Annotation of Genetic Variants
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1 Annotation of Genetic Variants Valerie Obenchain Fred Hutchinson Cancer Research Center February 2012
2 Read VCF Files Structural location of variants Amino acid coding changes Extras
3 Outline Read VCF Files Structural location of variants Amino acid coding changes Extras
4 VCF (Variant Call Format) Format description on 1000 Genomes Web site, fixed : CHROM, POS, ID, REF, ALT, QUAL, FILTER info : data in INFO field, includes values such as allele count or frequency, membership in dbsnp or HapMap, etc. geno : genotype information for samples defined in FORMAT field
5 readvcf VCF object > fl <- system.file("extdata", "ex1.vcf", package = "VariantAnnotation") > vcf <- readvcf(fl, "hg19") > vcf class: VCF dim: 10 2 genome: hg19 exptdata(1): HEADER fixed(4): REF ALT QUAL FILTER info(1): DP geno(3): GT GQ DP rownames(10): 16: : : : rowdata values names(1): rangeid colnames(2): A B coldata names(1): Samples
6 readvcf ScanVcfParam Specify subsets of data by genomic position (ranges) or VCF fields Ranges are specified with the which argument VCF elements are specified with fixed, info and geno arguments > param <- ScanVcfParam(which = GRanges("chr1", IRanges(1, 1e8)), + asgranges = FALSE, + fixed = c("alt", "FILTER"), + info = "DP", + geno = c("dp", "GT"))
7 seqlevels Helper functions to aid with renaming and subsetting seqlevels renameseqlevels renameseqlevels accepts a named character vector in the format of oldname=newname > library(txdb.hsapiens.ucsc.hg19.knowngene) > txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene > vcf_mod <- renameseqlevels(vcf, c("16"="chr16", "21"="chr21")) > intersect(seqlevels(vcf_mod), seqlevels(txdb)) [1] "chr16" "chr21" keepseqlevels keepseqlevels accepts a character vector of seqlevels to keep > vcf_21 <- keepseqlevels(vcf_mod, "chr21") > seqlevels(vcf_21) [1] "chr21"
8 Outline Read VCF Files Structural location of variants Amino acid coding changes Extras
9 locatevariants TranscriptDb annotations are used to identify variants that fall in coding, intron, 5 UTR or 3 UTR regions Output is a DataFrame with one row for each variant-transcript match > loc <- locatevariants(vcf_mod, txdb) > head(loc) DataFrame with 6 rows and 7 columns queryid location txid cdsid geneid precedesid followsid <integer> <factor> <integer> <integer> <character> <character> <character> 1 1 coding NA NA 2 2 coding NA NA 3 3 coding NA NA 4 4 intron NA NA 5 4 intron NA NA 6 4 intron NA NA
10 locatevariants Intergenic variants have gene IDs for precedes and follows > loc[loc$location == "intergenic",] DataFrame with 4 rows and 7 columns queryid location txid cdsid geneid precedesid followsid <integer> <factor> <integer> <integer> <character> <character> <character> 1 7 intergenic NA NA NA intergenic NA NA NA intergenic NA NA NA intergenic NA NA NA
11 Outline Read VCF Files Structural location of variants Amino acid coding changes Extras
12 predictcoding Compute amino acid codes with BSgenome or fasta file reference and user supplied variant alleles Output is a DataFrame with one row for each variant-transcript match. Results for coding variants only. > library(bsgenome.hsapiens.ucsc.hg19) > aa <- predictcoding(vcf_mod, txdb, Hsapiens) > head(aa, 5) DataFrame with 5 rows and 9 columns queryid consequence refseq varseq refaa <integer> <factor> <DNAStringSet> <DNAStringSet> <AAStringSet> 1 1 nonsynonymous GATTAG GTA D* 2 1 frameshift GATTAG GT D* 3 2 frameshift GCAGAG GGAG AE 4 2 nonsynonymous GCAGAG GGTAAG AE 5 3 synonymous AAGGTA AAGGTA KV varaa txid geneid cdsid <AAStringSet> <character> <factor> <integer> 1 V GK KV
13 Consequence of coding changes SIFT (Sort Intolerant From Tolerant) predicts possible impact of amino acid substitution on protein function protein evolution is correlated with protein function; positions important for function are conserved uses multiple alignment information to predict tolerated and deleterious substitutions for every position of the query PolyPhen (Polymorphism Phenotyping) predicts possible impact of amino acid substitution on protein function and structure applies empirical rules to the sequence, phylogenetic and structural information characterizing the substitution uses multiple alignment, UniProt features and structural databases
14 SIFT example > library(sift.hsapiens.dbsnp132) > rsids <- c("rs ", "rs ") > subst <- c("aachange", "METHOD", "AA", "PREDICTION", "SCORE") > select(sift.hsapiens.dbsnp132, keys = rsids, cols = subst) RSID AACHANGE METHOD AA PREDICTION SCORE 1 rs F430L BEST HITS L TOLERATED rs F430L BEST HITS F TOLERATED rs F430L ALL HITS L TOLERATED rs F430L ALL HITS F TOLERATED 1 5 rs G202D BEST HITS D DELETERIOUS rs G202D BEST HITS G TOLERATED rs G202D ALL HITS D DELETERIOUS rs G202D ALL HITS G TOLERATED 1
15 Outline Read VCF Files Structural location of variants Amino acid coding changes Extras
16 Other functions in VariantAnnotation long form GRanges : set asgranges=true in ScanVcfParam MatrixToSnpMatrix converts GT genotype in VCF object to a SnpMatrix writevcf in progress
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