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      Low copy number of the salivary amylase gene predisposes to obesity

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          Abstract

          Common multi-allelic copy-number variants (CNVs) appear enriched for phenotypic associations compared to their di-allelic counterparts 1-4 . Here we investigated the influence of gene-dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index and obesity, and replicated this finding in 6,200 subjects. Increased AMY1 copy-number was positively associated with both amylase gene expression (P=2.31×10−14) and serum enzyme levels (P<2.20×10−16), while reduced AMY1 copy-number was associated with increased BMI (per-estimated-copy:β=−0.15[0.02]kg/m2;P=6.93×10−10) and obesity risk (per-estimated-copy:OR=1.19[1.13-1.26]95%CI;P=1.46×10−10). The OR of 1.19 per-copy of AMY1 translates to about an eight-fold difference in risk of obesity between subjects in the top (CN>9) and bottom (CN<4) 10% of the copy-number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies. We designed a gene-centric association study (GCAS) to identify common CNVs overlapping genes and inducing a dosage effect on gene expression, hypothesising that these might be enriched for physiologically-relevant CNVs. To achieve this, we conducted a family-based association analysis of signal intensity data from DNA arrays (log R ratio and B-allele frequency) with transcriptomic data from adipose tissue using famCNV 5 in 149 Swedish families ascertained through siblings discordant for obesity 6 (Table 1;Figure 1;Supplementary Figure 1). A total of 76 probes located within putative CNVs showed a dosage effect on gene expression at 1% FDR (Supplementary Table 1). Of these probes, only cnvi0020639, located within a CNV overlapping the amylase gene cluster (including the AMY1 salivary and the AMY2 pancreatic amylase genes expression probeset 208498_s_at; FDR=6.88×10−3), was also associated with adiposity [both BMI (P=3.86×10−4) and fat mass (P=3.11×10−4)] (Supplementary Figures 2-4). Reduced signal intensity at this probe was associated with increased adiposity levels (Table 2;Figure 2;Supplementary Figures 2-4). This inverse association between copy-number in the amylase region and BMI was first replicated using signal intensity data from DNA arrays in 972 subjects from TwinsUK (Table 1) 7 . The strongest association was observed at cnvi0022844 (P=1.13×10−3;Table 2), which showed significant association with BMI after Bonferroni correction. When multiple probes were considered through principal component analysis, the BMI association signal actually extended over a region between cnvi0022844 and cnvi0016754 (P=1.32×10−3), which overlapped the cnvi0020639 probe associated with adiposity in the Swedish discovery families. These results, although supportive of the association in the amylase region, did not permit us to distinguish which of the salivary or pancreatic amylases was driving the association with adiposity, necessitating use of a non-array-based method of copy-number measurement. Consequently, we estimated copy-number at AMY1 and AMY2 in 481 subjects from the Swedish families (Table 1) using quantitative real-time PCR (qPCR). This approach generates a continuous intensity distribution from which integer copy-numbers can be inferred by comparison to a reference sample of known copy-number (Supplementary Information). Given the many technical challenges inherent in copy-number measurement at multi-allelic loci 2,8-11 , we treated these discretised measurements as relative estimates or surrogates correlated with the true underlying copy-number state, as opposed to absolute copy-number genotypes. Only three estimated copy-number states (one to three) were detected for the pancreatic amylase (AMY2) gene, and these were not associated with either BMI or fat mass (Supplementary Table 2). In contrast, copy-number estimates at AMY1 ranged from two to fourteen, and showed association with both BMI (P=8.08×10−3) and fat mass (P=8.53×10−3) confirming our previous DNA-array based analysis (Supplementary Table 2). We found greater correlation between signal intensity at cnvi0020639 and AMY1 (r=0.73; P<2.20×10−16) than AMY2 copy-number (r=0.35; P=1.25×10−8), suggesting that the GCAS association was mainly capturing copy-number variation at AMY1 as opposed to AMY2, justifying follow-up of the former. Furthermore, we validated accuracy of the AMY1 qPCR assay by using AMY1 copy-number estimates derived using whole-genome shotgun-sequencing data from the 1000 Genomes Project 12 , and observed a correlation of 0.94 (P<2.20×10−16) between AMY1 copy-number estimates derived by qPCR and sequencing (Supplementary Figures 5-8; Supplementary Table 3). To further validate the AMY1 qPCR assay, we also compared the copy-number measured by qPCR in 96 samples from the DESIR cohort 13 with AMY1 copy-number measured by digital PCR in the same samples, obtaining high correlation between the two methods (r=0.95; P<2.20×10−16; Supplementary Figure 9). Analogously, high correlation (r=0.98; P<2.20×10−16; Supplementary Figure 9) was also observed between copy-numbers measured using the qPCR assay used in this study and those obtained using a different qPCR assay on the same 96 DESIR samples. To replicate the observed association in a larger sample, we next estimated AMY1 copy-number by qPCR in an additional sample of 1,479 female subjects from TwinsUK 14 and 2,137 male and female subjects from DESIR 13 (Table 1). The two population samples showed a similar copy-number distribution (Wilcoxon test P>0.05) with estimated median copy-number of six, ranging from one to eighteen (Supplementary Figure 10; Supplementary Tables 4-5). Meta-analysis of AMY1 effects in TwinsUK and DESIR (total n=3,616) showed significant association between reduced AMY1 copy-number and increased BMI (per copy-number β=−0.15[0.02]kg/m2;P=6.93×10−10; Table 2;Figure 3;Supplementary Tables 6-9). Results of associations assessed using both the qPCR intensity signal as a continuous measure, as well as discretised using an unsupervised clustering approach (k-means), were concordant with those generated using integer copy-numbers (Supplementary Information). We then assessed the effect of AMY1 copy-number on obesity susceptibility by selecting obese cases (BMI≥30kg/m2) and normal-weight controls (BMI<25kg/m2) from TwinsUK and DESIR and by measuring AMY1 copy-number by qPCR in an additional 205 severely obese cases and 358 age-matched controls from the AOB 15 study (Table 1;Supplementary Information). In these European samples, subjects with lower estimated AMY1 copy-number showed significantly increased risk of obesity in each of the three samples (per-estimated AMY1 copy-number meta-analysis: OR=1.19[1.13-1.26]95%CI;P=1.46×10−10;Table 2;Figure 3). The AMY1 copy number distribution in our sample ranged from one to eighteen copies, with approximately 10% of subjects carrying fewer than four copies of AMY1, and 10% of subjects with an AMY1 copy number greater than nine (Table 2). Given the multi-allelic nature of the AMY1 CNV, this OR of 1.19 per copy of AMY1 translates to about an eight-fold difference in risk of obesity between subjects in the top (CN>9) and bottom (CN<4) decile of the estimated AMY1 copy-number distribution (OR=7.67[3.92-14.99]95%CI;P=2.52×10−9;Supplementary Table 10). Using a multi-factorial liability threshold model 16 , we estimated the proportion of total variance of obesity explained by estimated AMY1 copy-number to lie between 1.73-7.94%[95%CI] (Supplementary Table 11). Therefore, based on an estimated heritability of 40-70% 17,18 , copy-number variation at AMY1 may account for 2.47-19.86% of the total genetic variation of obesity. Analogously, we estimated that between 0.66% and 4.40% of the proportion of genetic variance of BMI could be explained by inferred AMY1 copy-number in these European samples. As all the samples included in our analyses were of European origin, we reasoned that replication in a sample of different ethnicity and under differing environmental influences on obesity would provide greater support for its physiological role. We therefore selected a Singaporean Chinese case-control sample from SP2 19 . A total of 136 obese and 197 overweight subjects were identified among the 2,431 Chinese subjects included in the SP2 cohort, with 325 matched lean Chinese SP2 normal-weight controls. AMY1 copy-number was measured by qPCR in all 658 subjects. Median copy-number in SP2 normal-weight subjects was 6 (ranging from 2 to 16), similar to our French DESIR and UK TwinsUK populations, and in line with previous observations by Perry et al 20 . Case-control association analysis in the Chinese sample showed reduced AMY1 estimated copy number to be associated with increased risk of obesity (per copy-number OR=1.17[1.05-1.29]95%CI;P=3.73×10−3). Extending the case sample to include the 197 overweight subjects further confirmed the results (per copy-number OR=1.13[1.06-1.21]95%CI;P=3.52×10−4). To validate our AMY1 genomic copy-number data at the protein level, we investigated the effect of copy-number variation at AMY1 and AMY2 on serum amylase enzyme levels, and their relationship with BMI using 468 French morbidly obese subjects from the ABOS study (Table 1;Supplementary Table 12). On average, salivary and pancreatic amylase proportions were approximately equal in serum (52% and 48%, respectively) and their levels showed close positive association with copy-number variation at their respective genes (P<2.20×10−16 and P=1.04×10−11, respectively; Supplementary Figure 11). BMI was inversely associated with serum salivary amylase (β=−0.23[0.04]kg/m2;P=2.26×10−7;Supplementary Figure 12) and to a lesser extent serum pancreatic amylase (β=−0.23[0.06]kg/m2;P=2.29×10−4;Supplementary Figure 12), likely reflecting the physiological correlation between the levels of the two enzymes (r=0.21;P=4.29×10−6). Salivary amylase catalyses hydrolysis of the α-1,4-glycosidic bonds of starch, initiating carbohydrate digestion in the oral cavity. While individual salivary amylase levels vary in response to environmental factors including psychological stress 21 , they are genetically influenced by and directly correlated with the highly variable copy-number at AMY1 20,22 . Increased gene copy-numbers at this locus are believed to have evolved in the human lineage as a consequence of a shift to a starch-rich diet 23 . Human populations traditionally consuming a high proportion of carbohydrates in their diet show higher copy-numbers and salivary amylase activity than those consuming a low-starch diet 20,24 . Both the salivary glands and pancreas contribute similarly to determine overall levels of serum amylase 25 , although enzyme activity is also detectable in other organs, including adipose tissue 26,27 . Indeed amylase was among the 30% most-highly expressed genes in adipose tissue in both our discovery sample and publicly-available data from the general population, thus suggesting that this gene is actively expressed in adipose tissue (Supplementary Information). Whether adipose tissue is functionally involved in the link between AMY1 copy-number and obesity, or whether this link implicates a different tissue in which AMY1 is also actively transcribed warrants further investigation. Decreased blood amylase levels have been observed in both obese humans 28 and rats 29 , and have recently been associated with increased risk of metabolic abnormalities 30,31 and reduced pre-absorptive insulin release 32 . Furthermore, a recent study in mice fed a high fat/high sugar diet suggested association between the amylase locus and weight gain 33 . In these mice, this locus was also shown to be associated with the proportion of Enterobacteriaceae in the gut microbiota 33 , which have been previously correlated with obesity in humans 34 . Rare copy-number variants have recently been implicated in highly-penetrant forms of obesity 35,36 and severe thinness 37 , through a gene dosage effect. Common bi-allelic CNVs have also been associated with BMI 38-41 , however, since most of these are reliably tagged by surrounding SNPs 42 , they share the same properties of small effect sizes and limited predictive value for obesity risk. In contrast, complex multi-allelic CNVs show decreased linkage disequilibrium with surrounding SNPs (Supplementary Table 13) and are consequently less detectable by SNP-based GWAS 43 . Surprisingly, FTO is the most-replicated obesity susceptibility gene identified through GWAS 41 , yet in our analyses estimated AMY1 copy-number appeared to show stronger association with BMI than FTO SNPs (Supplementary Tables 14-15). It is conceivable that high structural variability in the amylase region and subsequent low SNP coverage (Supplementary Figures 13-14) may have hampered previous SNP-based GWAS attempts to detect association between the amylase cluster and adiposity. Indeed, examination of data from the most recent BMI meta-analysis conducted by the GIANT consortium 41 revealed a large gap in SNP coverage across the locus encompassing the salivary amylase gene (Supplementary Figure 14). Present DNA high-throughput methods for CNV assessment, including array-, PCR- and sequencing-based approaches, are all affected by a wide number of variables including DNA source, extraction methods, quality and concentration, as well as experimental factors inducing batch effects 10,11,44 . These factors complicate copy-number measurement at multi-allelic CNVs and hinder pooling of data from multiple centres. The observed association of AMY1 with obesity may rekindle interest in the role of multi-allelic CNVs in common disease, driving development of novel technological approaches for accurate and high-throughput measurement of absolute copy-number at such loci. These technological improvements will enable high-quality association analyses at such loci in larger sample sizes similar to those included in SNP association studies, and are mandatory for disease risk-assessment at the individual level, paving the way towards personalized medicine. Our study provides a first genetic link between carbohydrate metabolism and obesity, with low copy-number at AMY1 resulting in decreased salivary amylase levels and a higher risk of obesity. This finding provides intriguing insight into some of the biological mechanisms underlying obesity, as well as a novel rationale for the investigation of innovative obesity treatments based on manipulation of digestive enzyme levels. ONLINE METHODS Further detailed methods are provided in the Supplementary Information. Associations were assessed using linear mixed effects models, including plate as a random effect and family structure as an additional random effect where appropriate. Age and sex were included as covariates. Discovery The discovery sample included 149 Swedish families (342 subjects) ascertained through an obesity-discordant sib-pair (BMI difference>10kg/m2) 6 . Gene expression for 29,546 transcripts (16,563 Ensembl genes) was measured in subcutaneous adipose tissue using the Affymetrix Human Genome U133 Plus 2.0 microarray. GWAS signal intensity data from Illumina 610K-Quad arrays were available for 348,150 probes lying within each transcript plus 30kb upstream and downstream to encompass the coding regions and their internal and nearby regulatory regions. Quantitative real-time PCR (qPCR) was carried out to infer relative copy-number measurements reflecting the underlying copy-number distribution at AMY1 and AMY2, respectively, using the TaqMan assays Hs07226362_cn and Hs04204136_cn on an Applied Biosystems 7900HT Real-Time PCR System. Association analyses were carried out for 481 subjects with complete data on BMI and dual-energy X-ray absorptiometry (DEXA)-derived fat mass. Replication In-silico replication of the BMI association was conducted using 972 female subjects from the UK adult twin registry (TwinsUK) cohort 14 using intensity signals from Illumina 610K-Quad arrays 7 . Association with BMI and obesity was analysed in two population samples using qPCR estimates of AMY1 copy-number for 1,479 female twins from TwinsUK 14 and 2,137 subjects from the French Data from the Epidemiological Study on the Insulin Resistance syndrome (DESIR) 13 cohort. Obesity association with qPCR data was also assessed in an additional case-control sample of 205 obese cases and 358 age-matched controls from the French Adult Obesity study (AOB) 15 . An additional case-control sample was extracted from the Singapore Prospective Study Program (SP2) cohort, a population-based study including 2,431 adult Chinese Singaporean subjects 19 . Obesity in the Chinese population was defined as BMI≥28kg/m2 and normal-weight as BMI<23kg/m2, based on criteria set by the Working Group on Obesity in China 45 and the WHO expert consultation for Asia 46 . Accordingly, a total of 136 obese and 197 overweight subjects were identified among the 2,431 Chinese subjects of the SP2 cohort, with 325 matched lean SP2 subjects selected as normal-weight controls. In order to avoid any potential population stratification impacting on our association analyses resulting from the known differences in AMY1 copy number distribution between populations traditionally consuming high versus low starch diets 20 , we carried out genotype principal component analysis using genome-wide SNP array data to ensure that samples included in each analysis were of the same ethnicity and genetic background. Furthermore, AMY1 association analyses were conducted separately in each of the study populations and then combined by meta-analysis using METAL 47 rather than pooling. Protein levels ‘Atlas Biologique de l’Obésité Sévère’ (ABOS) is a French cohort comprised of candidates for bariatric surgery. Serum pancreatic and total amylase levels for 468 patients were measured by an enzymatic colorimetric assay with an autoanalyzer (CoBAS Icobas® 8000 modular analyser series; kits AMYL2-03183742122 and AMY-P-20766623322, Hoffman-La Roche Ltd). Serum salivary amylase levels were calculated by subtracting serum pancreatic amylase levels from total serum amylase levels. Supplementary Material 1

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          • Abstract: found
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          Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults.

          For prevention of obesity in Chinese population, it is necessary to define the optimal range of healthy weight and the appropriate cut-off points of BMI and waist circumference for Chinese adults. The Working Group on Obesity in China under the support of International Life Sciences Institute Focal point in China organized a meta-analysis on the relation between BMI, waist circumference and risk factors of related chronic diseases (e.g., high diabetes, diabetes mellitus, and lipoprotein disorders). 13 population studies in all met the criteria for enrollment, with data of 239,972 adults (20-70 year) surveyed in the 1990s. Data on waist circumference was available for 111,411 persons and data on serum lipids and glucose were available for more than 80,000. The study populations located in 21 provinces, municipalities and autonomous regions in mainland China as well as in Taiwan. Each enrolled study provided data according to a common protocol and uniform format. The Center for data management in Department of Epidemiology, Fu Wai Hospital was responsible for statistical analysis. The prevalence of hypertension, diabetes, dyslipidemia and clustering of risk factors all increased with increasing levels of BMI or waist circumference. BMI at 24 with best sensitivity and specificity for identification of the risk factors, was recommended as the cut-off point for overweight, BMI at 28 which may identify the risk factors with specificity around 90% was recommended as the cut-off point for obesity. Waist circumference beyond 85 cm for men and beyond 80 cm for women were recommended as the cut-off points for central obesity. Analysis of population attributable risk percent illustrated that reducing BMI to normal range ( or = 28) with drugs could prevent 15%-17% clustering of risk factors. The waist circumference controlled under 85 cm for men and under 80 cm for women, could prevent 47%-58% clustering of risk factors. According to these, a classification of overweight and obesity for Chinese adults is recommended.
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            • Abstract: found
            • Article: not found

            Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

            Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.
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              Diet and the evolution of human amylase gene copy number variation.

              Starch consumption is a prominent characteristic of agricultural societies and hunter-gatherers in arid environments. In contrast, rainforest and circum-arctic hunter-gatherers and some pastoralists consume much less starch. This behavioral variation raises the possibility that different selective pressures have acted on amylase, the enzyme responsible for starch hydrolysis. We found that copy number of the salivary amylase gene (AMY1) is correlated positively with salivary amylase protein level and that individuals from populations with high-starch diets have, on average, more AMY1 copies than those with traditionally low-starch diets. Comparisons with other loci in a subset of these populations suggest that the extent of AMY1 copy number differentiation is highly unusual. This example of positive selection on a copy number-variable gene is, to our knowledge, one of the first discovered in the human genome. Higher AMY1 copy numbers and protein levels probably improve the digestion of starchy foods and may buffer against the fitness-reducing effects of intestinal disease.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                2 December 2014
                30 March 2014
                May 2014
                26 April 2019
                : 46
                : 5
                : 492-497
                Affiliations
                [1 ]Department of Genomics of Common Disease, Imperial College London, W12 0NN London, United Kingdom
                [2 ]Renal, Dialysis and Transplant Unit. Department of Emergency and Organ Transplantation (D.E.T.O.), University of Bari “Aldo Moro”, Italy
                [3 ]Centre National de la Recherche Scientifique (CNRS)-Unité mixte de recherche (UMR) 8199, Lille Pasteur Institute, 9000 France
                [4 ]Lille 2 University, Lille, 59000 France
                [5 ]Qatar Biomedical Institute (QBRI), Qatar Foundation, Doha, Qatar
                [6 ]European Genomic Institute for Diabetes (EGID) Institute, Lille, France
                [7 ]Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
                [8 ]Center for Cardiovascular and Metabolic Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
                [9 ]Department of Genome Sciences, University of Washington, Seattle, Washington, USA
                [10 ]Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
                [11 ]Research Division, Qatar Foundation (QF), Doha, Qatar
                [12 ]Department of Mathematics, Imperial College London, SW7 2AZ London, United Kingdom
                [13 ]Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
                [14 ]Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Canada
                [15 ]INSERM UMR 859, Lille, 59000 France
                [16 ]CHRU Lille, Lille 59000 France
                [17 ]Bariatric Program, Ottawa Hospital, Ottawa, Canada
                [18 ]Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas’ Hospital campus, Westminster Bridge Road, SE1 7EH London, United Kingdom
                [19 ]Norwich Medical School, University of East Anglia, Norwich, Norfolk NR4 7TJ, United Kingdom
                [20 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
                [21 ]Centre de Recherche en Épidémiologie et Santé des Populations, INSERM U1018, Epidemiology of Diabetes, Obesity, and Chronic Kidney Disease Over the Life Course, Villejuif, France
                [22 ]Université Paris-Sud 11, UMRS 1018, Villejuif, France
                [23 ]Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
                [24 ]Inserm-U695, Paris 7 University, Paris, France
                [25 ]UMR INSERM U1122 “IGE-PCV”, Université de Lorraine, Nancy, France
                [26 ]Paediatric Endocrine Unit, Lille teaching Hospital, France
                [27 ]William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, EC1M 6BQ United Kingdom
                [28 ]Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah 21589, Saudi Arabia
                [29 ]Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada
                [30 ]Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Canada
                [31 ]Saw Swee Hock School of Public Health, National University of Singapore, National University Hospital System, Singapore
                [32 ]Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Hospital System, Singapore
                [33 ]Duke-National University of Singapore Graduate Medical School, Singapore
                [34 ]Department of Human Genetics, Faculty of Medicine, McGill University, H3A 1A4 Montréal, Canada
                [35 ]Department of Medicine, Faculty of Medicine, McGill University, H3A 1A4 Montréal, Canada
                [36 ]McGill University and Génome Québec Innovation Centre, H3A 1A4 Montréal, Canada
                [37 ]Section of Genomic Medicine, National Heart and Lung Institute, Imperial College London, SW3 6LY, London, United Kingdom
                [38 ]Howard Hughes Medical Institute, Seattle, Washington, USA
                Author notes
                []Corresponding authors: Mario Falchi: m.falchi@ 123456imperial.ac.uk and Philippe Froguel: p.froguel@ 123456imperial.ac.uk

                Author Contributions

                MF conceived the study. MF, PF and TDS directed the project. MF, JSEM and PF wrote the paper. AB, TDS, RS, FPa, HS, PHS, LB, FPe, PT, RDo, PCS and EEE edited the paper. JSEM, PT, FPe, JCA, RDo, MNA, EO, AB, AD and MH performed the laboratory experiments. MF, JSEM, JCA, LB, PHS, EEE, PCS and HS performed the statistical analyses. RWD, AP, RDe, MaM, PGH, JS, MP, RC, VR, EV, SF, BB, MiM, SVS, JW, OP, PJ, LS, CJH, PD, RM, JL, EST, LMSC, AW, FPa, TDS and PF provided samples, data and/or reagents. MF and JSEM are joint-first authors of this study. PT, FPe, AB and JCA are joint-second authors. TDS and PF are joint-last authors. All authors commented on and approved the manuscript.

                [39,40,41]

                Authors within each of these three author groups contributed equally to this work

                Article
                EMS57335
                10.1038/ng.2939
                6485469
                24686848
                19d39fdb-4ff5-4b3d-bdee-9dc3523501a9

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