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- Wessel, Jennifer, et al.
(författare)
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Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
- 2015
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 6
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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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- Castellani, Carlo, et al.
(författare)
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Consensus on the use and interpretation of cystic fibrosis mutation analysis in clinical practice
- 2008
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Ingår i: Journal of Cystic Fibrosis. - : Elsevier BV. - 1569-1993 .- 1873-5010. ; 7:3, s. 179-96
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Tidskriftsartikel (refereegranskat)abstract
- It is often challenging for the clinician interested in cystic fibrosis (CF) to interpret molecular genetic results, and to integrate them in the diagnostic process. The limitations of genotyping technology, the choice of mutations to be tested, and the clinical context in which the test is administered can all influence how genetic information is interpreted. This paper describes the conclusions of a consensus conference to address the use and interpretation of CF mutation analysis in clinical settings. Although the diagnosis of CF is usually straightforward, care needs to be exercised in the use and interpretation of genetic tests: genotype information is not the final arbiter of a clinical diagnosis of CF or CF transmembrane conductance regulator (CFTR) protein related disorders. The diagnosis of these conditions is primarily based on the clinical presentation, and is supported by evaluation of CFTR function (sweat testing, nasal potential difference) and genetic analysis. None of these features are sufficient on their own to make a diagnosis of CF or CFTR-related disorders. Broad genotype/phenotype associations are useful in epidemiological studies, but CFTR genotype does not accurately predict individual outcome. The use of CFTR genotype for prediction of prognosis in people with CF at the time of their diagnosis is not recommended. The importance of communication between clinicians and medical genetic laboratories is emphasized. The results of testing and their implications should be reported in a manner understandable to the clinicians caring for CF patients.
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- Mahajan, Anubha, et al.
(författare)
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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
- 2018
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 50:4, s. 559-571
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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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