Genomic basis for RNA alterations in cancer.

Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

The mediating role of health behaviors in the association between depression, anxiety and cancer incidence: an individual participant data meta-analysis.

BACKGROUND: Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers). METHODS: Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs). RESULTS: Smoking (HRs range 1.04-1.10) and physical inactivity (HRs range 1.01-1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03-1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01). CONCLUSIONS: Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Prediction of acute myeloid leukaemia risk in healthy individuals.

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning.

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.

The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020-2023.

BACKGROUND: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. METHODS: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. RESULTS: By November 2021, 9.0% (95% credible interval [CrI] 7.3%-11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant - by Mar. 15, 2023, 76% (95% CrI 74%-79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. INTERPRETATION: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin.

BACKGROUND: Uromodulin, the most abundant protein excreted in normal urine, plays major roles in kidney physiology and disease. The mechanisms regulating the urinary excretion of uromodulin remain essentially unknown. METHODS: We conducted a meta-analysis of genome-wide association studies for raw (uUMOD) and indexed to creatinine (uUCR) urinary levels of uromodulin in 29,315 individuals of European ancestry from 13 cohorts. We tested the distribution of candidate genes in kidney segments and investigated the effects of keratin-40 (KRT40) on uromodulin processing. RESULTS: Two genome-wide significant signals were identified for uUMOD: a novel locus (P 1.24E-08) over the KRT40 gene coding for KRT40, a type 1 keratin expressed in the kidney, and the UMOD-PDILT locus (P 2.17E-88), with two independent sets of single nucleotide polymorphisms spread over UMOD and PDILT. Two genome-wide significant signals for uUCR were identified at the UMOD-PDILT locus and at the novel WDR72 locus previously associated with kidney function. The effect sizes for rs8067385, the index single nucleotide polymorphism in the KRT40 locus, were similar for both uUMOD and uUCR. KRT40 colocalized with uromodulin and modulating its expression in thick ascending limb (TAL) cells affected uromodulin processing and excretion. CONCLUSIONS: Common variants in KRT40, WDR72, UMOD, and PDILT associate with the levels of uromodulin in urine. The expression of KRT40 affects uromodulin processing in TAL cells. These results, although limited by lack of replication, provide insights into the biology of uromodulin, the role of keratins in the kidney, and the influence of the UMOD-PDILT locus on kidney function.

Lifestyle factors and lung cancer risk among never smokers in the Canadian Partnership for Tomorrow's Health (CanPath).

BACKGROUND: Although smoking is the primary risk factor for lung cancer, 15-25% of lung cancers occur in never smokers. Emerging evidence suggests lifestyle factors are associated with lung cancer risk, but few studies among never smokers exist. METHODS: A case-control study of never smokers within the Canadian Partnership for Tomorrow's Health was conducted. At recruitment, participants provided data on lifestyle, health history and sociodemographic factors. Incident lung cancers were identified through linkage with administrative health records. Cases (n = 190) were matched to controls (n = 760) on age, sex, and follow-up time. Logistic regression analyses, adjusted for matching factors and annual income, were used to identify associations between lifestyle factors and lung cancer risk. RESULTS: Consumption of < 5 servings of fruits and vegetables/day was associated with higher risk of lung cancer (OR 1.50, 95% CI 1.03-2.17). Short or long sleep (≤ 6 or > 9 h/night) was also associated with increased risk of lung cancer (OR 1.52, 95% CI 1.01-2.29). No associations were observed for obesity measures, alcohol consumption, or physical activity. CONCLUSION: Our findings provide evidence of a potential role between sleep, fruits and vegetable consumption, and lung cancer risk in a pan-Canadian, non-smoking population. However, the sample size is modest, and further investigation is needed.

Normal sex and age-specific parameters in a multi-ethnic population: a cardiovascular magnetic resonance study of the Canadian Alliance for Healthy Hearts and Minds cohort.

BACKGROUND: Despite the growing utility of cardiovascular magnetic resonance (CMR) for cardiac morphology and function, sex and age-specific normal reference values derived from large, multi-ethnic data sets are lacking. Furthermore, most available studies use a simplified tracing methodology. Using a large cohort of participants without history of cardiovascular disease (CVD) or risk factors from the Canadian Alliance for Healthy Heart and Minds, we sought to establish a robust set of reference values for ventricular and atrial parameters using an anatomically correct contouring method, and to determine the influence of age and sex on ventricular parameters. METHODS AND RESULTS: Participants (n = 3206, 65% females; age 55.2 ± 8.4 years for females and 55.1 ± 8.8 years for men) underwent CMR using standard methods for quantitative measurements of cardiac parameters. Normal ventricular and atrial reference values are provided: (1) for males and females, (2) stratified by four age categories, and (3) for different races/ethnicities. Values are reported as absolute, indexed to body surface area, or height. Ventricular volumes and mass were significantly larger for males than females (p < 0.001). Ventricular ejection fraction was significantly diminished in males as compared to females (p < 0.001). Indexed left ventricular (LV) end-systolic, end-diastolic volumes, mass and right ventricular (RV) parameters significantly decreased as age increased for both sexes (p < 0.001). For females, but not men, mean LV and RVEF significantly increased with age (p < 0.001). CONCLUSION: Using anatomically correct contouring methodology, we provide accurate sex and age-specific normal reference values for CMR parameters derived from the largest, multi-ethnic population free of CVD to date. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02220582. Registered 20 August 2014-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02220582 .

Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control.

AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS: The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10-21 and p = 9.6 × 10-31, respectively) and a 4.4-fold (p = 6.8 × 10-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION: This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.

Aberrant PRDM9 expression impacts the pan-cancer genomic landscape.

The binding of PRDM9 to chromatin is a key step in the induction of DNA double-strand breaks associated with meiotic recombination hotspots; it is normally expressed solely in germ cells. We interrogated 1879 cancer samples in 39 different cancer types and found that PRDM9 is unexpectedly expressed in 20% of these tumors even after stringent gene homology correction. The expression levels of PRDM9 in tumors are significantly higher than those found in healthy neighboring tissues and in healthy nongerm tissue databases. Recurrently mutated regions located within 5 Mb of the PRDM9 loci, as well as differentially expressed genes in meiotic pathways, correlate with PRDM9 expression. In samples with aberrant PRDM9 expression, structural variant breakpoints frequently neighbor the DNA motif recognized by PRDM9, and there is an enrichment of structural variants at sites of known meiotic PRDM9 activity. This study is the first to provide evidence of an association between aberrant expression of the meiosis-specific gene PRDM9 with genomic instability in cancer.

Score tests for scale effects, with application to genomic analysis.

Tests for variance or scale effects due to covariates are used in many areas and recently, in genomic and genetic association studies. We study score tests based on location-scale models with arbitrary error distributions that allow incorporation of additional adjustment covariates. Tests based on Gaussian and Laplacian double generalized linear models are examined in some detail. Numerical properties of the tests under Gaussian and other error distributions are examined. Our results show that the use of model-based asymptotic distributions with score tests for scale effects does not control type 1 error well in many settings of practical relevance. We consider simple statistics based on permutation distribution approximations, which correspond to well-known statistics derived by another approach. They are shown to give good type 1 error control under different error distributions and under covariate distribution imbalance. The methods are illustrated through a differential gene expression analysis involving breast cancer tumor samples.

Reduced Cognitive Assessment Scores Among Individuals With Magnetic Resonance Imaging-Detected Vascular Brain Injury.

Background and Purpose- Little is known about the association between covert vascular brain injury and cognitive impairment in middle-aged populations. We investigated if scores on a cognitive screen were lower in individuals with higher cardiovascular risk, and those with covert vascular brain injury. Methods- Seven thousand five hundred forty-seven adults, aged 35 to 69 years, free of cardiovascular disease underwent a cognitive assessment using the Digital Symbol Substitution test and Montreal Cognitive Assessment, and magnetic resonance imaging (MRI) to detect covert vascular brain injury (high white matter hyperintensities, lacunar, and nonlacunar brain infarctions). Cardiovascular risk factors were quantified using the INTERHEART (A Global Study of Risk Factors for Acute Myocardial Infarction) risk score. Multivariable mixed models tested for independent determinants of reduced cognitive scores. The population attributable risk of risk factors and MRI vascular brain injury on low cognitive scores was calculated. Results- The mean age of participants was 58 (SD, 9) years; 55% were women. Montreal Cognitive Assessment and Digital Symbol Substitution test scores decreased significantly with increasing age (P<0.0001), INTERHEART risk score (P<0.0001), and among individuals with high white matter hyperintensities, nonlacunar brain infarction, and individuals with 3+ silent brain infarctions. Adjusted for age, sex, education, ethnicity covariates, Digital Symbol Substitution test was significantly lowered by 1.0 (95% CI, -1.3 to -0.7) point per 5-point cardiovascular risk score increase, 1.9 (95% CI, -3.2 to -0.6) per high white matter hyperintensities, 3.5 (95% CI, -6.4 to -0.7) per nonlacunar stroke, and 6.8 (95% CI, -11.5 to -2.2) when 3+ silent brain infarctions were present. No postsecondary education accounted for 15% (95% CI, 12-17), moderate and high levels of cardiovascular risk factors accounted for 19% (95% CI, 8-30), and MRI vascular brain injury accounted for 10% (95% CI, -3 to 22) of low test scores. Conclusions- Among a middle-aged community-dwelling population, scores on a cognitive screen were lower in individuals with higher cardiovascular risk factors or MRI vascular brain injury. Much of the population attributable risk of low cognitive scores can be attributed to lower educational attainment, higher cardiovascular risk factors, and MRI vascular brain injury.

Allele-specific expression reveals interactions between genetic variation and environment.

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.

Long-term exposure to a mixture of industrial SO2, NO2, and PM2.5 and anti-citrullinated protein antibody positivity.

BACKGROUND: Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO2), nitrogen dioxide (NO2), and fine particles matter (PM2.5) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA). METHODS: Serum ACPA was determined for 7600 randomly selected CARTaGENE general population subjects in Quebec, Canada. Industrial SO2, NO2, and PM2.5 concentrations, estimated by the California Puff (CALPUFF) atmospheric dispersion model, were assigned based on residential postal codes at the time of sera collection. Single-exposure logistic regressions were performed for ACPA positivity defined by 20 U/ml, 40 U/ml, and 60 U/ml thresholds, adjusting for age, sex, French Canadian origin, smoking, and family income. Associations between regional overall PM2.5 exposure and ACPA positivity were also investigated. The associations between the combined three industrial exposures and the ACPA positivity were assessed by weighted quantile sum (WQS) regressions. RESULTS: Significant associations between individual industrial exposures and ACPA positivity defined by the 20 U/ml threshold were seen with single-exposure logistic regression models, for industrial emissions of PM2.5 (odds ratio, OR = 1.19, 95% confidence intervals, CI: 1.04-1.36) and SO2 (OR = 1.03, 95% CI: 1.00-1.06), without clear associations for NO2 (OR = 1.01, 95% CI: 0.86-1.17). Similar findings were seen for the 40 U/ml threshold, although at 60 U/ml, the results were very imprecise. The WQS model demonstrated a positive relationship between combined industrial exposures and ACPA positivity (OR = 1.36, 95% CI: 1.10-1.69 at 20 U/ml) and suggested that industrial PM2.5 may have a closer association with ACPA positivity than the other exposures. Again, similar findings were seen with the 40 U/ml threshold, though 60 U/ml results were imprecise. No clear association between ACPA and regional overall PM2.5 exposure was seen. CONCLUSIONS: We noted positive associations between ACPA and industrial emissions of PM2.5 and SO2. Industrial PM2.5 exposure may play a particularly important role in this regard.

Impact of the X Chromosome and sex on regulatory variation.

The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.

Family Based Whole Exome Sequencing Reveals the Multifaceted Role of Notch Signaling in Congenital Heart Disease.

Left-ventricular outflow tract obstructions (LVOTO) encompass a wide spectrum of phenotypically heterogeneous heart malformations which frequently cluster in families. We performed family based whole-exome and targeted re-sequencing on 182 individuals from 51 families with multiple affected members. Central to our approach is the family unit which serves as a reference to identify causal genotype-phenotype correlations. Screening a multitude of 10 overlapping phenotypes revealed disease associated and co-segregating variants in 12 families. These rare or novel protein altering mutations cluster predominantly in genes (NOTCH1, ARHGAP31, MAML1, SMARCA4, JARID2, JAG1) along the Notch signaling cascade. This is in line with a significant enrichment (Wilcoxon, p< 0.05) of variants with a higher pathogenicity in the Notch signaling pathway in patients compared to controls. The significant enrichment of novel protein truncating and missense mutations in NOTCH1 highlights the allelic and phenotypic heterogeneity in our pediatric cohort. We identified novel co-segregating pathogenic mutations in NOTCH1 associated with left and right-sided cardiac malformations in three independent families with a total of 15 affected individuals. In summary, our results suggest that a small but highly pathogenic fraction of family specific mutations along the Notch cascade are a common cause of LVOTO.

The Canadian Partnership for Tomorrow Project: a pan-Canadian platform for research on chronic disease prevention.

BACKGROUND: Understanding the complex interaction of risk factors that increase the likelihood of developing common diseases is challenging. The Canadian Partnership for Tomorrow Project (CPTP) is a prospective cohort study created as a population-health research platform for assessing the effect of genetics, behaviour, family health history and environment (among other factors) on chronic diseases. METHODS: Volunteer participants were recruited from the general Canadian population for a confederation of 5 regional cohorts. Participants were enrolled in the study and core information obtained using 2 approaches: attendance at a study assessment centre for all study measures (questionnaire, venous blood sample and physical measurements) or completion of the core questionnaire (online or paper), with later collection of other study measures where possible. Physical measurements included height, weight, percentage body fat and blood pressure. Participants consented to passive follow-up through linkage with administrative health databases and active follow-up through recontact. All participant data across the 5 regional cohorts were harmonized. RESULTS: A total of 307 017 participants aged 30-74 from 8 provinces were recruited. More than half provided a venous blood sample and/or other biological sample, and 33% completed physical measurements. A total of 709 harmonized variables were created; almost 25% are available for all participants and 60% for at least 220 000 participants. INTERPRETATION: Primary recruitment for the CPTP is complete, and data and biosamples are available to Canadian and international researchers through a data-access process. The CPTP will support research into how modifiable risk factors, genetics and the environment interact to affect the development of cancer and other chronic diseases, ultimately contributing evidence to reduce the global burden of chronic disease.