I have several thousand SNPs identified as significant predictors in a GWAS, and I would like to be be able to annotate them beyond the standard identifiers of associated genes and classification as missense/nonsense etc.
Specifically, I would like to be able to obtain information such as which SNPs may correspond to loss of function (partial or total), changes in expression levels, etc. I've been using ANNOVAR, but the standard databases at least do not provide this level of detail about any of the variants. Are there alternative packages (or specific libraries/databases in ANNOVAR) that anybody might recommend?
In case it matters, my vcfs give variant coordinates with respect to the Hg19 assembly.
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shpak.max •
VEP, SnpEFF have been around for years. Either of those will work.
https://github.com/KarchinLab/open-cravat is relatively new
Thank you, I'll take a look. Incidentally, I have been getting anomalous results with ANNOVAR - i.e. nearly every exonic SNP is listed as ExonicFunc.refGeneWithVer=synonymous_SNV, whereas only about half of SNVs are synonymous. Have you ever worked with ANNOVAR and had such results?
show us an example
Please clarify - it wouldn't be practical to post the whole output file to illustrate how nearly every exonic snp is synonymous.
Here's an example of a typical run of exonic SNPs identified as synonymous:
1 16337111 16337111 0 G . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.C3403C:p.R1135R;ALLELE_END
21 16337764 16337764 0 G . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.C2750C:p.S917S;ALLELE_END
21 16338106 16338106 0 G . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.C2408C:p.S803S;ALLELE_END
21 16338443 16338443 0 G . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.C2071C:p.P691P;ALLELE_END
21 16338814 16338814 0 T . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.A1700A:p.N567N;ALLELE_END
21 16339172 16339172 0 G . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.C1342C:p.R448R;ALLELE_END
21 16339348 16339348 0 A . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.T1166T:p.I389I;ALLELE_END
21 16339852 16339852 0 T . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.A662A:p.H221H;ALLELE_END
21 16340289 16340289 0 C . . .;ANNOVAR_DATE=2022-08-02;Func.refGeneWithVer=exonic;Gene.refGeneWithVer=NRIP1;GeneDetail.refGeneWithVer=.;ExonicFunc.refGeneWithVer=synonymous_SNV;AAChange.refGeneWithVer=NRIP1:NM_003489.4:exon4:c.G225G:p.G75G;ALLELE_END
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shpak.max •
hmm i'm not seeing ALTs here, just refs. i am not familiar enough with ANNOVAR to know what it is trying to say here.
QIAGEN Clinical Insight Interpret Translational (QCII-T) may be of interest to you, it offers a comprehensive workflow for efficient, evidence-powered variant assessment.
QCII-T is a software solution for researchers and labs to analyze genetic variants in a streamlined, evidence-powered workflow. It helps identify disease-causing variants in next-generation sequencing (NGS) data by using a comprehensive, manually curated knowledge base and advanced tools to automate variant assessment and classification. This allows for faster and more efficient research discoveries related to hereditary diseases and cancer without requiring extensive bioinformatics expertise.
Two week free trials available here: https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/qci-interpret-translational/
Good luck!
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