Contents

0.1 Working with Variants

Google Genomics implements the GA4GH variants API and this R package can retrieve data from that implementation. For more detail, see https://cloud.google.com/genomics/v1beta2/reference/variants

library(GoogleGenomics)
# This vignette is authenticated on package load from the env variable GOOGLE_API_KEY.
# When running interactively, call the authenticate method.
# ?authenticate

By default, this function retrieves variants for a small genomic region from the 1,000 Genomes phase 1 variants.

variants <- getVariants()
## Fetching variants page.
## Variants are now available.
length(variants)
## [1] 4

We can see that 4 individual variants were returned and that the JSON response was deserialized into an R list object:

class(variants)
## [1] "list"
mode(variants)
## [1] "list"

The top level field names are:

names(variants[[1]])
##  [1] "variantSetId"   "id"             "names"          "created"       
##  [5] "referenceName"  "start"          "end"            "referenceBases"
##  [9] "alternateBases" "quality"        "filter"         "info"          
## [13] "calls"

The variant contains nested calls:

length(variants[[1]]$calls)
## [1] 1092

With top level call field names:

names(variants[[1]]$calls[[1]])
## [1] "callSetId"          "callSetName"        "genotype"          
## [4] "phaseset"           "genotypeLikelihood" "info"

And examining a call for a particular variant:

variants[[1]]$referenceName
## [1] "22"
variants[[1]]$start
## [1] "16051452"
variants[[1]]$alternateBases
## [[1]]
## [1] "C"
variants[[1]]$calls[[1]]
## $callSetId
## [1] "10473108253681171589-0"
## 
## $callSetName
## [1] "HG00261"
## 
## $genotype
## $genotype[[1]]
## [1] 0
## 
## $genotype[[2]]
## [1] 0
## 
## 
## $phaseset
## [1] "*"
## 
## $genotypeLikelihood
## $genotypeLikelihood[[1]]
## [1] -0.1
## 
## $genotypeLikelihood[[2]]
## [1] -0.67
## 
## $genotypeLikelihood[[3]]
## [1] -3.3
## 
## 
## $info
## $info$DS
## $info$DS[[1]]
## [1] "0.000"

This is good, but this data becomes much more useful when it is converted to Bioconductor data types. For example, we can convert variants in this list representation to VRanges from VariantAnnotation:

variantsToVRanges(variants)
## VRanges object with 4 ranges and 2 metadata columns:
##               seqnames               ranges strand         ref
##                  <Rle>            <IRanges>  <Rle> <character>
##   rs143503259    chr22 [16051453, 16051453]      *           A
##   rs192339082    chr22 [16051477, 16051477]      *           C
##    rs79725552    chr22 [16051480, 16051480]      *           T
##   rs141578542    chr22 [16051497, 16051497]      *           A
##                            alt     totalDepth       refDepth       altDepth
##               <characterOrRle> <integerOrRle> <integerOrRle> <integerOrRle>
##   rs143503259                C           <NA>           <NA>           <NA>
##   rs192339082                A           <NA>           <NA>           <NA>
##    rs79725552                C           <NA>           <NA>           <NA>
##   rs141578542                G           <NA>           <NA>           <NA>
##                 sampleNames softFilterMatrix |      QUAL      FILTER
##               <factorOrRle>         <matrix> | <numeric> <character>
##   rs143503259          <NA>                  |       100        PASS
##   rs192339082          <NA>                  |       100        PASS
##    rs79725552          <NA>                  |       100        PASS
##   rs141578542          <NA>                  |       100        PASS
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
##   hardFilters: NULL

0.2 Annotating Variants

Next let’s use package VariantAnnotation to annotate some specific 1,000 Genomes Phase 1 variants.

library(VariantAnnotation)

Note that the parameters start and end are expressed in 0-based coordinates per the GA4GH specification but the Bioconductor data type converters in GoogleGenomics, by default, transform the returned data to 1-based coordinates.

granges <- getVariants(variantSetId="10473108253681171589",
                       chromosome="22",
                       start=50300077,
                       end=50303000,
                       converter=variantsToGRanges)
## Fetching variants page.
## Continuing variant query with the nextPageToken: CIWK_hcQ_Nr-8ZGlk78i
## Fetching variants page.
## Continuing variant query with the nextPageToken: CIaM_hcQvM_un46SmcJ0
## Fetching variants page.
## Continuing variant query with the nextPageToken: CJGN_hcQiYna3cSwse_RAQ
## Fetching variants page.
## Continuing variant query with the nextPageToken: CMKQ_hcQr9OUpKu4n_xz
## Fetching variants page.
## Continuing variant query with the nextPageToken: CMKS_hcQn-nIneaph7WJAQ
## Fetching variants page.
## Continuing variant query with the nextPageToken: CKOU_hcQ2eCCwsmBysdd
## Fetching variants page.
## Continuing variant query with the nextPageToken: CLWV_hcQmo-n-tX08uPyAQ
## Fetching variants page.
## Continuing variant query with the nextPageToken: CMqX_hcQ__3H4KKV9tUR
## Fetching variants page.
## Continuing variant query with the nextPageToken: CKuZ_hcQ9pHXwa742bKgAQ
## Fetching variants page.
## Continuing variant query with the nextPageToken: CLqa_hcQ-6zjo_Dc86gy
## Fetching variants page.
## Continuing variant query with the nextPageToken: CLSb_hcQ3vrDo5XJwsB_
## Fetching variants page.
## Continuing variant query with the nextPageToken: CKOd_hcQ6IPgk-PVjskK
## Fetching variants page.
## Continuing variant query with the nextPageToken: CPGe_hcQ-8Saw6Ojg8yXAQ
## Fetching variants page.
## Continuing variant query with the nextPageToken: CPGf_hcQuKDdodiCvLUm
## Fetching variants page.
## Variants are now available.

Now locate the protein coding variants in each:

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
granges_locations <- locateVariants(granges, txdb, CodingVariants())
## 'select()' returned many:1 mapping between keys and columns
granges_locations
## GRanges object with 7 ranges and 9 metadata columns:
##       seqnames               ranges strand | LOCATION  LOCSTART    LOCEND
##          <Rle>            <IRanges>  <Rle> | <factor> <integer> <integer>
##   [1]    chr22 [50301422, 50301422]      - |   coding       939       939
##   [2]    chr22 [50301476, 50301476]      - |   coding       885       885
##   [3]    chr22 [50301488, 50301488]      - |   coding       873       873
##   [4]    chr22 [50301494, 50301494]      - |   coding       867       867
##   [5]    chr22 [50301584, 50301584]      - |   coding       777       777
##   [6]    chr22 [50302962, 50302962]      - |   coding       698       698
##   [7]    chr22 [50302995, 50302995]      - |   coding       665       665
##         QUERYID        TXID         CDSID      GENEID       PRECEDEID
##       <integer> <character> <IntegerList> <character> <CharacterList>
##   [1]        24       75253        218562       79087                
##   [2]        25       75253        218562       79087                
##   [3]        26       75253        218562       79087                
##   [4]        27       75253        218562       79087                
##   [5]        28       75253        218562       79087                
##   [6]        57       75253        218563       79087                
##   [7]        58       75253        218563       79087                
##              FOLLOWID
##       <CharacterList>
##   [1]                
##   [2]                
##   [3]                
##   [4]                
##   [5]                
##   [6]                
##   [7]                
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

And predict the effect of the protein coding variants:

library(BSgenome.Hsapiens.UCSC.hg19)
granges_coding <- predictCoding(rep(granges, elementNROWS(granges$ALT)),
                                txdb,
                                seqSource=Hsapiens,
                                varAllele=unlist(granges$ALT, use.names=FALSE))

granges_coding
## GRanges object with 7 ranges and 16 metadata columns:
##               seqnames               ranges strand |            REF
##                  <Rle>            <IRanges>  <Rle> | <DNAStringSet>
##   rs114335781    chr22 [50301422, 50301422]      - |              G
##     rs8135963    chr22 [50301476, 50301476]      - |              T
##   rs200080075    chr22 [50301488, 50301488]      - |              C
##   rs147801200    chr22 [50301494, 50301494]      - |              G
##   rs138060012    chr22 [50301584, 50301584]      - |              C
##   rs114264124    chr22 [50302962, 50302962]      - |              C
##   rs149209714    chr22 [50302995, 50302995]      - |              C
##                              ALT      QUAL      FILTER      varAllele
##               <DNAStringSetList> <numeric> <character> <DNAStringSet>
##   rs114335781                  A       100        PASS              T
##     rs8135963                  C       100        PASS              G
##   rs200080075                  T       100        PASS              A
##   rs147801200                  A       100        PASS              T
##   rs138060012                  T       100        PASS              A
##   rs114264124                  T       100        PASS              A
##   rs149209714                  G       100        PASS              C
##                   CDSLOC    PROTEINLOC   QUERYID        TXID         CDSID
##                <IRanges> <IntegerList> <integer> <character> <IntegerList>
##   rs114335781 [939, 939]           313        24       75253        218562
##     rs8135963 [885, 885]           295        25       75253        218562
##   rs200080075 [873, 873]           291        26       75253        218562
##   rs147801200 [867, 867]           289        27       75253        218562
##   rs138060012 [777, 777]           259        28       75253        218562
##   rs114264124 [698, 698]           233        57       75253        218563
##   rs149209714 [665, 665]           222        58       75253        218563
##                    GENEID   CONSEQUENCE       REFCODON       VARCODON
##               <character>      <factor> <DNAStringSet> <DNAStringSet>
##   rs114335781       79087    synonymous            ATC            ATT
##     rs8135963       79087    synonymous            GCA            GCG
##   rs200080075       79087    synonymous            CCG            CCA
##   rs147801200       79087    synonymous            CAC            CAT
##   rs138060012       79087    synonymous            CCG            CCA
##   rs114264124       79087 nonsynonymous            CGG            CAG
##   rs149209714       79087 nonsynonymous            GGA            GCA
##                       REFAA         VARAA
##               <AAStringSet> <AAStringSet>
##   rs114335781             I             I
##     rs8135963             A             A
##   rs200080075             P             P
##   rs147801200             H             H
##   rs138060012             P             P
##   rs114264124             R             Q
##   rs149209714             G             A
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Finally, add gene information:

library(org.Hs.eg.db)
annots <- select(org.Hs.eg.db,
                 keys=granges_coding$GENEID,
                 keytype="ENTREZID",
                 columns=c("SYMBOL", "GENENAME","ENSEMBL"))
## 'select()' returned many:1 mapping between keys and columns
cbind(elementMetadata(granges_coding), annots)
## DataFrame with 7 rows and 20 columns
##              REF                ALT      QUAL      FILTER      varAllele
##   <DNAStringSet> <DNAStringSetList> <numeric> <character> <DNAStringSet>
## 1              G                  A       100        PASS              T
## 2              T                  C       100        PASS              G
## 3              C                  T       100        PASS              A
## 4              G                  A       100        PASS              T
## 5              C                  T       100        PASS              A
## 6              C                  T       100        PASS              A
## 7              C                  G       100        PASS              C
##       CDSLOC    PROTEINLOC   QUERYID        TXID         CDSID      GENEID
##    <IRanges> <IntegerList> <integer> <character> <IntegerList> <character>
## 1 [939, 939]           313        24       75253        218562       79087
## 2 [885, 885]           295        25       75253        218562       79087
## 3 [873, 873]           291        26       75253        218562       79087
## 4 [867, 867]           289        27       75253        218562       79087
## 5 [777, 777]           259        28       75253        218562       79087
## 6 [698, 698]           233        57       75253        218563       79087
## 7 [665, 665]           222        58       75253        218563       79087
##     CONSEQUENCE       REFCODON       VARCODON         REFAA         VARAA
##        <factor> <DNAStringSet> <DNAStringSet> <AAStringSet> <AAStringSet>
## 1    synonymous            ATC            ATT             I             I
## 2    synonymous            GCA            GCG             A             A
## 3    synonymous            CCG            CCA             P             P
## 4    synonymous            CAC            CAT             H             H
## 5    synonymous            CCG            CCA             P             P
## 6 nonsynonymous            CGG            CAG             R             Q
## 7 nonsynonymous            GGA            GCA             G             A
##      ENTREZID      SYMBOL                             GENENAME
##   <character> <character>                          <character>
## 1       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 2       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 3       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 4       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 5       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 6       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
## 7       79087       ALG12 ALG12, alpha-1,6-mannosyltransferase
##           ENSEMBL
##       <character>
## 1 ENSG00000182858
## 2 ENSG00000182858
## 3 ENSG00000182858
## 4 ENSG00000182858
## 5 ENSG00000182858
## 6 ENSG00000182858
## 7 ENSG00000182858

0.3 Provenance

sessionInfo()
## R Under development (unstable) (2017-10-20 r73567)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] org.Hs.eg.db_3.4.2                     
##  [2] BSgenome.Hsapiens.UCSC.hg19_1.4.0      
##  [3] BSgenome_1.47.0                        
##  [4] rtracklayer_1.39.0                     
##  [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [6] GenomicFeatures_1.31.0                 
##  [7] AnnotationDbi_1.41.0                   
##  [8] GoogleGenomics_2.1.0                   
##  [9] VariantAnnotation_1.25.0               
## [10] GenomicAlignments_1.15.0               
## [11] Rsamtools_1.31.0                       
## [12] Biostrings_2.47.0                      
## [13] XVector_0.19.0                         
## [14] SummarizedExperiment_1.9.0             
## [15] DelayedArray_0.5.0                     
## [16] matrixStats_0.52.2                     
## [17] Biobase_2.39.0                         
## [18] GenomicRanges_1.31.0                   
## [19] GenomeInfoDb_1.15.0                    
## [20] IRanges_2.13.0                         
## [21] S4Vectors_0.17.0                       
## [22] BiocGenerics_0.25.0                    
## [23] knitr_1.17                             
## [24] BiocStyle_2.7.0                        
## 
## loaded via a namespace (and not attached):
##  [1] progress_1.1.2          lattice_0.20-35         htmltools_0.3.6        
##  [4] yaml_2.1.14             blob_1.1.0              XML_3.98-1.9           
##  [7] rlang_0.1.2             DBI_0.7                 BiocParallel_1.13.0    
## [10] bit64_0.9-7             GenomeInfoDbData_0.99.1 stringr_1.2.0          
## [13] zlibbioc_1.25.0         evaluate_0.10.1         memoise_1.1.0          
## [16] biomaRt_2.35.0          curl_3.0                Rcpp_0.12.13           
## [19] backports_1.1.1         jsonlite_1.5            bit_1.1-12             
## [22] rjson_0.2.15            RMySQL_0.10.13          digest_0.6.12          
## [25] stringi_1.1.5           bookdown_0.5            grid_3.5.0             
## [28] rprojroot_1.2           tools_3.5.0             bitops_1.0-6           
## [31] magrittr_1.5            RCurl_1.95-4.8          RSQLite_2.0            
## [34] tibble_1.3.4            pkgconfig_2.0.1         Matrix_1.2-11          
## [37] prettyunits_1.0.2       assertthat_0.2.0        rmarkdown_1.6          
## [40] httr_1.3.1              R6_2.2.2                compiler_3.5.0