Subtopic Deep Dive
Asthma Genetics
Research Guide
What is Asthma Genetics?
Asthma Genetics studies genetic variants, genome-wide association studies (GWAS), and heritability underlying asthma phenotypes and bronchial hyperresponsiveness.
Key GWAS identified common alleles associated with asthma risk across ages (Moffatt et al., 2010, 2004 citations). Variants regulating ORMDL3 expression contribute to childhood asthma risk (Moffatt et al., 2007, 1595 citations). Bronchial hyperresponsiveness coinherits with a major gene for atopy (Postma et al., 1995, 762 citations). Over 10 major papers from 1995-2019 form the core literature.
Why It Matters
Asthma Genetics identifies causal variants for polygenic risk scores enabling personalized medicine (Moffatt et al., 2010). ORMDL3 variants link genetics to childhood asthma onset, guiding early interventions (Moffatt et al., 2007). Studies on bronchial hyperresponsiveness reveal heritable components tied to IgE levels and airway inflammation, informing therapeutic targets (Postma et al., 1995). Multiancestry GWAS colocalize risk loci with immune-cell enhancers, advancing diverse population treatments (Démenais et al., 2017).
Key Research Challenges
Genetic Heterogeneity
Asthma shows heterogeneous genetic profiles across ages and phenotypes (Moffatt et al., 2010). Common alleles explain limited heritability, requiring larger multiancestry studies (Démenais et al., 2017). Distinguishing causal from correlated variants remains difficult.
Gene-Environment Interactions
Heritability of bronchial hyperresponsiveness interacts with environmental triggers like IgE levels (Postma et al., 1995). Childhood asthma variants like ORMDL3 need interplay models with exposures (Moffatt et al., 2007). Modeling these interactions demands integrated datasets.
Polygenic Risk Scores
Translating GWAS loci into accurate polygenic scores for clinical use faces validation issues across ancestries (Démenais et al., 2017). Pulmonary function loci add complexity to asthma-specific scores (Hancock et al., 2009). Statistical refinement is needed for predictive power.
Essential Papers
A Large-Scale, Consortium-Based Genomewide Association Study of Asthma
Miriam F. Moffatt, Marta Gut, Florence Démenais et al. · 2010 · New England Journal of Medicine · 2.0K citations
Asthma is genetically heterogeneous. A few common alleles are associated with disease risk at all ages. Implicated genes suggest a role for communication of epithelial damage to the adaptive immune...
Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma
Miriam F. Moffatt, Michael Kabesch, Liming Liang et al. · 2007 · Nature · 1.6K citations
Epidemiology of Asthma in Children and Adults
Shyamali C. Dharmage, Jennifer L. Perret, Adnan Ćustović · 2019 · Frontiers in Pediatrics · 1.1K citations
Asthma is a globally significant non-communicable disease with major public health consequences for both children and adults, including high morbidity, and mortality in severe cases. We have summar...
After asthma: redefining airways diseases
Ian Pavord, Richard Beasley, Àlvar Agustí et al. · 2017 · The Lancet · 988 citations
International audience
A Genome-Wide Association Study in Chronic Obstructive Pulmonary Disease (COPD): Identification of Two Major Susceptibility Loci
Sreekumar Pillai, Dongliang Ge, Guohua Zhu et al. · 2009 · PLoS Genetics · 781 citations
There is considerable variability in the susceptibility of smokers to develop chronic obstructive pulmonary disease (COPD). The only known genetic risk factor is severe deficiency of alpha(1)-antit...
Genetic Susceptibility to Asthma — Bronchial Hyperresponsiveness Coinherited with a Major Gene for Atopy
Dirkje S. Postma, Eugene R. Bleecker, Pamela J. Amelung et al. · 1995 · New England Journal of Medicine · 762 citations
Bronchial hyperresponsiveness, a risk factor for asthma, consists of a heightened bronchoconstrictor response to a variety of stimuli. The condition has a heritable component and is closely related...
Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function
Dana B. Hancock, Mark Eijgelsheim, Jemma B. Wilk et al. · 2009 · Nature Genetics · 632 citations
Reading Guide
Foundational Papers
Start with Moffatt et al. (2010) for large-scale GWAS establishing common alleles, then Moffatt et al. (2007) for ORMDL3 in childhood asthma, and Postma et al. (1995) for heritability of bronchial hyperresponsiveness.
Recent Advances
Study Démenais et al. (2017) for multiancestry loci colocalizing with immune enhancers, Hancock et al. (2009) for pulmonary function meta-analyses.
Core Methods
Core techniques are GWAS for variant discovery (Moffatt et al., 2010), linkage analysis for atopy (Postma et al., 1995), and meta-analyses for function loci (Hancock et al., 2009).
How PapersFlow Helps You Research Asthma Genetics
Discover & Search
Research Agent uses searchPapers and exaSearch to find Moffatt et al. (2010) GWAS on asthma alleles, then citationGraph reveals 2004 citing papers including Démenais et al. (2017) multiancestry loci, and findSimilarPapers uncovers ORMDL3 studies like Moffatt et al. (2007).
Analyze & Verify
Analysis Agent applies readPaperContent to extract ORMDL3 variant effects from Moffatt et al. (2007), verifies GWAS p-values with verifyResponse (CoVe), and runs PythonAnalysis on heritability stats from Postma et al. (1995) using pandas for IgE correlations. GRADE grading scores evidence strength for causal claims in Moffatt et al. (2010).
Synthesize & Write
Synthesis Agent detects gaps in gene-environment data post-Moffatt et al. (2010), flags contradictions between childhood (Moffatt et al., 2007) and adult loci. Writing Agent uses latexEditText for PRS methods, latexSyncCitations for 10+ papers, latexCompile for review drafts, and exportMermaid diagrams GWAS networks.
Use Cases
"Compute polygenic risk score heritability from Postma 1995 and Hancock 2009 pulmonary loci."
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas/NumPy on extracted GWAS stats) → CSV of heritability estimates with confidence intervals.
"Draft LaTeX review of ORMDL3 variants in childhood asthma citing Moffatt 2007 and 2010."
Synthesis Agent → gap detection → Writing Agent → latexEditText → latexSyncCitations (10 papers) → latexCompile → PDF with ORMDL3 pathway figure.
"Find GitHub repos analyzing Moffatt 2010 GWAS data for asthma genetics replication."
Research Agent → citationGraph on Moffatt 2010 → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → List of 5 repos with replication scripts.
Automated Workflows
Deep Research workflow scans 50+ asthma GWAS papers starting with Moffatt et al. (2010), chains searchPapers → citationGraph → structured report on loci trends. DeepScan applies 7-step analysis to Postma et al. (1995) with CoVe checkpoints on atopy heritability claims. Theorizer generates hypotheses on ORMDL3-environment interactions from Moffatt et al. (2007).
Frequently Asked Questions
What defines Asthma Genetics?
Asthma Genetics examines GWAS, variants like ORMDL3, and heritability of phenotypes including bronchial hyperresponsiveness (Moffatt et al., 2007; Postma et al., 1995).
What are key methods in Asthma Genetics?
Genome-wide association studies (GWAS) identify risk loci (Moffatt et al., 2010), meta-analyses refine pulmonary function signals (Hancock et al., 2009), and multiancestry approaches colocalize enhancers (Démenais et al., 2017).
What are the most cited papers?
Moffatt et al. (2010, 2004 citations) on consortium GWAS, Moffatt et al. (2007, 1595 citations) on ORMDL3, and Postma et al. (1995, 762 citations) on hyperresponsiveness-atopy linkage.
What open problems exist?
Challenges include modeling gene-environment interactions beyond ORMDL3 (Moffatt et al., 2007), improving polygenic scores across ancestries (Démenais et al., 2017), and resolving heterogeneity (Moffatt et al., 2010).
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Part of the Asthma and respiratory diseases Research Guide