Example import of de novo variants from Rates of contributory de novo mutation in high and low-risk autism families

Let us import de novo variants from the Yoon, S., Munoz, A., Yamrom, B. et al. Rates of contributory de novo mutation in high and low-risk autism families. Commun Biol 4, 1026 (2021)..

We will focus on de novo variants from the SSC collection published in the aforementioned paper. To import these variants into the GPF system we need a list of de novo variants and a pedigree file describing the families. The list of de novo variants is available from Supplementary Data 2. The pedigree file for this study is not available. Instead, we have a list of children available from Supplementary Data 1.

Let us first export these Excel spreadsheets into CSV files. Let us say that the list of de novo variants from the SSC collection is saved into a file named SupplementaryData2_SSC.tsv and the list of children is saved into a TSV file named SupplementaryData1_Children.tsv.

Note

Input files for this example can be downloaded from denovo-in-high-and-low-risk-papter.tar.gz.

Preprocess the families data

To import the data into GPF we need a pedigree file describing the structure of the families. The SupplementaryData1_Children.tsv contains only the list of children. There is no information about their parents. Fortunately for the SSC collection it is not difficult to build the full families’ structures from the information we have. For the SSC collection if you have a family with ID <fam_id>, then the identifiers of the individuals in the family are going to be formed as follows:

• mother - <fam_id>.mo;

• father - <fam_id>.fa;

• proband - <fam_id>.p1;

• first sibling - <fam_id>.s1;

• second sibling - <fam_id>.s2.

Another important restriction for SSC is that the only affected person in the family is the proband. The affected status of the mother, father and siblings are unaffected.

Using all these conventions we can write a simple python script build_ssc_pedigree.py to convert SupplementaryData1_Children.tsv into a pedigree file ssc_denovo.ped:

"""Converts SupplementaryData1_Children.tsv into a pedigree file."""
import pandas as pd

children = pd.read_csv("SupplementaryData1_Children.tsv", sep="\t")
ssc = children[children.collection == "SSC"]

# list of all individuals in SSC
persons = []
# each person is represented by a tuple:
# (familyId, personId, dadId, momId, status, sex)

for fam_id, members in ssc.groupby("familyId"):
    persons.append((fam_id, f"{fam_id}.mo", "0", "0", "unaffected", "F"))
    persons.append((fam_id, f"{fam_id}.fa", "0", "0", "unaffected", "F"))
    for child in members.to_dict(orient="records"):
        persons.append((
            fam_id, child["personId"], f"{fam_id}.fa", f"{fam_id}.mo",
            child["affected status"], child["sex"]))

with open("ssc_denovo.ped", "wt", encoding="utf8") as output:
    output.write(
        "\t".join(("familyId", "personId", "dadId", "momId", "status", "sex")))
    output.write("\n")

    for person in persons:
        output.write("\t".join(person))
        output.write("\n")

If we run this script it will read SupplementaryData1_Children.tsv and produce the appropriate pedigree file ssc_denovo.ped.

Preprocess the variants data

The SupplementaryData2_SSC.tsv file contains 255231 variants. To import so many variants in in-memory genotype storage is not appropriate. For this example we are going to use a subset of 10000 variants:

head -n 10001 SupplementaryData2_SSC.tsv > ssc_denovo.tsv

Data import of ssc_denovo

Now we have a pedigree file ssc_denovo.ped and a list of de novo variants ssc_denovo.tsv. Let us prepare an import project configuration file ssc_denovo.yaml:

id: ssc_denovo

input:
  pedigree:
    file: ssc_denovo.ped

  denovo:
    files:
      - ssc_denovo.tsv
    person_id: personIds
    variant: variant
    location: location

To import the study we should run:

import_tools ssc_denovo.yaml

and when the import finishes we can run the development GPF server:

wgpf run

In the list of studies, we should have a new study ssc_denovo.