tools for genetic genealogy and the analysis of consumer DNA test results
lineage provides a framework for analyzing genotype (raw data) files from direct-to-consumer (DTC) DNA testing companies, primarily for the purposes of genetic genealogy.
- Compute centiMorgans (cMs) of shared DNA between individuals using the HapMap Phase II genetic map
- Plot shared DNA between individuals
- Determine genes shared between individuals (i.e., genes transcribed from shared DNA segments)
- Find discordant SNPs between child and parent(s)
- Read, write, merge, and remaps SNPs for an individual via the snps package
Supported Genotype Files
lineage supports all genotype files supported by snps.
lineage requires Python 3.5+ and the following Python packages:
On Linux systems, the following system-level installs may also be required:
$ sudo apt-get install python3-tk $ sudo apt-get install gfortran $ sudo apt-get install python-dev $ sudo apt-get install python-devel $ sudo apt-get install python3.X-dev # (where X == Python minor version)
$ pip install lineage
Initialize the lineage Framework
Import Lineage and instantiate a Lineage object:
>>> from lineage import Lineage >>> l = Lineage()
Download Example Data
First, let’s setup logging to get some helpful output:
>>> import logging, sys >>> logger = logging.getLogger() >>> logger.setLevel(logging.INFO) >>> logger.addHandler(logging.StreamHandler(sys.stdout))
Now we’re ready to download some example data from openSNP:
>>> paths = l.download_example_datasets() Downloading resources/662.23andme.304.txt.gz Downloading resources/662.23andme.340.txt.gz Downloading resources/662.ftdna-illumina.341.csv.gz Downloading resources/663.23andme.305.txt.gz Downloading resources/4583.ftdna-illumina.3482.csv.gz Downloading resources/4584.ftdna-illumina.3483.csv.gz
We’ll call these datasets User662, User663, User4583, and User4584.
Load Raw Data
Create an Individual in the context of the lineage framework to interact with the User662 dataset:
>>> user662 = l.create_individual('User662', 'resources/662.ftdna-illumina.341.csv.gz') Loading resources/662.ftdna-illumina.341.csv.gz
Here we created user662 with the name User662 and loaded a raw data file.
Oops! The data we just loaded is Build 36, but we want Build 37 since the other files in the datasets are Build 37… Let’s remap the SNPs:
>>> user662.build 36 >>> chromosomes_remapped, chromosomes_not_remapped = user662.remap_snps(37) Downloading resources/NCBI36_GRCh37.tar.gz >>> user662.build 37 >>> user662.assembly 'GRCh37'
SNPs can be re-mapped between Build 36 (NCBI36), Build 37 (GRCh37), and Build 38 (GRCh38).
Merge Raw Data Files
The dataset for User662 consists of three raw data files from two different DNA testing companies. Let’s load the remaining two files.
As the data gets added, it’s compared to the existing data, and SNP position and genotype discrepancies are identified. (The discrepancy thresholds can be tuned via parameters.)
>>> user662.snp_count 708092 >>> user662.load_snps(['resources/662.23andme.304.txt.gz', 'resources/662.23andme.340.txt.gz'], ... discrepant_genotypes_threshold=300) Loading resources/662.23andme.304.txt.gz 3 SNP positions were discrepant; keeping original positions 8 SNP genotypes were discrepant; marking those as null Loading resources/662.23andme.340.txt.gz 27 SNP positions were discrepant; keeping original positions 156 SNP genotypes were discrepant; marking those as null >>> len(user662.discrepant_positions) 30 >>> user662.snp_count 1006960
Ok, so far we’ve remapped the SNPs to the same build and merged the SNPs from three files, identifying discrepancies along the way. Let’s save the merged dataset consisting of over 1M+ SNPs to a CSV file:
>>> saved_snps = user662.save_snps() Saving output/User662_GRCh37.csv
All output files are saved to the output directory.
Let’s create another Individual for the User663 dataset:
>>> user663 = l.create_individual('User663', 'resources/663.23andme.305.txt.gz') Loading resources/663.23andme.305.txt.gz
Now we can perform some analysis between the User662 and User663 datasets.
Find Discordant SNPs
First, let’s find discordant SNPs (i.e., SNP data that is not consistent with Mendelian inheritance):
>>> discordant_snps = l.find_discordant_snps(user662, user663, save_output=True) Saving output/discordant_snps_User662_User663_GRCh37.csv
This method also returns a pandas.DataFrame, and it can be inspected interactively at the prompt, although the same output is available in the CSV file.
>>> len(discordant_snps.loc[discordant_snps['chrom'] != 'MT']) 37
Not counting mtDNA SNPs, there are 37 discordant SNPs between these two datasets.
Documentation is available here.
Copyright (C) 2016 Andrew Riha
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
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