A cautionary tale: the non-causal association between type 2 diabetes risk SNP, rs7756992, and levels of non-coding RNA, CDKAL1-v1

Journal ArticleCopyright © The Author(s) 2015. This article is published with open access at Springerlink.com.Aims/hypothesis: Intronic single nucleotide polymorphisms (SNPs) in the CDKAL1 gene are associated with risk of developing type 2 diabetes. A strong correlation between risk alleles and lower levels of the non-coding RNA, CDKAL1-v1, has recently been reported in whole blood extracted from Japanese individuals. We sought to replicate this association in two independent cohorts: one using whole blood from white UK-resident individuals, and one using a collection of human pancreatic islets, a more relevant tissue type to study with respect to the aetiology of diabetes. Methods: Levels of CDKAL1-v1 were measured by real-time PCR using RNA extracted from human whole blood (n = 70) and human pancreatic islets (n = 48). Expression with respect to genotype was then determined. Results: In a simple linear regression model, expression of CDKAL1-v1 was associated with the lead type 2 diabetes-associated SNP, rs7756992, in whole blood and islets. However, these associations were abolished or substantially reduced in multiple regression models taking into account rs9366357 genotype: a moderately linked SNP explaining a much larger amount of the variation in CDKAL1-v1 levels, but not strongly associated with risk of type 2 diabetes. Conclusions/interpretation: Contrary to previous findings, we provide evidence against a role for dysregulated expression of CDKAL1-v1 in mediating the association between intronic SNPs in CDKAL1 and susceptibility to type 2 diabetes. The results of this study illustrate how caution should be exercised when inferring causality from an association between disease-risk genotype and non-coding RNA expression.MRCNIH

A cautionary tale: the non-causal association between type 2 diabetes risk SNP, rs7756992, and levels of non-coding RNA, CDKAL1-v1

This is the final version of the article. Available from Springer Verlag via the DOI in this record.AIMS/HYPOTHESIS: Intronic single nucleotide polymorphisms (SNPs) in the CDKAL1 gene are associated with risk of developing type 2 diabetes. A strong correlation between risk alleles and lower levels of the non-coding RNA, CDKAL1-v1, has recently been reported in whole blood extracted from Japanese individuals. We sought to replicate this association in two independent cohorts: one using whole blood from white UK-resident individuals, and one using a collection of human pancreatic islets, a more relevant tissue type to study with respect to the aetiology of diabetes. METHODS: Levels of CDKAL1-v1 were measured by real-time PCR using RNA extracted from human whole blood (n = 70) and human pancreatic islets (n = 48). Expression with respect to genotype was then determined. RESULTS: In a simple linear regression model, expression of CDKAL1-v1 was associated with the lead type 2 diabetes-associated SNP, rs7756992, in whole blood and islets. However, these associations were abolished or substantially reduced in multiple regression models taking into account rs9366357 genotype: a moderately linked SNP explaining a much larger amount of the variation in CDKAL1-v1 levels, but not strongly associated with risk of type 2 diabetes. CONCLUSIONS/INTERPRETATION: Contrary to previous findings, we provide evidence against a role for dysregulated expression of CDKAL1-v1 in mediating the association between intronic SNPs in CDKAL1 and susceptibility to type 2 diabetes. The results of this study illustrate how caution should be exercised when inferring causality from an association between disease-risk genotype and non-coding RNA expression.This paper presents independent research funded by the Medical Research Council (grant number MR/J006777/1) and supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the Medical Research Council, UK National Health Service, NIHR or the UK Department of Health

Are the new drugs better? Changing UK prescribing of Type 2 diabetes medications and effects on HbA1c and weight, 2010 to 2016

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Aim: The availability of new glucose‐lowering drugs has changed UK National Institute of Clinical Excellence Type 2 diabetes guidelines, but there has been little evaluation of real‐world use of these drugs, or of the population‐level impact of their use. We examined changes in UK prescribing for patients starting second‐ and third‐line medications, and population‐level trends in glycaemic response and weight change. Methods: We extracted incident second‐ and third‐line oral prescription records for patients with Type 2 diabetes in the UK‐representative Clinical Practice Research Datalink, 2010 to 2016 (n = 68,902). Each year we calculated the proportion of each drug prescribed as the percentage of the total prescribed. We estimated annual mean six‐month HbA1c response and weight change using linear regression, standardised for clinical characteristics. Results: Use of Dipeptidyl peptidase‐4 (DPP4) inhibitors has increased markedly to overtake sulfonylureas as the most commonly prescribed second‐line drug in 2016 (43% vs 34% of total prescriptions compared with 18% v 59% in 2010). Use of sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors has increased rapidly to 14% of second‐line and 27% of third‐line prescriptions in 2016. Mean HbA1c response at six months was stable over time (2016: 13.5 (95% confidence interval 12.8, 14.1) mmol/mol vs 2010: 13.9 (13.6;14.2) mmol/mol, p = 0.21). We found mean weight loss at six months in 2016, in contrast to 2010 where there was mean weight gain (2016: −1.2 (−0.9; −1.5) kg vs 2010: +0.4 (+0.3; +0.5) kg, p < 0.001). Conclusion: The pattern of drug prescribing to manage patients with Type 2 diabetes has changed rapidly in the United Kingdom. Increasing use of DPP4 inhibitors and SGLT2 inhibitors has not resulted in improved glycaemic control but has improved the body weight of patients starting second‐ and third‐line therapy. Acknowledgement: This abstract is submitted on behalf of the MASTERMIND consortium

Pitfalls of haplotype phasing from amplicon-based long-read sequencing

This is the final version. Available on open access from Nature Research via the DOI in this recordThe long-read sequencers from Pacific Bioscience (PacBio) and Oxford Nanopore Technologies (ONT) offer the opportunity to phase mutations multiple kilobases apart directly from sequencing reads. In this study, we used long-range PCR with ONT and PacBio sequencing to phase two variants 9 kb apart in the RET gene. We also re-analysed data from a recent paper which had apparently successfully used ONT to phase clinically important haplotypes at the CYP2D6 and HLA loci. From these analyses, we demonstrate PCR-chimera formation during PCR amplification and reference alignment bias are pitfalls that need to be considered when attempting to phase variants using amplicon-based long-read sequencing technologies. These methodological pitfalls need to be avoided if the opportunities provided by long-read sequencers are to be fully exploited.Wellcome Trus

Adherence to oral glucose-lowering therapies and associations with 1-year HbA_1c:A retrospective cohort analysis in a large primary care database

JOURNAL ARTICLEOBJECTIVE: The impact of taking oral glucose-lowering medicines intermittently, rather than as recommended, is unclear. We conducted a retrospective cohort study using community-acquired U.K. clinical data (Clinical Practice Research Database [CPRD] and GoDARTS database) to examine the prevalence of nonadherence to treatment for type 2 diabetes and investigate its potential impact on HbA1c reduction stratified by type of glucose-lowering medication. RESEARCH DESIGN AND METHODS: Data were extracted for patients treated between 2004 and 2014 who were newly-prescribed metformin, sulfonylurea, thiazolidinedione, or dipeptidyl peptidase-4 inhibitors and who continued to obtain prescriptions over 1 year. Cohorts were defined by prescribed medication type, and good adherence was defined as a medication possession ratio ≥0.8. Linear regression was used to determine potential associations between adherence and 1-year baseline-adjusted HbA1c reduction. RESULTS: In CPRD and GoDARTS, 13% and 15% of patients, respectively, were nonadherent. Proportions of nonadherent patients varied by the oral glucose-lowering treatment prescribed (range 8.6% [thiazolidinedione] to 18.8% [metformin]). Nonadherent, compared with adherent, patients had a smaller HbA1c reduction (0.4% [4.4mmmol/mol] and 0.46% [5.0 mmol/mol] for CPRD and GoDARTs, respectively). Difference in HbA1c response for adherent compared with nonadherent patients varied by drug (range 0.38% [4.1 mmol/mol] to 0.75% [8.2 mmol/mol] lower in adherent group). Decreasing levels of adherence were consistently associated with a smaller reduction in HbA1c. CONCLUSIONS: Reduced medication adherence for commonly used glucose-lowering therapies among patients persisting with treatment is associated with smaller HbA1c reductions compared with those taking treatment as recommended. Differences observed in HbA1c responses to glucose-lowering treatments may be explained in part by their intermittent use.A.J.F. and R.R.H. are National Institute for Health Research (NIHR) Senior Investigators and receive additional support from the Oxford NIHR Biomedical Research Centre. M.N.W. was supported by a Wellcome Trust Institutional Strategic Support Award (WT097835MF). E.R.P. holds a Wellcome Trust New Investigator award. The MASTERMIND consortium is funded by the U.K. Medical Research Council MR-K005707-1. The funder of the trial had no role in study design, data collection, data analysis, data interpretation, or writing of the report

Penetrance and expressivity of mitochondrial variants in a large clinically unselected population

\ua9 The Author(s) 2023. Published by Oxford University Press. Whole genome sequencing (WGS) from large clinically unselected cohorts provides a unique opportunity to assess the penetrance and expressivity of rare and/or known pathogenic mitochondrial variants in population. Using WGS from 179 862 clinically unselected individuals from the UK Biobank, we performed extensive single and rare variant aggregation association analyses of 15 881 mtDNA variants and 73 known pathogenic variants with 15 mitochondrial disease-relevant phenotypes. We identified 12 homoplasmic and one heteroplasmic variant (m.3243A&gt;G) with genome-wide significant associations in our clinically unselected cohort. Heteroplasmic m.3243A&gt;G (MAF = 0.0002, a known pathogenic variant) was associated with diabetes, deafness and heart failure and 12 homoplasmic variants increased aspartate aminotransferase levels including three low-frequency variants (MAF ~0.002 and beta~0.3 SD). Most pathogenic mitochondrial disease variants (n = 66/74) were rare in the population (&lt;1:9000). Aggregated or single variant analysis of pathogenic variants showed low penetrance in unselected settings for the relevant phenotypes, except m.3243A&gt;G. Multi-system disease risk and penetrance of diabetes, deafness and heart failure greatly increased with m.3243A&gt;G level ≥ 10%. The odds ratio of these traits increased from 5.61, 12.3 and 10.1 to 25.1, 55.0 and 39.5, respectively. Diabetes risk with m.3243A&gt;G was further influenced by type 2 diabetes genetic risk. Our study of mitochondrial variation in a large-unselected population identified novel associations and demonstrated that pathogenic mitochondrial variants have lower penetrance in clinically unselected settings. m.3243A&gt;G was an exception at higher heteroplasmy showing a significant impact on health making it a good candidate for incidental reporting

SavvyCNV: Genome-wide CNV calling from off-target reads

This is the uncorrected proof. The final version is available on open access from Public Library of Science via the DOI in this recordData Availability: The SavvyCNV tool and the code used to run the benchmarking comparisons are freely available on github. The tool is available at https://github.com/rdemolgen/SavvySuite. The code used to run the benchmarking comparisons is available at: https://github.com/exeter-matthew-wakeling/SavvyCNV_benchmarking. Our study uses the ICR96 data set for benchmarking, which is publicly available and can be accessed through the European-Genome phenome Archive (EGA) under the accession number EGAS00001002428. The dataset of 2591 samples referred to the molecular genetics department at the Royal Devon and Exeter Hospital for genetic testing cannot be shared due to patient confidentiality issues, as the genotype data could be used to identify individuals and so cannot be made openly available. Requests for access to the anonymised data by researchers will be considered following an application to the Genetic Beta Cell Research Bank (https://www.diabetesgenes.org/current-research/genetic-beta-cell-research-bank/) with proposals reviewed by the Genetic Data Access Committee.Identifying copy number variants (CNVs) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data from exome and targeted sequencing data to call germline CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions. We have developed a new tool, SavvyCNV, to exploit this 'free data' to call CNVs across the genome. We benchmarked SavvyCNV against five state-of-the-art CNV callers using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming the five other tools at calling CNVs genome-wide, using off-target or on-target reads from targeted panel and exome sequencing. We then applied SavvyCNV to clinical samples sequenced using a targeted panel and were able to call previously undetected clinically-relevant CNVs, highlighting the utility of this tool within the diagnostic setting. SavvyCNV outperforms existing tools for calling CNVs from off-target reads. It can call CNVs genome-wide from targeted panel and exome data, increasing the utility and diagnostic yield of these tests. SavvyCNV is freely available at https://github.com/rdemolgen/SavvySuite.Medical Research Council (MRC)Research EnglandDiabetes U

The functional "KL-VS" variant of KLOTHO is not associated with type 2 diabetes in 5028 UK Caucasians

BACKGROUND: Klotho has an important role in insulin signalling and the development of ageing-like phenotypes in mice. The common functional "KL-VS" variant in the KLOTHO (KL) gene is associated with longevity in humans but its role in type 2 diabetes is not known. We performed a large case-control and family-based study to test the hypothesis that KL-VS is associated with type 2 diabetes in a UK Caucasian population. METHODS: We genotyped 1793 cases, 1619 controls and 1616 subjects from 509 families for the single nucleotide polymorphism (SNP) F352V (rs9536314) that defines the KL-VS variant. Allele and genotype frequencies were compared between cases and controls. Family-based analysis was used to test for over- or under-transmission of V352 to affected offspring. RESULTS: Despite good power to detect odds ratios of 1.2, there were no significant associations between alleles or genotypes and type 2 diabetes (V352 allele: odds ratio = 0.96 (0.84–1.09)). Additional analysis of quantitative trait data in 1177 healthy control subjects showed no association of the variant with fasting insulin, glucose, triglycerides, HDL- or LDL-cholesterol (all P > 0.05). However, the HDL-cholesterol levels observed across the genotype groups showed a similar, but non-significant, pattern to previously reported data. CONCLUSION: This is the first large-scale study to examine the association between common functional variation in KL and type 2 diabetes risk. We have found no evidence that the functional KL-VS variant is a risk factor for type 2 diabetes in a large UK Caucasian case-control and family-based study

Evaluation of Evidence for Pathogenicity Demonstrates that BLK, KLF11 and PAX4 Should not be Included in Diagnostic Testing for MODY

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this recordData and Resource Availability: UK Biobank data is accessible via application: https://www.ukbiobank.ac.uk/enable-yourresearch. GnomAD data is publically available: https://gnomad.broadinstitute.org/. The MODY cohort data is not publicly available due the limitations of the current ethics and to protect patient confidentiality but is available from the corresponding authors on reasonable request. No applicable resources were generated or analyzed during the current study.Maturity Onset Diabetes of the Young (MODY) is an autosomal dominant form of monogenic diabetes, reported to be caused by variants in 16 genes. Concern has been raised about whether variants in BLK (MODY11), KLF11 (MODY7) and PAX4 (MODY9) cause MODY. We examined variant-level genetic evidence (co-segregation with diabetes and frequency in population) for published putative pathogenic variants in these genes and used burden testing to test gene-level evidence in a MODY cohort (n=1227) compared to population control (UK Biobank, n=185,898). For comparison we analysed well-established causes of MODY, HNF1A and HNF4A. The published variants in BLK, KLF11 and PAX4 showed poor co-segregation with diabetes (combined LOD scores ≤1.2), compared to HNF1A and HNF4A (LOD scores >9), and are all too common to cause MODY (minor allele frequency >4.95x10-5). Ultra-rare missense and protein-truncating variants (PTVs) were not enriched in a MODY cohort compared to the UK Biobank (PTVs P>0.05, missense P>0.1 for all three genes) while HNF1A and HNF4A were enriched (P<10-6). Sensitivity analyses using different population cohorts supported our results. Variant and gene-level genetic evidence does not support BLK, KLF11 or PAX4 as causes of MODY. They should not be included in MODY diagnostic genetic testing.Medical Research Council (MRC)Diabetes UKResearch EnglandWellcome Trus

Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

This is the final version. Available on open access from the BMJ Publishing Group via the DOI in this recordData sharing: The data reported in this paper are available via application directly to the UK Biobank. Direct to consumer data are available from the Personal Genome Project website.Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.Wellcome TrustNational Institute for Health Research (NIHR