Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose.
Blood Glucose
/ genetics
Cohort Studies
CpG Islands
/ genetics
DNA Methylation
Fasting
/ blood
Genome-Wide Association Study
Genomics
/ methods
Glucose-6-Phosphatase
/ genetics
Humans
Longitudinal Studies
Mendelian Randomization Analysis
Polymorphism, Single Nucleotide
Quantitative Trait Loci
/ genetics
Taiwan
/ epidemiology
DNA methylation
Fasting glucose
GWAS
Han Chinese
Mendelian randomisation
Multi-omics analysis
Journal
Diabetologia
ISSN: 1432-0428
Titre abrégé: Diabetologia
Pays: Germany
ID NLM: 0006777
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
02
09
2020
accepted:
08
02
2021
pubmed:
13
4
2021
medline:
11
3
2022
entrez:
12
4
2021
Statut:
ppublish
Résumé
An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses. We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data. Our meta-analysis identified 18 significant (p < 5 × 10 Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.
Identifiants
pubmed: 33842983
doi: 10.1007/s00125-021-05449-9
pii: 10.1007/s00125-021-05449-9
doi:
Substances chimiques
Blood Glucose
0
Glucose-6-Phosphatase
EC 3.1.3.9
G6PC2 protein, human
EC 3.1.3.9.
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1613-1625Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001881
Pays : United States
Références
Brambilla P, La Valle E, Falbo R et al (2011) Normal fasting plasma glucose and risk of type 2 diabetes. Diabetes Care 34:1372–1374. https://doi.org/10.2337/dc10-2263
doi: 10.2337/dc10-2263
pubmed: 21498787
pmcid: 3114342
Chen WM, Erdos MR, Jackson AU et al (2008) Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J Clin Invest 118:2620–2628
pubmed: 18521185
pmcid: 2398737
Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C et al (2009) A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet 41:89–94. https://doi.org/10.1038/ng.277
doi: 10.1038/ng.277
pubmed: 19060909
Dupuis J, Langenberg C, Prokopenko I et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42:105–116. https://doi.org/10.1038/ng.520
doi: 10.1038/ng.520
pubmed: 20081858
pmcid: 3018764
Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44:991–1005. https://doi.org/10.1038/ng.2385
doi: 10.1038/ng.2385
pubmed: 22885924
pmcid: 3433394
Manning AK, Hivert MF, Scott RA et al (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44:659–669. https://doi.org/10.1038/ng.2274
doi: 10.1038/ng.2274
pubmed: 22581228
pmcid: 3613127
Nagy R, Boutin TS, Marten J et al (2017) Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants. Genome Med 9:23
doi: 10.1186/s13073-017-0414-4
pubmed: 28270201
pmcid: 5339960
Ingelsson E, Langenberg C, Hivert MF et al (2010) Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humans. Diabetes 59:1266–1275. https://doi.org/10.2337/db09-1568
doi: 10.2337/db09-1568
pubmed: 20185807
pmcid: 2857908
Lyssenko V, Laakso M (2013) Genetic screening for the risk of type 2 diabetes: worthless or valuable? Diabetes Care 36(Suppl 2):S120–S126
doi: 10.2337/dcS13-2009
pubmed: 23882036
pmcid: 3920800
Kong A, Steinthorsdottir V, Masson G et al (2009) Parental origin of sequence variants associated with complex diseases. Nature 462:868–874. https://doi.org/10.1038/nature08625
doi: 10.1038/nature08625
pubmed: 20016592
pmcid: 3746295
Olsson AH, Volkov P, Bacos K et al (2014) Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets. PLoS Genet 10:e1004735. https://doi.org/10.1371/journal.pgen.1004735
doi: 10.1371/journal.pgen.1004735
pubmed: 25375650
pmcid: 4222689
Xue A, Wu Y, Zhu Z et al (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:2941
doi: 10.1038/s41467-018-04951-w
pubmed: 30054458
pmcid: 6063971
Kim YJ, Go MJ, Hu C et al (2011) Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet 43:990–995. https://doi.org/10.1038/ng.939
doi: 10.1038/ng.939
pubmed: 21909109
Hwang JY, Sim X, Wu Y et al (2015) Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians. Diabetes 64:291–298
doi: 10.2337/db14-0563
pubmed: 25187374
Spracklen CN, Shi J, Vadlamudi S et al (2018) Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet 14:e1007275. https://doi.org/10.1371/journal.pgen.1007275
doi: 10.1371/journal.pgen.1007275
pubmed: 29621232
pmcid: 5886383
Kanai M, Akiyama M, Takahashi A et al (2018) Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet 50:390–400. https://doi.org/10.1038/s41588-018-0047-6
doi: 10.1038/s41588-018-0047-6
pubmed: 29403010
Hidalgo B, Irvin MR, Sha J et al (2014) Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network study. Diabetes 63:801–807. https://doi.org/10.2337/db13-1100
doi: 10.2337/db13-1100
pubmed: 24170695
pmcid: 3968438
Kriebel J, Herder C, Rathmann W et al (2016) Association between DNA Methylation in Whole Blood and Measures of Glucose Metabolism: KORA F4 Study. PLoS One 11:e0152314. https://doi.org/10.1371/journal.pone.0152314
doi: 10.1371/journal.pone.0152314
pubmed: 27019061
pmcid: 4809492
Kulkarni H, Kos MZ, Neary J et al (2015) Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet 24:5330–5344. https://doi.org/10.1093/hmg/ddv232
doi: 10.1093/hmg/ddv232
pubmed: 26101197
pmcid: 4550817
Walaszczyk E, Luijten M, Spijkerman AMW et al (2018) DNA methylation markers associated with type 2 diabetes, fasting glucose and HbA1c levels: a systematic review and replication in a case-control sample of the Lifelines study. Diabetologia 61:354–368. https://doi.org/10.1007/s00125-017-4497-7
doi: 10.1007/s00125-017-4497-7
pubmed: 29164275
Relton CL, Davey Smith G (2012) Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol 41:161–176. https://doi.org/10.1093/ije/dyr233
doi: 10.1093/ije/dyr233
pubmed: 22422451
pmcid: 3304531
Richardson TG, Zheng J, Davey Smith G et al (2017) Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk. Am J Hum Genet 101:590–602. https://doi.org/10.1016/j.ajhg.2017.09.003
doi: 10.1016/j.ajhg.2017.09.003
pubmed: 28985495
pmcid: 5630190
Juvinao-Quintero DL, Hivert MF, Sharp GC, Relton CL, Elliott HR (2019) DNA Methylation and Type 2 Diabetes: the Use of Mendelian Randomization to Assess Causality. Curr Genet Med Rep 7:191–207. https://doi.org/10.1007/s40142-019-00176-5
doi: 10.1007/s40142-019-00176-5
pubmed: 32274260
pmcid: 7145450
Liu J, Carnero-Montoro E, van Dongen J et al (2019) An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis. Nat Commun 10:2581
doi: 10.1038/s41467-019-10487-4
pubmed: 31197173
pmcid: 6565679
Fan CT, Lin JC, Lee CH (2008) Taiwan Biobank: a project aiming to aid Taiwan’s transition into a biomedical island. Pharmacogenomics 9:235–246. https://doi.org/10.2217/14622416.9.2.235
doi: 10.2217/14622416.9.2.235
pubmed: 18370851
Hsu CC, Chang HY, Wu IC et al (2017) Cohort Profile: The Healthy Aging Longitudinal Study in Taiwan (HALST). Int J Epidemiol 46:1106–1106j. https://doi.org/10.1093/ije/dyw331
doi: 10.1093/ije/dyw331
pubmed: 28369534
pmcid: 5837206
Wu KD, Hsiao CF, Ho LT et al (2002) Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families: a Stanford-Asian Pacific Program in Hypertension and Insulin Resistance sibling study. Hypertens Res 25:529–536. https://doi.org/10.1291/hypres.25.529
doi: 10.1291/hypres.25.529
pubmed: 12358137
Chen CH, Yang JH, Chiang CWK et al (2016) Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum Mol Genet 25:5321–5331. https://doi.org/10.1093/hmg/ddw346
doi: 10.1093/hmg/ddw346
pubmed: 27798100
pmcid: 6078601
Voight BF, Kang HM, Ding J et al (2012) The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 8:e1002793. https://doi.org/10.1371/journal.pgen.1002793
doi: 10.1371/journal.pgen.1002793
pubmed: 22876189
pmcid: 3410907
Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. https://doi.org/10.1086/519795
doi: 10.1086/519795
pubmed: 17701901
pmcid: 1950838
Staples J, Maxwell EK, Gosalia N et al (2018) Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes. Am J Hum Genet 102:874–889. https://doi.org/10.1016/j.ajhg.2018.03.012
doi: 10.1016/j.ajhg.2018.03.012
pubmed: 29727688
pmcid: 5986700
Chung RH, Chiu YF, Hung YJ et al (2017) Genome-wide copy number variation analysis identified deletions in SFMBT1 associated with fasting plasma glucose in a Han Chinese population. BMC Genomics 18:591
doi: 10.1186/s12864-017-3975-0
pubmed: 28789618
pmcid: 5549306
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7
doi: 10.1186/s13742-015-0047-8
pubmed: 25722852
pmcid: 4342193
R Core Team (2018) R: A Language and Environment for Statistical Computing. In. R Foundation for Statistical Computing, Vienna, Austria
Conomos MP, Miller MB, Thornton TA (2015) Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet Epidemiol 39:276–293. https://doi.org/10.1002/gepi.21896
doi: 10.1002/gepi.21896
pubmed: 25810074
pmcid: 4836868
Genomes Project C, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526:68–74. https://doi.org/10.1038/nature15393
doi: 10.1038/nature15393
Verma SS, de Andrade M, Tromp G et al (2014) Imputation and quality control steps for combining multiple genome-wide datasets. Front Genet 5:370. https://doi.org/10.3389/fgene.2014.00370
doi: 10.3389/fgene.2014.00370
pubmed: 25566314
pmcid: 4263197
Pidsley R, Zotenko E, Peters TJ et al (2016) Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol 17:208
doi: 10.1186/s13059-016-1066-1
pubmed: 27717381
pmcid: 5055731
Gorrie-Stone TJ, Smart MC, Saffari A et al (2019) Bigmelon: tools for analysing large DNA methylation datasets. Bioinformatics 35:981–986. https://doi.org/10.1093/bioinformatics/bty713
doi: 10.1093/bioinformatics/bty713
pubmed: 30875430
Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC (2013) A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14:293. https://doi.org/10.1186/1471-2164-14-293
doi: 10.1186/1471-2164-14-293
pubmed: 23631413
pmcid: 3769145
Hannon E, Gorrie-Stone TJ, Smart MC et al (2018) Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits. Am J Hum Genet 103:654–665. https://doi.org/10.1016/j.ajhg.2018.09.007
doi: 10.1016/j.ajhg.2018.09.007
pubmed: 30401456
pmcid: 6217758
McCartney DL, Walker RM, Morris SW, McIntosh AM, Porteous DJ, Evans KL (2016) Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom Data 9:22–24. https://doi.org/10.1016/j.gdata.2016.05.012
doi: 10.1016/j.gdata.2016.05.012
pubmed: 27330998
pmcid: 4909830
Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354. https://doi.org/10.1038/ng.548
doi: 10.1038/ng.548
pubmed: 20208533
pmcid: 3092069
Fuchsberger C, Flannick J, Teslovich TM et al (2016) The genetic architecture of type 2 diabetes. Nature 536:41–47. https://doi.org/10.1038/nature18642
doi: 10.1038/nature18642
pubmed: 27398621
pmcid: 5034897
Mahajan A, Taliun D, Thurner M et al (2018) Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 50:1505–1513. https://doi.org/10.1038/s41588-018-0241-6
doi: 10.1038/s41588-018-0241-6
pubmed: 30297969
pmcid: 6287706
Takeuchi F, Yokota M, Yamamoto K et al (2012) Genome-wide association study of coronary artery disease in the Japanese. Eur J Hum Genet 20:333–340. https://doi.org/10.1038/ejhg.2011.184
doi: 10.1038/ejhg.2011.184
pubmed: 21971053
Liu JZ, Tozzi F, Waterworth DM et al (2010) Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 42:436–440. https://doi.org/10.1038/ng.572
doi: 10.1038/ng.572
pubmed: 20418889
pmcid: 3612983
Yang J, Ferreira T, Morris AP et al (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44:369–375, S361-363. https://doi.org/10.1038/ng.2213
doi: 10.1038/ng.2213
pubmed: 22426310
pmcid: 3593158
MacArthur J, Bowler E, Cerezo M et al (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45:D896–D901. https://doi.org/10.1093/nar/gkw1133
doi: 10.1093/nar/gkw1133
pubmed: 27899670
Lagou V, Magi R, Hottenga JJ et al (2021) Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 12:24
doi: 10.1038/s41467-020-19366-9
pubmed: 33402679
pmcid: 7785747
Prokopenko I, Langenberg C, Florez JC et al (2009) Variants in MTNR1B influence fasting glucose levels. Nat Genet 41:77–81. https://doi.org/10.1038/ng.290
doi: 10.1038/ng.290
pubmed: 19060907
Yang J, Benyamin B, McEvoy BP et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569. https://doi.org/10.1038/ng.608
doi: 10.1038/ng.608
pubmed: 20562875
pmcid: 3232052
Henningsen A, Hamann JD (2007) systemfit: A package for estimating systems of simultaneous equations in R. J Stat Softw 23:1–40
doi: 10.18637/jss.v023.i04
Wahl S, Drong A, Lehne B et al (2017) Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541:81–86. https://doi.org/10.1038/nature20784
doi: 10.1038/nature20784
pubmed: 28002404
Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M (2016) FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32:1493–1501. https://doi.org/10.1093/bioinformatics/btw018
doi: 10.1093/bioinformatics/btw018
pubmed: 26773131
pmcid: 4866522
Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG, Consortium E-I (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30:543–552. https://doi.org/10.1007/s10654-015-0011-z
doi: 10.1007/s10654-015-0011-z
pubmed: 25773750
pmcid: 4516908
Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163. https://doi.org/10.1002/sim.3034
doi: 10.1002/sim.3034
pubmed: 17886233
Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164. https://doi.org/10.1093/nar/gkq603
doi: 10.1093/nar/gkq603
pubmed: 20601685
pmcid: 2938201
Gaunt TR, Shihab HA, Hemani G et al (2016) Systematic identification of genetic influences on methylation across the human life course. Genome Biol 17:61
doi: 10.1186/s13059-016-0926-z
pubmed: 27036880
pmcid: 4818469
Huan T, Joehanes R, Song C et al (2019) Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat Commun 10:4267
doi: 10.1038/s41467-019-12228-z
pubmed: 31537805
pmcid: 6753136
Marcolongo P, Fulceri R, Gamberucci A, Czegle I, Banhegyi G, Benedetti A (2013) Multiple roles of glucose-6-phosphatases in pathophysiology: state of the art and future trends. Biochim Biophys Acta 1830:2608–2618
doi: 10.1016/j.bbagen.2012.12.013
pubmed: 23266497
Arden SD, Zahn T, Steegers S et al (1999) Molecular cloning of a pancreatic islet-specific glucose-6-phosphatase catalytic subunit-related protein. Diabetes 48:531–542. https://doi.org/10.2337/diabetes.48.3.531
doi: 10.2337/diabetes.48.3.531
pubmed: 10078553
Wang Y, Martin CC, Oeser JK et al (2007) Deletion of the gene encoding the islet-specific glucose-6-phosphatase catalytic subunit-related protein autoantigen results in a mild metabolic phenotype. Diabetologia 50:774–778. https://doi.org/10.1007/s00125-006-0564-1
doi: 10.1007/s00125-006-0564-1
pubmed: 17265032
Pound LD, Oeser JK, O’Brien TP et al (2013) G6PC2: a negative regulator of basal glucose-stimulated insulin secretion. Diabetes 62:1547–1556. https://doi.org/10.2337/db12-1067
doi: 10.2337/db12-1067
pubmed: 23274894
pmcid: 3636628
O’Brien RM (2013) Moving on from GWAS: functional studies on the G6PC2 gene implicated in the regulation of fasting blood glucose. Curr Diab Rep 13:768–777. https://doi.org/10.1007/s11892-013-0422-8
doi: 10.1007/s11892-013-0422-8
pubmed: 24142592
pmcid: 4041587
Ng NHJ, Willems SM, Fernandez J et al (2019) Tissue-specific alteration of metabolic pathways influences glycemic regulation. bioRxiv: 790618
Wheeler E, Marenne G, Barroso I (2017) Genetic aetiology of glycaemic traits: approaches and insights. Hum Mol Genet 26:R172–R184. https://doi.org/10.1093/hmg/ddx293
doi: 10.1093/hmg/ddx293
pubmed: 28977447
pmcid: 5886471
Mahajan A, Sim X, Ng HJ et al (2015) Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet 11:e1004876. https://doi.org/10.1371/journal.pgen.1004876
doi: 10.1371/journal.pgen.1004876
pubmed: 25625282
pmcid: 4307976
Wessel J, Chu AY, Willems SM et al (2015) Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 6:5897
doi: 10.1038/ncomms6897
pubmed: 25631608
Flannick J, Fuchsberger C, Mahajan A et al (2017) Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data 4:170179
doi: 10.1038/sdata.2017.179
pubmed: 29257133
Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249. https://doi.org/10.1038/nmeth0410-248
doi: 10.1038/nmeth0410-248
pubmed: 20354512
pmcid: 2855889
Al-Daghri NM, Pontremoli C, Cagliani R et al (2017) Susceptibility to type 2 diabetes may be modulated by haplotypes in G6PC2, a target of positive selection. BMC Evol Biol 17:43
doi: 10.1186/s12862-017-0897-z
pubmed: 28173748
pmcid: 5297017
Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32:377–389
doi: 10.1007/s10654-017-0255-x
pubmed: 28527048
pmcid: 5506233
Dayeh T, Tuomi T, Almgren P et al (2016) DNA methylation of loci within ABCG1 and PHOSPHO1 in blood DNA is associated with future type 2 diabetes risk. Epigenetics 11:482–488. https://doi.org/10.1080/15592294.2016.1178418
doi: 10.1080/15592294.2016.1178418
pubmed: 27148772
pmcid: 4939923