Abstract

Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies, but the generalizability of such findings remains unknown. Here we genotype 1,267 individuals from a Greek population isolate on the Illumina HumanExome Beadchip, in search of functional coding variants associated with lipids traits. We find genome-wide significant evidence for association between R19X, a functional variant in APOC3, with increased high-density lipoprotein and decreased triglycerides levels. Approximately 3.8% of individuals are heterozygous for this cardioprotective variant, which was previously thought to be private to the Amish founder population. R19X is rare (<0.05% frequency) in outbred European populations. The increased frequency of R19X enables discovery of this lipid traits signal at genome-wide significance in a small sample size. This work exemplifies the value of isolated populations in successfully detecting transferable rare variant associations of high medical relevance.

Introduction

The escalating burden of cardiovascular and metabolic disease has growing health economics and societal implications. Blood lipid levels are measurable indices strongly associated with cardiometabolic disease endpoints. Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high clinical importance, such as body mass index, lipid levels and blood pressure. Population isolates have been proposed as useful tools in genetic association studies for complex traits, as they are characterized by reduced phenotypic, environmental and genetic heterogeneity1. Furthermore, isolated populations can empower next generation association studies focusing on rare variation2, as trait-associated alleles may have drifted to higher frequency. However, the generalizability of novel associations identified in population isolates has been the subject of debate. Variants private to the isolate may be highly relevant for that particular population but of limited transferability to other populations. For example, a null APOC3 variant, R19X, found to be associated with high-density lipoprotein (HDL) and triglycerides levels in the Amish was deemed to be population-specific as it was not detected in non-Amish individuals of European descent given available data at the time3.

There is growing empirical evidence that low-frequency and rare variants have an important role in complex human phenotypes. The Illumina HumanExome Beadchip has been developed as a cost-effective tool to study the role of exonic variants and has been successfully applied to the identification of new loci influencing insulin processing and secretion4.

To identify potentially functional coding variants associated with cardiometabolic-related traits, we perform exome chip genotyping of 1,267 individuals from a Greek population isolate stemming from Anogia and the surrounding Mylopotamos villages on Crete (HELIC (Hellenic Isolated Cohorts) MANOLIS (Minoan Isolates) study, http://www.helic.org). The Mylopotamos villages’ residents anecdotally demonstrate high rates of longevity. Although Cretans have low mortality rates from cardiovascular disease5, their serum lipids are comparable to Northern Europeans6,7.

We find genome-wide significant evidence for association between R19X, a functional variant in APOC3, and increased HDL and decreased triglycerides levels. R19X is rare (<0.05% frequency) in outbred European populations, whereas its frequency has increased to 1.9% in MANOLIS, which enables discovery of this lipid traits signal at genome-wide significance in a small sample size. Our findings additionally address the generalizability of associations discovered in population isolates, as this cardioprotective variant was previously thought to be private to the Amish founder population.

Results

Exome chip-wide association analysis

In an analysis of HDL levels using exome chip data in 1,256 individuals from the MANOLIS study (Supplementary Table S1), we find genome-wide significant evidence for association with common-frequency positive control variants in CETP (for example, rs1532624, P=1.1 × 10−11, likelihood ratio test), the well-established HDL-associated locus which has accrued robust evidence for association across multiple populations (Fig. 1; Supplementary Fig. S1; Table 1)8,9,10,11,12. Despite the small sample size, we also find genome-wide significant evidence for association with a functional variant in APOC3 (rs76353203, R19X, P=4.6 × 10−9, likelihood ratio test), which explains 2.9% of the trait variance (Table 2; Fig. 2). The strongest R19X variant association is with decreased triglyceride levels (P=1.1 × 10−11, likelihood ratio test) and explains 3.9% of trait variance (Table 2; Fig. 2). This clear cardioprotective effect is recapitulated in the strong association with high HDL as a dichotomized trait defined as >60 mg dl−1 (ref. 13) (P=4.3 × 10−10; Table 2, likelihood ratio test). We observe a trend for the minor allele frequency (MAF) to increase with age (Supplementary Fig. S2). There was no evidence for the association of R19X with C-reactive protein (CRP) levels, anthropometric, glycaemic and blood pressure traits and no attenuation of the signal was observed when conditioning on smoking status (Table 2; Supplementary Table S2). The single nucleotide protein (SNP) that led to the original R19X discovery in the Amish3 is located in the DSCAML1 gene (rs10892151) and was highly correlated to R19X in that founder population (D′=1, r2=0.85). In the MANOLIS cohort, we find that the two variants are poorly correlated (r2=0.001), whereas only three of the possible four haplotypes are observed (D′=1, due to low allele frequency).

Figure 1: Manhattan plot for HDL in MANOLIS.
Figure 1

Genome-wide statistical association evidence for HDL in MANOLIS. P-values are generated from the likelihood ratio test, as calculated by GEMMA software.

Table 1: CETP variants associated with HDL levels in MANOLIS.
Table 2: Distribution of lipid traits by R19X genotype.
Figure 2: Bean plot of HDL and triglycerides by APOC3 R19X genotype.
Figure 2

The y-axis shows the untransformed HDL (a) levels (mmol l−1) and the natural logarithm-transformed triglycerides (b) levels (mmol l−1), respectively. HDL P=4.65 × 10−9 and triglycerides P=1.10 × 10−11. P-values are generated from the likelihood ratio test, as calculated by GEMMA software.

The T allele, which is the derived allele, changes an arginine residue into a stop codon in exon 2 of all three coding transcripts of the APOC3 gene. The introduction of a premature stop codon at this position means that the mRNA transcripts are likely to be subject to non-sense-mediated decay and will not be translated.

R19X frequency and haplotype analysis

R19X has a frequency of 1.9% in the MANOLIS cohort, but is very rare (0.035% frequency; T:3, C:8585) in 4,924 European-descent exomes from the NHLBI data (Exome Variant Server, NHLBI, http://evs.gs.washington.edu/EVS/ (accessed February 2013)) and is not present in the 1,000 Genome Project data14. The R19X minor allele is carried by 4 out of 3,621 whole genome sequenced individuals in the UK10K study ( http://www.uk10k.org) (MAF=0.05%). Individuals with the R19X variant do not tend to be closely related in MANOLIS, with 97.3% of carrier pairs having ≤10% of alleles identical by descent exome array-wide (Supplementary Table S3).

The age of the R19X variant in the general European population is estimated at 3.0KYA (95% CI 0–9.0KYA)15, which supports the idea that this variant was present at a low frequency in Europe before this Cretan isolate was established. To test this, we examined local haplotypes from UK10K and find that the four chromosomes with the T (stop) alleles share the same ~700 kb haplotype (~200 kb-stop-~500 kb) as we might expect given the frequency of the allele and its age estimated from this frequency. The MANOLIS carriers also all share the same ~400 kb downstream haplotype, which matches ~400 kb of the ~500 kb UK10K haplotype, and some share the entire region. We also examined the Amish T carrier haplotype data and found that the Amish also share almost the same haplotype (181 kb-stop-390 kb) (Supplementary Fig. S3). These findings suggest that R19X has a single recent origin in Europe that predates the establishment of the MANOLIS and Amish isolates, and that it has risen to a relatively high frequency in the two isolates due to genetic drift.

To examine patterns of haplotype sharing around R19X more broadly in the MANOLIS cohort, we identified stretches of maximal haplotype identity along chromosome 11 between pairs of samples using an adaptation of the long-range phasing algorithm of Kong et al.16 As expected from an isolated population, haplotype sharing is extensive, such that the median size of maximal haplotype identity around a randomly sampled chromosomal location is 14.8 Mb (15.3 cM). However, around R19X, haplotype sharing is no more extensive than elsewhere on the chromosome, nor is it greater among carriers than non-carriers (Kolmogorov–Smirnov test P=0.8; Supplementary Fig. S4). Moreover, there is no evidence for cryptic relatedness, substructure or for any extended correlation between genetic background and carrier status (Supplementary Fig. S5), as excess relatedness decays exponentially away from the risk locus as expected. Around R19X, the profile of maximal haplotype identity is well-approximated by a model in which the common ancestor to the genealogical nearest neighbour occurred an average of 4.1 generations ago. This provides a lower bound for the age of the mutation at c. 100 years ago assuming a generation time of 25 years. Interestingly, around other variants of the same frequency, we find greater levels of cryptic relatedness (Supplementary Fig. S6), suggesting that these are typically more recent.

APOC3 variant associations with lipid traits

Functional coding mutations in the APOC3 locus have previously been implicated in several traits related to triglycerides and HDL cholesterol levels17,18,19,20,21. Variants in the promoter of this gene have also been associated with hypertriglyceridemia22,23, changes in response to insulin regulation24,25, and in the case of rs2542052, with low serum APOC3 levels and longevity26. We find no evidence for association between rs2542052 and HDL level in the MANOLIS cohort (P=0.76, likelihood ratio test).

A large-scale meta-analysis of genome-wide association studies across ~100,000 individuals has also identified robust associations between a common-frequency variant (rs964184) 51.5 kb upstream of APOC3 and HDL and triglycerides levels12. We find little evidence for linkage disequilibrium between rs964184 and R19X (r2=0.01, D′=0.34) in the MANOLIS cohort (Supplementary Table S4), indicating that these are two distinct signals within the same locus. This is further supported by conditional analysis (Supplementary Table S5).

Discussion

In this study, we examine a newly characterized population isolate from Crete in Greece. Our findings in the Greek isolate demonstrate the utility of the exome array in the discovery of low-frequency variant associations in traits of medical relevance. We find R19X to be associated with increased HDL and reduced triglycerides levels at genome-wide significance. This work provides insights into the allelic architecture of the APOC3 locus, in which common variants have also been found to be associated with lipid traits by genome-wide association study (for example, Teslovich et al.12) and highlights the dramatic gains in power that can be afforded by studying population isolates. The R19X functional rare variant has risen in frequency to ~2% in this population, enabling discovery of this lipid traits signal at genome-wide significance in a small sample size. The equivalent sample size needed to achieve 80% power to detect the observed effect size in an outbred European population would be 67,000. Our findings additionally address the generalizability of associations discovered in population isolates both at the gene and at the variant level. The R19X variant has also risen in frequency and shows association with HDL in the Amish founder population3. A summary statistic-based meta-analysis across the MANOLIS and Amish3 population isolates for R19X leads to a combined P-value of 8.1 × 10−32, achieved with a total sample size of ~2,700. APOC3 R19X constitutes to our knowledge the first known example of a clinically important variant that was previously thought to be private to a population but which has in fact drifted in frequency in two independent population isolates and is strongly associated with traits of high clinical relevance. Next generation association studies in search of low frequency and rare variants associated with complex disease can be greatly assisted by the study of population isolates.

Methods

Anogia population history

Anogia is a geographically isolated mountainous village on the island of Crete, Greece (population size 4,000). It is located on mount Idi and is part of the municipality of Mylopotamos, which includes further isolated villages. The name Anogia means ‘Upper Earth’ and refers to the altitude at which the village is located (740 m). The residents of Anogia demonstrate characteristic Cretan resolve, great pride in their land and uphold ancient traditions. In 1593, the settlement numbered 911 citizens. Anogia is historically renowned for its residents’ resistance to conquerors. The village has been built three times, as it was burnt down twice by the Turks (in 1822 and 1866) and once by the Germans (in 1944). The local dialect is still strongly influenced by the ancient Dorian language.

HELIC cohort collection

The HELIC-MANOLIS collection focuses on Anogia and surrounding Mylopotamos villages. Recruitment of this population-based sample was primarily carried out at the village medical centres. All individuals were older than 17 years and had to have at least one parent from the Mylopotamos area. The study includes biological sample collection for DNA extraction and lab-based blood measurements, and interview-based questionnaire filling. The phenotypes collected include anthropometric and biometric measurements, clinical evaluation data, biochemical and haematological profiles, self-reported medical history, demographic, socioeconomic and lifestyle information. Biochemical measurements were obtained using enzymatic colorimetric assays and included glucose (hexokinase method), total cholesterol (cholesterol oxidase – phenol aminophenazone method), HDL-cholesterol, triglycerides (glycerol-3-phosphate oxidase-phenol aminophenazone) and iron. Insulin and ferritin were measured via chemiluminescence and CRP through an immunoturbidimetric method. Low-density lipoprotein (LDL)-cholesterol levels were calculated according to Friedewald equation27. The study was approved by the Harokopio University Bioethics Committee, and informed consent was obtained from human subjects.

Genotyping and QC

Samples were genotyped using the Infinium HumanExome and genotypes were called using Illumina Genome Studio Gencall followed by zCall28. We undertook a staged quality control (QC) approach; briefly, we performed an initial prefilter on the GenCalled data excluding samples and variants that had call rate <90%. We carried out a pre-zCall sample QC, excluding samples that had <98% call rate or that were heterozygosity outliers based on the distribution at two different MAF thresholds (MAF <1% and MAF ≥1%). We also excluded samples that were gender discordant and samples that had genotype discordances when we compared these exome chip genotypes to genome-wide genotypes that were established previously using the Illumina OmniExpress platform. No variant exclusions were made at this stage because zCall performs variant QC (which includes call rate >99%, MAF >5%, Hardy Weinberg Equilibrium (HWE) <0.00001). Following zCall, we performed variant QC using the GenCalled genotypes (call rate <95%, HWE P<0.0001, cluster separation score <0.4). Sample QC was then carried out on the zCall data excluding samples that were ethnicity outliers or duplicates, that had a call rate <99%, or that were heterozygosity outliers, performed at two different MAF thresholds (MAF <1% and MAF ≥1%). We excluded variants with a zCall call rate <99%. We applied a final sample QC step examining again the multidimensional scaling results, MAF <1% heterozygosity distribution in combination with the number of singletons per sample, and excluded combined visual outliers and any samples that had a minor allele count of 1 for >100 variants. We also applied a final HWE step excluding variants with HWE P<0.0001. A total of 1,267 samples and 61,638 variants that were not monomorphic passed QC (Supplementary Table S1).

Trait transformations and analysis

Trait values were considered to be outliers if they were five standard deviations away from the average value, and were removed from the analysis. Also, LDL values were excluded for individuals with triglycerides level of >400 mg dl−1. All phenotypes were analysed directly, apart from triglycerides, insulin, CRP and ferritin, which were normalized using the natural logarithm transformation. Age was inverse normalized. An individual was considered to have high HDL if HDL was over or equal to 60 mg dl−1, whereas low HDL was defined as HDL <40 mg dl−1. We performed trait association analysis using GEMMA29 which incorporates a kinship matrix to account for the relatedness between individuals. P-values were produced using the likelihood ratio test. The kinship matrix was generated using exome array genotype data. For HDL and triglycerides, we performed a three-way meta-analysis using the Fisher’s method across the MANOLIS data and the results reported by Pollin et al.3 (809 Old Order Amish individuals from the Heredity and Phenotype Intervention Heart Study, and 698 Amish individuals from the Amish Family Calcification Study).

Haplotype analysis to estimate the variant age

Four individuals in the UK10K data set each have one stop allele at R19X. We extracted 1 Mb haplotypes each side of the variant from these individuals as well as from 20 other randomly chosen individuals from the UK data set using only the sites which overlapped with the MANOLIS data set. They shared the same ~700 kb haplotype (~200 kb-stop-~500 kb) as we might expect given the frequency of the allele and its age estimated from this frequency (3.0 KYA, 95% CI 0–9.0 KYA). We then constructed haplotypes for the 48 chromosomes with the stop allele in MANOLIS in the same way. One major inferred ancestral haplotype (accounting for 99/3,740 haplotypes (2.6%) in 99/1,870 individuals (5.3%) of the Amish cohort) with 53 SNPs from Illumina HumanExome Beadchip data was compared with the shared haplotype between UK10K and MANOLIS. Forty-five SNPs were shared among the three data sets. The Amish haplotype shares at least 571 kb (181 kb-stop-390 kb, 116,520,527–117,091,609) with the HELIC and UK10K haplotypes. There are no SNPs in the Amish data between 116,973,929 and 117,100,594 where the HELIC-UK10K shared haplotype ends, and the next SNP available in the Amish data shows that the haplotype is different from the UK10K haplotype. The shared haplotype between the Amish and HELIC-UK10K may extend some way beyond 117,091,609.

Identification of maximal haplotype sharing

To identify stretches of extended haplotype sharing between individuals, we used a modification of the long-range phasing method of Kong et al.16 Briefly, for each target individual, we identify a pair of other individuals from the sample as surrogate parents, such that the target individual’s genotype is compatible with Mendelian transmission from the pseudo-parents. We allow for a low level of genotyping error (here 1%) and recombination (here proportional to the fine-scale HapMap genetic map30, such that between any pair of SNPs, one of the pseudo-parents can change). We find the most likely parent-pair path using the Viterbi algorithm and reconstruct shared haplotype lengths. Informally, the algorithm can be thought of as attempting to find, for each of an individual’s two chromosomes, those other samples that contain genealogical nearest neighbours. In isolated populations, the method can be used to detect cryptic relatedness and stratification within a sample. The analysis was restricted to those with low levels of relatedness (<0.2; 754 individuals of whom 32 are carriers for the risk variant; results are similar with <0.1).

Additional information

How to cite this article: Tachmazidou, I. et al. A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat. Commun. 4:2872 doi: 10.1038/ncomms3872 (2013).

Accession codes: The HELIC-MANOLIS study genotype data have been deposited to the European Genome-phenome Archive (EGA) under the accession code EGAS00001000630. UK10K sequence data have been deposited to the EGA under the accession codes EGAS00001000108 and EGAS00001000090.

Accessions

References

  1. 1.

    , & Use of population isolates for mapping complex traits. Nat. Rev. Genet. 1, 182–190 (2000).

  2. 2.

    Next-generation association studies for complex traits. Nat. Genet. 43, 287–288 (2011).

  3. 3.

    et al. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322, 1702–1705 (2008).

  4. 4.

    et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat. Genet. 45, 197–201 (2013).

  5. 5.

    Coronary Heart Disease in seven countries. Circulation 41, 1–211 (1970).

  6. 6.

    et al. Food intake patterns and 25-year mortality from coronary heart disease: cross-cultural correlations in the Seven Countries Study. The Seven Countries Study Research Group. Eur. J. Epidemiol. 15, 507–515 (1999).

  7. 7.

    et al. Cretan Mediterranean diet for prevention of coronary heart disease. Am. J. Clin. Nutr. 61, 1360S–1367S (1995).

  8. 8.

    et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 41, 47–55 (2009).

  9. 9.

    et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

  10. 10.

    et al. Polymorphism in the CETP gene region, HDL cholesterol, and risk of future myocardial infarction: genomewide analysis among 18 245 initially healthy women from the Women’s Genome Health Study. Circ. Cardiovasc. Genet. 2, 26–33 (2009).

  11. 11.

    et al. Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study. PLoS Genet. 6, (2010).

  12. 12.

    et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

  13. 13.

    Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285, 2486–2497 (2001).

  14. 14.

    et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  15. 15.

    et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

  16. 16.

    et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40, 1068–1075 (2008).

  17. 17.

    et al. Electrophoretic screening for genetic variation in apolipoprotein C-III: identification of a novel apoC-III variant, apoC-III(Asp45-->Asn), in a Turkish patient. J. Lipid. Res. 35, 1431–1440 (1994).

  18. 18.

    , , , & Molecular cloning of a human apoC-III variant: Thr 74—Ala 74 mutation prevents O-glycosylation. J. Lipid. Res. 28, 1405–1409 (1987).

  19. 19.

    et al. A novel apolipoprotein C-III variant, apoC-III(Gln38—>Lys), associated with moderate hypertriglyceridemia in a large kindred of Mexican origin. J. Lipid. Res. 38, 1833–1840 (1997).

  20. 20.

    et al. Apolipoprotein C-III(Lys58—Glu). Identification of an apolipoprotein C-III variant in a family with hyperalphalipoproteinemia. J. Clin. Invest. 87, 1724–1731 (1991).

  21. 21.

    et al. Association between genetic variation at the APO AI-CIII-AIV gene cluster and familial combined hyperlipidaemia. Clin. Genet. 46, 385–397 (1994).

  22. 22.

    , , , & An apolipoprotein CIII haplotype protective against hypertriglyceridemia is specified by promoter and 3' untranslated region polymorphisms. Proc. Natl Acad. Sci. USA 90, 4562–4566 (1993).

  23. 23.

    et al. Relative contribution of variation within the APOC3/A4/A5 gene cluster in determining plasma triglycerides. Hum. Mol. Genet. 11, 3039–3046 (2002).

  24. 24.

    et al. Common genetic variation in the promoter of the human apo CIII gene abolishes regulation by insulin and may contribute to hypertriglyceridemia. J. Clin. Invest. 96, 2601–2605 (1995).

  25. 25.

    et al. Variants in the APOC3 promoter insulin responsive element modulate insulin secretion and lipids in middle-aged men. Biochim. Biophys. Acta. 1637, 200–206 (2003).

  26. 26.

    et al. Lipoprotein genotype and conserved pathway for exceptional longevity in humans. PLoS Biol. 4, e113 (2006).

  27. 27.

    , & Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 18, 499–502 (1972).

  28. 28.

    et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

  29. 29.

    & Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

  30. 30.

    et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

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Acknowledgements

This work was funded by the Wellcome Trust (098051) and the European Research Council (ERC-2011-StG 280559-SEPI). The Amish study was supported by the following grants: R01 HL104193, NIH U01 HL072515, NIH U01 HL105198, NIH P30 DK072488 and NIH K01 HL116770. The MANOLIS cohort is named in honour of Manolis Giannakakis, 1978–2010. We thank the residents of Anogia and surrounding Mylopotamos villages for taking part. The HELIC study ( www.helic.org) has been supported by many individuals who have contributed to sample collection (including Olina Balafouti, Christina Batzaki, Georgios Daskalakis, Eleni Emmanouil, Chrisoula Giannakaki, Margarita Giannakopoulou, Anastasia Kaparou, Vasiliki Kariakli, Stella Koinaki, Dimitra Kokori, Maria Konidari, Hara Koundouraki, Dimitris Koutoukidis, Eirini Mamalaki, Eirini Mpamiaki, Maria Tsoukana, Dimitra Tzakou, Katerina Vosdogianni and Niovi Xenaki), data entry (Thanos Antonos, Dimitra Papagrigoriou and Betty Spiliopoulou), sample logistics (Sarah Edkins and Emma Gray), genotyping (Robert Andrews, Hannah Blackburn, Doug Simpkin and Siobhan Whitehead), research administration (Anja Kolb-Kokocinski and Carol Smee) and informatics (Martin Pollard and Josh Randall). We would also like to thank Klaudia Walter and Dawn Muddyman for their assistance with accessing UK10K data. This study makes use of data generated by the UK10K Consortium, derived from samples from UK10K_COHORTS_TWINSUK (The TwinsUK Cohort) and UK10K_COHORT_ALSPAC (the Avon Longitudinal Study of Parents and Children). A full list of the investigators who contributed to the generation of the data is available from www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310.

Author information

Affiliations

  1. Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK

    • Ioanna Tachmazidou
    • , Lorraine Southam
    • , Graham R. S. Ritchie
    • , Angela Matchan
    • , Konstantinos Hatzikotoulas
    • , Nigel W. Rayner
    • , Yuan Chen
    • , Kalliope Panoutsopoulou
    • , Jeremy Schwartzentruber
    • , Chris Tyler-Smith
    • , Yali Xue
    •  & Eleftheria Zeggini
  2. Department of Dietetics-Nutrition, Harokopio, University of Athens, Athens 17671, Greece

    • George Dedoussis
    •  & Aliki-Eleni Farmaki
  3. Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK

    • Lorraine Southam
    • , Dionysia K. Xifara
    • , Nigel W. Rayner
    • , Loukas Moutsianas
    •  & Gil McVean
  4. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, CB10 1SD, UK

    • Graham R. S. Ritchie
  5. Department of Statistics, University of Oxford, Oxford OX1 3TG, UK

    • Dionysia K. Xifara
  6. Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201-1595, USA

    • Toni I. Pollin
    • , Jeffrey R. O’Connell
    •  & Laura M. Yerges-Armstrong
  7. Anogia Medical Centre, Anogia 74051, Greece

    • Chrysoula Kiagiadaki
    •  & Emmanouil Tsafantakis

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  1. UK10K consortium

    A list of consortium members are presented in Supplementary Note 1.

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Contributions

Cohort collection: G.D., A.-E.F., C.K., E.T. and E.Z. Phenotype cleaning: A.-E.F., K.H., A.M., N.W.R., I.T. and E.Z. Bioinformatics: G.R.S.R., L.S., I.T. and E.Z. Genotype data processing and cleaning: K.H., A.M., K.P., N.W.R., J.S., L.S., I.T. and E.Z. Genotype–phenotype association testing: L.S., I.T. and E.Z. UK10K haplotype analyses: Y.C., UK10K Consortium, Chris Tyler-Smith, Yali Xue. Amish haplotype analysis: T.I.P., J.R.O’C., L.M.Y.-A. and Y.X. HELIC haplotype sharing: G.M., L.M. and D.K.X. Manuscript drafting: G.R.S.R., L.S., I.T., D.K.X., Y.X. and E.Z.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Eleftheria Zeggini.

Supplementary information

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    Supplementary Information

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