SNP annotation tool options (alternatives to ANNOVAR)

Question

[shpak.max]

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.

Answer 1

[Jeremy Leipzig]

VEP, SnpEFF have been around for years. Either of those will work.

https://github.com/KarchinLab/open-cravat is relatively new

[shpak.max]

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?

[Jeremy Leipzig]

show us an example

[shpak.max]

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

[Jeremy Leipzig]

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.

Answer 2

[Nicole]

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!