Large, three-generation CEPH families reveal post-zygotic mosaicism and variability in germline mutation accumulation
Thomas A. Sasani, Brent S. Pedersen, Ziyue Gao, Lisa M. Baird, Molly Przeworski, Lynn B. Jorde, Aaron R. Quinlan
###https://doi.org/10.7554/eLife.46922
This repository contains the necessary Jupyter Notebooks and data files to reproduce figures in the manuscript (also see the link to a Binder environment below).
Below is an example figure from the MS, which summarizes our finding that the paternal age effect on DNM counts significantly differs between CEPH families. This figure can be created (in multiple parts) using this repository.
Two notebooks, ms_figs.R.ipynb and ms_figs.python.ipynb, can be used to reproduce figures from the manuscript. A notebook called inter-family-variability.ipynb can be used to reproduce the statistical analyses associated with inter-family variability in parental age effects. Finally, figures presented in the main response to reviewers can be reproduced using response_figures.ipynb. Figures in the manuscript were generated with the versions of each library listed below, though more recent versions (if applicable) will likely work, as well.
To mitigate compatability/version issues, we have packaged all notebooks into a binder environment; to access the environment, simply click on the badge below. It might take a minute to load everything.
The files included in the data directory are organized as follows:
second_gen.dnms.txt and third_gen.dnms.txt contain a row for every DNM identified in the second or third generation, respectively. gonosomal.dnms.txt and post-pgcs.dnms.txt contain a row for every DNM identified as being gonosomal or a post-PGCS mosaic mutation, respectively. Each row in these files is formatted like a heavily annotated BED entry, where the first three columns indicate the chromosome, start position, and end position of the variant, followed by additional columns with per-variant information, such as the reference and alternate alleles, depth and genotype qualities in the proband and parents, etc.
For example, the first few lines of second_gen.dnms.txt are shown below:
chrom start end new_sample_id new_family_id ref alt mut new_paternal_id new_maternal_id kid_ref_depth kid_alt_depth kid_total_depth kid_allele_balance mom_ref_depth mom_alt_depth mom_total_depth dad_ref_depth dad_alt_depth dad_total_depth kid_qual mom_qual dad_qual paternal_age_at_birth maternal_age_at_birth phase
1 1142254 1142255 538 29 G A CpG>TpG 544 543 17 15 32 0.46875 24 0 24 35 0 35 99.0 63.0 99.0 32.69999999999999 27.0 paternal
1 1461136 1461137 257 16 G C C>G 261 263 12 17 29 0.5862068965517241 27 0 27 45 0 45 99.0 81.0 81.0 30.800000000000008 22.0 paternal
second_gen.dnms.summary.csv, third_gen.dnms.summary.csv, gonosomal.dnms.summary.csv, and post-pgcs.dnms.summary.csv are summary CSV files, and contain a row for every sample in the second, third, second (again, but for the gonosomal DNMs), and third (again, but for the post-PGCS DNMs) generations, respectively. Each row contains summary information about the sample, including the total number of DNMs identified, the numbers that were phased to either parental allele, the callable autosomal fraction in the sample, etc.
For example, the first few lines of second_gen.dnms.summary.csv is shown below:
all_dnms,alpha,autosomal_callable_fraction,autosomal_dnms,dad_age,dad_dnms,dad_dnms_auto,dad_dnms_auto_snv,dad_dnms_snv,family_id,maternal_id,mom_age,mom_dnms,mom_dnms_auto,mom_dnms_auto_snv,mom_dnms_snv,n_children,n_sibs,paternal_id,phased_frac,sample_id,snv_autosomal_dnms,snv_dnms,mean_depth
57.0,0.6545454545454545,2587148570.0,57.0,22.8,36.0,36.0,34.0,17.0,20,329,21.599999999999994,19.0,19.0,17.0,17.0,9.0,1.0,330,0.9649122807017544,328,52.0,52.0,36.822442140553065
86.0,0.8604651162790697,2560784557.0,86.0,30.800000000000008,74.0,74.0,69.0,11.0,16,263,22.0,12.0,12.0,11.0,11.0,9.0,1.0,261,1.0,257,80.0,80.0,33.838202333693275
scipy v1.2.1
seaborn v0.9.0
matplotlib v2.2.3
numpy v1.16.2
pandas 0.24.2
statsmodels v0.9.0
ggplot2 v3.1.0
cowplot v0.9.3
ggridges v0.5.1
viridis v0.5.1
Simply clone the repository and run a notebook as follows:
git clone https://github.com/tomsasani/ceph-dnm-manuscript
cd ceph-dnm-manuscript/notebooks
jupyter lab ms_figs.R.ipynb
jupyter lab ms_figs.python.ipynb
jupyter lab inter-family-variability.ipynb