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Sökning: WFRF:(Bombieri Cristina)

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  • Wessel, Jennifer, et al. (författare)
  • Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
  • 2015
  • Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723 .- 2041-1723. ; 6
  • Tidskriftsartikel (refereegranskat)abstract
    • Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF = 1.4%) with lower FG (beta = -0.09 +/- 0.01 mmol l(-1), P = 3.4 x 10(-12)), T2D risk (OR[95% CI] = 0.86[0.76-0.96], P = 0.010), early insulin secretion (beta = -0.07 +/- 0.035 pmol(insulin) mmol(glucose)(-1), P = 0.048), but higher 2-h glucose (beta = 0.16 +/- 0.05 mmol l(-1), P = 4.3 x 10(-4)). We identify a gene-based association with FG at G6PC2 (p(SKAT) = 6.8 x 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF = 20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (beta = 0.02 +/- 0.004 mmol l(-1), P = 1.3 x 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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3.
  • Huffman, Jennifer E., et al. (författare)
  • Modulation of Genetic Associations with Serum Urate Levels by Body-Mass-Index in Humans
  • 2015
  • Ingår i: PLOS ONE. - 1932-6203. ; 10:3
  • Tidskriftsartikel (refereegranskat)abstract
    • We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, P-inter= 2.6 x 10(-8)). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDAR-ADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10(-8)), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10(-8)), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10(-4)). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.
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4.
  • Mahajan, Anubha, et al. (författare)
  • Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
  • 2018
  • Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 50:4, s. 559-571
  • Tidskriftsartikel (refereegranskat)abstract
    • We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10−7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent ‘false leads’ with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
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