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1.  Puberty timing associated with diabetes, cardiovascular disease and also diverse health outcomes in men and women: the UK Biobank study 
Scientific Reports  2015;5:11208.
Early puberty timing is associated with higher risks for type 2 diabetes (T2D) and cardiovascular disease in women and therefore represents a potential target for early preventive interventions. We characterised the range of diseases and other adverse health outcomes associated with early or late puberty timing in men and women in the very large UK Biobank study. Recalled puberty timing and past/current diseases were self-reported by questionnaire. We limited analyses to individuals of White ethnicity (250,037 women; 197,714 men) and to disease outcomes with at least 500 cases (~0·2% prevalence) and we applied stringent correction for multiple testing (corrected threshold P < 7.48 × 10–5). In models adjusted for socioeconomic position and adiposity/body composition variables, both in women and men separately, earlier puberty timing was associated with higher risks for angina, hypertension and T2D. Furthermore, compared to the median/average group, earlier or later puberty timing in women or men was associated with higher risks for 48 adverse outcomes, across a range of cancers, cardio-metabolic, gynaecological/obstetric, gastrointestinal, musculoskeletal, and neuro-cognitive categories. Notably, both early and late menarche were associated with higher risks for early natural menopause in women. Puberty timing in both men and women appears to have a profound impact on later health.
doi:10.1038/srep11208
PMCID: PMC4471670  PMID: 26084728
2.  Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study 
PLoS Medicine  2015;12(6):e1001841.
Background
Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR).
Methods and Findings
We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP–AD associations from the International Genomics of Alzheimer’s Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62–0.91]; p = 3.4 × 10−3). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10−8). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51–0.89]; p = 6.5 × 10−3), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses.
Conclusions
Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure—or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications—may reduce AD risk.
Robert A. Scott and colleagues use genetic instruments to identify causal associations between known risk factors and Alzheimer's disease.
Editors' Summary
Background
Worldwide, about 44 million people have dementia, a group of brain degeneration disorders characterized by an irreversible decline in memory, communication, and other “cognitive” functions. Dementia mainly affects older people, and because people are living longer, experts estimate that more than 135 million people will have dementia by 2050. The most common form of dementia, which accounts for 60%–70% of cases, is Alzheimer disease (AD). The earliest sign of AD is often increasing forgetfulness. As the disease progresses, affected individuals gradually lose the ability to look after themselves, they may become anxious or aggressive, and they may have difficulty recognizing friends and relatives. People with late stage disease may lose control of their bladder and of other physical functions. At present, there is no cure for AD, although some of its symptoms can be managed with drugs. Most people with AD are initially cared for at home by relatives and other caregivers, but many affected individuals end their days in a care home or specialist nursing home.
Why Was This Study Done?
Researchers are interested in identifying risk factors for AD, particularly modifiable risk factors, because if such risk factors exist, it might be possible to limit the predicted increase in future AD cases. Epidemiological studies (investigations that examine patterns of disease in populations) have identified several potential risk factors for AD, including hypertension (high blood pressure), obesity, smoking, and dyslipidemia (changes in how the body handles fats). However, epidemiological studies cannot prove that a specific risk factor causes AD. For example, people with hypertension might share another characteristic that causes both hypertension and AD (confounding) or AD might cause hypertension (reverse causation). Information on causality is needed to decide which risk factors to target to help prevent AD. Here, the researchers use “Mendelian randomization” to examine whether differences in several epidemiologically identified risk factors for AD have a causal impact on AD risk. In Mendelian randomization, causal associations are inferred from the effects of genetic variants (which predict levels of modifiable risk factors) on the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if hypertension actually causes AD, genetic variants that affect hypertension should be associated with an altered risk of AD.
What Did the Researchers Do and Find?
The researchers identified causal associations between potentially modifiable risk factors and AD risk by analyzing the occurrence of single nucleotide polymorphisms (SNPs, a type of gene variant) known to predict levels of each risk factor, in genetic data from 17,008 individuals with AD and 37,154 cognitively normal elderly controls collected by the International Genomics of Alzheimer’s Project. They report that genetically predicted higher systolic blood pressure (SBP; the pressure exerted on the inside of large blood vessels when the heart is pumping out blood) was associated with lower AD risk (and with a higher probability of taking antihypertensive medication). Predicted smoking quantity was also associated with lower AD risk, but there was no evidence of causal associations between any of the other risk factors investigated and AD risk.
What Do These Findings Mean?
In contrast to some epidemiological studies, these findings suggest that hypertension is associated with lower AD risk. However, because genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication, it could be that exposure to such drugs, rather than having hypertension, reduces AD risk. Like all Mendelian randomization studies, the reliability of these findings depends on the validity of several assumptions made by the researchers and on the ability of the SNPs used in the analyses to explain variations in exposure to the various risk factors. Moreover, because all the participants in the International Genomics of Alzheimer’s Project are of European ancestry, these findings may not be valid for other ethnic groups. Given that hypertension is a risk factor for cardiovascular disease, the researchers do not advocate raising blood pressure as a measure to prevent AD (neither do they advocate that people smoke more cigarettes to lower AD risk). Rather, given the strong association between higher SBP gene scores and the probability of exposure to antihypertensive treatment, they suggest that the possibility that antihypertensive drugs might reduce AD risk independently of their effects on blood pressure should be investigated as a priority.
Additional Information
This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001841.
The UK National Health Service Choices website provides information (including personal stories) about Alzheimer disease
The UK not-for-profit organization Alzheimer’s Society provides information for patients and carers about dementia, including personal experiences of living with Alzheimer disease
The US not-for-profit organization Alzheimer’s Association also provides information for patients and carers about dementia and personal stories about dementia
Alzheimer’s Disease International is the federation of Alzheimer disease associations around the world; it provides links to individual Alzheimer associations, information about dementia, and links to world Alzheimer reports
MedlinePlus provides links to additional resources about Alzheimer disease (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
A PLOS Medicine Research Article by Proitsi et al. describes a Mendelian randomization study that looked for a causal association between dyslipidemia and Alzheimer disease
doi:10.1371/journal.pmed.1001841
PMCID: PMC4469461  PMID: 26079503
5.  Whole-genome sequence-based analysis of thyroid function 
Nature Communications  2015;6:5681.
Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N=2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF≥1%) associated with TSH and FT4 (N=16,335). For TSH, we identify a novel variant in SYN2 (MAF=23.5%, P=6.15 × 10−9) and a new independent variant in PDE8B (MAF=10.4%, P=5.94 × 10−14). For FT4, we report a low-frequency variant near B4GALT6/SLC25A52 (MAF=3.2%, P=1.27 × 10−9) tagging a rare TTR variant (MAF=0.4%, P=2.14 × 10−11). All common variants explain ≥20% of the variance in TSH and FT4. Analysis of rare variants (MAF<1%) using sequence kernel association testing reveals a novel association with FT4 in NRG1. Our results demonstrate that increased coverage in whole-genome sequence association studies identifies novel variants associated with thyroid function.
Levels of circulating thyrotropin and free thyroxine reflect thyroid function, however, their genetic underpinnings remain poorly understood. Taylor et al. take advantage of whole-genome sequence data from cohorts within the UK10K project to identify novel variants associated with these traits.
doi:10.1038/ncomms6681
PMCID: PMC4366514  PMID: 25743335
6.  Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach 
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
7.  Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes 
Human Molecular Genetics  2014;24(5):1504-1512.
Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10−06 (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10−10. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.
doi:10.1093/hmg/ddu560
PMCID: PMC4321449  PMID: 25378555
8.  A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans 
Nature Communications  2014;5:4871.
The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (−1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10−8)) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (−1.0 s.d. (s.e.=0.173), P-value=7.32 × 10−9). This is consistent with an effect between 0.5 and 1.5 mmol l−1 dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.
Population-based genome sequencing provides an increasingly rich resource for the identification of low-frequency, large effect variants associated with clinically important phenotypes. Timpson et al. use UK10K data to identify a variant of the APOC3 gene strongly associated with plasma triglyceride levels.
doi:10.1038/ncomms5871
PMCID: PMC4167609  PMID: 25225788
9.  Association of Adiposity Genetic Variants With Menarche Timing in 92,105 Women of European Descent 
Fernández-Rhodes, Lindsay | Demerath, Ellen W. | Cousminer, Diana L. | Tao, Ran | Dreyfus, Jill G. | Esko, Tõnu | Smith, Albert V. | Gudnason, Vilmundur | Harris, Tamara B. | Launer, Lenore | McArdle, Patrick F. | Yerges-Armstrong, Laura M. | Elks, Cathy E. | Strachan, David P. | Kutalik, Zoltán | Vollenweider, Peter | Feenstra, Bjarke | Boyd, Heather A. | Metspalu, Andres | Mihailov, Evelin | Broer, Linda | Zillikens, M. Carola | Oostra, Ben | van Duijn, Cornelia M. | Lunetta, Kathryn L. | Perry, John R. B. | Murray, Anna | Koller, Daniel L. | Lai, Dongbing | Corre, Tanguy | Toniolo, Daniela | Albrecht, Eva | Stöckl, Doris | Grallert, Harald | Gieger, Christian | Hayward, Caroline | Polasek, Ozren | Rudan, Igor | Wilson, James F. | He, Chunyan | Kraft, Peter | Hu, Frank B. | Hunter, David J. | Hottenga, Jouke-Jan | Willemsen, Gonneke | Boomsma, Dorret I. | Byrne, Enda M. | Martin, Nicholas G. | Montgomery, Grant W. | Warrington, Nicole M. | Pennell, Craig E. | Stolk, Lisette | Visser, Jenny A. | Hofman, Albert | Uitterlinden, André G. | Rivadeneira, Fernando | Lin, Peng | Fisher, Sherri L. | Bierut, Laura J. | Crisponi, Laura | Porcu, Eleonora | Mangino, Massimo | Zhai, Guangju | Spector, Tim D. | Buring, Julie E. | Rose, Lynda M. | Ridker, Paul M. | Poole, Charles | Hirschhorn, Joel N. | Murabito, Joanne M. | Chasman, Daniel I. | Widen, Elisabeth | North, Kari E. | Ong, Ken K. | Franceschini, Nora
American Journal of Epidemiology  2013;178(3):451-460.
Obesity is of global health concern. There are well-described inverse relationships between female pubertal timing and obesity. Recent genome-wide association studies of age at menarche identified several obesity-related variants. Using data from the ReproGen Consortium, we employed meta-analytical techniques to estimate the associations of 95 a priori and recently identified obesity-related (body mass index (weight (kg)/height (m)2), waist circumference, and waist:hip ratio) single-nucleotide polymorphisms (SNPs) with age at menarche in 92,116 women of European descent from 38 studies (1970–2010), in order to estimate associations between genetic variants associated with central or overall adiposity and pubertal timing in girls. Investigators in each study performed a separate analysis of associations between the selected SNPs and age at menarche (ages 9–17 years) using linear regression models and adjusting for birth year, site (as appropriate), and population stratification. Heterogeneity of effect-measure estimates was investigated using meta-regression. Six novel associations of body mass index loci with age at menarche were identified, and 11 adiposity loci previously reported to be associated with age at menarche were confirmed, but none of the central adiposity variants individually showed significant associations. These findings suggest complex genetic relationships between menarche and overall obesity, and to a lesser extent central obesity, in normal processes of growth and development.
doi:10.1093/aje/kws473
PMCID: PMC3816344  PMID: 23558354
adiposity; body mass index; genetic association studies; menarche; obesity; waist circumference; waist:hip ratio; women's health
10.  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
11.  DNA mismatch repair gene MSH6 implicated in determining age at natural menopause 
Perry, John R.B. | Hsu, Yi-Hsiang | Chasman, Daniel I. | Johnson, Andrew D. | Elks, Cathy | Albrecht, Eva | Andrulis, Irene L. | Beesley, Jonathan | Berenson, Gerald S. | Bergmann, Sven | Bojesen, Stig E. | Bolla, Manjeet K. | Brown, Judith | Buring, Julie E. | Campbell, Harry | Chang-Claude, Jenny | Chenevix-Trench, Georgia | Corre, Tanguy | Couch, Fergus J. | Cox, Angela | Czene, Kamila | D'adamo, Adamo Pio | Davies, Gail | Deary, Ian J. | Dennis, Joe | Easton, Douglas F. | Engelhardt, Ellen G. | Eriksson, Johan G. | Esko, Tõnu | Fasching, Peter A. | Figueroa, Jonine D. | Flyger, Henrik | Fraser, Abigail | Garcia-Closas, Montse | Gasparini, Paolo | Gieger, Christian | Giles, Graham | Guenel, Pascal | Hägg, Sara | Hall, Per | Hayward, Caroline | Hopper, John | Ingelsson, Erik | Kardia, Sharon L.R. | Kasiman, Katherine | Knight, Julia A. | Lahti, Jari | Lawlor, Debbie A. | Magnusson, Patrik K.E. | Margolin, Sara | Marsh, Julie A. | Metspalu, Andres | Olson, Janet E. | Pennell, Craig E. | Polasek, Ozren | Rahman, Iffat | Ridker, Paul M. | Robino, Antonietta | Rudan, Igor | Rudolph, Anja | Salumets, Andres | Schmidt, Marjanka K. | Schoemaker, Minouk J. | Smith, Erin N. | Smith, Jennifer A. | Southey, Melissa | Stöckl, Doris | Swerdlow, Anthony J. | Thompson, Deborah J. | Truong, Therese | Ulivi, Sheila | Waldenberger, Melanie | Wang, Qin | Wild, Sarah | Wilson, James F | Wright, Alan F. | Zgaga, Lina | Ong, Ken K. | Murabito, Joanne M. | Karasik, David | Murray, Anna
Human Molecular Genetics  2013;23(9):2490-2497.
The length of female reproductive lifespan is associated with multiple adverse outcomes, including breast cancer, cardiovascular disease and infertility. The biological processes that govern the timing of the beginning and end of reproductive life are not well understood. Genetic variants are known to contribute to ∼50% of the variation in both age at menarche and menopause, but to date the known genes explain <15% of the genetic component. We have used genome-wide association in a bivariate meta-analysis of both traits to identify genes involved in determining reproductive lifespan. We observed significant genetic correlation between the two traits using genome-wide complex trait analysis. However, we found no robust statistical evidence for individual variants with an effect on both traits. A novel association with age at menopause was detected for a variant rs1800932 in the mismatch repair gene MSH6 (P = 1.9 × 10−9), which was also associated with altered expression levels of MSH6 mRNA in multiple tissues. This study contributes to the growing evidence that DNA repair processes play a key role in ovarian ageing and could be an important therapeutic target for infertility.
doi:10.1093/hmg/ddt620
PMCID: PMC3976329  PMID: 24357391
12.  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
13.  Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant - Phenotype Associations Undetected by HapMap Based Imputation 
PLoS ONE  2013;8(5):e64343.
Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10−8 based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10−11 respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10−8 in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10−12). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations.
doi:10.1371/journal.pone.0064343
PMCID: PMC3655956  PMID: 23696881
14.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228
15.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
16.  Low-Frequency Variants in HMGA1 Are Not Associated With Type 2 Diabetes Risk 
Diabetes  2012;61(2):524-530.
It has recently been suggested that the low-frequency c.136–14_136–13insC variant in high-mobility group A1 (HMGA1) may strongly contribute to insulin resistance and type 2 diabetes risk. In our study, we attempted to confirm that HMGA1 is a novel type 2 diabetes locus in French Caucasians. The gene was sequenced in 368 type 2 diabetic case subjects with a family history of type 2 diabetes and 372 normoglycemic control subjects without a family history of type 2 diabetes. None of the 41 genetic variations identified were associated with type 2 diabetes. The lack of association between the c.136–14_136–13insC variant and type 2 diabetes was confirmed in an independent French group of 4,538 case subjects and 4,015 control subjects and in a large meta-analysis of 16,605 case subjects and 46,179 control subjects. Finally, this variant had no effects on metabolic traits and was not involved in variations of HMGA1 and insulin receptor (INSR) expressions. The c.136–14_136–13insC variant was not associated with type 2 diabetes in individuals of European descent. Our study emphasizes the need to analyze a large number of subjects to reliably assess the association of low-frequency variants with the disease.
doi:10.2337/db11-0728
PMCID: PMC3266400  PMID: 22210315
17.  A genome-wide association study of early menopause and the combined impact of identified variants 
Human Molecular Genetics  2013;22(7):1465-1472.
Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably. Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in 3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered, but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT) were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology which overlaps variation in normal age at menopause and is at least partly explained by the additive effects of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smoking.
doi:10.1093/hmg/dds551
PMCID: PMC3596848  PMID: 23307926
18.  Genome-wide meta-analysis of common variant differences between men and women 
Boraska, Vesna | Jerončić, Ana | Colonna, Vincenza | Southam, Lorraine | Nyholt, Dale R. | William Rayner, Nigel | Perry, John R.B. | Toniolo, Daniela | Albrecht, Eva | Ang, Wei | Bandinelli, Stefania | Barbalic, Maja | Barroso, Inês | Beckmann, Jacques S. | Biffar, Reiner | Boomsma, Dorret | Campbell, Harry | Corre, Tanguy | Erdmann, Jeanette | Esko, Tõnu | Fischer, Krista | Franceschini, Nora | Frayling, Timothy M. | Girotto, Giorgia | Gonzalez, Juan R. | Harris, Tamara B. | Heath, Andrew C. | Heid, Iris M. | Hoffmann, Wolfgang | Hofman, Albert | Horikoshi, Momoko | Hua Zhao, Jing | Jackson, Anne U. | Hottenga, Jouke-Jan | Jula, Antti | Kähönen, Mika | Khaw, Kay-Tee | Kiemeney, Lambertus A. | Klopp, Norman | Kutalik, Zoltán | Lagou, Vasiliki | Launer, Lenore J. | Lehtimäki, Terho | Lemire, Mathieu | Lokki, Marja-Liisa | Loley, Christina | Luan, Jian'an | Mangino, Massimo | Mateo Leach, Irene | Medland, Sarah E. | Mihailov, Evelin | Montgomery, Grant W. | Navis, Gerjan | Newnham, John | Nieminen, Markku S. | Palotie, Aarno | Panoutsopoulou, Kalliope | Peters, Annette | Pirastu, Nicola | Polašek, Ozren | Rehnström, Karola | Ripatti, Samuli | Ritchie, Graham R.S. | Rivadeneira, Fernando | Robino, Antonietta | Samani, Nilesh J. | Shin, So-Youn | Sinisalo, Juha | Smit, Johannes H. | Soranzo, Nicole | Stolk, Lisette | Swinkels, Dorine W. | Tanaka, Toshiko | Teumer, Alexander | Tönjes, Anke | Traglia, Michela | Tuomilehto, Jaakko | Valsesia, Armand | van Gilst, Wiek H. | van Meurs, Joyce B.J. | Smith, Albert Vernon | Viikari, Jorma | Vink, Jacqueline M. | Waeber, Gerard | Warrington, Nicole M. | Widen, Elisabeth | Willemsen, Gonneke | Wright, Alan F. | Zanke, Brent W. | Zgaga, Lina | Boehnke, Michael | d'Adamo, Adamo Pio | de Geus, Eco | Demerath, Ellen W. | den Heijer, Martin | Eriksson, Johan G. | Ferrucci, Luigi | Gieger, Christian | Gudnason, Vilmundur | Hayward, Caroline | Hengstenberg, Christian | Hudson, Thomas J. | Järvelin, Marjo-Riitta | Kogevinas, Manolis | Loos, Ruth J.F. | Martin, Nicholas G. | Metspalu, Andres | Pennell, Craig E. | Penninx, Brenda W. | Perola, Markus | Raitakari, Olli | Salomaa, Veikko | Schreiber, Stefan | Schunkert, Heribert | Spector, Tim D. | Stumvoll, Michael | Uitterlinden, André G. | Ulivi, Sheila | van der Harst, Pim | Vollenweider, Peter | Völzke, Henry | Wareham, Nicholas J. | Wichmann, H.-Erich | Wilson, James F. | Rudan, Igor | Xue, Yali | Zeggini, Eleftheria
Human Molecular Genetics  2012;21(21):4805-4815.
The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 × 10−8) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ∼115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.
doi:10.1093/hmg/dds304
PMCID: PMC3471397  PMID: 22843499
19.  A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation 
Coviello, Andrea D. | Haring, Robin | Wellons, Melissa | Vaidya, Dhananjay | Lehtimäki, Terho | Keildson, Sarah | Lunetta, Kathryn L. | He, Chunyan | Fornage, Myriam | Lagou, Vasiliki | Mangino, Massimo | Onland-Moret, N. Charlotte | Chen, Brian | Eriksson, Joel | Garcia, Melissa | Liu, Yong Mei | Koster, Annemarie | Lohman, Kurt | Lyytikäinen, Leo-Pekka | Petersen, Ann-Kristin | Prescott, Jennifer | Stolk, Lisette | Vandenput, Liesbeth | Wood, Andrew R. | Zhuang, Wei Vivian | Ruokonen, Aimo | Hartikainen, Anna-Liisa | Pouta, Anneli | Bandinelli, Stefania | Biffar, Reiner | Brabant, Georg | Cox, David G. | Chen, Yuhui | Cummings, Steven | Ferrucci, Luigi | Gunter, Marc J. | Hankinson, Susan E. | Martikainen, Hannu | Hofman, Albert | Homuth, Georg | Illig, Thomas | Jansson, John-Olov | Johnson, Andrew D. | Karasik, David | Karlsson, Magnus | Kettunen, Johannes | Kiel, Douglas P. | Kraft, Peter | Liu, Jingmin | Ljunggren, Östen | Lorentzon, Mattias | Maggio, Marcello | Markus, Marcello R. P. | Mellström, Dan | Miljkovic, Iva | Mirel, Daniel | Nelson, Sarah | Morin Papunen, Laure | Peeters, Petra H. M. | Prokopenko, Inga | Raffel, Leslie | Reincke, Martin | Reiner, Alex P. | Rexrode, Kathryn | Rivadeneira, Fernando | Schwartz, Stephen M. | Siscovick, David | Soranzo, Nicole | Stöckl, Doris | Tworoger, Shelley | Uitterlinden, André G. | van Gils, Carla H. | Vasan, Ramachandran S. | Wichmann, H.-Erich | Zhai, Guangju | Bhasin, Shalender | Bidlingmaier, Martin | Chanock, Stephen J. | De Vivo, Immaculata | Harris, Tamara B. | Hunter, David J. | Kähönen, Mika | Liu, Simin | Ouyang, Pamela | Spector, Tim D. | van der Schouw, Yvonne T. | Viikari, Jorma | Wallaschofski, Henri | McCarthy, Mark I. | Frayling, Timothy M. | Murray, Anna | Franks, Steve | Järvelin, Marjo-Riitta | de Jong, Frank H. | Raitakari, Olli | Teumer, Alexander | Ohlsson, Claes | Murabito, Joanne M. | Perry, John R. B.
PLoS Genetics  2012;8(7):e1002805.
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
Author Summary
Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.
doi:10.1371/journal.pgen.1002805
PMCID: PMC3400553  PMID: 22829776
20.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455
21.  Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals 
Dastani, Zari | Hivert, Marie-France | Timpson, Nicholas | Perry, John R. B. | Yuan, Xin | Scott, Robert A. | Henneman, Peter | Heid, Iris M. | Kizer, Jorge R. | Lyytikäinen, Leo-Pekka | Fuchsberger, Christian | Tanaka, Toshiko | Morris, Andrew P. | Small, Kerrin | Isaacs, Aaron | Beekman, Marian | Coassin, Stefan | Lohman, Kurt | Qi, Lu | Kanoni, Stavroula | Pankow, James S. | Uh, Hae-Won | Wu, Ying | Bidulescu, Aurelian | Rasmussen-Torvik, Laura J. | Greenwood, Celia M. T. | Ladouceur, Martin | Grimsby, Jonna | Manning, Alisa K. | Liu, Ching-Ti | Kooner, Jaspal | Mooser, Vincent E. | Vollenweider, Peter | Kapur, Karen A. | Chambers, John | Wareham, Nicholas J. | Langenberg, Claudia | Frants, Rune | Willems-vanDijk, Ko | Oostra, Ben A. | Willems, Sara M. | Lamina, Claudia | Winkler, Thomas W. | Psaty, Bruce M. | Tracy, Russell P. | Brody, Jennifer | Chen, Ida | Viikari, Jorma | Kähönen, Mika | Pramstaller, Peter P. | Evans, David M. | St. Pourcain, Beate | Sattar, Naveed | Wood, Andrew R. | Bandinelli, Stefania | Carlson, Olga D. | Egan, Josephine M. | Böhringer, Stefan | van Heemst, Diana | Kedenko, Lyudmyla | Kristiansson, Kati | Nuotio, Marja-Liisa | Loo, Britt-Marie | Harris, Tamara | Garcia, Melissa | Kanaya, Alka | Haun, Margot | Klopp, Norman | Wichmann, H.-Erich | Deloukas, Panos | Katsareli, Efi | Couper, David J. | Duncan, Bruce B. | Kloppenburg, Margreet | Adair, Linda S. | Borja, Judith B. | Wilson, James G. | Musani, Solomon | Guo, Xiuqing | Johnson, Toby | Semple, Robert | Teslovich, Tanya M. | Allison, Matthew A. | Redline, Susan | Buxbaum, Sarah G. | Mohlke, Karen L. | Meulenbelt, Ingrid | Ballantyne, Christie M. | Dedoussis, George V. | Hu, Frank B. | Liu, Yongmei | Paulweber, Bernhard | Spector, Timothy D. | Slagboom, P. Eline | Ferrucci, Luigi | Jula, Antti | Perola, Markus | Raitakari, Olli | Florez, Jose C. | Salomaa, Veikko | Eriksson, Johan G. | Frayling, Timothy M. | Hicks, Andrew A. | Lehtimäki, Terho | Smith, George Davey | Siscovick, David S. | Kronenberg, Florian | van Duijn, Cornelia | Loos, Ruth J. F. | Waterworth, Dawn M. | Meigs, James B. | Dupuis, Josee | Richards, J. Brent
PLoS Genetics  2012;8(3):e1002607.
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8–1.2×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10−4). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10−3, n = 22,044), increased triglycerides (p = 2.6×10−14, n = 93,440), increased waist-to-hip ratio (p = 1.8×10−5, n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10−3, n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10−13, n = 96,748) and decreased BMI (p = 1.4×10−4, n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
Author Summary
Serum adiponectin levels are highly heritable and are inversely correlated with the risk of type 2 diabetes (T2D), coronary artery disease, stroke, and several metabolic traits. To identify common genetic variants associated with adiponectin levels and risk of T2D and metabolic traits, we conducted a meta-analysis of genome-wide association studies of 45,891 multi-ethnic individuals. In addition to confirming that variants at the ADIPOQ and CDH13 loci influence adiponectin levels, our analyses revealed that 10 new loci also affecting circulating adiponectin levels. We demonstrated that expression levels of several genes in these candidate regions are associated with serum adiponectin levels. Using a powerful novel method to assess the contribution of the identified variants with other traits using summary-level results from large-scale GWAS consortia, we provide evidence that the risk alleles for adiponectin are associated with deleterious changes in T2D risk and metabolic syndrome traits (triglycerides, HDL, post-prandial glucose, insulin, and waist-to-hip ratio), demonstrating that the identified loci, taken together, impact upon metabolic disease.
doi:10.1371/journal.pgen.1002607
PMCID: PMC3315470  PMID: 22479202
22.  Genome-Wide Association Study to Identify Common Variants Associated with Brachial Circumference: A Meta-Analysis of 14 Cohorts 
PLoS ONE  2012;7(3):e31369.
Brachial circumference (BC), also known as upper arm or mid arm circumference, can be used as an indicator of muscle mass and fat tissue, which are distributed differently in men and women. Analysis of anthropometric measures of peripheral fat distribution such as BC could help in understanding the complex pathophysiology behind overweight and obesity. The purpose of this study is to identify genetic variants associated with BC through a large-scale genome-wide association scan (GWAS) meta-analysis. We used fixed-effects meta-analysis to synthesise summary results across 14 GWAS discovery and 4 replication cohorts comprising overall 22,376 individuals (12,031 women and 10,345 men) of European ancestry. Individual analyses were carried out for men, women, and combined across sexes using linear regression and an additive genetic model: adjusted for age and adjusted for age and BMI. We prioritised signals for follow-up in two-stages. We did not detect any signals reaching genome-wide significance. The FTO rs9939609 SNP showed nominal evidence for association (p<0.05) in the age-adjusted strata for men and across both sexes. In this first GWAS meta-analysis for BC to date, we have not identified any genome-wide significant signals and do not observe robust association of previously established obesity loci with BC. Large-scale collaborations will be necessary to achieve higher power to detect loci underlying BC.
doi:10.1371/journal.pone.0031369
PMCID: PMC3315559  PMID: 22479309
23.  Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies 
Elks, Cathy E. | Perry, John R.B. | Sulem, Patrick | Chasman, Daniel I. | Franceschini, Nora | He, Chunyan | Lunetta, Kathryn L. | Visser, Jenny A. | Byrne, Enda M. | Cousminer, Diana L. | Gudbjartsson, Daniel F. | Esko, Tõnu | Feenstra, Bjarke | Hottenga, Jouke-Jan | Koller, Daniel L. | Kutalik, Zoltán | Lin, Peng | Mangino, Massimo | Marongiu, Mara | McArdle, Patrick F. | Smith, Albert V. | Stolk, Lisette | van Wingerden, Sophie W. | Zhao, Jing Hua | Albrecht, Eva | Corre, Tanguy | Ingelsson, Erik | Hayward, Caroline | Magnusson, Patrik K.E. | Smith, Erin N. | Ulivi, Shelia | Warrington, Nicole M. | Zgaga, Lina | Alavere, Helen | Amin, Najaf | Aspelund, Thor | Bandinelli, Stefania | Barroso, Ines | Berenson, Gerald S. | Bergmann, Sven | Blackburn, Hannah | Boerwinkle, Eric | Buring, Julie E. | Busonero, Fabio | Campbell, Harry | Chanock, Stephen J. | Chen, Wei | Cornelis, Marilyn C. | Couper, David | Coviello, Andrea D. | d’Adamo, Pio | de Faire, Ulf | de Geus, Eco J.C. | Deloukas, Panos | Döring, Angela | Smith, George Davey | Easton, Douglas F. | Eiriksdottir, Gudny | Emilsson, Valur | Eriksson, Johan | Ferrucci, Luigi | Folsom, Aaron R. | Foroud, Tatiana | Garcia, Melissa | Gasparini, Paolo | Geller, Frank | Gieger, Christian | Gudnason, Vilmundur | Hall, Per | Hankinson, Susan E. | Ferreli, Liana | Heath, Andrew C. | Hernandez, Dena G. | Hofman, Albert | Hu, Frank B. | Illig, Thomas | Järvelin, Marjo-Riitta | Johnson, Andrew D. | Karasik, David | Khaw, Kay-Tee | Kiel, Douglas P. | Kilpeläinen, Tuomas O. | Kolcic, Ivana | Kraft, Peter | Launer, Lenore J. | Laven, Joop S.E. | Li, Shengxu | Liu, Jianjun | Levy, Daniel | Martin, Nicholas G. | McArdle, Wendy L. | Melbye, Mads | Mooser, Vincent | Murray, Jeffrey C. | Murray, Sarah S. | Nalls, Michael A. | Navarro, Pau | Nelis, Mari | Ness, Andrew R. | Northstone, Kate | Oostra, Ben A. | Peacock, Munro | Palmer, Lyle J. | Palotie, Aarno | Paré, Guillaume | Parker, Alex N. | Pedersen, Nancy L. | Peltonen, Leena | Pennell, Craig E. | Pharoah, Paul | Polasek, Ozren | Plump, Andrew S. | Pouta, Anneli | Porcu, Eleonora | Rafnar, Thorunn | Rice, John P. | Ring, Susan M. | Rivadeneira, Fernando | Rudan, Igor | Sala, Cinzia | Salomaa, Veikko | Sanna, Serena | Schlessinger, David | Schork, Nicholas J. | Scuteri, Angelo | Segrè, Ayellet V. | Shuldiner, Alan R. | Soranzo, Nicole | Sovio, Ulla | Srinivasan, Sathanur R. | Strachan, David P. | Tammesoo, Mar-Liis | Tikkanen, Emmi | Toniolo, Daniela | Tsui, Kim | Tryggvadottir, Laufey | Tyrer, Jonathon | Uda, Manuela | van Dam, Rob M. | van Meurs, Joyve B.J. | Vollenweider, Peter | Waeber, Gerard | Wareham, Nicholas J. | Waterworth, Dawn M. | Weedon, Michael N. | Wichmann, H. Erich | Willemsen, Gonneke | Wilson, James F. | Wright, Alan F. | Young, Lauren | Zhai, Guangju | Zhuang, Wei Vivian | Bierut, Laura J. | Boomsma, Dorret I. | Boyd, Heather A. | Crisponi, Laura | Demerath, Ellen W. | van Duijn, Cornelia M. | Econs, Michael J. | Harris, Tamara B. | Hunter, David J. | Loos, Ruth J.F. | Metspalu, Andres | Montgomery, Grant W. | Ridker, Paul M. | Spector, Tim D. | Streeten, Elizabeth A. | Stefansson, Kari | Thorsteinsdottir, Unnur | Uitterlinden, André G. | Widen, Elisabeth | Murabito, Joanne M. | Ong, Ken K. | Murray, Anna
Nature genetics  2010;42(12):1077-1085.
To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P=5.4×10−60) and 9q31.2 (P=2.2×10−33), we identified 30 novel menarche loci (all P<5×10−8) and found suggestive evidence for a further 10 loci (P<1.9×10−6). New loci included four previously associated with BMI (in/near FTO, SEC16B, TRA2B and TMEM18), three in/near other genes implicated in energy homeostasis (BSX, CRTC1, and MCHR2), and three in/near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and MAGENTA pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.
doi:10.1038/ng.714
PMCID: PMC3140055  PMID: 21102462
24.  Genetic Determinants of Serum Testosterone Concentrations in Men 
PLoS Genetics  2011;7(10):e1002313.
Testosterone concentrations in men are associated with cardiovascular morbidity, osteoporosis, and mortality and are affected by age, smoking, and obesity. Because of serum testosterone's high heritability, we performed a meta-analysis of genome-wide association data in 8,938 men from seven cohorts and followed up the genome-wide significant findings in one in silico (n = 871) and two de novo replication cohorts (n = 4,620) to identify genetic loci significantly associated with serum testosterone concentration in men. All these loci were also associated with low serum testosterone concentration defined as <300 ng/dl. Two single-nucleotide polymorphisms at the sex hormone-binding globulin (SHBG) locus (17p13-p12) were identified as independently associated with serum testosterone concentration (rs12150660, p = 1.2×10−41 and rs6258, p = 2.3×10−22). Subjects with ≥3 risk alleles of these variants had 6.5-fold higher risk of having low serum testosterone than subjects with no risk allele. The rs5934505 polymorphism near FAM9B on the X chromosome was also associated with testosterone concentrations (p = 5.6×10−16). The rs6258 polymorphism in exon 4 of SHBG affected SHBG's affinity for binding testosterone and the measured free testosterone fraction (p<0.01). Genetic variants in the SHBG locus and on the X chromosome are associated with a substantial variation in testosterone concentrations and increased risk of low testosterone. rs6258 is the first reported SHBG polymorphism, which affects testosterone binding to SHBG and the free testosterone fraction and could therefore influence the calculation of free testosterone using law-of-mass-action equation.
Author Summary
Testosterone is the most important testicular androgen in men. Low serum testosterone concentrations are associated with cardiovascular morbidity, metabolic syndrome, type 2 diabetes mellitus, atherosclerosis, osteoporosis, sarcopenia, and increased mortality risk. Thus, there is growing evidence that serum testosterone is a valuable biomarker of men's overall health status. Studies in male twins indicate that there is a strong heritability of serum testosterone. Here we perform a large-scale genome-wide association study to examine the effects of common genetic variants on serum testosterone concentrations. By examining 14,429 men, we show that genetic variants in the sex hormone-binding globulin (SHBG) locus and on the X chromosome are associated with a substantial variation in serum testosterone concentrations and increased risk of low testosterone. The reported associations may now be used in order to better understand the functional background of recently identified disease associations related to low testosterone. Importantly, we identified the first known genetic variant, which affects SHBG's affinity for binding testosterone and the free testosterone fraction and could therefore influence the calculation of free testosterone. This finding suggests that individual-based SHBG-testosterone affinity constants are required depending on the genotype of this single-nucleotide polymorphism.
doi:10.1371/journal.pgen.1002313
PMCID: PMC3188559  PMID: 21998597
25.  A common variant of HMGA2 is associated with adult and childhood height in the general population 
Nature genetics  2007;39(10):1245-1250.
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
doi:10.1038/ng2121
PMCID: PMC3086278  PMID: 17767157

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