18,109 research outputs found

    Genome wide association study of Preserved Ratio Impaired Spirometry (PRISm)

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    Background: Preserved Ratio Impaired Spirometry (PRISm) is defined as FEV1 &lt;80% predicted, FEV1/FVC ≥0.70. PRISm is associated with respiratory symptoms and co-morbidities. Our objective was to discover novel genetic signals for PRISm and see if they provide insight into the pathogenesis of PRISm and associated co-morbidities.Methods: We undertook a genome-wide association study (GWAS) of PRISm in UK Biobank participants (Stage 1), and selected SNPs reaching genome-wide significance for replication in 13 cohorts (Stage 2). A combined meta-analysis of Stage 1 and Stage 2 was done to determine top SNPs. We used cross-trait Linkage Disequilibrium score regression to estimate genome-wide genetic correlation between PRISM and pulmonary and extra-pulmonary traits. Phenome-wide association studies of top SNPs was performed. Results: 22 signals reached significance in the joint meta-analysis, including four signals novel for lung function. A strong genome-wide genetic correlation (rg) between PRISm and spirometric COPD (rg = 0.62, p-value &lt;0.001) was observed, and genetic correlation with type II diabetes (rg = 0.12, p-value 0.007). PheWAS showed that 18 of 22 signals were associated with diabetic traits and 7 with blood pressure traits.Discussion: This is the first GWAS to successfully identify SNPs associated with PRISm. Four of the signals; rs7652391 (nearest gene MECOM), rs9431040 (HLX), rs62018863 (TMEM114) and rs185937162 (HLA-B) have not been described in association with lung function before, demonstrating the utility of using different lung function phenotypes in GWAS. Genetic factors associated with PRISm are strongly correlated with risk of both other lung diseases and extra-pulmonary co-morbidity.<br/

    Idiopathic pulmonary fibrosis is associated with common genetic variants and limited rare variants

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    Rationale: Idiopathic pulmonary fibrosis is a rare, irreversible, and progressive disease of the lungs. Common genetic variants, in addition to non-genetic factors, have been consistently associated with IPF. Rare variants identified by candidate gene, family-based, and exome studies have also been reported to associate with IPF. However, the extent to which rare variants genome-wide may contribute to the risk of IPF remains unknown. Objectives: We used whole-genome sequencing to investigate the role of rare variants, genome-wide, on IPF risk. Methods: As part of the Trans-Omics for Precision Medicine Program, we sequenced 2,180 cases of IPF. Association testing focused on the aggregated effect of rare variants (minor allele frequency ≤0.01) within genes or regions. We also identified individual variants that are influential within genes and estimated the heritability of IPF based on rare and common variants. Measurements and Main Results: Rare variants in both TERT and RTEL1 were significantly associated with IPF. A single rare variant in each of the TERT and RTEL1 genes was found to consistently influence the aggregated test statistics. There was no significant evidence of association with other previously reported rare variants. The SNP-heritability of IPF was estimated to be 32% (s.e. 3%). Conclusions: Rare variants within the TERT and RTEL1 genes and well-established common variants have the largest contribution to IPF risk overall. Efforts in risk profiling or development of therapies for IPF that focus on TERT, RTEL1, common variants, and environmental risk factors are likely to have the largest impact on this complex disease

    Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

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    Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders

    Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans

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    Background: Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. Methods: New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case–control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. Results: The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91–1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10−8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. Conclusion: Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD

    The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

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    Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences

    Supplementary Table S2 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

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    AREG/EREG with variation in tissue processing</p

    Supplementary Figure S1 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

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    Example AREG and EREG immunohistochemistry</p

    Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer:Mendelian randomization

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    BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer (PC). Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for PC. METHODS: Data from genome-wide association studies within the Pancreatic Cancer Cohort Consortium (PanScan; cases n=5090, controls n=8733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n=4,163, controls n=3,792) were analyzed. We used data on 68 genetic variants with four different MR methods (inverse variance weighting [IVW], MR-Egger, simple median, and penalized weighted median) separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and PC risk, using logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and PC risk in the PanScan or PanC4 samples (e.g., PanScan, IVW OR=1.04, 95% CI: 0.88-1.22, MR-Egger OR=0.89, 95% CI: 0.65-1.21; PanC4, IVW OR=1.07, 95% CI: 0.90-1.27, MR-Egger OR=0.93, 95% CI: 0.67-1.28). None of the four MR methods indicated an association between genetically predicted NAFLD and PC risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with PC risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and PC might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and PC
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