Expressive

rs11088268 - LINC00649

Magnitude 2.0 · 4 studies on file

Reported associations

  • Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction. - Nature genetics (2026) · He Y, Lu W, Jee YH, Shih MY, Wang Y, Tsuo K, Qian DC, Diao JA, Huang H, Patel CJ, Byun J, Pasaniuc B, Atkinson EG, Amos CI, Feng YA, Moll M, Cho MH, Martin AR · PubMed 41565855

    While respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma share many risk factors, most studies investigate them in isolation and in predominantly European-ancestry populations. Here, we conducted the most powerful multi-trait and multi-ancestry genetic analysis of respiratory diseases and auxiliary traits to date, identifying 25 new loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross-trait and cross-ancestry), a multi-trait and multi-ancestry polygenic risk score (PRS) approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD and lung cancer compared to trait- and ancestry-matched PRSs in a multi-an

  • Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. - Nature genetics (2023) · Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An TH, Lai D, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F · PubMed 37069358

    Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exac

  • Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis - Unknown journal (n.d.) · Unknown authors · PubMed 37426090

    ABSTRACT: Introduction The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection). Methods In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trai

  • Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk - Unknown journal (n.d.) · Unknown authors · PubMed 36914875

    ABSTRACT: Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 588,452 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of interve

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