lineage 1.0b1

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.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.

Find Shared DNA

lineage uses the probabilistic recombination rates throughout the human genome from the International HapMap Project to compute the shared DNA (in centiMorgans) between two individuals. Additionally, lineage denotes when the shared DNA is shared on either one or both chromosomes in a pair. For example, when siblings share a segment of DNA on both chromosomes, they inherited the same DNA from their mother and father for that segment.

With that background, let’s find the shared DNA between the User662 and User663 datasets, calculating the centiMorgans of shared DNA and plotting the results:

>>> l.find_shared_dna(user662, user663, cM_threshold=0.75, snp_threshold=1100)
Downloading resources/hapmap_h37.tar.gz
Downloading resources/cytoband_h37.txt.gz
Saving output/shared_dna_User662_User663.png
Saving output/shared_dna_one_chrom_User662_User663.csv

Notice that the centiMorgan and SNP thresholds for each DNA segment can be tuned. Additionally, notice that two files were downloaded to facilitate the analysis and plotting - future analyses will used the downloaded files instead of downloading the files again.

Here, the output consists of a CSV file that details the shared segments of DNA on one chromosome. Additionally, a plot is also generated that illustrates the shared DNA:

Find Shared Genes

The Central Dogma of Molecular Biology states that genetic information flows from DNA to mRNA to proteins: DNA is transcribed into mRNA, and mRNA is translated into a protein. It’s more complicated than this (it’s biology after all), but generally, one mRNA produces one protein, and the mRNA / protein is considered a gene.

Therefore, it would be interesting to understand not just what DNA is shared between individuals, but what genes are shared between individuals with the same variations. (In other words, what genes are producing the same proteins?) Since lineage can determine the shared DNA between individuals, it can use that information to determine what genes are also shared on either one or both chromosomes.

For this example, let’s create two more Individuals for the User4583 and User4584 datasets:

>>> user4583 = l.create_individual('User4583', 'resources/4583.ftdna-illumina.3482.csv.gz')
Loading resources/4583.ftdna-illumina.3482.csv.gz

>>> user4584 = l.create_individual('User4584', 'resources/4584.ftdna-illumina.3483.csv.gz')
Loading resources/4584.ftdna-illumina.3483.csv.gz

Now let’s find the shared genes:

>>> l.find_shared_dna(user4583, user4584, shared_genes=True)
Saving output/shared_dna_User4583_User4584.png
Saving output/shared_dna_one_chrom_User4583_User4584.csv
Downloading resources/knownGene_h37.txt.gz
Downloading resources/kgXref_h37.txt.gz
Saving output/shared_genes_one_chrom_User4583_User4584.csv
Saving output/shared_dna_two_chroms_User4583_User4584.csv
Saving output/shared_genes_two_chroms_User4583_User4584.csv

The plot that illustrates the shared DNA is shown below. Note that in addition to outputting the shared DNA segments on either one or both chromosomes, the shared genes on either one or both chromosomes are also output. These output files are detailed in the documentation.