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

Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O'Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, WTCCCZabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ, Barroso I, Meigs JB - Diabetes (2010)

Bottom Line: We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B).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.

View Article: PubMed Central - PubMed

Affiliation: Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.

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).

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Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.
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Figure 3: Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.

Mentions: We used net reclassification analysis to estimate the population-level impact of the seven nonglycemic loci when HbA1c ≥6.5 (%) is used as the reference cutoff for diabetes diagnosis, as recently proposed (18). We calculated the net reclassification around this threshold attributable to effects of the seven nonglycemic HbA1c loci that might be expected when screening a general European ancestry population for undiagnosed diabetes using HbA1c. We studied the FHS and ARIC cohorts combined (N = 10,110), and included individuals with undiagnosed diabetes for detection by screening. We compared the measured distribution of HbA1c to the distribution adjusted for the seven nonglycemic SNPs (Fig. 3). The net reclassification was −1.86% (P = 0.002), indicating that the population-level effect size of the 7 nonglycemic HbA1c-associated SNPs is equivalent to reclassification of about 2% of an European ancestry population sample according to HbA1c-determined diabetes status.


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

Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Mägi R, Magnusson PK, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O'Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, WTCCCZabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ, Barroso I, Meigs JB - Diabetes (2010)

Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992787&req=5

Figure 3: Net reclassification when screening for undiagnosed diabetes, using HbA1c as a population-level measure of genetic effect size. The figure shows the distribution of HbA1c in the FHS and ARIC cohorts combined (N = 10,110), stratified by individuals with undiagnosed type 2 diabetes (UnDx DM, N = 593, black lines) or without diabetes (Non DM, N = 9,517, gray lines), and by HbA1c without adjustment (solid lines) or after adjustment for seven nonglycemic SNPs (dashed lines). The vertical dashed line is the diabetes diagnostic threshold at HbA1c ≥6.5(%). Net reclassification is the overall proportion of the population appropriately moved above or below this line by considering the genetic information. For instance, among individuals with undiagnosed diabetes, 39.5% had an unadjusted HbA1c level ≥6.5 (%) and 37.4% had a seven SNP-adjusted HbA1c level ≥6.5 (%), and among those with undiagnosed diabetes, 2.02% of those with undiagnosed diabetes were misclassified by the influence of the seven SNPs. The net reclassification is calculated as the difference −2.02% − (−0.17%) = −1.86%.
Mentions: We used net reclassification analysis to estimate the population-level impact of the seven nonglycemic loci when HbA1c ≥6.5 (%) is used as the reference cutoff for diabetes diagnosis, as recently proposed (18). We calculated the net reclassification around this threshold attributable to effects of the seven nonglycemic HbA1c loci that might be expected when screening a general European ancestry population for undiagnosed diabetes using HbA1c. We studied the FHS and ARIC cohorts combined (N = 10,110), and included individuals with undiagnosed diabetes for detection by screening. We compared the measured distribution of HbA1c to the distribution adjusted for the seven nonglycemic SNPs (Fig. 3). The net reclassification was −1.86% (P = 0.002), indicating that the population-level effect size of the 7 nonglycemic HbA1c-associated SNPs is equivalent to reclassification of about 2% of an European ancestry population sample according to HbA1c-determined diabetes status.

Bottom Line: We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B).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.

View Article: PubMed Central - PubMed

Affiliation: Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K.

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).

Show MeSH
Related in: MedlinePlus