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.

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 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.

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 3 10 À8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

Several diseases have been clinically or genetically related to cystic fibrosis (CF), but a consensus definition is lacking. Here, we present a proposal for consensus guidelines on cystic fibrosis transmembrane conductance regulator (CFTR)-related disorders (CFTR-RDs), reached after expert discussion and two dedicated workshops. A CFTR-RD may be defined as "a clinical entity associated with CFTR dysfunction that does not fulfil diagnostic criteria for CF". The utility of sweat testing, mutation analysis, nasal potential difference, and/or intestinal current measurement for the differential diagnosis of CF and CFTR-RD is discussed. Algorithms which use genetic and functional diagnostic tests to distinguish CF and CFTR-RDs are presented. According to present knowledge, congenital bilateral absence of vas deferens (CBAVD), acute recurrent or chronic pancreatitis and disseminated bronchiectasis, all with CFTR dysfunction, are CFTR-RDs.

An abbreviated tract of five thymidines (5T) in intron 8 of the cystic fibrosis transmembrane conductance regulator (CFTR) gene is found in approximately 10% of individuals in the general population. When found in trans with a severe CFTR mutation, 5T can result in male infertility, nonclassic cystic fibrosis, or a normal phenotype. To test whether the number of TG repeats adjacent to 5T influences disease penetrance, we determined TG repeat number in 98 patients with male infertility due to congenital absence of the vas deferens, 9 patients with nonclassic CF, and 27 unaffected individuals (fertile men). Each of the individuals in this study had a severe CFTR mutation on one CFTR gene and 5T on the other. Of the unaffected individuals, 78% (21 of 27) had 5T adjacent to 11 TG repeats, compared with 9% (10 of 107) of affected individuals. Conversely, 91% (97 of 107) of affected individuals had 12 or 13 TG repeats, versus only 22% (6 of 27) of unaffected individuals (P<.00001). Those individuals with 5T adjacent to either 12 or 13 TG repeats were substantially more likely to exhibit an abnormal phenotype than those with 5T adjacent to 11 TG repeats (odds ratio 34.0, 95% CI 11.1-103.7, P<.00001). Thus, determination of TG repeat number will allow for more accurate prediction of benign versus pathogenic 5T alleles.