Shared variants
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Entering edit mode
7 months ago
priya.bmg ▴ 20

Hello

I have exome data sets from 6 individuals, in which 4 are affected and 2 are not affected. I have to identify the variants which are shared between the four affected individuals. I did the joint call genotyping for the 4 affected individuals and filtered the SNPs and Indels by hard filtering. How do I identify the shared variants between 4 affected individuals

Thanks

Priya

filtering variants GATK • 466 views
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Entering edit mode
7 months ago

using VcfFilterJdk http://lindenb.github.io/jvarkit/VcfFilterJdk.html

 java -jar dist/vcffilterjdk.jar -e 'final Set<String> sns = new HashSet<>(Arrays.asList("SAMPLE1","SAMPLE2","SAMPLE3","SAMPLE4")); return sns.stream().map(S->variant.getGenotype(S)).allMatch(G->G.isHet() || G.isHomVar()) && variant.getGenotypes().stream().filter(G->!sns.contains(G.getSampleName())).noneMatch(G->G.isHet() || G.isHomVar());' input.vcf
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Entering edit mode
7 months ago
sbstevenlee ▴ 430

If you are a Python user, you may want to checkout the pyvcf submodule from the fuc package I wrote:

>>> from fuc import pyvcf
>>> data = {
...     'CHROM': ['chr1', 'chr1', 'chr1', 'chr1'],
...     'POS': [100, 101, 102, 103],
...     'ID': ['.', '.', '.', '.'],
...     'REF': ['G', 'T', 'T', 'T'],
...     'ALT': ['A', 'C', 'A', 'C'],
...     'QUAL': ['.', '.', '.', '.'],
...     'FILTER': ['.', '.', '.', '.'],
...     'INFO': ['.', '.', '.', '.'],
...     'FORMAT': ['GT', 'GT', 'GT', 'GT'],
...     'Affected1': ['0/0', '0/0', '1/1', '0/1'],
...     'Affected2': ['0/1', '0/0', '0/1', '0/0'],
...     'Affected3': ['0/0', '0/0', '1/1', '0/0'],
...     'Affected4': ['0/0', '0/0', '0/1', '0/1'],
...     'Unaffected1': ['0/0', '0/0', '0/0', '0/0'],
...     'Unaffected2': ['0/1', '0/1', '0/0', '0/0'],
... }
>>> vf = pyvcf.VcfFrame.from_dict([], data)
>>> vf.df
  CHROM  POS ID REF ALT QUAL FILTER INFO FORMAT Affected1 Affected2 Affected3 Affected4 Unaffected1 Unaffected2
0  chr1  100  .   G   A    .      .    .     GT       0/0       0/1       0/0       0/0         0/0         0/1
1  chr1  101  .   T   C    .      .    .     GT       0/0       0/0       0/0       0/0         0/0         0/1
2  chr1  102  .   T   A    .      .    .     GT       1/1       0/1       1/1       0/1         0/0         0/0
3  chr1  103  .   T   C    .      .    .     GT       0/1       0/0       0/0       0/1         0/0         0/0
>>> affected_samples = ['Affected1', 'Affected2', 'Affected3', 'Affected4']
>>> filtered_vf = vf.filter_sampall(samples=affected_samples)
>>> filtered_vf.df
  CHROM  POS ID REF ALT QUAL FILTER INFO FORMAT Affected1 Affected2 Affected3 Affected4 Unaffected1 Unaffected2
0  chr1  102  .   T   A    .      .    .     GT       1/1       0/1       1/1       0/1         0/0         0/0
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