Abstract
We examined common variants in the fatty acid binding protein 4 gene (FABP4) and plasma levels of FABP4 in adults aged 65 and older from the Cardiovascular Health Study. We genotyped rs16909187, rs1054135, rs16909192, rs10808846, rs7018409, rs2290201, and rs6992708 and measured circulating FABP4 levels among 3190 European Americans and 660 African Americans. Among European Americans, the minor alleles of six single nucleotide polymorphisms (SNP) were associated with lower FABP4 levels (all p ≤ 0.01). Among African Americans, the SNP with the lowest minor allele frequency was associated with lower FABP4 levels (p = 0.015). The C-A haplotype of rs16909192 and rs2290201 was associated with lower FABP4 levels in both European Americans (frequency = 16 %; p = 0.001) and African Americans (frequency = 8 %; p = 0.04). The haplotype combined a SNP in the first intron with one in the 3′untranslated region. However, the alleles associated with lower FABP4 levels were associated with higher fasting glucose in meta-analyses from the MAGIC consortium. These results demonstrate associations of common SNP and haplotypes in the FABP4 gene with lower plasma FABP4 but higher fasting glucose levels.
Keywords: Fatty acid binding proteins, Metabolism, Genetics
Introduction
Adipose tissues produce multiple adipokines that influence inflammation, thrombogenicity, insulin resistance, and other metabolic pathways [1]. One of these adipokines is fatty acid binding protein 4 (FABP4 or aP2), a carrier for fatty acids and other lipophilic substances between extra- and intra-cellular membranes [2]. FABP4 is expressed by macrophages and adipocytes [3, 4]. FABP4 expression in adipocytes has been associated with insulin resistance in animal and human studies [5, 6]. FABP4 effects on insulin and glucose metabolism are thought to result from its effects on fatty acid release and transport, insulin secretion from beta-cells, and inflammation [4, 5, 7, 8]. We have previously demonstrated a positive association between plasma FABP4 and incident diabetes in the Cardiovascular Health Study (CHS) [9]. Plasma FABP4 levels were also associated with increased risk of diabetes in a prospective case–control study from the Physicians’ Health Study [10].
Expression of FABP4 is modulated by the FABP4 gene. A functional mutation (T-87C) in the FABP4 gene reduces expression of FABP4 in adipocytes and is associated with a lower risk of type 2 diabetes (especially among obese subjects) [6]. Recently however, the Women’s Health Initiative (WHI) reported null results for common variants across the FABP4 gene in relation to diabetes risk, which included the T-87C SNP [11]. Among children, a variant in exon 4 has been related to obesity and FABP4 plasma levels in obese and non-obese children, but the T-87C variant was not assessed [12]. The exon 4 SNP (rs1054135) was not associated with T2D risk in the WHI [11]. It is unknown whether other polymorphisms in the FABP4 gene influence plasma FABP4 concentrations.
Because CHS participants underwent measurement of plasma FABP4 levels and genotyping for FABP4 single-nucleotide polymorphisms as part of the candidate gene association resource (CARe) project [13], it offers a unique opportunity to evaluate their association in a large cohort of older adults. Data on eight SNP in or near the gene on chromosome 8 were available from CARe, but the T-87C was not genotyped. The current project examined whether common FABP4 polymorphisms are associated with plasma levels of FABP4 in older adults. Additionally, we examined published GWAS results from the MAGIC and GIANT consortia to determine the relation of these SNP to fasting glucose, fasting insulin, and body mass index.
Materials and Methods
CHS is a population-based cohort study of 5,201 adults aged 65 years and older that began in 1989–1990; a supplemental cohort of 687 predominantly African–Americans was enrolled in 1992–1993 [14]. Participants in the current study are limited to those included in the CARe genotyping project and who had a blood sample from the 1992–1993 examination available for the measurement of plasma FABP4. These totaled 3,190 European Americans [mean (SD) age 75.2 ± 5.2 years, 43 % male] and 660 African Americans (73.3 ± 5.7 years, 37 % male). All CHS participants provided written informed consent and participants included in this study provided additional consent for analysis of genetic data. Plasma FABP4 concentration was measured using BioVendor ELISA kits. Genotyping was performed using an Illumina custom SNP array with 50,000 SNP, which has been previously described [15]. In the current project, we evaluated the eight SNP near the FABP4 gene, one of which had a minor allele occurring only twice in the CHS participants, and thus was too rare for further analysis.
We analyzed the association between seven genotyped SNP and natural log transformed plasma FABP4 levels using race-specific linear regression models adjusted for age, sex, body mass index (BMI), and clinic. SNP were analyzed using an additive genetic model coding for the minor allele, which in two instances differed by race. Linkage disequilibrium (LD) was evaluated using Haploview [16] to determine the number of independent tests for Bonferroni correction. Haplotypes were analyzed using PLINK [17], which estimates phased haplotypes using an Expectation–Maximization algorithm. Linear models with the same covariates were implemented, and each haplotype was tested against a collapsed group of the other haplotypes. The SNP with the strongest association in race-specific single-SNP models was paired with each of the other SNP to evaluate 2-SNP haplotypes. The best 2-SNP haplotype was then evaluated with each of the remaining SNP.
To address the potential for FABP4 SNP to influence glycemic traits and obesity, we accessed GWAS results from three large consortia meta-analyses. For fasting glucose and insulin, we examined results from the MAGIC consortium, downloaded from www.magicinvestigators.org, which were generated from analyses of 46,186 non-diabetic participants [18]. For BMI, we examined results from the GIANT consortium, downloaded from http://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files, which analyzed over 122,800 participants at each of the FABP4 SNP [19].
Results
Mean levels of FABP4 were higher in African Americans (38.3 ± 24.3 ng/mL) than European Americans (33.3 ± 17.4 ng/mL), as was the prevalence of obesity and diabetes. In the European American sample, all SNP were consistent with Hardy–Weinberg equilibrium (HWE) and had over 98 % successful genotyping (call rate). In the African American sample, three SNP (SNP 2–4) exhibited departure from HWE with p-values 0.013–0.016, and all SNP had over 99 % call rate.
In European Americans, we observed a nominal association (p ≤ 0.05) with six of the seven SNP examined and the minor alleles were all associated with lower FABP4 levels (Table 1). In an LD plot (Fig. 1), we observed two sets of SNP with r2 > 0.8 among them (SNP 1–3–5 and 4–6–7), and SNP 2 was independent of the others. Therefore, we set a Bonferroni threshold of 0.017 to account for the three comparisons, and both sets of correlated SNP met this criterion. The strongest association was observed for SNP 5 (rs7018409), which had a minor allele (T) related to lower FABP4 levels. This SNP explained 0.006 of the variance and was located in an intron (Fig. 2).
Table 1.
SNP association to FABP4 levels adjusted for age, sex, BMI and clinic
| # | SNP | European American |
African–American |
||||||
|---|---|---|---|---|---|---|---|---|---|
| Minor allele | MAF | Beta | p-value | Minor allele | MAF | Beta | p-value | ||
| 1 | rs16909187 | A | 0.17 | −0.044 | 1.6E- 04* |
A | 0.16 | −0.042 | 0.16 |
| 2 | rs1054135 | T | 0.10 | 0.023 | 0.11 | T | 0.25 | 0.015 | 0.57 |
| 3 | rs16909192 | C | 0.17 | −0.042 | 3.8E- 04* |
C | 0.08 | −0.093 | 1.5E- 02 |
| 4 | rs10808846 | T | 0.27 | −0.024 | 1.5E- 02* |
G | 0.46 | 0.013 | 0.57 |
| 5 | rs7018409 | T | 0.17 | −0.050 | 2.4E- 05* |
T | 0.16 | −0.053 | 7.8E- 02 |
| 6 | rs2290201 | A | 0.27 | −0.026 | 1.0E- 02* |
G | 0.34 | 0.002 | 0.93 |
| 7 | rs6992708 | C | 0.29 | −0.027 | 5.7E- 03* |
C | 0.47 | −0.014 | 0.52 |
Beta estimate from in-transformed FABP4 levels in ng/mL
Statistically significant after Bonferroni correction for multiple testing
Fig. 1.
a Linkage disequilibrium in European Americans presented as r2 values, and b linkage disequilibrium in African Americans presented as r2 values
Fig. 2.
FABP genetic region, association with FABP4 levels in CHS (European) and r2 for LD with top-associated SNP (rs7018409)
In African–Americans, the smaller sample size reduced power and there was considerably less LD among the SNP (Fig. 1b), which would require a more stringent correction for multiple testing. The minor allele for two SNP (SNP 4 and 6) was opposite from that observed in European Americans. SNP 3 was nominally associated with FABP4 levels (p = 0.015) but not statistically significant after correction for multiple testing, although it provides independent replication of the effect observed in European Americans. The C allele of SNP 3 (rs16909192) was associated with lower FABP4 levels. This SNP explained 0.009 of the variance and was located in the 3′ untranslated region of FABP4.
In general, haplotype analyses did not identify combinations of SNP with stronger effects than observed in single SNP analyses. For both races, the best haplotype combination came from combining the strongest individual SNP result with SNP6 (rs2290201), which is one that had inverse allele frequencies between the races. We present the result for the 2-SNP haplotype of SNP 3 and 6 (rs16909192-rs2290201) for comparison between the races (Table 2). In both races, the C-A haplotype was associated with lower FABP4 levels.
Table 2.
rs16909192-rs2290201 haplotype association to FABP4 levels
| Haplotype | European American omnibus p-value = 0.001 |
African-American omnibus p-value = 0.04 |
||||
|---|---|---|---|---|---|---|
| Freq | Beta | p-value | Freq | Beta | p-value | |
| C–A | 0.163 | −0.049 | 4.4E-05 | 0.076 | −0.0954 | 1.3E-02 |
| A–A | 0.108 | 0.018 | 0.23 | 0.58 | 0.0311 | 0.17 |
| A–G | 0.722 | 0.022 | 2.7E-02 | 0.343 | 0.00205 | 0.93 |
We examined results from the MAGIC and GIANT consortia to identify association between FABP4 SNP and diabetes-related traits (Table 3). None of the FABP4 SNP studied in the current project were associated with fasting insulin (smallest p value = 0.12), but for fasting glucose, five SNP were nominally associated. The LD block that included SNP 1-3-5 had p-values for association between 0.012 and 0.017, meeting our criteria for Bonferroni corrected statistical significance, and the alleles associated with lower FABP4 levels were associated with higher fasting glucose concentrations. For BMI, none of the SNP were statistically significant, but the same block of SNP [1–3] had p-values between 0.08 and 0.1 and the alleles associated with lower FABP4 levels were related to lower BMI.
Table 3.
FABP4 SNP results for diabetes-related traits from MAGIC [18] and GIANT [19]
| # | SNP | MAGIC(18) N = 46,186 non-diabetica |
GIANT[19] N C 122,804b |
|||||
|---|---|---|---|---|---|---|---|---|
| Fasting glucose |
Fasting insulin |
BMI |
||||||
| Effect allele | Effect |
p- value |
Effect | p-value | Allele decreasing BMI |
p- value |
||
| 1 | rs16909187 | A | 0.012 | 0.013 | 0.0003 | 0.96 | A | 0.08 |
| 2 | rs1054135 | T | 0.0014 | 0.83 | 0.011 | 0.12 | T | 0.91 |
| 3 | rs16909192 | A | −0.012 | 0.012 | −0.001 | 0.85 | C | 0.09 |
| 4 | rs10808846 | T | 0.008 | 0.06 | 0.0041 | 0.35 | T | 0.25 |
| 5 | rs7018409 | T | 0.012 | 0.017 | 0.0011 | 0.84 | T | 0.10 |
| 6 | rs2290201 | A | 0.0085 | 0.04 | 0.0045 | 0.29 | A | 0.15 |
| 7 | rs6992708 | A | −0.0086 | 0.04 | −0.0054 | 0.22 | C | 0.22 |
Data on glycemic traits have been contributed by MAGIC investigators and downloaded from www.magicinvestigators.org
Discussion
Common variants in the FABP4 gene are associated with plasma FABP4 levels measured in a sample of European American older adults. Although the sample size among African Americans was smaller, we observed larger effect sizes in African–Americans. Among African–Americans, the SNP with the smallest minor allele frequency was nominally associated with a beta estimate of −0.09, compared to an effect of −0.04 in European Americans. Haplotype results were driven by the effects observed in single SNP results, and we observed some consistent effects across races despite large differences in allele frequency.
FABP4 levels have been associated with a higher risk of incident diabetes in the CHS cohort and in the Physicians’ Health Study [9, 10]. However, a search of the NHGRI catalog of published genome-wide association studies (http://www.genome.gov/gwastudies/ accessed 6/26/2012) [20] does not identify the FABP4 gene as being associated with diabetes, which is consistent with the results from the WHI [11]. One of the SNP examined here, rs1054135 (SNP2) was previously shown to be associated with FABP4 levels in children [12], but we could not replicate that association. Our meta-analyses identified an association between FABP4 SNP and fasting glucose levels, but not fasting insulin or BMI.
The association between the FABP4 SNP and lower FABP4 levels but higher fasting glucose appears to be paradoxical in relation to the observed association between higher FABP4 levels and higher risk of diabetes. While SNP in the FABP4 gene exhibit association to plasma levels, variants in other genes may also influence FABP4 levels, both through gene expression and metabolism. The SNP examined in the current analysis account for only a small amount of the variability in FABP4 levels, and given the observed association of FABP4 levels with diabetes, genome-wide studies of FABP4 levels are warranted.
Important limitations of our work include its restriction to previously genotyped SNP, the small sample size of African–Americans, and lack of circulating FABP4 levels outside of CHS. Nonetheless, our results confirm the structure of the genetic association between FABP4 SNP and FABP4 levels and illustrate the complexity of relating these to metabolic traits.
Acknowledgments
Funding for CARe genotyping was provided by NHLBI Contract N01-HC-65226. The research reported in this article was supported by contracts HHSN268201200036C, N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, and grants HL094555 (to Drs. Djousse, Ix, Mukamal, Zieman, and Kizer) and HL080295 from the NHLBI, with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm.
Abbreviations
- BMI
Body mass index
- CARe
Candidate gene association resource
- CHS
Cardiovascular Health Study
- FABP4
Fatty acid binding protein 4
- GWAS
Genome-wide association study
- HWE
Hardy-Weinberg equilibrium
- LD
Linkage disequilibrium
- SNP
Single nucleotide polymorphism
- WHI
Women’s Health Initiative
Footnotes
K. J. Mukamal and J. B. Wilk contributed equally to this manuscript.
References
- 1.Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metab. 2004;89:2548–2556. doi: 10.1210/jc.2004-0395. [DOI] [PubMed] [Google Scholar]
- 2.Makowski L, Hotamisligil GS. Fatty acid binding proteins—the evolutionary crossroads of inflammatory and metabolic responses. J Nutr. 2004;134:2464S–2468S. doi: 10.1093/jn/134.9.2464S. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Xu A, Tso AW, Cheung BM, Wang Y, Wat NM, Fong CH, Yeung DC, Janus ED, Sham PC, Lam KS. Circulating adipocyte-fatty acid binding protein levels predict the development of the metabolic syndrome: a 5-year prospective study. Circulation. 2007;115:1537–1543. doi: 10.1161/CIRCULATIONAHA.106.647503. [DOI] [PubMed] [Google Scholar]
- 4.Xu A, Wang Y, Xu JY, Stejskal D, Tam S, Zhang J, Wat NM, Wong WK, Lam KS. Adipocyte fatty acid-binding protein is a plasma biomarker closely associated with obesity and metabolic syndrome. Clin Chem. 2006;52:405–413. doi: 10.1373/clinchem.2005.062463. [DOI] [PubMed] [Google Scholar]
- 5.Boord JB, Maeda K, Makowski L, Babaev VR, Fazio S, Linton MF, Hotamisligil GS. Combined adipocyte-macrophage fatty acid-binding protein deficiency improves metabolism, atherosclerosis, and survival in apolipoprotein E-deficient mice. Circulation. 2004;110:1492–1498. doi: 10.1161/01.CIR.0000141735.13202.B6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Tuncman G, Erbay E, Hom X, De Vivo I, Campos H, Rimm EB, Hotamisligil GS. A genetic variant at the fatty acid-binding protein aP2 locus reduces the risk for hypertriglyceridemia, type 2 diabetes, and cardiovascular disease. Proc Natl Acad Sci U S A. 2006;103:6970–6975. doi: 10.1073/pnas.0602178103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Baar RA, Dingfelder CS, Smith LA, Bernlohr DA, Wu C, Lange AJ, Parks EJ. Investigation of in vivo fatty acid metabolism in AFABP/aP2(−/−) mice. Am J Physiol Endocrinol Metab. 2005;288:E187–E193. doi: 10.1152/ajpendo.00256.2004. [DOI] [PubMed] [Google Scholar]
- 8.Scheja L, Makowski L, Uysal KT, Wiesbrock SM, Shimshek DR, Meyers DS, Morgan M, Parker RA, Hotamisligil GS. Altered insulin secretion associated with reduced lipolytic efficiency in aP2−/− mice. Diabetes. 1999;48:1987–1994. doi: 10.2337/diabetes.48.10.1987. [DOI] [PubMed] [Google Scholar]
- 9.Djousse L, Khawaja O, Bartz TM, Biggs ML, Ix JH, Zieman SJ, Kizer JR, Tracy RP, Siscovick DS, Mukamal KJ. Plasma fatty acid-binding protein 4, nonesterified fatty acids, and incident diabetes in older adults. Diabetes Care. 2012;35(8):1700–1707. doi: 10.2337/dc11-1690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Djousse L, Gaziano JM. Plasma levels of FABP4, but not FABP3, are associated with increased risk of diabetes. Lipids. 2012;47(8):757–762. doi: 10.1007/s11745-012-3689-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chan KH, Song Y, Hsu YH, You NC, FTinker L, Liu S. Common genetic variants in fatty acid-binding protein-4 (FABP4) and clinical diabetes risk in the Women’s Health Initiative Observational Study. Obesity (Silver Spring) 2010;18:1812–1820. doi: 10.1038/oby.2009.496. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Khalyfa A, Bhushan B, Hegazi M, Kim J, Kheirandish-Gozal L, Bhattacharjee R, Capdevila OS, Gozal D. Fatty-acid binding protein 4 gene variants and childhood obesity: potential implications for insulin sensitivity and CRP levels. Lipids Health Dis. 2010;9:18. doi: 10.1186/1476-511X-9-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, Ejebe KG, Keating BJ, Yang Q, Chen MH, Lapchyk N, Crenshaw A, Ziaugra L, Rachupka A, Benjamin EJ, Cupples LA, Fornage M, Fox ER, Heckbert SR, Hirschhorn JN, Newton-Cheh C, Nizzari MM, Paltoo DN, Papanicolaou GJ, Patel SR, Psaty BM, Rader DJ, Redline S, Rich SS, Rotter JI, Taylor HA, Jr, Tracy RP, Vasan RS, Wilson JG, Kathiresan S, Fabsitz RR, Boerwinkle E, Gabriel SB. Resource NCGA: candidate gene association resource (CARe): design, methods, and proof of concept. Circ Cardiovasc Genet. 2010;3:267–275. doi: 10.1161/CIRCGENETICS.109.882696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio TA, Mittelmark MB, Newman A, et al. The cardiovascular health study: design and rationale. Ann Epidemiol. 1991;1:263–276. doi: 10.1016/1047-2797(91)90005-w. [DOI] [PubMed] [Google Scholar]
- 15.Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, Glessner JT, Galver L, Barrett JC, Grant SF, Farlow DN, Chandrupatla HR, Hansen M, Ajmal S, Papanicolaou GJ, Guo Y, Li M, Derohannessian S, de Bakker PI, Bailey SD, Montpetit A, Edmondson AC, Taylor K, Gai X, Wang SS, Fornage M, Shaikh T, Groop L, Boehnke M, Hall AS, Hattersley AT, Frackelton E, Patterson N, Chiang CW, Kim CE, Fabsitz RR, Ouwehand W, Price AL, Munroe P, Caulfield M, Drake T, Boerwinkle E, Reich D, Whitehead AS, Cappola TP, Samani NJ, Lusis AJ, Schadt E, Wilson JG, Koenig W, McCarthy MI, Kathiresan S, Gabriel SB, Hakonarson H, Anand SS, Reilly M, Engert JC, Nickerson DA, Rader DJ, Hirschhorn JN, Fitzgerald GA. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS One. 2008;3:e3583. doi: 10.1371/journal.pone.0003583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265. doi: 10.1093/bioinformatics/bth457. [DOI] [PubMed] [Google Scholar]
- 17.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Magi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparso T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proenca C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Bottcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PR, Jorgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martinez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orru M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvanen AC, Tanaka T, Thorand B, Tichet J, Tonjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Consortium D, Consortium G, Global BC, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Rios M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Anders Hamsten, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–116. doi: 10.1038/ng.520. on behalf of Procardis C, investigators M. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpelainen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proenca C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grassler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jorgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, Konig IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaloy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimaki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, Magic, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O’Donnell CJ, O’Rahilly S, Ong KK, Oostra B, Pare G, Parker AN, Perola M, Pichler I, Pietilainen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstrale M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tonjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kahonen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Gronberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis C, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O’Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, Loos RJ. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–948. doi: 10.1038/ng.686. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106:9362–9367. doi: 10.1073/pnas.0903103106. [DOI] [PMC free article] [PubMed] [Google Scholar]