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Comparing power to detect rare variant aggregate association using exome sequence data vs imputed data

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Rare Variant Aggregate Association Analysis of Imputed Variants

Aims


This project aims to explore whether imputed data is a viable alternative to detect rare-variant aggregate associations when exome sequence data is unavailable

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  • analysis: analysis of exome sequence data and imputed genotypes

    • exome_sequencde_processing.ipynb: annotation and filtering steps for exome sequencing data

    • imputed_data_processing.ipynb: annotation and filtering steps for imputed data (HRC, TOPMed)

    • data_summary_analysis.ipynb: notebook that generates data summary tables and plots

    • pcsk9_apoc3_brv_analysis.ipynb: analysis of PCSK9 and APOC3 versus LDL and TG respectively, using BRV

  • workflow: simulations and applications of rare variant association studies

    • simulation_pipeline.ipynb: notebook documenting simulation steps and result analysis, accompanied by dsc_pipeline folder, where code for simulations can be found
    • additional_queries.ipynb: notebook investigating $R^2$ (imputation quality) and $r^2$ (correlation) between imputed dataset and simulation data

Collaborators


This project is a joint effort between Suzanne (CU), Gao (CU), Andy (Yale) and Paul (MCW), with contributions from

  • Amanda/Tianyi (CU)
  • Elnaz (MSK)

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Comparing power to detect rare variant aggregate association using exome sequence data vs imputed data

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