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1.  Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma 
Genome Medicine  2013;5(2):13.
Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.
doi:10.1186/gm417
PMCID: PMC3706909  PMID: 23414815
2.  On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies 
BMC Bioinformatics  2012;13:120.
Background
Genome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain.
Results
Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.
Conclusions
We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.
doi:10.1186/1471-2105-13-120
PMCID: PMC3537592  PMID: 22672667
p-gain; Metabolomics; MWAS; GWAS; Genome-wide association studies; Metabolome-wide association studies
3.  Human metabolic individuality in biomedical and pharmaceutical research 
Nature  2011;477(7362):10.1038/nature10354.
SUMMARY
Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 exhibit effect sizes that are unusually high for GWAS and account for 10-60% of metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism, and Crohn’s disease. Taken together our study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
doi:10.1038/nature10354
PMCID: PMC3832838  PMID: 21886157
4.  Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits 
Human Molecular Genetics  2013;23(2):534-545.
Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here, we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis, we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study for association with methylation of 457 004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome–metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style-dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (P = 3.9 × 10−20 to 2.0 × 10−108, r2 = 0.036 to 0.221). Seven loci display CpG site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (P = 9.2 × 10−14 to 2.7 × 10−27, r2 = 0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3-beta,17-beta-diol disulfate. In these cases, the association between CpG-methylation and metabotype is likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggest that DNA methylation plays an important role in regulating human metabolism.
doi:10.1093/hmg/ddt430
PMCID: PMC3869358  PMID: 24014485
5.  Metabolomic markers reveal novel pathways of ageing and early development in human populations 
Background Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age.
Methods Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels.
Results We identified a panel of 22 metabolites which combined are strongly correlated with age (R2 = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10−157) and lung function (FEV1 beta = −0.04, SE = 0.008, P = 1.8 × 10−8 adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = −0.01, SE = 0.002, P = 1.9 × 10−6) and birthweight (beta = −0.06, SE = 0.01, P = 2.5 × 10−9). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10−6). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = −0.20, SE = 0.04, P = 2.9 × 10−8). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes.
Conclusions Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.
doi:10.1093/ije/dyt094
PMCID: PMC3781000  PMID: 23838602
Ageing; metabolomics; epigenetics; twin studies; developmental origins of health and disease; birthweight
6.  Body Fat Free Mass Is Associated with the Serum Metabolite Profile in a Population-Based Study 
PLoS ONE  2012;7(6):e40009.
Objective
To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study.
Subjects and Methods
Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).
Results
We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75×10−16–8.95×10−06) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.
Conclusion
A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.
doi:10.1371/journal.pone.0040009
PMCID: PMC3384624  PMID: 22761945
7.  A genome-wide association study identifies novel loci associated with circulating IGF-I and IGFBP-3 
Human Molecular Genetics  2011;20(6):1241-1251.
Insulin-like growth factor-I (IGF-I) and insulin-like growth factor-binding protein-3 (IGFBP-3) are involved in cell replication, proliferation, differentiation, protein synthesis, carbohydrate homeostasis and bone metabolism. Circulating IGF-I and IGFBP-3 concentrations predict anthropometric traits and risk of cancer and cardiovascular disease. In a genome-wide association study of 10 280 middle-aged and older men and women from four community-based cohort studies, we confirmed a known association of single nucleotide polymorphisms in the IGFBP3 gene region on chromosome 7p12.3 with IGFBP-3 concentrations using a significance threshold of P < 5 × 10−8 (P = 3.3 × 10−101). Furthermore, the same IGFBP3 gene locus (e.g. rs11977526) that was associated with IGFBP-3 concentrations was also associated with the opposite direction of effect, with IGF-I concentration after adjustment for IGFBP-3 concentration (P = 1.9 × 10−26). A novel and independent locus on chromosome 7p12.3 (rs700752) had genome-wide significant associations with higher IGFBP-3 (P = 4.4 × 10−21) and higher IGF-I (P = 4.9 × 10−9) concentrations; when the two measurements were adjusted for one another, the IGF-I association was attenuated but the IGFBP-3 association was not. Two additional loci demonstrated genome-wide significant associations with IGFBP-3 concentration (rs1065656, chromosome 16p13.3, P = 1.2 × 10−11, IGFALS, a confirmatory finding; and rs4234798, chromosome 4p16.1, P = 4.5 × 10−10, SORCS2, a novel finding). Together, the four genome-wide significant loci explained 6.5% of the population variation in IGFBP-3 concentration. Furthermore, we observed a borderline statistically significant association between IGF-I concentration and FOXO3 (rs2153960, chromosome 6q21, P = 5.1 × 10−7), a locus associated with longevity. These genetic loci deserve further investigation to elucidate the biological basis for the observed associations and clarify their possible role in IGF-mediated regulation of cell growth and metabolism.
doi:10.1093/hmg/ddq560
PMCID: PMC3043664  PMID: 21216879
8.  Genetic Determinants of Serum Testosterone Concentrations in Men 
PLoS Genetics  2011;7(10):e1002313.
Testosterone concentrations in men are associated with cardiovascular morbidity, osteoporosis, and mortality and are affected by age, smoking, and obesity. Because of serum testosterone's high heritability, we performed a meta-analysis of genome-wide association data in 8,938 men from seven cohorts and followed up the genome-wide significant findings in one in silico (n = 871) and two de novo replication cohorts (n = 4,620) to identify genetic loci significantly associated with serum testosterone concentration in men. All these loci were also associated with low serum testosterone concentration defined as <300 ng/dl. Two single-nucleotide polymorphisms at the sex hormone-binding globulin (SHBG) locus (17p13-p12) were identified as independently associated with serum testosterone concentration (rs12150660, p = 1.2×10−41 and rs6258, p = 2.3×10−22). Subjects with ≥3 risk alleles of these variants had 6.5-fold higher risk of having low serum testosterone than subjects with no risk allele. The rs5934505 polymorphism near FAM9B on the X chromosome was also associated with testosterone concentrations (p = 5.6×10−16). The rs6258 polymorphism in exon 4 of SHBG affected SHBG's affinity for binding testosterone and the measured free testosterone fraction (p<0.01). Genetic variants in the SHBG locus and on the X chromosome are associated with a substantial variation in testosterone concentrations and increased risk of low testosterone. rs6258 is the first reported SHBG polymorphism, which affects testosterone binding to SHBG and the free testosterone fraction and could therefore influence the calculation of free testosterone using law-of-mass-action equation.
Author Summary
Testosterone is the most important testicular androgen in men. Low serum testosterone concentrations are associated with cardiovascular morbidity, metabolic syndrome, type 2 diabetes mellitus, atherosclerosis, osteoporosis, sarcopenia, and increased mortality risk. Thus, there is growing evidence that serum testosterone is a valuable biomarker of men's overall health status. Studies in male twins indicate that there is a strong heritability of serum testosterone. Here we perform a large-scale genome-wide association study to examine the effects of common genetic variants on serum testosterone concentrations. By examining 14,429 men, we show that genetic variants in the sex hormone-binding globulin (SHBG) locus and on the X chromosome are associated with a substantial variation in serum testosterone concentrations and increased risk of low testosterone. The reported associations may now be used in order to better understand the functional background of recently identified disease associations related to low testosterone. Importantly, we identified the first known genetic variant, which affects SHBG's affinity for binding testosterone and the free testosterone fraction and could therefore influence the calculation of free testosterone. This finding suggests that individual-based SHBG-testosterone affinity constants are required depending on the genotype of this single-nucleotide polymorphism.
doi:10.1371/journal.pgen.1002313
PMCID: PMC3188559  PMID: 21998597
9.  A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation 
Coviello, Andrea D. | Haring, Robin | Wellons, Melissa | Vaidya, Dhananjay | Lehtimäki, Terho | Keildson, Sarah | Lunetta, Kathryn L. | He, Chunyan | Fornage, Myriam | Lagou, Vasiliki | Mangino, Massimo | Onland-Moret, N. Charlotte | Chen, Brian | Eriksson, Joel | Garcia, Melissa | Liu, Yong Mei | Koster, Annemarie | Lohman, Kurt | Lyytikäinen, Leo-Pekka | Petersen, Ann-Kristin | Prescott, Jennifer | Stolk, Lisette | Vandenput, Liesbeth | Wood, Andrew R. | Zhuang, Wei Vivian | Ruokonen, Aimo | Hartikainen, Anna-Liisa | Pouta, Anneli | Bandinelli, Stefania | Biffar, Reiner | Brabant, Georg | Cox, David G. | Chen, Yuhui | Cummings, Steven | Ferrucci, Luigi | Gunter, Marc J. | Hankinson, Susan E. | Martikainen, Hannu | Hofman, Albert | Homuth, Georg | Illig, Thomas | Jansson, John-Olov | Johnson, Andrew D. | Karasik, David | Karlsson, Magnus | Kettunen, Johannes | Kiel, Douglas P. | Kraft, Peter | Liu, Jingmin | Ljunggren, Östen | Lorentzon, Mattias | Maggio, Marcello | Markus, Marcello R. P. | Mellström, Dan | Miljkovic, Iva | Mirel, Daniel | Nelson, Sarah | Morin Papunen, Laure | Peeters, Petra H. M. | Prokopenko, Inga | Raffel, Leslie | Reincke, Martin | Reiner, Alex P. | Rexrode, Kathryn | Rivadeneira, Fernando | Schwartz, Stephen M. | Siscovick, David | Soranzo, Nicole | Stöckl, Doris | Tworoger, Shelley | Uitterlinden, André G. | van Gils, Carla H. | Vasan, Ramachandran S. | Wichmann, H.-Erich | Zhai, Guangju | Bhasin, Shalender | Bidlingmaier, Martin | Chanock, Stephen J. | De Vivo, Immaculata | Harris, Tamara B. | Hunter, David J. | Kähönen, Mika | Liu, Simin | Ouyang, Pamela | Spector, Tim D. | van der Schouw, Yvonne T. | Viikari, Jorma | Wallaschofski, Henri | McCarthy, Mark I. | Frayling, Timothy M. | Murray, Anna | Franks, Steve | Järvelin, Marjo-Riitta | de Jong, Frank H. | Raitakari, Olli | Teumer, Alexander | Ohlsson, Claes | Murabito, Joanne M. | Perry, John R. B. | Gibson, Greg
PLoS Genetics  2012;8(7):e1002805.
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
Author Summary
Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.
doi:10.1371/journal.pgen.1002805
PMCID: PMC3400553  PMID: 22829776
10.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane | Gibson, Greg
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455
11.  A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol 
Surakka, Ida | Isaacs, Aaron | Karssen, Lennart C. | Laurila, Pirkka-Pekka P. | Middelberg, Rita P. S. | Tikkanen, Emmi | Ried, Janina S. | Lamina, Claudia | Mangino, Massimo | Igl, Wilmar | Hottenga, Jouke-Jan | Lagou, Vasiliki | van der Harst, Pim | Mateo Leach, Irene | Esko, Tõnu | Kutalik, Zoltán | Wainwright, Nicholas W. | Struchalin, Maksim V. | Sarin, Antti-Pekka | Kangas, Antti J. | Viikari, Jorma S. | Perola, Markus | Rantanen, Taina | Petersen, Ann-Kristin | Soininen, Pasi | Johansson, Åsa | Soranzo, Nicole | Heath, Andrew C. | Papamarkou, Theodore | Prokopenko, Inga | Tönjes, Anke | Kronenberg, Florian | Döring, Angela | Rivadeneira, Fernando | Montgomery, Grant W. | Whitfield, John B. | Kähönen, Mika | Lehtimäki, Terho | Freimer, Nelson B. | Willemsen, Gonneke | de Geus, Eco J. C. | Palotie, Aarno | Sandhu, Manj S. | Waterworth, Dawn M. | Metspalu, Andres | Stumvoll, Michael | Uitterlinden, André G. | Jula, Antti | Navis, Gerjan | Wijmenga, Cisca | Wolffenbuttel, Bruce H. R. | Taskinen, Marja-Riitta | Ala-Korpela, Mika | Kaprio, Jaakko | Kyvik, Kirsten O. | Boomsma, Dorret I. | Pedersen, Nancy L. | Gyllensten, Ulf | Wilson, James F. | Rudan, Igor | Campbell, Harry | Pramstaller, Peter P. | Spector, Tim D. | Witteman, Jacqueline C. M. | Eriksson, Johan G. | Salomaa, Veikko | Oostra, Ben A. | Raitakari, Olli T. | Wichmann, H.-Erich | Gieger, Christian | Järvelin, Marjo-Riitta | Martin, Nicholas G. | Hofman, Albert | McCarthy, Mark I. | Peltonen, Leena | van Duijn, Cornelia M. | Aulchenko, Yurii S. | Ripatti, Samuli | Gibson, Greg
PLoS Genetics  2011;7(10):e1002333.
Recent genome-wide association (GWA) studies described 95 loci controlling serum lipid levels. These common variants explain ∼25% of the heritability of the phenotypes. To date, no unbiased screen for gene–environment interactions for circulating lipids has been reported. We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association (GWA) data from 18 population-based cohorts with European ancestry (maximum N = 32,225). We collected 8 further cohorts (N = 17,102) for replication, and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio (WHR) on total cholesterol (TC) with a combined P-value of 4.79×10−9. There were two potential candidate genes in the region, PCDH7 and CCKAR, with differential expression levels for rs6448771 genotypes in adipose tissue. The effect of WHR on TC was strongest for individuals carrying two copies of G allele, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference in TC concentration, while for A allele homozygous the difference was 0.12 sd. Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles. However, more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus.
Author Summary
Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases. Serum lipid levels are partly inherited, and already 95 loci affecting high- and low-density lipoprotein cholesterol, total cholesterol, and triglycerides have been found. Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition, lifestyle, and sex. It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids, but to date only candidate gene studies for interactions have been reported. We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30,000 population-based samples. When combining results from our initial datasets and 8 additional replication cohorts (maximum N = 17,102), we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4.79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol. In the area surrounding this genetic variant, there were two genes having association between the genotypes and the gene expression in adipose tissue, and we also found enrichment of association in genes belonging to lipid metabolism related functions.
doi:10.1371/journal.pgen.1002333
PMCID: PMC3197672  PMID: 22028671

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