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Bouwland-Both M.I.,Erasmus Medical Center | Van Mil N.H.,Erasmus Medical Center | Stolk L.,Erasmus Medical Center | Eilers P.H.C.,Erasmus Medical Center | And 8 more authors.
PLoS ONE | Year: 2013

Changes in epigenetic programming of embryonic growth genes during pregnancy seem to affect fetal growth. Therefore, in a population-based prospective birth cohort in the Netherlands, we examined associations between fetal and infant growth and DNA methylation of IGF2DMR , H19 and MTHFR. For this study, we selected 69 case children born small-forgestational age (SGA, birth weight <-2SDS) and 471 control children. Fetal growth was assessed with serial ultrasound measurements. Information on birth outcomes was retrieved from medical records. Infant weight was assessed at three and six months. Methylation was assessed in DNA extracted from umbilical cord white blood cells. Analyses were performed using linear mixed models with DNA methylation as dependent variable. The DNA methylation levels of IGF2DMR and H19 in the control group were, median (90% range), 53.6% (44.5-61.6) and 30.0% (25.6-34.2) and in the SGA group 52.0% (43.9-60.9) and 30.5% (23.9-32.9), respectively. The MTHFR region was found to be hypomethylated with limited variability in the control and SGA group, 2.5% (1.4-4.0) and 2.4% (1.5-3.8), respectively. SGA was associated with lower IGF2DMR DNA methylation (β = -1.07, 95% CI -1.93; -0.21, P-value = 0.015), but not with H19 methylation. A weight gain in the first three months after birth was associated with lower IGF2DMR DNA methylation (β = -0.53, 95% CI -0.91; -0.16, P-value = 0.005). Genetic variants in the IGF2/H19 locus were associated with IGF2DMR DNA methylation (P-value<0.05), but not with H19 methylation. Furthermore, our results suggest a possibility of mediation of DNA methylation in the association between the genetic variants and SGA. To conclude, IGF2DMR and H19 DNA methylation is associated with fetal and infant growth. © 2013 Bouwland-Both et al. Source

Mihaescu R.,Erasmus Medical Center | Van Zitteren M.,Erasmus Medical Center | Van Hoek M.,Erasmus Medical Center | Sijbrands E.J.G.,Netherlands Consortium of Healthy Aging | And 7 more authors.
American Journal of Epidemiology | Year: 2010

Reclassification is observed even when there is no or minimal improvement in the area under the receiver operating characteristic curve (AUC), and it is unclear whether it indicates improved clinical utility. The authors investigated total reclassification, net reclassification improvement, and integrated discrimination improvement for different ΔAUC using empirical and simulated data. Empirical analyses compared prediction of type 2 diabetes risk based on age, sex, and body mass index with prediction updated with 18 established genetic risk factors. Simulated data were used to investigate measures of reclassification against ΔAUCs of 0.005, 0.05, and 0.10. Total reclassification and net reclassification improvement were calculated for all possible cutoff values. The AUC of type 2 diabetes risk prediction improved from 0.63 to 0.66 when 18 polymorphisms were added, whereas total reclassification ranged from 0% to 22.5% depending on the cutoff value chosen. In the simulation study, total reclassification, net reclassification improvement, and integrated discrimination improvement increased with higher ΔAUC. When ΔAUC was low (0.005), net reclassification improvement values were close to zero, integrated discrimination improvement was 0.08% (P > 0.05), but total reclassification ranged from 0 to 6.7%. Reclassification increases with increasing AUC but predominantly varies with the cutoff values chosen. Reclassification observed in the absence of AUC increase is unlikely to improve clinical utility. © 2010 The Author. Source

Brautbar A.,Marshfield Clinic | Brautbar A.,Baylor College of Medicine | Brautbar A.,Center for Cardiovascular Disease Prevention | Pompeii L.A.,University of Texas Health Science Center at Houston | And 20 more authors.
Atherosclerosis | Year: 2012

Objective: Multiple studies have identified single-nucleotide polymorphisms (SNPs) that are associated with coronary heart disease (CHD). We examined whether SNPs selected based on predefined criteria will improve CHD risk prediction when added to traditional risk factors (TRFs). Methods: SNPs were selected from the literature based on association with CHD, lack of association with a known CHD risk factor, and successful replication. A genetic risk score (GRS) was constructed based on these SNPs. Cox proportional hazards model was used to calculate CHD risk based on the Atherosclerosis Risk in Communities (ARIC) and Framingham CHD risk scores with and without the GRS. Results: The GRS was associated with risk for CHD (hazard ratio [HR] = 1.10; 95% confidence interval [CI]: 1.07-1.13). Addition of the GRS to the ARIC risk score significantly improved discrimination, reclassification, and calibration beyond that afforded by TRFs alone in non-Hispanic whites in the ARIC study. The area under the receiver operating characteristic curve (AUC) increased from 0.742 to 0.749 (Δ = 0.007; 95% CI, 0.004-0.013), and the net reclassification index (NRI) was 6.3%. Although the risk estimates for CHD in the Framingham Offspring (HR = 1.12; 95% CI: 1.10-1.14) and Rotterdam (HR = 1.08; 95% CI: 1.02-1.14) Studies were significantly improved by adding the GRS to TRFs, improvements in AUC and NRI were modest. Conclusion: Addition of a GRS based on direct associations with CHD to TRFs significantly improved discrimination and reclassification in white participants of the ARIC Study, with no significant improvement in the Rotterdam and Framingham Offspring Studies. © 2012 Elsevier Ireland Ltd. Source

Coviello A.D.,Boston University | Coviello A.D.,Lung and Blood Institutes The Framingham Heart Study | Haring R.,University of Greifswald | Wellons M.,University of Alabama at Birmingham | And 114 more authors.
PLoS Genetics | Year: 2012

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. Source

Stolk L.,Erasmus University Rotterdam | Perry J.R.B.,University of Exeter | Perry J.R.B.,University of Oxford | Chasman D.I.,Brigham and Womens Hospital | And 222 more authors.
Nature Genetics | Year: 2012

To newly identify loci for age at natural menopause, we carried out a meta-analysis of 22 genome-wide association studies (GWAS) in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 loci newly associated with age at natural menopause (at P < 5 - 10 g8). Candidate genes located at these newly associated loci include genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG and PRIM1) and immune function (IL11, NLRP11 and PRRC2A (also known as BAT2)). Gene-set enrichment pathway analyses using the full GWAS data set identified exoDNase, NF-I °B signaling and mitochondrial dysfunction as biological processes related to timing of menopause. © 2012 Nature America, Inc. All rights reserved. Source

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