Dataset Information

Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

ABSTRACT:

Objective

Glycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels.

Research design and methods

We studied associations with HbA₁(c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA₁(c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.

Results

Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10⁻²⁶), HFE (rs1800562/P = 2.6 × 10⁻²⁰), TMPRSS6 (rs855791/P = 2.7 × 10⁻¹⁴), ANK1 (rs4737009/P = 6.1 × 10⁻¹²), SPTA1 (rs2779116/P = 2.8 × 10⁻⁹) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10⁻⁹), and four known HbA₁(c) loci: HK1 (rs16926246/P = 3.1 × 10⁻⁵⁴), MTNR1B (rs1387153/P = 4.0 × 10⁻¹¹), GCK (rs1799884/P = 1.5 × 10⁻²⁰) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10⁻¹⁸). We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA₁(c)) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA₁(c).

Conclusions

GWAS identified 10 genetic loci reproducibly associated with HbA₁(c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA₁(c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA₁(c).

SUBMITTER: Soranzo N

PROVIDER: S-EPMC2992787 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Publications

Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

Soranzo Nicole N Sanna Serena S Wheeler Eleanor E Gieger Christian C Radke Dörte D Dupuis Josée J Bouatia-Naji Nabila N Langenberg Claudia C Prokopenko Inga I Stolerman Elliot E Sandhu Manjinder S MS Heeney Matthew M MM Devaney Joseph M JM Reilly Muredach P MP Ricketts Sally L SL Stewart Alexandre F R AF Voight Benjamin F BF Willenborg Christina C Wright Benjamin B Altshuler David D Arking Dan D Balkau Beverley B Barnes Daniel D Boerwinkle Eric E Böhm Bernhard B Bonnefond Amélie A Bonnycastle Lori L LL Boomsma Dorret I DI Bornstein Stefan R SR Böttcher Yvonne Y Bumpstead Suzannah S Burnett-Miller Mary Susan MS Campbell Harry H Cao Antonio A Chambers John J Clark Robert R Collins Francis S FS Coresh Josef J de Geus Eco J C EJ Dei Mariano M Deloukas Panos P Döring Angela A Egan Josephine M JM Elosua Roberto R Ferrucci Luigi L Forouhi Nita N Fox Caroline S CS Franklin Christopher C Franzosi Maria Grazia MG Gallina Sophie S Goel Anuj A Graessler Jürgen J Grallert Harald H Greinacher Andreas A Hadley David D Hall Alistair A Hamsten Anders A Hayward Caroline C Heath Simon S Herder Christian C Homuth Georg G Hottenga Jouke-Jan JJ Hunter-Merrill Rachel R Illig Thomas T Jackson Anne U AU Jula Antti A Kleber Marcus M Knouff Christopher W CW Kong Augustine A Kooner Jaspal J Köttgen Anna A Kovacs Peter P Krohn Knut K Kühnel Brigitte B Kuusisto Johanna J Laakso Markku M Lathrop Mark M Lecoeur Cécile C Li Man M Li Mingyao M Loos Ruth J F RJ Luan Jian'an J Lyssenko Valeriya V Mägi Reedik R Magnusson Patrik K E PK Mälarstig Anders A Mangino Massimo M Martínez-Larrad María Teresa MT März Winfried W McArdle Wendy L WL McPherson Ruth R Meisinger Christa C Meitinger Thomas T Melander Olle O Mohlke Karen L KL Mooser Vincent E VE Morken Mario A MA Narisu Narisu N Nathan David M DM Nauck Matthias M O'Donnell Chris C Oexle Konrad K Olla Nazario N Pankow James S JS Payne Felicity F Peden John F JF Pedersen Nancy L NL Peltonen Leena L Perola Markus M Polasek Ozren O Porcu Eleonora E Rader Daniel J DJ Rathmann Wolfgang W Ripatti Samuli S Rocheleau Ghislain G Roden Michael M Rudan Igor I Salomaa Veikko V Saxena Richa R Schlessinger David D Schunkert Heribert H Schwarz Peter P Seedorf Udo U Selvin Elizabeth E Serrano-Ríos Manuel M Shrader Peter P Silveira Angela A Siscovick David D Song Kjioung K Spector Timothy D TD Stefansson Kari K Steinthorsdottir Valgerdur V Strachan David P DP Strawbridge Rona R Stumvoll Michael M Surakka Ida I Swift Amy J AJ Tanaka Toshiko T Teumer Alexander A Thorleifsson Gudmar G Thorsteinsdottir Unnur U Tönjes Anke A Usala Gianluca G Vitart Veronique V Völzke Henry H Wallaschofski Henri H Waterworth Dawn M DM Watkins Hugh H Wichmann H-Erich HE Wild Sarah H SH Willemsen Gonneke G Williams Gordon H GH Wilson James F JF Winkelmann Juliane J Wright Alan F AF Zabena Carina C Zhao Jing Hua JH Epstein Stephen E SE Erdmann Jeanette J Hakonarson Hakon H HH Kathiresan Sekar S Khaw Kay-Tee KT Roberts Robert R Samani Nilesh J NJ Fleming Mark D MD Sladek Robert R Abecasis Gonçalo G Boehnke Michael M Froguel Philippe P Groop Leif L McCarthy Mark I MI Kao W H Linda WH Florez Jose C JC Uda Manuela M Wareham Nicholas J NJ Barroso Inês I Meigs James B JB

Diabetes 20100921 12

<h4>Objective</h4>Glycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels.<h4>Research design and methods</h4>We studied associations with ...[more]

PMID: 20858683

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Project description:Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P?<?0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Project description:BackgroundHigh sensitivity C-reactive protein (hsCRP) levels are shown to be influenced by genetic variants in Europeans; however, little is explored in Indian population.MethodsHerein, we comprehensively evaluated association of all previously reported genetic determinants of hsCRP levels, including 18 cis (proximal to CRP gene) and 73 trans-acting (distal to CRP gene) variants in 4,200 North Indians of Indo-European ethnicity. First, we evaluated association of 91 variants from 12 candidate loci with hsCRP levels in 2,115 North Indians (1,042 non-diabetic subjects and 1,073 patients with type 2 diabetes). Then, cis and trans-acting variants contributing maximally to hsCRP level variation were further replicated in an independent 2,085 North Indians (1,047 patients with type 2 diabetes and 1,038 non-diabetic subjects).ResultsWe found association of 12 variants from CRP, LEPR, IL1A, IL6, and IL6R with hsCRP levels in non-diabetic subjects. However, only rs3093059-CRP [β = 0.33, P = 9.6×10⁻⁵] and the haplotype harboring rs3093059 risk allele [β = 0.32 µg/mL, P = 1.4×10⁻⁴/P(perm) = 9.0×10⁻⁴] retained significance after correcting for multiple testing. The cis-acting variant rs3093059-CRP had maximum contribution to the variance in hsCRP levels (1.14%). Among, trans-acting variants, rs1892534-LEPR was observed to contribute maximally to hsCRP level variance (0.59%). Associations of rs3093059-CRP and rs1892534-LEPR were confirmed by replication and attained higher significance after meta-analysis [β(meta) = 0.26/0.22; P(meta) = 4.3×10⁻⁷/7.4×10⁻³ and β(meta) = -0.15/-0.12; P(meta) = 2.0×10⁻⁶/1.6×10⁻⁶ for rs3093059 and rs1892534, respectively in non-diabetic subjects and all subjects taken together].ConclusionIn conclusion, we identified rs3093059 in CRP and rs1892534 in LEPR as major cis and trans-acting contributor respectively, to the variance in hsCRP levels in North Indian population.

Dataset Information

Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

Objective

Research design and methods

Results

Conclusions

Publications

Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

Similar Datasets

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