Hi, friends I have finished call variants via GTAK best practices(joint-call) with 20 WES samples. My goal is to find the genes that may causes the disease from these 20 samples, I also tried some tools and methods, but I don't whether they are rigth. So, Is there a good guidance documentation like GTAK best practices to get my target?
I have tried an annotation tool named annova,
First, I transform vcf into the annova input format, more precisely, I got 20 annova input file for each sample.
convert2annovar.pl -format vcf4 relapse.filtered.snps.indels.vcf -allsample -filter PASS -out out/relapse
For each sample, I did the following:
1.filtering the irrelevant variants via 1000 Genomes Project dataset with MAF=0.01
2.annotating each variants with gene info, due to the data is WES, I got the "exonic variantfunction"
intronic KCNMA1 chr10 77008214 77008214 T C het 127.30 51
exonic CDHR4 chr3 49795287 49795287 C T het 8460.17 274
exonic CLCN5 chrX 50081733 50081733 A G hom 69769.27 255
intronic TUFT1 chr1 151566031 151566031 - C hom 7453.64 18
intronic PDE4D chr5 60147674 60147674 T G het 1524.08 112
intronic USF1 chr1 161041947 161041948 GA - het 2536.97 91
intronic RPS6KB2 chr11 67432552 67432552 A G het 2772.98 156
intergenic LINC01296(dist=73295),DUXAP10(dist=113998) chr14 19180792 19180792 C A het 2378.33 172
3.Combining there 20 output file with .MAF format to generate a waterfall plot, however, the percent of mutant is almost 100%, I don't think that is a right result.
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jiangpeng59 •
I take it this is a mendelian rare disease that you are looking for a causal variant? Are the 20 samples trios (parents-proband) or singletons (unrelated cases with similar symptoms), as this will change the filtering strategy? Do you have family history? Population details / Phenotype details?
If trios you can probably filter autosomal variants present in parents (who do not exhibit the disease - be careful here of reduced penetrance). If singletons you would probably start with MAF (1% is still very high for a rare disease) specific for that population (after annotation - Gnomad), then look at nonsense > splicing variants > missense > rare synonymous variant present in a high percentage of the samples (less likely and much harder to prove). You might also want to run a CNV caller to pick up larger structural variants. You are probably looking for a HMZ variant (HTZ in both parents), possibly a denovo HTZ (will not be present in parents), depending on the severity of the phenotype / symptoms. This is a very brief overview since there's alot more you would do depending on the samples / family history / phenotype, including HPO / OMIM lookup of phenotype terms to provide possible candidate genes etc.
Thanks for your reply, Garan, I think the sample is a singletons, more precisely, it is a tumor(glioma). these samples come from 20 individual and no normal sample.
I guess if you're looking for somatic mutations not germline variants then this complicates things, without non-tumor samples to compare to. I guess you'd also want to take into account the source and age profile of the Gnomad/Exac variant information since I believe some of datasets were recruited from patients with various cancers, and the age profiles (can be seen in the age histograms) are sometimes skewered towards older participants (more likely to carry somatic variants). Annotation with COSMIC is probably high on the agenda (https://cancer.sanger.ac.uk/cosmic)?
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You mean GATK, right?
Yes, I got a vcf with gatk_v4.1.0.0, here is part of the output.
but I have no ideal what should I do to find these disease genes. is there a common practice ?
What have you tried?
Thanks for your reply, I added a description of the problem ,and I tried an annotation tool named 'annova'
You should consider changing your question to the actual problem you are having, which is "find the genes that may causes the disease". GATK helps you get mutations, but it will not help you with understanding them.
Thanks for your reply, I added a description of the problem.
Thanks for your reply, I added a description of the problem.