Identifying dominant gene candidates with GEMINI

Transcription

1 Identifying dominant gene candidates with GEMINI Aaron Quinlan University of Utah! quinlanlab.org Please refer to the following Github Gist to find each command for this session. Commands should be copy/pasted from this Gist 1

2 Autosomal dominant pedigrees 2 Jessica Chong

3 Create a PED file (done for you) 3 Jessica Chong

4 Create a GEMINI database from a VCF Notes: 1. The VCF has been normalized and decomposed with VT 2. The VCF has been annotated with VEP. $ curl tutorials/trio.trim.vep.vcf.gz > trio.trim.vep.vcf.gz $ curl tutorials/dominant.ped > dominant.ped $ gemini load - - cores 4 \ - v trio.trim.vep.vcf.gz \ - t VEP \ - - skip- gene- tables \! - p dominant.ped \ trio.trim.vep.dominant.db to avoid errors 4

5 Running the autosomal_dominant tool $ gemini autosomal_dominant --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ head \ column -t chrom start end ref alt gene impact cadd_raw variant_id family_id family_members family_genotypes samples family_count chr T C AC mature_mirna family1 1805,1847,4805 T/C,T/T,T/C 1805, chr G C AC downstream family1 1805,1847,4805 G/C,G/G,G/C 1805, chr G A AC upstream family1 1805,1847,4805 G/A,G/G,G/A 1805, chr C T AC upstream family1 1805,1847,4805 C/T,C/C,C/T 1805, chr C T AC upstream family1 1805,1847,4805 C/T,C/C,C/T 1805, chr C G AC mature_mirna family1 1805,1847,4805 C/G,C/C,C/G 1805, chr C A ACAN non_syn_coding family1 1805,1847,4805 C/A,C/C,C/A 1805, chr G A ADAMTS17 synonymous_coding family1 1805,1847,4805 G/A,G/G,G/A 1805, chr A G ADAMTS17 non_syn_coding family1 1805,1847,4805 A/G,A/A,A/G 1805,

6 Time to start filtering $ gemini autosomal_dominant \ --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ wc -l 1515 candidates 6

7 Again, use the - - filter option Exclude variants that failed GATK filters $ gemini autosomal_dominant \ --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ --filter "filter is NULL" \ wc -l 1288 candidates 7

8 Further restrict variants with functional consequence $ gemini autosomal_dominant \ --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ --filter "filter is NULL and impact_severity!= 'LOW'" \ wc -l 347 candidates 8

9 Use ESP and ExAC to focus on rare variants $ gemini autosomal_dominant \ --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ --filter "filter is NULL and impact_severity!= 'LOW' and (aaf_esp_ea <= 0.01 or aaf_esp_ea is NULL) and (aaf_exac_all <= 0.01 or aaf_exac_all is NULL)" wc -l 40 candidates 9

10 Let s be more strict with the functional consequence $ gemini autosomal_dominant \ --columns "chrom, start, end, ref, alt, gene, impact, cadd_raw" \ --filter "filter is NULL and impact_severity == 'HIGH' and (aaf_esp_ea <= 0.01 or aaf_esp_ea is NULL) and (aaf_exac_all <= 0.01 or aaf_exac_all is NULL)" wc -l chrom start end ref alt gene impact cadd_raw variant_id family_id family_members family_genotypes samples family_count chr T C CPD stop_loss family1 1805,1847,4805 T/C,T/T,T/C 1805, chr G A APOB stop_gain family1 1805,1847,4805 G/A,G/G,G/A 1805, chr T C TUBA3FP splice_acceptor family1 1805,1847,4805 T/C,T/T,T/C 1805,

11 Are any of these variants known to underlie a clinical phenotype? How could you extend the query from the previous slide to answer this? Hint: use the gemini documentation chrom start end ref alt gene impact cadd_raw variant_id family_id family_members family_genotypes samples family_count chr T C CPD stop_loss family1 1805,1847,4805 T/C,T/T,T/C 1805, chr G A APOB stop_gain family1 1805,1847,4805 G/A,G/G,G/A 1805, chr T C TUBA3FP splice_acceptor family1 1805,1847,4805 T/C,T/T,T/C 1805,

12 Custom analyses: Use the query module to identify autosomal dominant variants 12

13 Using the - - gt- filter We need to use the query module to enforce an autosomal dominant inheritance pattern. 13

14 Using the - - gt- filter $ gemini query \ -q "SELECT chrom, start, end, ref, alt, gene, impact, (gts).(*) \ FROM variants" \ --header \ --gt-filter "gt_types.4805 == HET \ and gt_types.1805 == HET \ and gt_types.1847 == HOM_REF" \ head \ column -t chrom start end ref alt gene impact gts.1805 gts.1847 gts.4805 chr A G SH3YL1 intron A/G A/A A/G chr G A ACP1 downstream G/A G/G G/A chr G T TMEM18 intron G/T G/G G/T chr A T AC upstream A/T A/A A/T chr C T PXDN UTR_3_prime C/T C/C C/T chr T C PXDN intron T/C T/T T/C chr C A COLEC11 intron C/A C/C C/A chr G A COLEC11 intron G/A G/G G/A chr G A COLEC11 intron G/A G/G G/A 14

15 What about large pedigrees or multiple families? --gt- filter wildcards 15

16 Using the --gt- filter wildcards $ gemini query \ -q "SELECT chrom, start, end, ref, alt, gene, impact, (gts).(*) \ FROM variants" \ --header \ --gt-filter "(gt_types).(phenotype==2).(==het).(all) \ and (gt_types).(phenotype==1).(==hom_ref).(all) \ head \ column -t Affected individuals must be HET Unffected individuals must be HOM_REF chrom start end ref alt gene impact gts.1805 gts.1847 gts.4805 chr A G SH3YL1 intron A/G A/A A/G chr G A ACP1 downstream G/A G/G G/A chr G T TMEM18 intron G/T G/G G/T chr A T AC upstream A/T A/A A/T chr C T PXDN UTR_3_prime C/T C/C C/T chr T C PXDN intron T/C T/T T/C chr C A COLEC11 intron C/A C/C C/A chr G A COLEC11 intron G/A G/G G/A chr G A COLEC11 16 intron G/A G/G G/A

17 Can apply multiple --gt- filter wildcards $ gemini query \ -q "SELECT chrom, start, end, ref, alt, gene, impact, \ (gts).(*), (gt_depths).(*) \ FROM variants" \ --header \ --gt-filter "(gt_types).(phenotype==2).(==het).(all) \ and (gt_types).(phenotype==1).(==hom_ref).(all) \ and (gt_depths).(*).(>=20).(all)" \ head \ column -t Affected individuals must be HET Unaffected individuals must be HOM_REF Everyone must have sequence depth >=20 chrom start end ref alt gene impact gts.1805 gts.1847 gts.4805 gt_depths.1805 gt_depths.1847 gt_depths.4805 chr A G SH3YL1 intron A/G A/A A/G chr G A ACP1 downstream G/A G/G G/A chr A T AC upstream A/T A/A A/T chr C T PXDN UTR_3_prime C/T C/C C/T chr T C PXDN intron T/C T/T T/C chr C A COLEC11 intron C/A C/C C/A chr G A COLEC11 intron G/A G/G G/A chr G A COLEC11 intron G/A G/G G/A chr T A LINC00487 intron T/A T/T T/A

18 We have the inheritance model, now apply the annotation filters $ gemini query \ -q "SELECT chrom, start, end, ref, alt, gene, impact, \ (gts).(*), (gt_depths).(*) \ FROM variants \ WHERE filter is NULL and impact_severity == 'HIGH' and (aaf_esp_ea <= 0.01 or aaf_esp_ea is NULL) and (aaf_exac_all <= 0.01 or aaf_exac_all is NULL)" \ --header \ --gt-filter "(gt_types).(phenotype==2).(==het).(all) \ and (gt_types).(phenotype==1).(==hom_ref).(all) \ and (gt_depths).(*).(>=20).(all)" \ column -t chrom start end ref alt gene impact gts.1805 gts.1847 gts.4805 gt_depths.1805 gt_depths.1847 gt_depths.4805 chr G A APOB stop_gain G/A G/G G/A chr T C CPD stop_loss T/C T/T T/C chr T C TUBA3FP splice_acceptor T/C T/T T/C Note that (pleasingly) these are the same three candidates as detected with the autosomal_dominant tool. 18

19 Load the following files into IGV (Load from URL) and inspect your candidates BAM alignment files:! tutorials/1805.workshop.bam tutorials/1847.workshop.bam tutorials/4805.workshop.bam VCF variant file:! tutorials/trio.trim.vep.vcf.gz! 19