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- Coviello, Andrea D, et al.
(författare)
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A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple Loci implicated in sex steroid hormone regulation.
- 2012
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Ingår i: PLoS genetics. - : Public Library of Science (PLoS). - 1553-7404 .- 1553-7390. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Haring, Robin, et al.
(författare)
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A Network-Based Approach to Visualize Prevalence and Progression of Metabolic Syndrome Components
- 2012
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Ingår i: PLOS ONE. - San Francisco : Public Library of Science. - 1932-6203. ; 7:6, s. e39461-
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Tidskriftsartikel (refereegranskat)abstract
- Background: The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time. Methods & Results: We used data from 3,187 individuals aged 20-79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (2(5)) different states and highlight the most important transitions between the 1,024 (32(2)) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up. Conclusions: We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor.
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