Stratification of glioma based on stemness scores in bulk and single-cell transcriptomes.

BACKGROUND: Brain tumours are known to have a high mortality and morbidity rate due to their localised and frequent invasive growth. The concept that glioma resistance could originate from the dissimilarity in the vulnerability of clonogenic glial stem cells to chemotherapeutic drugs and radiation has driven the scientific community to reexamine the comprehension of glioma growth and strategies that target these cells or modify their stemness. METHODS: Based on the enrichment scores of 12 stemness signatures, we identified glioma subtypes in both tumour bulks and single cells by clustering analysis. Furthermore, we comprehensively compared molecular and clinical features among the glioma subtypes. RESULTS: Consistently, in seven different datasets, hierarchical clustering uncovered three subtypes of glioma, termed Stem-H, Stem-M, and Stem-L, with high, medium, and low stemness signatures, respectively. Stem-H and Stem-L exhibited the most unfavorable and favourable overall and disease-free survival, respectively. Stem-H showed the highest enrichment scores of the EMT, invasion, proliferation, differentiation, and metastasis processes signatures, while Stem-L displayed the lowest. Stem-H harboured a greater proportion of late-stage tumours compared to Stem-L. Moreover, Stem-H manifested higher tumour mutation burden, DNA damage repair and cell cycle activity, intratumour heterogeneity, and a more frequent incidence of TP53 and EGFR mutations than Stem-L. In contrast, Stem-L had higher O6-Methylguanine-DNA Methyltransferase (MGMT) methylation levels. CONCLUSION: The classification of glioma based on stemness may offer new insights into the biology of the tumour, as well as more accurate clinical management of the disease.

Genetic and Epigenetic Associations with Post-Transplant Diabetes Mellitus.

Post-transplant diabetes mellitus (PTDM) is a common complication of solid organ transplantation. PTDM prevalence varies due to different diabetes definitions. Consensus guidelines for the diagnosis of PTDM have been published based on random blood glucose levels, glycated hemoglobin (HbA1c), and oral glucose tolerance test (OGTT). The task of diagnosing PTDM continues to pose challenges, given the potential for diabetes to manifest at different time points after transplantation, thus demanding constant clinical vigilance and repeated testing. Interpreting HbA1c levels can be challenging after renal transplantation. Pre-transplant risk factors for PTDM include obesity, sedentary lifestyle, family history of diabetes, ethnicity (e.g., African-Caribbean or South Asian ancestry), and genetic risk factors. Risk factors for PTDM include immunosuppressive drugs, weight gain, hepatitis C, and cytomegalovirus infection. There is also emerging evidence that genetic and epigenetic variation in the organ transplant recipient may influence the risk of developing PTDM. This review outlines many known risk factors for PTDM and details some of the pathways, genetic variants, and epigenetic features associated with PTDM. Improved understanding of established and emerging risk factors may help identify people at risk of developing PTDM and may reduce the risk of developing PTDM or improve the management of this complication of organ transplantation.

When should we offer antenatal sequencing for urinary tract malformations? A systematic review, cohort study and meta-analysis.

OBJECTIVE: Determine the incremental yield of prenatal exome sequencing (PES) over chromosome microarray (CMA) and/or karyotype for urinary tract malformations (UTMs). METHOD: A prospective cohort study encompassing data from the English Genomic Medicine Service North Thames Laboratory Hub for fetuses with bilateral echogenic kidneys (BEKs) was combined with data from a systematic review. MEDLINE, EMBASE, Web of Science, MedRxiv and GreyLit were searched from 01/2010-02/2023 for studies reporting on the yield of PES over CMA or karyotype in fetuses with UTMs. Pooled incremental yield was determined using a random effects model. PROSPERO CRD42023364544. RESULTS: Fourteen studies (410 cases) were included. The incremental yield for multisystem UTMs, any isolated UTMs, and BEKs was 31% [95% CI, 18%-46%; I2  = 78%], 16% [95% CI, 6%-26%; I2  = 80%] and 51% [95% CI, 27%-75%; I2  = 34%]. The most common clinical diseases and syndromes identified, based on the variant genes detected, were Bardet-Biedl syndrome (BBS genes), dominant and recessive polycystic kidney diseases (PKD1, PKD2 and PKHD1) and renal cysts and diabetes syndrome (HNF1B). CONCLUSION: There was a notable incremental genetic diagnostic yield when PES was applied to multisystem UTMs and BEKs. There was a modest incremental yield when this technique was used for UTMs other than BEKs.

Stakeholder perceptions of complementary and alternative medicine from people living with rare diseases in Northern Ireland.

INTRODUCTION: Only five percent of rare diseases have an orthodox modern medicine treatment available, therefore patients often utilise complementary and alternative medicines (CAMs) to help manage their condition. Several CAMs are supported by scientific evidence as effective for aiding management of rare diseases, however the methodological rigour of published literature may be sub-optimal and it can be difficult to show that an outcome is a direct result of the CAM intervention. Patients and healthcare professionals must weigh up the safety, efficacy, practical logistics, and financial implications of utilising CAMs for rare diseases. This study aimed to elucidate perspectives of stakeholders, (individuals with rare diseases, carers, family members, CAMs practitioners and healthcare professionals) on the usage of CAMs for rare diseases across Northern Ireland. METHODS: An online survey was open January - February 2019 (n = 29 responses). Themes identified from the survey were then discussed with stakeholders in a semi-structured discussion workshop in March, 2019. RESULTS: Patients with rare diseases reported CAMs as effective in the management of their condition, in particular acupuncture, dietary supplements, herbal medicines, homeopathy, hydrotherapy / swimming, kinesiology, mindfulness, pilates, reflexology, tai chi, and yoga. In this study, seven survey respondents experienced a negative side effect from CAMs. Workshop participants raised the lack of information available about CAMs and rare disease. Both the survey and workshop identified inequality of access with participants reporting CAMs to be expensive and expressing a desire for more therapies to be available within the United Kingdom (UK) National Healthcare Service (NHS). CONCLUSIONS: Many patients with rare diseases report that CAMs are helpful in the management of their condition. More information, high quality research, and education about CAMs is required for patients and healthcare professionals to help make informed decisions about the usage of CAMs for rare diseases. Improved communication, information, and health and social care in general would help individuals be more confident and knowledgeable about therapeutic options in relation to their rare disease(s).

Albuminuria-Related Genetic Biomarkers: Replication and Predictive Evaluation in Individuals with and without Diabetes from the UK Biobank.

Increased albuminuria indicates underlying glomerular pathology and is associated with worse renal disease outcomes, especially in diabetic kidney disease. Many single nucleotide polymorphisms (SNPs), associated with albuminuria, could be potentially useful to construct polygenic risk scores (PRSs) for kidney disease. We investigated the diagnostic accuracy of SNPs, previously associated with albuminuria-related traits, on albuminuria and renal injury in the UK Biobank population, with a particular interest in diabetes. Multivariable logistic regression was used to evaluate the influence of 91 SNPs on urine albumin-to-creatinine ratio (UACR)-related traits and kidney damage (any pathology indicating renal injury), stratifying by diabetes. Weighted PRSs for microalbuminuria and UACR from previous studies were used to calculate the area under the receiver operating characteristic curve (AUROC). CUBN-rs1801239 and DDR1-rs116772905 were associated with all the UACR-derived phenotypes, in both the overall and non-diabetic cohorts, but not with kidney damage. Several SNPs demonstrated different effects in individuals with diabetes compared to those without. SNPs did not improve the AUROC over currently used clinical variables. Many SNPs are associated with UACR or renal injury, suggesting a role in kidney dysfunction, dependent on the presence of diabetes in some cases. However, individual SNPs or PRSs did not improve the diagnostic accuracy for albuminuria or renal injury compared to standard clinical variables.

Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes.

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case-control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case-control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10-6). Telomere length was also significantly reduced (p = 6.6 × 10-5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10-8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

Four missense genetic variants in CUBN are associated with higher levels of eGFR in non-diabetes but not in diabetes mellitus or its subtypes: A genetic association study in Europeans.

Aim: Rare genetic variants in the CUBN gene encoding the main albumin-transporter in the proximal tubule of the kidneys have previously been associated with microalbuminuria and higher urine albumin levels, also in diabetes. Sequencing studies in isolated proteinuria suggest that these variants might not affect kidney function, despite proteinuria. However, the relation of these CUBN missense variants to the estimated glomerular filtration rate (eGFR) is largely unexplored. We hereby broadly examine the associations between four CUBN missense variants and eGFRcreatinine in Europeans with Type 1 (T1D) and Type 2 Diabetes (T2D). Furthermore, we sought to deepen our understanding of these variants in a range of single- and aggregate- variant analyses of other kidney-related traits in individuals with and without diabetes mellitus. Methods: We carried out a genetic association-based linear regression analysis between four CUBN missense variants (rs141640975, rs144360241, rs45551835, rs1801239) and eGFRcreatinine (ml/min/1.73 m2, CKD-EPIcreatinine(2012), natural log-transformed) in populations with T1D (n ~ 3,588) or T2D (n ~ 31,155) from multiple European studies and in individuals without diabetes from UK Biobank (UKBB, n ~ 370,061) with replication in deCODE (n = 127,090). Summary results of the diabetes-group were meta-analyzed using the fixed-effect inverse-variance method. Results: Albeit we did not observe associations between eGFRcreatinine and CUBN in the diabetes-group, we found significant positive associations between the minor alleles of all four variants and eGFRcreatinine in the UKBB individuals without diabetes with rs141640975 being the strongest (Effect=0.02, PeGFR_creatinine=2.2 × 10-9). We replicated the findings for rs141640975 in the Icelandic non-diabetes population (Effect=0.026, PeGFR_creatinine=7.7 × 10-4). For rs141640975, the eGFRcreatinine-association showed significant interaction with albuminuria levels (normo-, micro-, and macroalbuminuria; p = 0.03). An aggregated genetic risk score (GRS) was associated with higher urine albumin levels and eGFRcreatinine. The rs141640975 variant was also associated with higher levels of eGFRcreatinine-cystatin C (ml/min/1.73 m2, CKD-EPI2021, natural log-transformed) and lower circulating cystatin C levels. Conclusions: The positive associations between the four CUBN missense variants and eGFR in a large population without diabetes suggests a pleiotropic role of CUBN as a novel eGFR-locus in addition to it being a known albuminuria-locus. Additional associations with diverse renal function measures (lower cystatin C and higher eGFRcreatinine-cystatin C levels) and a CUBN-focused GRS further suggests an important role of CUBN in the future personalization of chronic kidney disease management in people without diabetes.

Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease.

The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.

A genome-wide association study identifies a possible role for cannabinoid signalling in the pathogenesis of diabetic kidney disease.

Diabetic kidney disease (DKD), also known as diabetic nephropathy, is the leading cause of renal impairment and end-stage renal disease. Patients with diabetes are at risk for DKD because of poor control of their blood glucose, as well as nonmodifiable risk factors including age, ethnicity, and genetics. This genome-wide association study (GWAS) was conducted for the first time in the Emirati population to investigate possible genetic factors associated with the development and progression of DKD. We included data on 7,921,925 single nucleotide polymorphism (SNPs) in 258 cases of type 2 diabetes mellitus (T2DM) who developed DKD and 938 control subjects with T2DM who did not develop DKD. GWAS suggestive results (P < 1 × 10-5) were further replicated using summary statistics from three cohorts with T2DM-induced DKD (Bio Bank Japan data, UK Biobank, and FinnGen Project data) and T1DM-induced DKD (UK-ROI cohort data from Belfast, UK). When conducting a multiple linear regression model for gene-set analyses, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10-6. SNPs in CNR2 gene, encodes cannabinoid receptor 2 or CB2, were replicated in Japanese samples with the leading SNP rs2501391 showing a Pcombined = 9.3 × 10-7, and odds ratio = 0.67 in association with DKD associated with T2DM, but not with T1DM, without any significant association with T2DM itself. The allele frequencies of our cohort and those of the replication cohorts were in most cases markedly different. In addition, we replicated the association between rs1564939 in the GLRA3 gene and DKD in T2DM (P = 0.016, odds ratio = 0.54 per allele C). Our findings suggest evidence that cannabinoid signalling may be involved in the development of DKD through CB2, which is expressed in different kidney regions and known to be involved in insulin resistance, inflammation, and the development of kidney fibrosis.

Longitudinal Epigenome-Wide Analysis of Kidney Transplant Recipients Pretransplant and Posttransplant.

Introduction: Kidney transplantation remains the gold standard of treatment for end-stage renal disease (ESRD), with improved patient outcomes compared with dialysis. Epigenome-Wide Association Analysis (EWAS) of DNA methylation may identify markers that contribute to an individual's risk of adverse transplant outcomes, yet only a limited number of EWAS have been conducted in kidney transplant recipients. This EWAS aimed to interrogate the methylation profile of a kidney transplant recipient cohort with minimal posttransplant complications, exploring differences in samples pretransplant and posttransplant. Methods: We compared differentially methylated cytosine-phosphate-guanine sites (dmCpGs) in samples derived from peripheral blood mononuclear cells of the same kidney transplant recipients, collected both pretransplant and posttransplant (N = 154), using the Infinium MethylationEPIC microarray (Illumina, San Diego, CA). Recipients received kidneys from deceased donors and had a mean of 17 years of follow-up. Results: Five top-ranked dmCpGs were significantly different at false discovery rate (FDR) adjusted P ≤ 9 × 10-8; cg23597162 within JAZF1, cg25187293 within BTNL8, cg17944885, located between ZNF788P and ZNF625-ZNF20, cg14655917 located between ASB4 and PDK4 and cg09839120 located between GIMAP6 and EIF2AP3. Conclusion: Five dmCpGs were identified at the generally accepted EWAS critical significance level of FDR adjusted P (P FDRadj) ≤ 9 × 10-8, including cg23597162 (within JAZF1) and cg17944885, which have prior associations with chronic kidney disease (CKD). Comparing individuals with no evidence of posttransplant complications (N = 105) demonstrated that 693,555 CpGs (89.57%) did not display any significant difference in methylation (P FDRadj ≥ 0.05), thereby this study establishes an important reference for future epigenetic studies that seek to identify markers of posttransplant complications.

Differential methylation in CD44 and SEC23A is associated with time preference in older individuals.

Time preference is a measure used to ascertain the level of which individuals prefer smaller, immediate rewards over larger, delayed rewards. We explored how an individual's time preference associates with their epigenetic profile. Time preferences were ascertained by asking participants of the Northern Ireland COhort for the Longitudinal study of Ageing to make a series of choices between two hypothetical income scenarios. From these, eight 'time preference' categories were derived, ranging from "patient" to "impatient" on an ordinal scale. The Infinium High Density Methylation Assay, MethylationEPIC (Illumina) was used to evaluate the status of 862,927 CpGs. Time preference and DNA methylation data were obtained for 1648 individuals. Four analyses were conducted, assessing the methylation patterns at single site resolution between patient and impatient individuals using two adjustment models. In this discovery cohort analysis, two CpG sites were identified with significantly different levels of methylation (p < 9 × 10-8) between the individuals allocated to the patient group and the remaining population following adjustment for covariates; cg08845621 within CD44 and cg18127619 within SEC23A. Neither of these genes have previously been linked to time preference. Epigenetic modifications have not previously been linked to time preference using a population cohort but they may represent important biomarkers of accumulated, complex determinants of this trait. Further analysis is warranted of both the top-ranked results and of DNA methylation as an important link between measurable biomarkers and health behaviours.

Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study.

PURPOSE: To report prevalence and risk factor associations for age-related macular degeneration (AMD) and AMD features from multimodal retinal grading in a multidisciplinary longitudinal population-based study of aging in Northern Ireland. STUDY DESIGN: Population-based longitudinal cohort study. METHODS: Retinal imaging at the Norther Ireland Cohort for the Longitudinal Aging Study health assessment included stereo Colour Fundus Photography (CFP) (Canon CX-1, Tokyo, Japan) and Spectral-Domain Optical Coherence Tomography (SD-OCT) ((Heidelberg Retinal Angopgraph (HRA)+OCT; Heidelberg Engineering, Heidelberg, Germany). Medical history and demographic information was obtained during a home interview. Descriptive statistics were used to describe the prevalence of AMD and individual AMD features. Multiple imputation followed by multiple regression modelling was used to explore risk factor associations including relationships with AMD genetic risk score. RESULTS: Retinal images from 3386 participants were available for analysis. Mean age of the sample was 63.4 (SD 9.01, range: 36-99). Population weighted prevalence of AMD using colour grading in those over 55 years was: no drusen: 6 0.4%; drusen <63 µm: 15.9%; drusen 63-125 µm: 13.7%; drusen >125 µm or pigmentary changes: 8.3%; late AMD: 1.6%. Prevalence of AMD features in those over 55 years was: OCT drusen 27.5%, complete outer retinal pigment epithelium and outer retinal atrophy (cRORA) on OCT was 4.3%, reticular drusen 3.2% and subretinal drusenoid deposits 25.7%. The genetic risk score was significantly associated with drusen and cRORA but less so for SDD alone and non-significant for hyperpigmentation or vitelliform lesions. CONCLUSIONS: Multimodal imaging-based classification has provided evidence of some divergence of genetic risk associations between classical drusen and SDD. Our findings support an urgent review of current AMD severity classification systems.

Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease.

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.

Needs of informal caregivers of people with a rare disease: a rapid review of the literature.

OBJECTIVES: Many people living with a rare disease (RD) are cared for by a family member. Due to a frequent lack of individual RD knowledge from healthcare professionals, the patient and their informal caregiver are frequently obliged to become 'experts' in their specific condition. This puts a huge strain on family life and results in caregivers juggling multiple roles in addition to unique caring roles including as advocate, case manager and medical navigator. We conducted a rapid review of literature reporting on the unmet needs of informal caregivers for people living with an RD. All searches were conducted on 14 September 2021, followed by a manual searches of reference lists on 21 September 2021. SETTING: Searches were conducted in Medline, Embase, Web of Science, GreyLit and OpenGrey. RESULTS: Thirty-five papers were included in the final review and data extracted. This rapid review presents several unmet needs identified by informal caregivers of persons with an RD. The related literature was organised thematically: caregiver burden, support through the diagnosis process, social needs, financial needs, psychological needs, information and communication needs and acknowledgement from healthcare professionals. CONCLUSIONS: This review provides evidence that increased meaningful support is required for caregivers. Active engagement should be encouraged from this cohort in future research and awareness raised of the support available to improve the quality of life for families living with an RD. The unmet needs identified through this review will benefit people living with an RD, caregivers, healthcare professionals and policy makers.

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events.

BACKGROUND: Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the 'next-generation' epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. RESULTS: Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). CONCLUSIONS: We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures.

A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland.

Background: The UK 100,000 Genomes Project was a transformational research project which facilitated whole genome sequencing (WGS) diagnostics for rare diseases. We evaluated experiences of introducing WGS in Northern Ireland, providing recommendations for future projects. Methods: This formative evaluation included (1) an appraisal of the logistics of implementing and delivering WGS, (2) a survey of participant self-reported views and experiences, (3) semi-structured interviews with healthcare staff as key informants who were involved in the delivery of WGS and (4) a workshop discussion about interprofessional collaboration with respect to molecular diagnostics. Results: We engaged with >400 participants, with detailed reflections obtained from 74 participants including patients, caregivers, key National Health Service (NHS) informants, and researchers (patient survey n = 42; semi-structured interviews n = 19; attendees of the discussion workshop n = 13). Overarching themes included the need to improve rare disease awareness, education, and support services, as well as interprofessional collaboration being central to an effective, mainstreamed molecular diagnostic service. Conclusions: Recommendations for streamlining precision medicine for patients with rare diseases include administrative improvements (e.g., streamlining of the consent process), educational improvements (e.g., rare disease training provided from undergraduate to postgraduate education alongside genomics training for non-genetic specialists) and analytical improvements (e.g., multidisciplinary collaboration and improved computational infrastructure).

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. METHODS: We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. RESULTS: The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]). CONCLUSIONS/INTERPRETATION: Altogether, the results point to novel genes contributing to the pathogenesis of DKD. DATA AVAILABILITY: The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

The Role of Epigenetic Clocks in Explaining Educational Inequalities in Mortality: A Multicohort Study and Meta-analysis.

Educational inequalities in all-cause mortality have been observed for decades. However, the underlying biological mechanisms are not well known. We aimed to assess the role of DNA methylation changes in blood captured by epigenetic clocks in explaining these inequalities. Data were from 8 prospective population-based cohort studies, representing 13 021 participants. First, educational inequalities and their portion explained by Horvath DNAmAge, Hannum DNAmAge, DNAmPhenoAge, and DNAmGrimAge epigenetic clocks were assessed in each cohort via counterfactual-based mediation models, on both absolute (hazard difference) and relative (hazard ratio) scales, and by sex. Second, estimates from each cohort were pooled through a random effect meta-analysis model. Men with low education had excess mortality from all causes of 57 deaths per 10 000 person-years (95% confidence interval [CI]: 38, 76) compared with their more advantaged counterparts. For women, the excess mortality was 4 deaths per 10 000 person-years (95% CI: -11, 19). On the relative scale, educational inequalities corresponded to hazard ratios of 1.33 (95% CI: 1.12, 1.57) for men and 1.15 (95% CI: 0.96, 1.37) for women. DNAmGrimAge accounted for the largest proportion, approximately 50%, of the educational inequalities for men, while the proportion was negligible for women. Most of this mediation was explained by differential effects of unhealthy lifestyles and morbidities of the World Health Organization (WHO) risk factors for premature mortality. These results support DNA methylation-based epigenetic aging as a signature of educational inequalities in life expectancy emphasizing the need for policies to address the unequal social distribution of these WHO risk factors.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease.

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

An investigation into DNA methylation patterns associated with risk preference in older individuals.

Risk preference is a complex trait governed by psycho-social, environmental and genetic determinants. We aimed to examine how an individual's risk preference associates with their epigenetic profile.Risk preferences were ascertained by asking participants of the Northern Ireland COhort for the Longitudinal study of Ageing to make a series of choices between hypothetical income scenarios. From these, four risk preference categories were derived, ranging from risk-averse to risk-seeking. Illumina's Infinium High-Density Methylation Assay was used to evaluate the status of 862,927 CpGs.Risk preference and DNA methylation data were obtained for 1,656 individuals. The distribution of single-site DNA methylation levels between risk-averse and risk-seeking individuals was assessed whilst adjusting for age, sex and peripheral white cell counts. In this discovery cohort, 55 CpGs were identified with significantly different levels of methylation (p≤x10-5) between risk-averse and risk-seeking individuals when adjusting for the maximum number of covariates. No CpGs were significantly differentially methylated in any of the risk preference groups at an epigenome-wide association level (p<9x10-8) following covariate adjustment.Protein-coding genes NWD1 and LRP1 were among the genes in which the top-ranked dmCpGs were located for all analyses conducted. Mutations in these genes have previously been linked to neurological conditions.Epigenetic modifications have not previously been linked to risk-aversion using a population cohort, but may represent important biomarkers of accumulated, complex determinants of this trait. Several striking results from this study support further analysis of DNA methylation as an important link between measurable biomarkers and health behaviours.