated with exacerbations in asthmatic children and young adults despite their ICS use. Genetic variation in the glucocorticoid signaling pathway may contribute to the interindividual variability in clinical response to ICS treatment in children and young adults.
doi:10.1111/cea.12492
PMCID: PMC4461653  PMID: 25616159
Childhood asthma; corticosteroids; exacerbations; pharmacogenomics; ST13
9.  A Genome-Wide Association Study Identifies Variants in KCNIP4 Associated with ACE Inhibitor Induced Cough 
The pharmacogenomics journal  2015;16(3):231-237.
The most common side effect of angiotensin converting enzyme inhibitor drugs (ACEi) is a cough. We conducted a genome wide association study (GWAS) of ACEi-induced cough among 7,080 subjects of diverse ancestries in the eMERGE network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1,595 cases and 5,485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (MAF=0.33, OR=1.3 [95%CI: 1.2–1.4], p=1.0×10−8). Replication for six SNPs in KCNIP4 was tested in a second eMERGE population (n=926) and in the GoDARTS cohort (n=4,309). Replication was observed at rs7675300 (OR=1.32 [1.01–1.70], p=0.04) in eMERGE and rs16870989 and rs1495509 (OR=1.15 [1.01–1.30], p=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 [1.15–1.32], p=1.9×10−9). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.
doi:10.1038/tpj.2015.51
PMCID: PMC4713364  PMID: 26169577
ACE inhibitor; angiotensin converting enzyme inhibitor; GWAS; KCNIP4; Drug Related Side Effects and Adverse Reactions; pharmacogenetics
10.  Glycemic Exposure and Blood Pressure Influencing Progression and Remission of Diabetic Retinopathy 
Diabetes Care  2013;36(12):3979-3984.
OBJECTIVE
This study sought to investigate the progression and regression of diabetic retinopathy (DR) and the effects of population risk factors on the rates of transition across retinopathy stages.
RESEARCH DESIGN AND METHODS
The study cohort consisted of 44,871 observed DR events between the calendar years 1990 and 2011 for 4,758 diabetic patients who were diagnosed at 35 years of age or older. The first retinal observation was recorded within a year from diagnosis, and the result was recorded as free of retinopathy. A multistate Markov model was applied for analyzing the development of DR and its relation to the patterns of changes in risk factors.
RESULTS
We observed a consistent risk effect of HbA1c on the progression (no retinopathy to mild background DR [BDR] hazard ratio per SD of HbA1c [HR] 1.42 [95% CI 1.32–1.52], mild BDR to observable BDR HR 1.32 [95% CI 1.08–1.60], and observable BDR to severe nonproliferative/proliferative DR HR 2.23 [95% CI 1.16–4.29]). Similarly, systolic blood pressure (SBP) and diastolic blood pressure increased the risk for the transition from the asymptomatic phase to mild BDR (HR 1.20 [95% CI 1.11–1.30]) and the mild BDR to observable BDR (HR 1.87 [95% CI 1.46–2.40]), respectively. Regression from mild BDR to no DR was associated with lower SBP (HR 0.79 [95% CI 0.64–0.97]) and lower HbA1c (HR 0.76 [95% CI 0.64–0.89]).
CONCLUSIONS
Progression and regression of DR were strongly associated with blood pressure and glycemic exposure.
doi:10.2337/dc12-2392
PMCID: PMC3836116  PMID: 24170761
11.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease 
Nikpay, Majid | Goel, Anuj | Won, Hong-Hee | Hall, Leanne M | Willenborg, Christina | Kanoni, Stavroula | Saleheen, Danish | Kyriakou, Theodosios | Nelson, Christopher P | Hopewell, Jemma C | Webb, Thomas R | Zeng, Lingyao | Dehghan, Abbas | Alver, Maris | Armasu, Sebastian M | Auro, Kirsi | Bjonnes, Andrew | Chasman, Daniel I | Chen, Shufeng | Ford, Ian | Franceschini, Nora | Gieger, Christian | Grace, Christopher | Gustafsson, Stefan | Huang, Jie | Hwang, Shih-Jen | Kim, Yun Kyoung | Kleber, Marcus E | Lau, King Wai | Lu, Xiangfeng | Lu, Yingchang | Lyytikäinen, Leo-Pekka | Mihailov, Evelin | Morrison, Alanna C | Pervjakova, Natalia | Qu, Liming | Rose, Lynda M | Salfati, Elias | Saxena, Richa | Scholz, Markus | Smith, Albert V | Tikkanen, Emmi | Uitterlinden, Andre | Yang, Xueli | Zhang, Weihua | Zhao, Wei | de Andrade, Mariza | de Vries, Paul S | van Zuydam, Natalie R | Anand, Sonia S | Bertram, Lars | Beutner, Frank | Dedoussis, George | Frossard, Philippe | Gauguier, Dominique | Goodall, Alison H | Gottesman, Omri | Haber, Marc | Han, Bok-Ghee | Huang, Jianfeng | Jalilzadeh, Shapour | Kessler, Thorsten | König, Inke R | Lannfelt, Lars | Lieb, Wolfgang | Lind, Lars | Lindgren, Cecilia M | Lokki, Marja-Liisa | Magnusson, Patrik K | Mallick, Nadeem H | Mehra, Narinder | Meitinger, Thomas | Memon, Fazal-ur-Rehman | Morris, Andrew P | Nieminen, Markku S | Pedersen, Nancy L | Peters, Annette | Rallidis, Loukianos S | Rasheed, Asif | Samuel, Maria | Shah, Svati H | Sinisalo, Juha | Stirrups, Kathleen E | Trompet, Stella | Wang, Laiyuan | Zaman, Khan S | Ardissino, Diego | Boerwinkle, Eric | Borecki, Ingrid B | Bottinger, Erwin P | Buring, Julie E | Chambers, John C | Collins, Rory | Cupples, L Adrienne | Danesh, John | Demuth, Ilja | Elosua, Roberto | Epstein, Stephen E | Esko, Tõnu | Feitosa, Mary F | Franco, Oscar H | Franzosi, Maria Grazia | Granger, Christopher B | Gu, Dongfeng | Gudnason, Vilmundur | Hall, Alistair S | Hamsten, Anders | Harris, Tamara B | Hazen, Stanley L | Hengstenberg, Christian | Hofman, Albert | Ingelsson, Erik | Iribarren, Carlos | Jukema, J Wouter | Karhunen, Pekka J | Kim, Bong-Jo | Kooner, Jaspal S | Kullo, Iftikhar J | Lehtimäki, Terho | Loos, Ruth J F | Melander, Olle | Metspalu, Andres | März, Winfried | Palmer, Colin N | Perola, Markus | Quertermous, Thomas | Rader, Daniel J | Ridker, Paul M | Ripatti, Samuli | Roberts, Robert | Salomaa, Veikko | Sanghera, Dharambir K | Schwartz, Stephen M | Seedorf, Udo | Stewart, Alexandre F | Stott, David J | Thiery, Joachim | Zalloua, Pierre A | O’Donnell, Christopher J | Reilly, Muredach P | Assimes, Themistocles L | Thompson, John R | Erdmann, Jeanette | Clarke, Robert | Watkins, Hugh | Kathiresan, Sekar | McPherson, Ruth | Deloukas, Panos | Schunkert, Heribert | Samani, Nilesh J | Farrall, Martin
Nature genetics  2015;47(10):1121-1130.
Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005
doi:10.1038/ng.3396
PMCID: PMC4589895  PMID: 26343387
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
Joshi, Peter K. | Esko, Tonu | Mattsson, Hannele | Eklund, Niina | Gandin, Ilaria | Nutile, Teresa | Jackson, Anne U. | Schurmann, Claudia | Smith, Albert V. | Zhang, Weihua | Okada, Yukinori | Stančáková, Alena | Faul, Jessica D. | Zhao, Wei | Bartz, Traci M. | Concas, Maria Pina | Franceschini, Nora | Enroth, Stefan | Vitart, Veronique | Trompet, Stella | Guo, Xiuqing | Chasman, Daniel I. | O’Connel, Jeffery R. | Corre, Tanguy | Nongmaithem, Suraj S. | Chen, Yuning | Mangino, Massimo | Ruggiero, Daniela | Traglia, Michela | Farmaki, Aliki-Eleni | Kacprowski, Tim | Bjonnes, Andrew | van der Spek, Ashley | Wu, Ying | Giri, Anil K. | Yanek, Lisa R. | Wang, Lihua | Hofer, Edith | Rietveld, Cornelius A. | McLeod, Olga | Cornelis, Marilyn C. | Pattaro, Cristian | Verweij, Niek | Baumbach, Clemens | Abdellaoui, Abdel | Warren, Helen R. | Vuckovic, Dragana | Mei, Hao | Bouchard, Claude | Perry, John R.B. | Cappellani, Stefania | Mirza, Saira S. | Benton, Miles C. | Broeckel, Ulrich | Medland, Sarah E. | Lind, Penelope A. | Malerba, Giovanni | Drong, Alexander | Yengo, Loic | Bielak, Lawrence F. | Zhi, Degui | van der Most, Peter J. | Shriner, Daniel | Mägi, Reedik | Hemani, Gibran | Karaderi, Tugce | Wang, Zhaoming | Liu, Tian | Demuth, Ilja | Zhao, Jing Hua | Meng, Weihua | Lataniotis, Lazaros | van der Laan, Sander W. | Bradfield, Jonathan P. | Wood, Andrew R. | Bonnefond, Amelie | Ahluwalia, Tarunveer S. | Hall, Leanne M. | Salvi, Erika | Yazar, Seyhan | Carstensen, Lisbeth | de Haan, Hugoline G. | Abney, Mark | Afzal, Uzma | Allison, Matthew A. | Amin, Najaf | Asselbergs, Folkert W. | Bakker, Stephan J.L. | Barr, R. Graham | Baumeister, Sebastian E. | Benjamin, Daniel J. | Bergmann, Sven | Boerwinkle, Eric | Bottinger, Erwin P. | Campbell, Archie | Chakravarti, Aravinda | Chan, Yingleong | Chanock, Stephen J. | Chen, Constance | Chen, Y.-D. Ida | Collins, Francis S. | Connell, John | Correa, Adolfo | Cupples, L. Adrienne | Smith, George Davey | Davies, Gail | Dörr, Marcus | Ehret, Georg | Ellis, Stephen B. | Feenstra, Bjarke | Feitosa, Mary F. | Ford, Ian | Fox, Caroline S. | Frayling, Timothy M. | Friedrich, Nele | Geller, Frank | Scotland, Generation | Gillham-Nasenya, Irina | Gottesman, Omri | Graff, Misa | Grodstein, Francine | Gu, Charles | Haley, Chris | Hammond, Christopher J. | Harris, Sarah E. | Harris, Tamara B. | Hastie, Nicholas D. | Heard-Costa, Nancy L. | Heikkilä, Kauko | Hocking, Lynne J. | Homuth, Georg | Hottenga, Jouke-Jan | Huang, Jinyan | Huffman, Jennifer E. | Hysi, Pirro G. | Ikram, M. Arfan | Ingelsson, Erik | Joensuu, Anni | Johansson, Åsa | Jousilahti, Pekka | Jukema, J. Wouter | Kähönen, Mika | Kamatani, Yoichiro | Kanoni, Stavroula | Kerr, Shona M. | Khan, Nazir M. | Koellinger, Philipp | Koistinen, Heikki A. | Kooner, Manraj K. | Kubo, Michiaki | Kuusisto, Johanna | Lahti, Jari | Launer, Lenore J. | Lea, Rodney A. | Lehne, Benjamin | Lehtimäki, Terho | Liewald, David C.M. | Lind, Lars | Loh, Marie | Lokki, Marja-Liisa | London, Stephanie J. | Loomis, Stephanie J. | Loukola, Anu | Lu, Yingchang | Lumley, Thomas | Lundqvist, Annamari | Männistö, Satu | Marques-Vidal, Pedro | Masciullo, Corrado | Matchan, Angela | Mathias, Rasika A. | Matsuda, Koichi | Meigs, James B. | Meisinger, Christa | Meitinger, Thomas | Menni, Cristina | Mentch, Frank D. | Mihailov, Evelin | Milani, Lili | Montasser, May E. | Montgomery, Grant W. | Morrison, Alanna | Myers, Richard H. | Nadukuru, Rajiv | Navarro, Pau | Nelis, Mari | Nieminen, Markku S. | Nolte, Ilja M. | O’Connor, George T. | Ogunniyi, Adesola | Padmanabhan, Sandosh | Palmas, Walter R. | Pankow, James S. | Patarcic, Inga | Pavani, Francesca | Peyser, Patricia A. | Pietilainen, Kirsi | Poulter, Neil | Prokopenko, Inga | Ralhan, Sarju | Redmond, Paul | Rich, Stephen S. | Rissanen, Harri | Robino, Antonietta | Rose, Lynda M. | Rose, Richard | Sala, Cinzia | Salako, Babatunde | Salomaa, Veikko | Sarin, Antti-Pekka | Saxena, Richa | Schmidt, Helena | Scott, Laura J. | Scott, William R. | Sennblad, Bengt | Seshadri, Sudha | Sever, Peter | Shrestha, Smeeta | Smith, Blair H. | Smith, Jennifer A. | Soranzo, Nicole | Sotoodehnia, Nona | Southam, Lorraine | Stanton, Alice V. | Stathopoulou, Maria G. | Strauch, Konstantin | Strawbridge, Rona J. | Suderman, Matthew J. | Tandon, Nikhil | Tang, Sian-Tsun | Taylor, Kent D. | Tayo, Bamidele O. | Töglhofer, Anna Maria | Tomaszewski, Maciej | Tšernikova, Natalia | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vaidya, Dhananjay | van Hylckama Vlieg, Astrid | van Setten, Jessica | Vasankari, Tuula | Vedantam, Sailaja | Vlachopoulou, Efthymia | Vozzi, Diego | Vuoksimaa, Eero | Waldenberger, Melanie | Ware, Erin B. | Wentworth-Shields, William | Whitfield, John B. | Wild, Sarah | Willemsen, Gonneke | Yajnik, Chittaranjan S. | Yao, Jie | Zaza, Gianluigi | Zhu, Xiaofeng | Project, The BioBank Japan | Salem, Rany M. | Melbye, Mads | Bisgaard, Hans | Samani, Nilesh J. | Cusi, Daniele | Mackey, David A. | Cooper, Richard S. | Froguel, Philippe | Pasterkamp, Gerard | Grant, Struan F.A. | Hakonarson, Hakon | Ferrucci, Luigi | Scott, Robert A. | Morris, Andrew D. | Palmer, Colin N.A. | Dedoussis, George | Deloukas, Panos | Bertram, Lars | Lindenberger, Ulman | Berndt, Sonja I. | Lindgren, Cecilia M. | Timpson, Nicholas J. | Tönjes, Anke | Munroe, Patricia B. | Sørensen, Thorkild I.A. | Rotimi, Charles N. | Arnett, Donna K. | Oldehinkel, Albertine J. | Kardia, Sharon L.R. | Balkau, Beverley | Gambaro, Giovanni | Morris, Andrew P. | Eriksson, Johan G. | Wright, Margie J. | Martin, Nicholas G. | Hunt, Steven C. | Starr, John M. | Deary, Ian J. | Griffiths, Lyn R. | Tiemeier, Henning | Pirastu, Nicola | Kaprio, Jaakko | Wareham, Nicholas J. | Pérusse, Louis | Wilson, James G. | Girotto, Giorgia | Caulfield, Mark J. | Raitakari, Olli | Boomsma, Dorret I. | Gieger, Christian | van der Harst, Pim | Hicks, Andrew A. | Kraft, Peter | Sinisalo, Juha | Knekt, Paul | Johannesson, Magnus | Magnusson, Patrik K.E. | Hamsten, Anders | Schmidt, Reinhold | Borecki, Ingrid B. | Vartiainen, Erkki | Becker, Diane M. | Bharadwaj, Dwaipayan | Mohlke, Karen L. | Boehnke, Michael | van Duijn, Cornelia M. | Sanghera, Dharambir K. | Teumer, Alexander | Zeggini, Eleftheria | Metspalu, Andres | Gasparini, Paolo | Ulivi, Sheila | Ober, Carole | Toniolo, Daniela | Rudan, Igor | Porteous, David J. | Ciullo, Marina | Spector, Tim D. | Hayward, Caroline | Dupuis, Josée | Loos, Ruth J.F. | Wright, Alan F. | Chandak, Giriraj R. | Vollenweider, Peter | Shuldiner, Alan | Ridker, Paul M. | Rotter, Jerome I. | Sattar, Naveed | Gyllensten, Ulf | North, Kari E. | Pirastu, Mario | Psaty, Bruce M. | Weir, David R. | Laakso, Markku | Gudnason, Vilmundur | Takahashi, Atsushi | Chambers, John C. | Kooner, Jaspal S. | Strachan, David P. | Campbell, Harry | Hirschhorn, Joel N. | Perola, Markus | Polašek, Ozren | Wilson, James F.
Nature  2015;523(7561):459-462.
Homozygosity has long been associated with rare, often devastating, Mendelian disorders1 and Darwin was one of the first to recognise that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity, ROH), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3,4. Here we use ROH to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts and find statistically significant associations between summed runs of homozygosity (SROH) and four complex traits: height, forced expiratory lung volume in 1 second (FEV1), general cognitive ability (g) and educational attainment (nominal p<1 × 10−300, 2.1 × 10−6, 2.5 × 10−10, 1.8 × 10−10). In each case increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing convincing evidence for the first time that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5,6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.
doi:10.1038/nature14618
PMCID: PMC4516141  PMID: 26131930
Circulation. Heart failure  2015;8(2):236-242.
Background
Type 2 diabetes is an independent risk factor for heart failure development, but the relationship between incident heart failure and antecedent glycaemia has not been evaluated.
Methods and Results
The Go-DARTS study holds data for 8683 individuals with type 2 diabetes. Dispensed prescribing, hospital admission data and echocardiography reports were linked to extract incident heart failure cases from December 1998 to August 2011. All available HbA1c measures until heart failure development or end of study were used to model HbA1c time-dependently. Individuals were observed from study enrolment until heart failure development or end of study. Proportional hazard regression calculated heart failure development risk associated with specific HbA1c ranges accounting for comorbidities associated with heart failure, including blood pressure, body mass index and coronary artery disease. Seven hundred and one individuals with type 2 diabetes (8%) developed heart failure during follow up (mean 5.5 years, ±2.8 years). Time-updated analysis with longitudinal HbA1c showed that both HbA1c <6% (HR = 1.60, 95% CI 1.38-1.86 P-value= <0.0001) as well as HbA1c> 10% (HR = 1.80, 95% CI 1.60-2.16 P-value= <0.0001) were independently associated with the risk of HF.
Conclusions
Both high and low HbA1c predicted heart failure development in our cohort, forming a U-shaped relationship.
doi:10.1161/CIRCHEARTFAILURE.113.000920
PMCID: PMC4366571  PMID: 25561089
diabetes mellitus; glucose; echocardiography
PPAR Research  2012;2012:216817.
The nuclear receptor, NR1C2 or peroxisome proliferator-activated receptor (PPAR)-δ, is ubiquitously expressed and important for placental development, fatty acid metabolism, wound healing, inflammation, and tumour development. PPARδ has been hypothesized to function as both a ligand activated transcription factor and a repressor of transcription in the absence of agonist. In this paper, treatment of mice conditionally expressing human PPARδ with GW501516 resulted in a marked loss in body weight that was not evident in nontransgenic animals or animals expressing a dominant negative derivative of PPARδ. Expression of either functional or dominant negative hPPARδ blocked bezafibrate-induced PPARα-dependent hepatomegaly and blocked the effect of bezafibrate on the transcription of PPARα target genes. These data demonstrate, for the first time, that PPARδ could inhibit the activation of PPARα in vivo and provide novel models for the investigation of the role of PPARδ in pathophysiology.
doi:10.1155/2012/216817
PMCID: PMC3324915  PMID: 22550474
EBioMedicine  2015;2(10):1386-1393.
Neuropathic pain is defined as pain arising as a direct consequence of a lesion or a disease affecting the somatosensory system and it affects around 1 in 4 diabetic patients in the UK. The purpose of this genome-wide association study (GWAS) was to identify genetic contributors to this disorder. Cases of neuropathic pain were defined as diabetic patients with a multiple prescription history of at least one of five drugs specifically indicated for the treatment of neuropathic pain. Controls were diabetic individuals who were not prescribed any of these drugs, nor amitriptyline, carbamazepine, or nortriptyline. Overall, 961 diabetic neuropathic pain cases and 3260 diabetic controls in the Genetics of Diabetes Audit and Research Tayside (GoDARTS) cohort were identified. We found a cluster in the Chr1p35.1 (ZSCAN20-TLR12P) with a lowest P value of 2.74 × 10− 7 at rs71647933 in females and a cluster in the Chr8p23.1, next to HMGB1P46 with a lowest P value of 8.02 × 10− 7 at rs6986153 in males. Sex-specific narrow sense heritability was higher in males (30.0%) than in females (14.7%). This GWAS on diabetic neuropathic pain provides evidence for the sex-specific involvement of Chr1p35.1 (ZSCAN20-TLR12P) and Chr8p23.1 (HMGB1P46) with the disorder, indicating the need for further research.
Highlights
•The case definition of diabetic neuropathic pain in this study is matched with those used in epidemiological studies.•We confirmed that diabetic neuropathic pain is a heritable trait.•We provided new genetic evidence of sex-specific involvement of two chromosome loci with diabetic neuropathic pain.
Using a pragmatic case definition, we identified two new genetic areas that may be involved in diabetic neuropathic pain, a common and debilitating complication of diabetes. One of these is more significant in males, the other in females. Furthermore, we calculated that the contribution of genes to developing neuropathic pain in diabetic men is about twice that in diabetic women. These findings help to explain why some people are more vulnerable to this complication, and help to elucidate the biological mechanisms of neuropathic pain. They will inform personalised (gender-specific) approaches to treatment, and the development of new drugs.
doi:10.1016/j.ebiom.2015.08.001
PMCID: PMC4634194  PMID: 26629533
Neuropathic pain; GWAS; Heritability; Sex-specific
Background
Plasma adiponectin levels have previously been inversely associated with carotid intima-media thickness (IMT), a marker of subclinical atherosclerosis. In this study, we used a sex-stratified Mendelian randomization approach to investigate whether adiponectin has a causal protective influence on IMT.
Methods and Results
Baseline plasma adiponectin concentration was tested for association with baseline IMT, IMT progression over 30 months, and occurrence of cardiovascular events within 3 years in 3430 participants (women, n =1777; men, n =1653) with high cardiovascular risk but no prevalent disease. Plasma adiponectin levels were inversely associated with baseline mean bifurcation IMT after adjustment for established risk factors (β =−0.018, P<0.001) in men but not in women (β =−0.006, P =0.185; P for interaction =0.061). Adiponectin levels were inversely associated with progression of mean common carotid IMT in men (β =−0.0022, P =0.047), whereas no association was seen in women (0.0007, P =0.475; P for interaction =0.018). Moreover, we observed that adiponectin levels were inversely associated with coronary events in women (hazard ratio 0.57, 95% CI 0.37 to 0.87) but not in men (hazard ratio 0.82, 95% CI 0.54 to 1.25). A gene score of adiponectin-raising alleles in 6 loci, reported recently in a large multi-ethnic meta-analysis, was inversely associated with baseline mean bifurcation IMT in men (β =−0.0008, P =0.004) but not in women (β =−0.0003, P =0.522; P for interaction =0.007).
Conclusions
This report provides some evidence for adiponectin protecting against atherosclerosis, with effects being confined to men; however, compared with established cardiovascular risk factors, the effect of plasma adiponectin was modest. Further investigation involving mechanistic studies is warranted.
doi:10.1161/JAHA.115.001853
PMCID: PMC4599454  PMID: 26276317
adiponectin; atherosclerosis; carotid intima-media thickness; genetics; Mendelian randomization
Diabetes  2009;58(11):2444-2447.
doi:10.2337/db09-1153
PMCID: PMC2768184  PMID: 19875620
Locke, Adam E. | Kahali, Bratati | Berndt, Sonja I. | Justice, Anne E. | Pers, Tune H. | Day, Felix R. | Powell, Corey | Vedantam, Sailaja | Buchkovich, Martin L. | Yang, Jian | Croteau-Chonka, Damien C. | Esko, Tonu | Fall, Tove | Ferreira, Teresa | Gustafsson, Stefan | Kutalik, Zoltán | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Winkler, Thomas W. | Wood, Andrew R. | Workalemahu, Tsegaselassie | Faul, Jessica D. | Smith, Jennifer A. | Zhao, Jing Hua | Zhao, Wei | Chen, Jin | Fehrmann, Rudolf | Hedman, Åsa K. | Karjalainen, Juha | Schmidt, Ellen M. | Absher, Devin | Amin, Najaf | Anderson, Denise | Beekman, Marian | Bolton, Jennifer L. | Bragg-Gresham, Jennifer L. | Buyske, Steven | Demirkan, Ayse | Deng, Guohong | Ehret, Georg B. | Feenstra, Bjarke | Feitosa, Mary F. | Fischer, Krista | Goel, Anuj | Gong, Jian | Jackson, Anne U. | Kanoni, Stavroula | Kleber, Marcus E. | Kristiansson, Kati | Lim, Unhee | Lotay, Vaneet | Mangino, Massimo | Leach, Irene Mateo | Medina-Gomez, Carolina | Medland, Sarah E. | Nalls, Michael A. | Palmer, Cameron D. | Pasko, Dorota | Pechlivanis, Sonali | Peters, Marjolein J. | Prokopenko, Inga | Shungin, Dmitry | Stančáková, Alena | Strawbridge, Rona J. | Sung, Yun Ju | Tanaka, Toshiko | Teumer, Alexander | Trompet, Stella | van der Laan, Sander W. | van Setten, Jessica | Van Vliet-Ostaptchouk, Jana V. | Wang, Zhaoming | Yengo, Loïc | Zhang, Weihua | Isaacs, Aaron | Albrecht, Eva | Ärnlöv, Johan | Arscott, Gillian M. | Attwood, Antony P. | Bandinelli, Stefania | Barrett, Amy | Bas, Isabelita N. | Bellis, Claire | Bennett, Amanda J. | Berne, Christian | Blagieva, Roza | Blüher, Matthias | Böhringer, Stefan | Bonnycastle, Lori L. | Böttcher, Yvonne | Boyd, Heather A. | Bruinenberg, Marcel | Caspersen, Ida H. | Chen, Yii-Der Ida | Clarke, Robert | Daw, E. 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F. | Eklund, Niina | Estrada, Karol | Eury, Elodie | Folkersen, Lasse | Fraser, Ross M. | Garcia, Melissa E. | Geller, Frank | Giedraitis, Vilmantas | Gigante, Bruna | Go, Alan S. | Golay, Alain | Goodall, Alison H. | Gordon, Scott D. | Gorski, Mathias | Grabe, Hans-Jörgen | Grallert, Harald | Grammer, Tanja B. | Gräßler, Jürgen | Grönberg, Henrik | Groves, Christopher J. | Gusto, Gaëlle | Haessler, Jeffrey | Hall, Per | Haller, Toomas | Hallmans, Goran | Hartman, Catharina A. | Hassinen, Maija | Hayward, Caroline | Heard-Costa, Nancy L. | Helmer, Quinta | Hengstenberg, Christian | Holmen, Oddgeir | Hottenga, Jouke-Jan | James, Alan L. | Jeff, Janina M. | Johansson, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Kinnunen, Leena | Koenig, Wolfgang | Koskenvuo, Markku | Kratzer, Wolfgang | Laitinen, Jaana | Lamina, Claudia | Leander, Karin | Lee, Nanette R. | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lo, Ken Sin | Lobbens, Stéphane | Lorbeer, Roberto | Lu, Yingchang | Mach, François | Magnusson, Patrik K. E. | Mahajan, Anubha | McArdle, Wendy L. | McLachlan, Stela | Menni, Cristina | Merger, Sigrun | Mihailov, Evelin | Milani, Lili | Moayyeri, Alireza | Monda, Keri L. | Morken, Mario A. | Mulas, Antonella | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W. | Nagaraja, Ramaiah | Nöthen, Markus M. | Nolte, Ilja M. | Pilz, Stefan | Rayner, Nigel W. | Renstrom, Frida | Rettig, Rainer | Ried, Janina S. | Ripke, Stephan | Robertson, Neil R. | Rose, Lynda M. | Sanna, Serena | Scharnagl, Hubert | Scholtens, Salome | Schumacher, Fredrick R. | Scott, William R. | Seufferlein, Thomas | Shi, Jianxin | Smith, Albert Vernon | Smolonska, Joanna | Stanton, Alice V. | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stringham, Heather M. | Sundström, Johan | Swertz, Morris A. | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Tayo, Bamidele O. | Thorand, Barbara | Thorleifsson, Gudmar | Tyrer, Jonathan P. | Uh, Hae-Won | Vandenput, Liesbeth | Verhulst, Frank C. | Vermeulen, Sita H. | Verweij, Niek | Vonk, Judith M. | Waite, Lindsay L. | Warren, Helen R. | Waterworth, Dawn | Weedon, Michael N. | Wilkens, Lynne R. | Willenborg, Christina | Wilsgaard, Tom | Wojczynski, Mary K. | Wong, Andrew | Wright, Alan F. | Zhang, Qunyuan | Brennan, Eoin P. | Choi, Murim | Dastani, Zari | Drong, Alexander W. | Eriksson, Per | Franco-Cereceda, Anders | Gådin, Jesper R. | Gharavi, Ali G. | Goddard, Michael E. | Handsaker, Robert E. | Huang, Jinyan | Karpe, Fredrik | Kathiresan, Sekar | Keildson, Sarah | Kiryluk, Krzysztof | Kubo, Michiaki | Lee, Jong-Young | Liang, Liming | Lifton, Richard P. | Ma, Baoshan | McCarroll, Steven A. | McKnight, Amy J. | Min, Josine L. | Moffatt, Miriam F. | Montgomery, Grant W. | Murabito, Joanne M. | Nicholson, George | Nyholt, Dale R. | Okada, Yukinori | Perry, John R. B. | Dorajoo, Rajkumar | Reinmaa, Eva | Salem, Rany M. | Sandholm, Niina | Scott, Robert A. | Stolk, Lisette | Takahashi, Atsushi | Tanaka, Toshihiro | van ’t Hooft, Ferdinand M. | Vinkhuyzen, Anna A. E. | Westra, Harm-Jan | Zheng, Wei | Zondervan, Krina T. | Heath, Andrew C. | Arveiler, Dominique | Bakker, Stephan J. L. | Beilby, John | Bergman, Richard N. | Blangero, John | Bovet, Pascal | Campbell, Harry | Caulfield, Mark J. | Cesana, Giancarlo | Chakravarti, Aravinda | Chasman, Daniel I. | Chines, Peter S. | Collins, Francis S. | Crawford, Dana C. | Cupples, L. Adrienne | Cusi, Daniele | Danesh, John | de Faire, Ulf | den Ruijter, Hester M. | Dominiczak, Anna F. | Erbel, Raimund | Erdmann, Jeanette | Eriksson, Johan G. | Farrall, Martin | Felix, Stephan B. | Ferrannini, Ele | Ferrières, Jean | Ford, Ian | Forouhi, Nita G. | Forrester, Terrence | Franco, Oscar H. | Gansevoort, Ron T. | Gejman, Pablo V. | Gieger, Christian | Gottesman, Omri | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Alistair S. | Harris, Tamara B. | Hattersley, Andrew T. | Hicks, Andrew A. | Hindorff, Lucia A. | Hingorani, Aroon D. | Hofman, Albert | Homuth, Georg | Hovingh, G. Kees | Humphries, Steve E. | Hunt, Steven C. | Hyppönen, Elina | Illig, Thomas | Jacobs, Kevin B. | Jarvelin, Marjo-Riitta | Jöckel, Karl-Heinz | Johansen, Berit | Jousilahti, Pekka | Jukema, J. Wouter | Jula, Antti M. | Kaprio, Jaakko | Kastelein, John J. P. | Keinanen-Kiukaanniemi, Sirkka M. | Kiemeney, Lambertus A. | Knekt, Paul | Kooner, Jaspal S. | Kooperberg, Charles | Kovacs, Peter | Kraja, Aldi T. | Kumari, Meena | Kuusisto, Johanna | Lakka, Timo A. | Langenberg, Claudia | Marchand, Loic Le | Lehtimäki, Terho | Lyssenko, Valeriya | Männistö, Satu | Marette, André | Matise, Tara C. | McKenzie, Colin A. | McKnight, Barbara | Moll, Frans L. | Morris, Andrew D. | Morris, Andrew P. | Murray, Jeffrey C. | Nelis, Mari | Ohlsson, Claes | Oldehinkel, Albertine J. | Ong, Ken K. | Madden, Pamela A. F. | Pasterkamp, Gerard | Peden, John F. | Peters, Annette | Postma, Dirkje S. | Pramstaller, Peter P. | Price, Jackie F. | Qi, Lu | Raitakari, Olli T. | Rankinen, Tuomo | Rao, D. C. | Rice, Treva K. | Ridker, Paul M. | Rioux, John D. | Ritchie, Marylyn D. | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J. | Saramies, Jouko | Sarzynski, Mark A. | Schunkert, Heribert | Schwarz, Peter E. H. | Sever, Peter | Shuldiner, Alan R. | Sinisalo, Juha | Stolk, Ronald P. | Strauch, Konstantin | Tönjes, Anke | Trégouët, David-Alexandre | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Völker, Uwe | Waeber, Gérard | Willemsen, Gonneke | Witteman, Jacqueline C. | Zillikens, M. Carola | Adair, Linda S. | Amouyel, Philippe | Asselbergs, Folkert W. | Assimes, Themistocles L. | Bochud, Murielle | Boehm, Bernhard O. | Boerwinkle, Eric | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C. | Chanock, Stephen J. | Cooper, Richard S. | de Bakker, Paul I. W. | Dedoussis, George | Ferrucci, Luigi | Franks, Paul W. | Froguel, Philippe | Groop, Leif C. | Haiman, Christopher A. | Hamsten, Anders | Hui, Jennie | Hunter, David J. | Hveem, Kristian | Kaplan, Robert C. | Kivimaki, Mika | Kuh, Diana | Laakso, Markku | Liu, Yongmei | Martin, Nicholas G. | März, Winfried | Melbye, Mads | Metspalu, Andres | Moebus, Susanne | Munroe, Patricia B. | Njølstad, Inger | Oostra, Ben A. | Palmer, Colin N. A. | Pedersen, Nancy L. | Perola, Markus | Pérusse, Louis | Peters, Ulrike | Power, Chris | Quertermous, Thomas | Rauramaa, Rainer | Rivadeneira, Fernando | Saaristo, Timo E. | Saleheen, Danish | Sattar, Naveed | Schadt, Eric E. | Schlessinger, David | Slagboom, P. Eline | Snieder, Harold | Spector, Tim D. | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | van der Harst, Pim | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Weir, David R. | Wichmann, H-Erich | Wilson, James F. | Zanen, Pieter | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Heid, Iris M. | O’Connell, Jeffrey R. | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Abecasis, Gonçalo R. | Franke, Lude | Frayling, Timothy M. | McCarthy, Mark I. | Visscher, Peter M. | Scherag, André | Willer, Cristen J. | Boehnke, Michael | Mohlke, Karen L. | Lindgren, Cecilia M. | Beckmann, Jacques S. | Barroso, Inês | North, Kari E. | Ingelsson, Erik | Hirschhorn, Joel N. | Loos, Ruth J. F. | Speliotes, Elizabeth K.
Nature  2015;518(7538):197-206.
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
doi:10.1038/nature14177
PMCID: PMC4382211  PMID: 25673413
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
Diabetes  2013;62(12):4270-4276.
Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.
doi:10.2337/db13-0570
PMCID: PMC3837024  PMID: 23884885
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
Diabetes  2013;62(9):3275-3281.
The incretin hormone glucagon-like peptide 1 (GLP-1) promotes glucose homeostasis and enhances β-cell function. GLP-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors, which inhibit the physiological inactivation of endogenous GLP-1, are used for the treatment of type 2 diabetes. Using the Metabochip, we identified three novel genetic loci with large effects (30–40%) on GLP-1–stimulated insulin secretion during hyperglycemic clamps in nondiabetic Caucasian individuals (TMEM114; CHST3 and CTRB1/2; n = 232; all P ≤ 8.8 × 10−7). rs7202877 near CTRB1/2, a known diabetes risk locus, also associated with an absolute 0.51 ± 0.16% (5.6 ± 1.7 mmol/mol) lower A1C response to DPP-4 inhibitor treatment in G-allele carriers, but there was no effect on GLP-1 RA treatment in type 2 diabetic patients (n = 527). Furthermore, in pancreatic tissue, we show that rs7202877 acts as expression quantitative trait locus for CTRB1 and CTRB2, encoding chymotrypsinogen, and increases fecal chymotrypsin activity in healthy carriers. Chymotrypsin is one of the most abundant digestive enzymes in the gut where it cleaves food proteins into smaller peptide fragments. Our data identify chymotrypsin in the regulation of the incretin pathway, development of diabetes, and response to DPP-4 inhibitor treatment.
doi:10.2337/db13-0227
PMCID: PMC3749354  PMID: 23674605
Nature Communications  2014;5:4204.
Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.
Understanding the genetic basis of cognitive traits could aid the development of numeracy and literacy skills in children. Here the authors show that reading and mathematics have a large overlapping genetic component and suggest that a child's learning environment has a key role in creating differences between them.
doi:10.1038/ncomms5204
PMCID: PMC4102107  PMID: 25003214
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

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