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analysis
detection_pipeline
variant_calling
ADfile.example_full_annotation.txt
ADfile.example_minimal.txt
README.md

README.md

Mosaicism in Congenital Heart Disease

Early post-zygotic mutations contribute to congenital heart disease

Alexander Hsieh, Sarah U Morton, Jon AL Willcox, Joshua M Gorham, Angela C Tai, Hongjian Qi, Steven DePalma, David McKean, Emily Griffin, Kathryn B Manheimer, Daniel Bernstein, Richard W Kim, Jane W Newburger, George A Porter Jr., Deepak Srivastava, Martin Tristani-Firouzi, Martina Brueckner, Richard P Lifton, Elizabeth Goldmuntz, Bruce D Gelb, Wendy K Chung, Christine Seidman, J G Seidman, Yufeng Shen

https://www.biorxiv.org/content/10.1101/733105v1

Project Aims

  1. to develop a method to detect mosaic (post-zygotic) SNVs from exome sequencing data
  2. to estimate the contribution of mosaicism to Congenital Heart Disease.

Directories

  • variant_calling/ - contains scripts used to call de novo variants from BAM files
  • detection_pipeline/ - contains scripts used to QC de novo variants and call mosaic SNVs
  • analysis/ - contains scripts used to analyze detected mosaics

Test Data

  • ADfile.example_minimal.txt - example de novo callset for testing (contains only columns required for mosaic detection: id, chr, pos, ref, alt, refdp, altdp)
  • ADfile.example_full_annotation.txt - contains additional columns and annotations used to QC/filter variants

Key Scripts and Usage

  • detection_pipeline/generate_candidates.POST.R - detects candidate mosaic SNVs by calculating posterior odds for each de novo SNV following annotation and QC
# Call mosaics from input de novo callset (ADfile.example_minimal.txt) with output prefix 'test', posterior odds cutoff '10', and cohort size '2400'

Rscript generate_candidates.POST.R ADfile.example_minimal.txt test 10 2400

# Output Files
* test.candidates.txt = contains mosaic variants with posterior odds > cutoff
* test.denovo.txt = contains all de novo variants in input passing filters, annotated with posterior odds score, etc
* test.all_denovos.txt = contains all de novo variants (including sites failing filters), annotated with exclusion criteria
* test.outlier_samples.txt = contains IDs of all outlier samples (based on cohort size)

# Output Plots
* test.EM.pdf = histogram of variant allele fraction of input variants, with EM-estimated mosaic fraction
* test.dp_vs_vaf.pdf = scatterplot of DP vs. VAF, colored by germline/mosaic
* test.vaf_vs_post.df = scatterplot of VAF vs. log-scaled posterior odds, colored by germline/mosiac
* test.fdr_min_nalt.pdf = scatterplot of DP vs. Nalt, with line indicating FDR-based minimum Nalt value
* test.overdispersion.pdf = overdispersion plot
* test.QQ.pdf = QQ plot
  • analysis/power_analysis.R - estimates mosaic detection power as a function of sample average depth
# Plot detection power as a function of sample average depth with model parameters determined from generate_candidates.POST.R 
# Estimate the true frequency of mosaics with VAF>0.1 using 'test.denovo.txt' (with parameters LR cutoff = 41, Theta estimate = 59, cohortsize = 2400, sample avg depth = 80)

Rscript power_analysis.R test.denovo.txt test 41 59 2400 80

# Output Files
* test.pwsite.log.txt = data used for estimating detection power given variant site DP
* test.pwsamp.log.txt = data used to plot detection power as a function of sample avg DP
* test.vaf_pw_adj.log.txt = data used in adjusting mosaic counts

# Output Plots
* test.power_sample_dp.pdf = plot of detection power curves as a function of sample avg DP
* test.vaf_pw_adj.pdf = histogram of adjusted mosaic counts, raw mosaic rate, adjusted mosaic rate

Prerequisites

Authors

Acknowledgments

Thanks to Jiayao and Hongjian for help with the variant calling and IGV automation

README.md template from PurpleBooth (https://gist.github.com/PurpleBooth)

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