Querying the GEMINI database

The real power in the GEMINI framework lies in the fact that all of your genetic variants have been stored in a convenient database in the context of a wealth of genome annotations that facilitate variant interpretation. The expressive power of SQL allows one to pose intricate questions of one’s variation data.

Note

If you are unfamiliar with SQL, sqlzoo has a decent online tutorial describing the basics. Really all you need to learn is the SELECT statement, and the examples below will give you a flavor of how to compose base SQL queries against the GEMINI framework.

Basic queries

GEMINI has a specific tool for querying a gemini database that has been load``ed using the ``gemini load command. That’s right, the tool is called gemini query. Below are a few basic queries that give you a sense of how to interact with the gemini database using the query tool.

  1. Extract all transitions with a call rate > 95%
$ gemini query -q "select * from variants \
                      where sub_type = 'ts' \
                      and call_rate >= 0.95" my.db
  1. Extract all loss-of-function variants with an alternate allele frequency < 1%:
$ gemini query -q "select * from variants \
                      where is_lof = 1 \
                      and aaf >= 0.01" my.db
  1. Extract the nucleotide diversity for each variant:
$ gemini query -q "select chrom, start, end, pi from variants" my.db
  1. Combine GEMINI with bedtools to compute nucleotide diversity estimates across 100kb windows:
$ gemini query -q "select chrom, start, end, pi from variants \
                      order by chrom, start, end" my.db | \
  bedtools map -a hg19.windows.bed -b - -c 4 -o mean

Selecting sample genotypes

The above examples illustrate ad hoc queries that do not request or filter upon the genotypes of individual samples. Since GEMINI stores the genotype information for each variant in compressed arrays that are stored as BLOBs in the database, standard SQL queries cannot directly access individual genotypes. However, we have enhanced the SQL syntax to support such queries with C “struct-like” access. For example, to retrieve the alleles for a given sample’s (in this case, sample 1094PC0009), one would add gts.1094PC0009 to the select statement.

Here is an example of selecting the genotype alleles for four different samples (note the examples below use the test.snpEff.vcf.db file that is created in the ./test directory when you run the bash master-test.sh command as described above):

$ gemini query -q "select chrom, start, end, ref, alt, gene, \
                          gts.1094PC0005, \
                          gts.1094PC0009, \
                          gts.1094PC0012, \
                          gts.1094PC0013 \
                   from variants" test.snpEff.vcf.db

chr1        30547   30548   T       G       FAM138A ./.     ./.     ./.     ./.
chr1        30859   30860   G       C       FAM138A G/G     G/G     G/G     G/G
chr1        30866   30869   CCT     C       FAM138A CCT/CCT CCT/CCT CCT/C   CCT/CCT
chr1        30894   30895   T       C       FAM138A T/C     T/C     T/T     T/T
chr1        30922   30923   G       T       FAM138A ./.     ./.     ./.     ./.
chr1        69269   69270   A       G       OR4F5   ./.     ./.     G/G     G/G
chr1        69427   69428   T       G       OR4F5   T/T     T/T     T/T     T/T
chr1        69510   69511   A       G       OR4F5   ./.     ./.     A/G     A/G
chr1        69760   69761   A       T       OR4F5   A/A     A/T     A/A     A/A
chr1        69870   69871   G       A       OR4F5   ./.     G/G     G/G     G/G

You can also add a header so that you can keep track of who’s who:

$ gemini query -q "select chrom, start, end, ref, alt, gene, \
                          gts.1094PC0005, \
                          gts.1094PC0009, \
                          gts.1094PC0012, \
                          gts.1094PC0013 \
                   from variants" \
                   --header test.snpEff.vcf.db

chrom       start   end     ref     alt     gene gts.1094PC0005     gts.1094PC0009  gts.1094PC0012  gts.1094PC0013
chr1        30547   30548   T       G       FAM138A ./.     ./.     ./.     ./.
chr1        30859   30860   G       C       FAM138A G/G     G/G     G/G     G/G
chr1        30866   30869   CCT     C       FAM138A CCT/CCT CCT/CCT CCT/C   CCT/CCT
chr1        30894   30895   T       C       FAM138A T/C     T/C     T/T     T/T
chr1        30922   30923   G       T       FAM138A ./.     ./.     ./.     ./.
chr1        69269   69270   A       G       OR4F5   ./.     ./.     G/G     G/G
chr1        69427   69428   T       G       OR4F5   T/T     T/T     T/T     T/T
chr1        69510   69511   A       G       OR4F5   ./.     ./.     A/G     A/G
chr1        69760   69761   A       T       OR4F5   A/A     A/T     A/A     A/A
chr1        69870   69871   G       A       OR4F5   ./.     G/G     G/G     G/G

Let’s now get the genotype and the depth of aligned sequence observed for a sample so that we can assess the confidence in the genotype:

$ gemini query -q "select chrom, start, end, ref, alt, gene,
                      gts.1094PC0005, \
                      gt_depths.1094PC0005 \
               from variants" test.snpEff.vcf.db

chr1    30547   30548   T       G       FAM138A ./.     -1
chr1    30859   30860   G       C       FAM138A G/G     7
chr1    30866   30869   CCT     C       FAM138A CCT/CCT 8
chr1    30894   30895   T       C       FAM138A T/C     8
chr1    30922   30923   G       T       FAM138A ./.     -1
chr1    69269   69270   A       G       OR4F5   ./.     -1
chr1    69427   69428   T       G       OR4F5   T/T     2
chr1    69510   69511   A       G       OR4F5   ./.     -1
chr1    69760   69761   A       T       OR4F5   A/A     1
chr1    69870   69871   G       A       OR4F5   ./.     -1

--gt-filter Filtering on genotypes

Now, we often want to focus only on variants where a given sample has a specific genotype (e.g., looking for homozygous variants in family trios). Unfortunately, we cannot directly do this in the SQL query, but the gemini query tool has an option called –gt-filter that allows one to specify filters to apply to the returned rows. The rules followed in the –gt-filter option follow Python syntax.

Tip

As you will see from the examples below, appropriate use of the –gt-filter option will allow you to compose queries that return variants meeting inheritance patterns that are relevant to the disease model of interest in your study.

As an example, let’s only return rows where sample 1094PC0012 is heterozygous. In order to do this, we apply a filter to the gt_types columns for this individual:

$ gemini query -q "select chrom, start, end, ref, alt, gene,
                      gts.1094PC0005, \
                      gts.1094PC0009, \
                      gts.1094PC0012, \
                      gts.1094PC0013 \
               from variants" \
               --gt-filter "gt_types.1094PC0012 == HET" \
               --header \
               test.snpEff.vcf.db

chrom   start   end     ref     alt     gene gts.1094PC0005     gts.1094PC0009  gts.1094PC0012  gts.1094PC0013
chr1    30866   30869   CCT     C       FAM138A CCT/CCT CCT/CCT CCT/C   CCT/CCT
chr1    69510   69511   A       G       OR4F5   ./.     ./.     A/G     A/G

Now let’s be a bit less restrictive and return variants where either sample 1094PC0012 is heterozygous or sample 1094PC0005 is homozygous for the reference allele:

$ gemini query -q "select chrom, start, end, ref, alt, gene,
                      gts.1094PC0005, \
                      gts.1094PC0009, \
                      gts.1094PC0012, \
                      gts.1094PC0013 \
               from variants" \
               --gt-filter "gt_types.1094PC0012 == HET or \
               gt_types.1094PC0005 == HOM_REF" \
               --header \
               test.snpEff.vcf.db

chrom   start   end     ref     alt     gene gts.1094PC0005     gts.1094PC0009  gts.1094PC0012  gts.1094PC0013
chr1    30859   30860   G       C       FAM138A G/G     G/G     G/G     G/G
chr1    30866   30869   CCT     C       FAM138A CCT/CCT CCT/CCT CCT/C   CCT/CCT
chr1    69427   69428   T       G       OR4F5   T/T     T/T     T/T     T/T
chr1    69510   69511   A       G       OR4F5   ./.     ./.     A/G     A/G
chr1    69760   69761   A       T       OR4F5   A/A     A/T     A/A     A/A

Eh, I changed my mind, let’s restrict the above to those variants where sample 1094PC0012 must also be heterozygous:

$ gemini query -q "select chrom, start, end, ref, alt, gene,
                      gts.1094PC0005, \
                      gts.1094PC0009, \
                      gts.1094PC0012, \
                      gts.1094PC0013 \
               from variants" \
               --gt-filter "(gt_types.1094PC0012 == HET or \
               gt_types.1094PC0005 == HOM_REF) \
               and \
               (gt_types.1094PC0013 == HET)" \
               --header \
               test.snpEff.vcf.db

 chrom  start   end     ref     alt     gene gts.1094PC0005     gts.1094PC0009  gts.1094PC0012  gts.1094PC0013

--show-samples Finding out which samples have a variant

While exploring your data you might hit on a set of interesting variants and want to know which of your samples have that variant in them. You can display the samples containing a variant with the –show-sample-variants flag:

$ gemini query --header --show-samples -q "select chrom, start, end, ref, alt \
                                from variants where is_lof=1 limit 5" test.query.db

chrom   start   end     ref     alt     variant_samples HET_samples     HOM_ALT_samples
chr1    874815  874816  C       CT      1478PC0006B,1478PC0007B,1478PC0010,1478PC0013B,1478PC0022B,1478PC0023B,1478PC0025,1719PC0007,1719PC0009,1719PC0010,1719PC0022   1478PC0006B,1478PC0007B,1478PC0010,1478PC0013B,1478PC0022B,1478PC0023B,1719PC0007,1719PC0009,1719PC0010 1478PC0025,1719PC0022
chr1    1140811 1140813 TC      T       1478PC0011      1478PC0011
chr1    1219381 1219382 C       G       1719PC0012      1719PC0012
chr1    1221487 1221490 CAA     C       1478PC0004      1478PC0004

variant_samples is a list of all of the samples with a variant, HET_samples is the subset of those heterozygous for the variant and HOM_ALT_samples is the subset homozygous for the variant.

--show-families Finding out which families have a variant

This works exactly like --show-samples except lists all of the families with a variant instead of the individual samples.

--region Restrict a query to a specified region

If you are only interested in a specific region, you can restrict queries to that region using the --region tool.

$ gemini query --region chr1:30859-30900 -q "select chrom, start, end, ref, alt \
             from variants"  test1.snpeff.db
chr1 30859   30860   G       C

--sample-filter Restrict a query to specified samples

The --sample-filter option allows you to select samples that a variant must be in by doing a SQL query on the samples table. For example if you wanted to show the set of variants that appear in all samples with a phenotype status of 2, you could do that query with:

$ gemini query --sample-filter "phenotype=2" -q "select gts, gt_types from variants" test.family.db
T/T,T/T,T/C,T/T,T/T,T/T,T/T,T/T,C/C  0,0,1,0,0,0,0,0,3       1_kid,3_kid     1_kid   3_kid
T/T,T/T,T/C,T/T,T/T,T/C,T/T,T/T,T/C  0,0,1,0,0,1,0,0,1       1_kid,2_kid,3_kid       1_kid,2_kid,3_kid
T/T,T/T,T/T,T/T,T/T,T/T,T/T,T/T,T/C  0,0,0,0,0,0,0,0,1       3_kid   3_kid

By default –sample-filter will show the variant if at least one sample contains the variant. You can change this behavior by using the --in option along with --sample-filter. --in all will return a variant if all samples matching the query have the variant. in none will return a variant if the variant does not appear in any of the matching samples. --in only will return a variant if the variant is only in the matching samples and not in any of the non-matching samples. --in only all will show all of the variant which are in all of the matching samples and not in any of the non-matching samples.

The --family-wise flag applies the --sample-filter and --in behavior on a family-wise basis. For example to show all variants that are only in samples with a phenotype status of 2 in at least one family:

$ gemini query --family-wise --in only all --sample-filter "phenotype=2" -q "select gts, gt_types from variants" test.family.db
T/T,T/T,T/C,T/T,T/T,T/T,T/T,T/T,C/C  0,0,1,0,0,0,0,0,3       1_kid,3_kid     1_kid   3_kid
T/T,T/T,T/C,T/T,T/T,T/C,T/T,T/T,T/C  0,0,1,0,0,1,0,0,1       1_kid,2_kid,3_kid       1_kid,2_kid,3_kid
T/T,T/T,T/T,T/T,T/T,T/T,T/T,T/T,T/C  0,0,0,0,0,0,0,0,1       3_kid   3_kid

You can also specify that a variant passes this filter in multiple families with the --min-kindreds option. So if you want to do the same query above, but restrict it such that at least three families have to pass the filter:

$ gemini query --min-kindreds 3 --family-wise --in only all --sample-filter "phenotype=2" -q "select gts, gt_types from variants" test.family.db
T/T,T/T,T/C,T/T,T/T,T/C,T/T,T/T,T/C  0,0,1,0,0,1,0,0,1       1_kid,2_kid,3_kid       1_kid,2_kid,3_kid

If the PED file you loaded has extra fields in it, those will also work with the --sample-filter option. For example if you had a hair_color extended field, you could query on that as well as phenotype:

$ gemini query  --in only all --sample-filter "phenotype=1 and hair_color='blue'" -q "select gts, gt_types from variants" extended_ped.db
G/G,G/G,G/G,G/A      0,0,0,1 M128215 M128215

--sample-delim Changing the sample list delimiter

One can modify the default comma delimiter used by the --show-samples option through the use of the --sample-delim option. For example, to use a semi-colon instead of a comma, one would do the following:

  $ gemini query --header --show-samples --sample-delim ";" \
                 -q "select chrom, start, end, ref, alt \
                     from variants where is_lof=1 limit 5" test.query.db

chrom start end ref alt variant_samples HET_samples HOM_ALT_samples
chr1  874815  874816  C CT  1478PC0006B;1478PC0007B;1478PC0010,1478PC0013B;1478PC0022B;1478PC0023B;1478PC0025;1719PC0007;1719PC0009;1719PC0010;1719PC0022 1478PC0006B;1478PC0007B;1478PC0010;1478PC0013B;1478PC0022B;1478PC0023B;1719PC0007;1719PC0009;1719PC0010 1478PC0025;1719PC0022
chr1  1140811 1140813 TC  T 1478PC0011  1478PC0011
chr1  1219381 1219382 C G 1719PC0012  1719PC0012
chr1  1221487 1221490 CAA C 1478PC0004  1478PC0004

--format Reporting query output in an alternate format.

The results of GEMINI queries can automatically be formatted for use with other programs using the –format command. Supported alternative formats are JSON and TPED (Transposed PED) format.

Reporting query output in JSON format may enable HTML/Javascript apps to query GEMINI and retrieve the output in a format that is amenable to web development protocols.

Here is a basic query:

$ gemini query -q "select chrom, start, end from variants" my.db | head
chr1  10067 10069
chr1  10230 10231
chr1  12782 12783
chr1  13109 13110
chr1  13115 13116
chr1  13117 13118
chr1  13272 13273
chr1  13301 13302
chr1  13416 13417
chr1  13417 13418

To report in JSON format, use the --format json option. For example:

$ gemini query --format json -q "select chrom, start, end from variants" my.db | head
{"chrom": "chr1", "start": 10067, "end": 10069}
{"chrom": "chr1", "start": 10230, "end": 10231}
{"chrom": "chr1", "start": 12782, "end": 12783}
{"chrom": "chr1", "start": 13109, "end": 13110}
{"chrom": "chr1", "start": 13115, "end": 13116}
{"chrom": "chr1", "start": 13117, "end": 13118}
{"chrom": "chr1", "start": 13272, "end": 13273}
{"chrom": "chr1", "start": 13301, "end": 13302}
{"chrom": "chr1", "start": 13416, "end": 13417}
{"chrom": "chr1", "start": 13417, "end": 13418}

If you would to use tools such as PLINK that use the PED format, you can dump out a set of variants matching any query in TPED (Transposed PED) format by adding the ``–tped``flag to your query:

$ gemini query --format tped -q "select * from variants where chrom=10" test4.snpeff.db
10 rs10794716 0 1142207 C/C C/C C/C C/C
10 rs142685947 0 48003991 T/T C/T C/T C/C
10 rs2842123 0 52004314 ./. ./. C/C C/C
10 rs4935178 0 52497528 ./. C/C C/C ./.
16 rs201947120 0 72057434 C/T C/C C/C C/C
10 rs73373169 0 126678091 G/G G/G G/G G/A
10 rs2265637 0 135210790 T/T C/C C/C T/T
10 rs6537611 0 135336655 ./. A/A ./. A/A
10 rs3747881 0 135369531 T/T T/C T/C T/T

You can pass –header to get a header to see which samples have which variant. To use the TPED format you also need to generate a corresponing TFAM file from your data as well, which you can get from the GEMINI dump tool:

$ gemini dump  --tfam test4.snpeff.db > obs
None    M10475  None    None    None    None
None    M10478  None    None    None    None
None    M10500  None    None    None    None
None    M128215 None    None    None    None

--carrier-summary-by-phenotype Summarize carrier status

For prioritizing variants sometimes it is useful to have summary counts of the carrier status for all samples with a variant stratified across a phenotype. --carrier-summary-by-phenotype takes a column in the samples table that you want to summarize the carrier status of and adds a set of counts of carrier/non-carrier status for each phenotype in the given column. For example, to get a summary of how a set of variants segregate with affected status:

$ gemini query --show-samples --carrier-summary-by-phenotype affected --header -q "select chrom, start, ref, alt, gt_types from variants" extended_ped_test.db
chrom   start   ref     alt     gt_types        variant_samples HET_samples     HOM_ALT_samples unaffected_carrier      affected_carrier        unaffected_noncarrier   affected_noncarrier     unknown
chr10   1142207 T       C       3,3,3,3 M10475,M10478,M10500,M128215            M10475,M10478,M10500,M128215    2       2       0       0       0
chr10   48003991        C       T       3,1,1,0 M10475,M10478,M10500    M10478,M10500   M10475  1       2       1       0       0
chr10   52004314        T       C       2,2,3,3 M10500,M128215          M10500,M128215  1       1       0       0       2
chr10   52497528        G       C       2,3,3,2 M10478,M10500           M10478,M10500   0       2       0       0       2
chr16   72057434        C       T       1,0,0,0 M10475  M10475          1       0       1       2       0
chr10   126678091       G       A       0,0,0,1 M128215 M128215         1       0       1       2       0
chr10   135210790       T       C       0,3,3,0 M10478,M10500           M10478,M10500   0       2       2       0       0
chr10   135336655       G       A       2,3,2,3 M10478,M128215          M10478,M128215  1       1       0       0       2
chr10   135369531       T       C       0,1,1,0 M10478,M10500   M10478,M10500           0       2       2       0       0

Or if you have another phenotypic feature you are interested in summarizing, like hair color:

$ gemini query --show-samples --carrier-summary-by-phenotype hair_color --header -q "select chrom, start, ref, alt, gt_types from variants" extended_ped.db
chrom   start   ref     alt     gt_types        variant_samples HET_samples     HOM_ALT_samples blue_carrier    brown_carrier   purple_carrier  blue_noncarrier brown_noncarrier        purple_noncarrier       unknown
chr10   1142207 T       C       3,3,3,3 M10475,M10478,M10500,M128215            M10475,M10478,M10500,M128215    1       2       1       0       0       0       0
chr10   48003991        C       T       3,1,1,0 M10475,M10478,M10500    M10478,M10500   M10475  0       2       1       1       0       0       0
chr10   52004314        T       C       2,2,3,3 M10500,M128215          M10500,M128215  1       0       1       0       0       0       2
chr10   52497528        G       C       2,3,3,2 M10478,M10500           M10478,M10500   0       1       1       0       0       0       2
chr16   72057434        C       T       1,0,0,0 M10475  M10475          0       1       0       1       1       1       0
chr10   126678091       G       A       0,0,0,1 M128215 M128215         1       0       0       0       2       1       0
chr10   135210790       T       C       0,3,3,0 M10478,M10500           M10478,M10500   0       1       1       1       1       0       0
chr10   135336655       G       A       2,3,2,3 M10478,M128215          M10478,M128215  1       1       0       0       0       0       2
chr10   135369531       T       C       0,1,1,0 M10478,M10500   M10478,M10500           0       1       1       1       1       0       0

Querying the gene tables

The gene tables viz. gene_detailed table and the gene_summary table have been built on version 73 of the ensembl genes. The column specifications are available at The Gemini database schema. These tables contain gene specific information e.g. gene synonyms, RVIS percentile scores(Petrovski et.al 2013), strand specifications, cancer gene census etc. While the former is more detailed, the later lacks transcript wise information and summarizes some aspects of the former. For e.g. while the gene_detailed table lists all transcripts of a gene with their start and end co-ordinates, the gene_summary table reports only the minimum start and maximum end co-ordinates of the gene transcripts. The chrom, gene and the transcript columns of the gene tables may be used to join on the variants and the variant_impacts tables.

Query the gene_detailed table with a join on variants table:

$ gemini query --header -q "select v.variant_id, v.chrom, v.gene, \
                   g.transcript_status, g.transcript, g.transcript_start, \
                           g.transcript_end, g.synonym, g.rvis_pct, g.protein_length, \
                               v.impact from variants v, gene_detailed g \

                               WHERE v.chrom = g.chrom AND \
                                             v.gene = g.gene AND v.impact_severity='HIGH' AND \
                                             v.biotype='protein_coding' AND \
                                             v.transcript = g.transcript" test.query.db

    variant_id      chrom   gene    transcript_status       transcript      transcript_start        transcript_end  synonym rvis_pct        protein_length  impact
    46      chr1    SAMD11  KNOWN   ENST00000342066 861118  879955  MGC45873        None    681     frame_shift
    578     chr1    TNFRSF18        PUTATIVE        ENST00000486728 1139224 1141060 AITR,CD357,GITR None    169     frame_shift
    733     chr1    SCNN1D  NOVEL   ENST00000470022 1217305 1221548 ENaCdelta,dNaCh 96.77990092     138     stop_gain

Query the gene_detailed table with a join on the variant_impacts table:

$ gemini query --header -q "select v.gene, g.transcript_status,g.transcript, g.transcript_start, \
                   g.transcript_end, g.synonym, g.rvis_pct, g.protein_length, \
               v.impact from variant_impacts v, gene_detailed g \

                               WHERE v.transcript = g.transcript AND \
                     v.gene = g.gene AND \
                         v.impact_severity='HIGH' AND \
                     v.biotype='protein_coding'" test.query.db

    gene    transcript_status       transcript      transcript_start        transcript_end  synonym rvis_pct        protein_length  impact
    SAMD11  KNOWN   ENST00000342066 861118  879955  MGC45873        None    681     frame_shift
    TNFRSF18        PUTATIVE        ENST00000486728 1139224 1141060 AITR,CD357,GITR None    169     frame_shift
    TNFRSF18        KNOWN   ENST00000379265 1139224 1141951 AITR,CD357,GITR None    234     frame_shift
    TNFRSF18        KNOWN   ENST00000379268 1138891 1142071 AITR,CD357,GITR None    241     frame_shift
    TNFRSF18        KNOWN   ENST00000328596 1138888 1141951 AITR,CD357,GITR None    255     frame_shift
    SCNN1D  NOVEL   ENST00000470022 1217305 1221548 ENaCdelta,dNaCh 96.77990092     138     stop_gain
    SCNN1D  NOVEL   ENST00000470022 1217305 1221548 ENaCdelta,dNaCh 96.77990092     138     frame_shift
    SCNN1D  KNOWN   ENST00000325425 1217489 1227404 ENaCdelta,dNaCh 96.77990092     704     frame_shift
    SCNN1D  KNOWN   ENST00000379116 1215816 1227399 ENaCdelta,dNaCh 96.77990092     802     frame_shift
    SCNN1D  KNOWN   ENST00000338555 1215968 1227404 ENaCdelta,dNaCh 96.77990092     638     frame_shift
    SCNN1D  KNOWN   ENST00000400928 1217576 1227409 ENaCdelta,dNaCh 96.77990092     638     frame_shift

Query the gene_summary table with a join on the variants table:

$ gemini query --header -q "select v.chrom, v.gene, g.strand, g.transcript_min_start, g.transcript_max_end, \
               g.synonym, g.rvis_pct, v.impact from variants v, gene_summary g \

                               WHERE v.chrom = g.chrom AND \
                     v.gene = g.gene AND \
                     v.impact_severity='HIGH'" test.query.db

    chrom   gene    strand  transcript_min_start    transcript_max_end      synonym rvis_pct        impact
    chr1    SAMD11  1       860260  879955  MGC45873        None    frame_shift
    chr1    TNFRSF18        -1      1138888 1142071 AITR,CD357,GITR None    frame_shift
    chr1    SCNN1D  1       1215816 1227409 ENaCdelta,dNaCh 96.77990092     stop_gain
    chr1    SCNN1D  1       1215816 1227409 ENaCdelta,dNaCh 96.77990092     frame_shift

Query the gene_summary table with a join on the variant_impacts table:

$ gemini query --header -q "select g.gene, v.impact, v.transcript, \
                   g.transcript_min_start, g.transcript_max_end, g.rvis_pct, g.synonym \
                       from gene_summary g, variant_impacts v \

                               WHERE g.gene=v.gene AND \
                                     g.gene ='SCNN1D' AND \
                                     v.impact ='stop_gain'" test.query.db

    gene    impact  transcript      transcript_min_start    transcript_max_end      rvis_pct        synonym
    SCNN1D  stop_gain       ENST00000470022 1215816 1227409 96.77990092     ENaCdelta,dNaCh
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