141 research outputs found
New genetic loci link adipose and insulin biology to body fat distribution
No full textBody fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms
Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: The CKDGen Consortium
No full textInterrogation of the six novel loci uncovered in the European ancestry (EA) individuals (CKDGen consortium) in individuals of African ancestry (AA) from the CARe consortium for the trait eGFRcrea.
No full text<p>Ref./Non-Ref. All.: reference/non-reference alleles; RAF: reference allele frequency; SE: standard error.</p>*<p>Characteristics of the six lead SNPs in the EA individuals from the CKDGen consortium can be found in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002584#pgen-1002584-t001" target="_blank">Table 1</a>.</p>§<p>The gene closest to the SNP is listed first and is in boldface if the SNP is located within the gene.</p>**<p>S = number of independent, typed SNPs interrogated.</p>†<p>No LD information available in the HapMap database between the target SNP and the best SNP in the DDX1 region.</p>‡<p>The SNP rs11078903 was not present in the CARe consortium database.</p
Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.
No full textThe prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity
Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: The CKDGen consortium
No full text10.1053/j.ajkd.2012.12.024American Journal of Kidney Diseases616889-898AJKD
Validated SNPs for eGFR and their associations with albuminuria
No full textAlbuminuria and reduced glomerular filtration rate are manifestations of chronic kidney disease (CKD) that predict end-stage renal disease, acute kidney injury, cardiovascular disease and death. We hypothesized that SNPs identified in association with the estimated glomerular filtration rate (eGFR) would also be associated with albuminuria. Within the CKDGen Consortium cohort (n= 31 580, European ancestry), we tested 16 eGFR-associated SNPs for association with the urinary albumin-to-creatinine ratio (UACR) and albuminuria [UACR >25 mg/g (women); 17 mg/g (men)]. In parallel, within the CARe Renal Consortium (n= 5569, African ancestry), we tested seven eGFR-associated SNPs for association with the UACR. We used a Bonferroni-corrected P-value of 0.003 (0.05/16) in CKDGen and 0.007 (0.05/7) in CARe. We also assessed whether the 16 eGFR SNPs were associated with the UACR in aggregate using a beta-weighted genotype score. In the CKDGen Consortium, the minor A allele of rs17319721 in the SHROOM3 gene, known to be associated with a lower eGFR, was associated with lower ln(UACR) levels (beta = -0.034, P-value = 0.0002). No additional eGFR-associated SNPs met the Bonferroni-corrected P-value threshold of 0.003 for either UACR or albuminuria. In the CARe Renal Consortium, there were no associations between SNPs and UACR with a P< 0.007. Although we found the genotype score to be associated with albuminuria (P= 0.0006), this result was driven almost entirely by the known SHROOM3 variant, rs17319721. Removal of rs17319721 resulted in a P-value 0.03, indicating a weak residual aggregate signal. No alleles, previously demonstrated to be associated with a lower eGFR, were associated with the UACR or albuminuria, suggesting that there may be distinct genetic components for these traits
Additional file 2 of Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation
No full textCKDGen study contributions assigned to replication set for illustration of procedures controlling type I error. (DOCX 18 kb
Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: the CKDGen consortium.
No full textChronic kidney disease is associated with cardiovascular disease. We tested for evidence of a shared genetic basis to these traits.We conducted 2 targeted analyses. First, we examined whether known single-nucleotide polymorphisms (SNPs) underpinning kidney traits were associated with a series of vascular phenotypes. Additionally, we tested whether vascular SNPs were associated with markers of kidney damage. Significance was set to 1.5×10(-4) (0.05/325 tests).Vascular outcomes were analyzed in participants from the AortaGen (20,634), CARDIoGRAM (86,995), CHARGE Eye (15,358), CHARGE IMT (31,181), ICBP (69,395), and NeuroCHARGE (12,385) consortia. Tests for kidney outcomes were conducted in up to 67,093 participants from the CKDGen consortium.We used 19 kidney SNPs and 64 vascular SNPs.Vascular outcomes tested were blood pressure, coronary artery disease, carotid intima-media thickness, pulse wave velocity, retinal venular caliber, and brain white matter lesions. Kidney outcomes were estimated glomerular filtration rate and albuminuria.In general, we found that kidney disease variants were not associated with vascular phenotypes (127 of 133 tests were nonsignificant). The one exception was rs653178 near SH2B3 (SH2B adaptor protein 3), which showed direction-consistent association with systolic (P = 9.3 ×10(-10)) and diastolic (P = 1.6 ×10(-14)) blood pressure and coronary artery disease (P = 2.2 ×10(-6)), all previously reported. Similarly, the 64 SNPs associated with vascular phenotypes were not associated with kidney phenotypes (187 of 192 tests were nonsignificant), with the exception of 2 high-correlated SNPs at the SH2B3 locus (P = 1.06 ×10(-07) and P = 7.05 ×10(-08)).The combined effect size of the SNPs for kidney and vascular outcomes may be too low to detect shared genetic associations.Overall, although we confirmed one locus (SH2B3) as associated with both kidney and cardiovascular disease, our primary findings suggest that there is little overlap between kidney and cardiovascular disease risk variants in the overall population. The reciprocal risks of kidney and cardiovascular disease may not be genetically mediated, but rather a function of the disease milieu itself
Genome-wide association and functional follow-up reveals new loci for kidney function
No full textChronic kidney disease (CKD) is an important public health problem with a genetic component. We performed genome-wide association studies in up to 130,600 European ancestry participants overall, and stratified for key CKD risk factors. We uncovered 6 new loci in association with estimated glomerular filtration rate (eGFR), the primary clinical measure of CKD, in or near MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. Morpholino knockdown of mpped2 and casp9 in zebrafish embryos revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. By providing new insights into genes that regulate renal function, these results could further our understanding of the pathogenesis of CKD
Additional file 2 of A Mendelian randomization study of serum uric acid with the risk of venous thromboembolism
No full textAdditional file 2: Supplementary Figure 1. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (CKDGen consortium) on venous thromboembolism. Supplementary Figure 2. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (UKB) on venous thromboembolism. Supplementary Figure 3. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (CKDGen consortium) on deep venous thrombosis. Supplementary Figure 4. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (UKB) on deep venous thrombosis. Supplementary Figure 5. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (CKDGen consortium) on pulmonary embolism. Supplementary Figure 6. The plots of “leave-one-out” analysis method to show the influence of individual SNP on the causal effect of genetically predicted uric acid (UKB) on pulmonary embolism
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