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Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality worldwide. Genetic risk loci provide new insights into disease pathogenesis. We performed a genome-wide association study in 35,735 cases and 222,076 controls from the UK Biobank and additional studies from the International COPD Genetics Consortium. We identified 82 loci associated with P < 5 × 10 −8 ; 47 of these were previously described in association with either COPD or population-based measures of lung function. Of the remaining 35 new loci, 13 were associated with lung function in 79,055 individuals from the SpiroMeta consortium. Using gene expression and regulation data, we identified functional enrichment of COPD risk loci in lung tissue, smooth muscle, and several lung cell types. We found 14 COPD loci shared with either asthma or pulmonary fibrosis. COPD genetic risk loci clustered into groups based on associations with quantitative imaging features and comorbidities. Our analyses provide further support for the genetic susceptibility and heterogeneity of COPD. Genome-wide analysis of chronic obstructive pulmonary disease identifies 82 loci, 35 of which are new. Integration of gene expression and genomic annotation data shows enrichment of signals in lung tissue, smooth muscle and several lung cell types.

Polygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their utility in various clinical situations. Here we show that among 122,978 women in the FinnGen study with 8401 breast cancer cases, the PRS modifies the breast cancer risk of two high-impact frameshift risk variants. Similarly, we show that after the breast cancer diagnosis, individuals with elevated PRS have an elevated risk of developing contralateral breast cancer, and that the PRS can considerably improve risk assessment among their female first-degree relatives. In more detail, women with the c.1592delT variant in PALB2 (242-fold enrichment in Finland, 336 carriers) and an average PRS (10–90 th percentile) have a lifetime risk of breast cancer at 55% (95% CI 49–61%), which increases to 84% (71–97%) with a high PRS ( > 90 th percentile), and decreases to 49% (30–68%) with a low PRS ( < 10 th percentile). Similarly, for c.1100delC in CHEK2 (3.7–fold enrichment; 1648 carriers), the respective lifetime risks are 29% (27–32%), 59% (52–66%), and 9% (5–14%). The PRS also refines the risk assessment of women with first-degree relatives diagnosed with breast cancer, particularly among women with positive family history of early-onset breast cancer. Here we demonstrate the opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members. Identifying women at high risk of breast cancer has important implications for screening. Here, the authors demonstrate that polygenic risk scores improve breast cancer risk prediction in the population, in women with mutations in high-risk genes and in women with close relatives with the disease.

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10 −8 ), 10 of which associate with CVD risk including five new loci- COL5A1 , GLTPD2 , SPTLC3 , MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD. Cardiovascular diseases (CVD) are associated with plasma lipid levels. Here, Tabassum et al . perform genome-wide association studies for lipidomic profiles with 141 (non-standard) lipid species which highlights shared genetic loci with CVD and that traditional lipids have low genetic correlation with other lipids.

The coexistence of asthma and chronic rhinosinusitis with nasal polyposis (CRSwNP) is associated with allergic phenotypes, disease severity and failure of first-line treatment for both asthma and CRSwNP. Recent studies have highlighted shared genetic components for these diseases. To better understand this shared component, we perform genome-wide meta-analyses of asthma (n = 71,481), CRSwNP (n = 9626) and chronic rhinosinusitis without nasal polyposis (CRSsNP, n = 15,448) in FinnGen and UKB (685,602 controls). We detect 131 genomic associations, including 17 novel loci for asthma, 33 novel loci for CRSwNP, and one for CRSsNP. A shared impact on asthma and CRSwNP is observed at 71 loci. A cross-trait meta-analysis using all disorders further implicates 17 loci associated with asthma or asthma and CRSwNP. We also find 17 nonsynonymous associating variants, including a novel TP63 missense variant association with CRSwNP (OR = 1.519 [1.331–1.734]). Gene set analyses confirm enrichment of genes involved with type 2 inflammation, Jak-STAT signaling, and FOXP3 signaling. Our results highlight new shared and separate genetic pathways for CRSwNP and asthma. These provide several avenues of further investigation in functional and epidemiological follow-up, and evidence for immunological and non-immunological mechanisms behind both diseases. Shared heritability between asthma and rhinosinusitis has not been extensively explored. Here the authors find that genetic loci shared between asthma and chronic rhinosinusitis highlight Jak-STAT signaling, and link a Finnish-enriched TP63 variant with nasal polyps.

PurposeObstructive sleep apnea (OSA) has been associated with a 2- to 7-fold risk of motor vehicle accidents (MVAs). Continuous positive airway pressure (CPAP) treatment may reduce MVA risk. We further explored this issue in long-term CPAP users and untreated controls.MethodsWe used both before-after and case-control study designs. The observational cohort consisted of CPAP-treated and untreated patients matched for gender, age, and apnea-hypopnea index. All MVAs reported to the police were identified.ResultsA total of 2060 patients (75.8% male, mean age 56.0 ± 10.5 years) were included. The CPAP-treated patients (N = 1030) were screened for MVAs for a median of 9.0 years before and after treatment. The median CPAP usage was 6.4 h/day. The control patients (N = 1030) were screened for MVAs for a median of 6.5 years after discontinuation of CPAP. No significant differences were observed between the incidences of MVAs per 1000 person years before treatment (3.2), after treatment (3.9), or in controls (2.6). Compared with controls, patients who had MVA after treatment had a higher body mass index (BMI), but did not differ in terms of other baseline characteristics, sleep study data, or accident conditions. In the majority of these patients, daytime sleepiness was reduced, whereas BMI tended to increase during treatment.ConclusionsThe MVA incidence did not change after CPAP treatment. Among the patients who had MVA, BMI was the only baseline characteristic that differed between the groups and tended to further increase after CPAP treatment. Differences in sleep study data or accident conditions were not observed.

Susceptibility to asthma depends on variation at an unknown number of genetic loci. To identify susceptibility genes on chromosome 7p, we adopted a hierarchical genotyping design, leading to the identification of a 133-kilobase risk-conferring segment containing two genes. One of these coded for an orphan G protein-coupled receptor named GPRA (G protein-coupled receptor for asthma susceptibility), which showed distinct distribution of protein isoforms between bronchial biopsies from healthy and asthmatic individuals. In three cohorts from Finland and Canada, single nucleotide polymorphism-tagged haplotypes associated with high serum immunoglobulin E or asthma. The murine ortholog of GPRA was up-regulated in a mouse model of ovalbumin-induced inflammation. Together, these data implicate GPRA in the pathogenesis of atopy and asthma.

Background and Objectives: Evaluation of data from electronic health care records could help in guiding towards more rational drug treatments. This single center study evaluated clinical characteristics that could be associated with disease progression. Methods: This was a real world data (RWD) study using existing data from the registries of a university hospital. Patients had lung adenocarcinoma and they had received 1st line treatment. Treatment patterns and survival parameters were characterized and clinical characteristics of the patients were evaluated together with their association with disease progression. Results: 80 stage III/IV patients fulfilling inclusion criteria were identified. Mean age was 62 years and 61% were men. In total, 65% were current smokers and 82% had performance status (ECOG) 0/1. Median progression free survival (mPFS) and median overall survival (mOS) for stage III and IV patients were 8.5 and 5.4 months, and 21.9 and 8.6 months, respectively. The study found that 69% of patients progressed within 9 months from the start of the 1st line treatment. Poor performance status (ECOG 3), male gender, and smoking suggested faster disease progression. Most had received cis/carboplatin-based treatment in the 1st line. Cisplatin regimens were associated with more complete responses and better PFS and OS than the carboplatin ones. Conclusions: By combining algorithmic and manual validation of electronic health care records, clinically valid characteristics and outcomes could be evaluated and presented. This approach forms a basis for tools such as quality registries that can guide treatment decisions.

Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients' smoking status into electronic health records (EHR) and deliver smoking cessation assistance. We analysed the results using a combination of rule and deep learning-based algorithms. Narrative reports of all adult patients, whose treatment started between years 2010 and 2016 for one of seven common chronic diseases, were followed for two years. Smoking related sentences were first extracted with a rule-based algorithm. Subsequently, pre-trained ULMFiT-based algorithm classified each patient's smoking status as a current smoker, ex-smoker, or never smoker. A rule-based algorithm was then again used to analyse the physician-patient discussions on smoking cessation among current smokers. A total of 35,650 patients were studied. Of all patients, 60% were found to have a smoking status in EHR and the documentation improved over time. Smoking status was documented more actively among COPD (86%) and sleep apnoea (83%) patients compared to patients with asthma, type 1&2 diabetes, cerebral infarction and ischemic heart disease (range 44-61%). Of the current smokers (N=7,105), 49% had discussed smoking cessation with their physician. The performance of ULMFiT-based classifier was good with F-scores 79-92. Ee found that smoking status was documented in 60% of patients with chronic disease and that the clinician had discussed smoking cessation in 49% of patients who were current smokers. ULMFiT-based classifier showed good/excellent performance and allowed us to efficiently study a large number of patients' medical narratives.

Asthma has substantial morbidity and mortality and a strong genetic component, but identification of genetic risk factors is limited by availability of suitable studies. To test if population-based cohorts with self-reported physician-diagnosed asthma and genome-wide association (GWA) data could be used to validate known associations with asthma and identify novel associations. The APCAT (Analysis in Population-based Cohorts of Asthma Traits) consortium consists of 1,716 individuals with asthma and 16,888 healthy controls from six European-descent population-based cohorts. We examined associations in APCAT of thirteen variants previously reported as genome-wide significant (P<5 x 10(-8)) and three variants reported as suggestive (P<5× 10(-7)). We also searched for novel associations in APCAT (Stage 1) and followed-up the most promising variants in 4,035 asthmatics and 11,251 healthy controls (Stage 2). Finally, we conducted the first genome-wide screen for interactions with smoking or hay fever. We observed association in the same direction for all thirteen previously reported variants and nominally replicated ten of them. One variant that was previously suggestive, rs11071559 in RORA, now reaches genome-wide significance when combined with our data (P = 2.4 × 10(-9)). We also identified two genome-wide significant associations: rs13408661 near IL1RL1/IL18R1 (P(Stage1+Stage2) = 1.1x10(-9)), which is correlated with a variant recently shown to be associated with asthma (rs3771180), and rs9268516 in the HLA region (P(Stage1+Stage2) = 1.1x10(-8)), which appears to be independent of previously reported associations in this locus. Finally, we found no strong evidence for gene-environment interactions with smoking or hay fever status. Population-based cohorts with simple asthma phenotypes represent a valuable and largely untapped resource for genetic studies of asthma.

Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10–4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.