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Principal Investigator: Sung Chun
Organization: BOSTON CHILDREN'S HOSPITAL
Fiscal Year: 2024
Award: $827,753
Funding agency: National Heart Lung and Blood Institute
Idiopathic Pulmonary Fibrosis (IPF) inflicts a significant healthcare burden and high rate of mortality in the U.S.
Current treatment options can slow the rate of lung function decline, but the five-year survival remains low,
therefore there is clear unmet medical needs for novel therapeutics. Here, human genetics can catalyze drug
development process by identifying new biological targets and elucidating the underlying pathways. As a rare
polygenic disorder, however, IPF poses challenges to existing disease gene-mapping strategies due to the
extensive locus heterogeneity and difficulty of assembling massive sample sizes typical of common polygenic
disorders. This project aims to develop more effective gene-mapping methods for rare polygenic disorders
such as IPF. Our approach is motivated by three complementary strategies for small genetic studies: (i)
pleiotropy-informed SNP association tests, which can be extended to take advantage of the pleiotropy of
disease SNPs with gene expression traits and to further boost power by accounting for the network
connectivity to known disease genes; (ii) Polygenic Risk Scores (PRS), which can be highly useful for rare
disorders by capturing the effect of disease modifiers in the genetic background; and (iii) highly modular
network structure of disease genes, which can be leveraged to reduce genetic heterogeneity among cases. In
Aim 1, by extending a pleiotropy-informed association test we had previously proposed, we will develop a new
network model-based association test informed by pleiotropy to gene expression traits. We will apply this
method to publicly available IPF GWAS data and expression Quantitative Trait Loci (eQTL) of IPF-relevant
tissues and cell populations. In Aim 2, we will develop a new rare-variant association test directly accounting
for the contribution of genetic background using PRS. We will apply the new method to sequencing data of
~1,500 IPF cases and ~15,000 unaffected controls from CGS-PF and TOPMed studies. In Aim 3, we will
identify novel IPF genes by leveraging the association between disease gene modules and comorbidities.
Known IPF genes are clustered in multiple tightly inter-connected gene modules in biological networks, and
mutations disrupting each network modules cause a distinct set of comorbidities in IPF patients. We will
leverage the modularity of IPF genes and comorbidity to find novel IPF genes in exome data of UK Biobank
and MGB Biobank. In reverse, we also will test if genotypes of key IPF gene modules can inform the course of
comorbidity development in patients by inviting 10 CGS-PF study participants to a reverse genetics study.
Ultimately, the findings from these studies will uncover novel genes and pathways underlying IPF and develop
new computational strategies generally applicable to rare polygenic disorders.
Terms: <65 and older><65 or older><65 years of age and older><65 years of age or more><65 years of age or older><65+ years><65+ years old><> 65 years><Accounting><Address><Aged 65 and Over><Atrophic Arthritis><Biogenesis><Biological><Body Tissues><Candidate Disease Gene><Candidate Gene><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><Chromosome Mapping><Clinic><Computing Methodologies><Data><Data Set><Death Rate><Development><Disease><Disease Outcome><Disorder><Fibrosing Alveolitis><GWA study><GWAS><Gene Cluster><Gene Expression><Gene Localization><Gene Mapping><Gene Mapping Genetics><Genes><Genetic><Genetic Alteration><Genetic Change><Genetic Heterogeneity><Genetic Risk><Genetic analyses><Genetic defect><Genetic study><Genotype><Goals><Heterogeneity><Human Genetics><Individual><Intracellular Communication and Signaling><Libraries><Linkage Mapping><Lung Surfactant><Lysosomes><Maps><Medical><Mendelian disease><Mendelian disorder><Mendelian genetic disorder><Methods><Modeling><Mutation><Network-based><Origin of Life><Orphan Disease><Participant><Pathway interactions><Patients><Pattern><Penetrance><Population><Position><Positioning Attribute><Predicting Risk><Prevalence><Probabilistic Models><Probability Models><Process><Pulmonary Surfactants><QTL><Quantitative Trait Loci><Rare Diseases><Rare Disorder><Rest><Rheumatoid Arthritis><Sample Size><Sampling><Scientist><Signal Transduction><Signal Transduction Systems><Signaling><Statistical Models><Structure><System><TOPMed><Techniques><Telomere Maintenance><Testing><Time><Tissues><Total Human and Non-Human Gene Mapping><Trans-Omics for Precision Medicine><Validation><Variant><Variation><above age 65><after age 65><age 65 and greater><age 65 and older><age 65 or older><age > 65><age of 65 years onward><aged 65 and greater><aged 65+><aged ≥65><biobank><biologic><biological signal transduction><biorepository><cell type><co-morbid><co-morbidity><cohort><comorbidity><computational methodology><computational methods><computer based method><computer methods><computing method><developmental><diffuse interstitial pulmonary fibrosis><disease heterogeneity><drug development><exome><exome sequencing><exome-seq><exomes><forecasting risk><genetic analysis><genetic approach><genetic mapping><genetic strategy><genome mutation><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genomewide association scan><genomewide association studies><genomewide association study><healthcare burden><high risk><human old age (65+)><idiopathic pulmonary fibrosis><life-threatening COVID><life-threatening COVID-19><life-threatening SARS-CoV-2><life-threatening coronavirus disease><life-threatening coronavirus disease 2019><life-threatening severe acute respiratory syndrome coronavirus 2><loss of function><lung function decline><monogenic disease><monogenic disorder><mortality rate><mortality ratio><network models><new drug treatments><new drugs><new pharmacological therapeutic><new therapeutics><new therapy><next generation therapeutics><novel><novel drug treatments><novel drugs><novel pharmaco-therapeutic><novel pharmacological therapeutic><novel therapeutics><novel therapy><old age><orphan disorder><over 65 years><pathway><phenomics><pleiotropic effect><pleiotropism><pleiotropy><polygenic risk score><predict risk><predict risks><predicted risk><predicted risks><predicting risks><predictive risk><predicts risk><pulmonary function decline><rare allele><rare mutation><rare variant><reverse genetics><rheumatic arthritis><risk prediction><risk predictions><serious COVID><serious COVID-19><serious SARS-CoV-2><serious coronavirus disease><serious coronavirus disease 2019><serious severe acute respiratory syndrome coronavirus 2><severe COVID><severe COVID-19><severe COVID19><severe SARS-CoV-2><severe coronavirus disease><severe coronavirus disease 19><severe coronavirus disease 2019><severe severe acute respiratory syndrome coronavirus 2><single-gene disease><single-gene disorder><statistical linear mixed models><statistical linear models><trait><validations><whole genome association analysis><whole genome association studies><whole genome association study><≥65 years>