Convert GT to numeric arrays

Most often we need to convert genetic data to numeric arrays (matrix of minor allele counts) for statistical analysis.

Example VCF file

We need an example VCF file for demonstation. You can manually download it from link (877KB) and put the file in your current working directory. Or, within Julia,

isfile("test.08Jun17.d8b.vcf.gz") || download("http://faculty.washington.edu/browning/beagle/test.08Jun17.d8b.vcf.gz", 
    joinpath(pwd(), "test.08Jun17.d8b.vcf.gz"))
stat("test.08Jun17.d8b.vcf.gz")
StatStruct(mode=0o100644, size=876514)

The first 5 markers in this VCF file are

using VCFTools

fh = openvcf("test.08Jun17.d8b.vcf.gz", "r")
for l in 1:35
    println(readline(fh))
end
close(fh)
##fileformat=VCFv4.1
##INFO=<ID=LDAF,Number=1,Type=Float,Description="MLE Allele Frequency Accounting for LD">
##INFO=<ID=AVGPOST,Number=1,Type=Float,Description="Average posterior probability from MaCH/Thunder">
##INFO=<ID=RSQ,Number=1,Type=Float,Description="Genotype imputation quality from MaCH/Thunder">
##INFO=<ID=ERATE,Number=1,Type=Float,Description="Per-marker Mutation rate from MaCH/Thunder">
##INFO=<ID=THETA,Number=1,Type=Float,Description="Per-marker Transition rate from MaCH/Thunder">
##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">
##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants">
##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
##INFO=<ID=HOMLEN,Number=.,Type=Integer,Description="Length of base pair identical micro-homology at event breakpoints">
##INFO=<ID=HOMSEQ,Number=.,Type=String,Description="Sequence of base pair identical micro-homology at event breakpoints">
##INFO=<ID=SVLEN,Number=1,Type=Integer,Description="Difference in length between REF and ALT alleles">
##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
##INFO=<ID=AC,Number=.,Type=Integer,Description="Alternate Allele Count">
##INFO=<ID=AN,Number=1,Type=Integer,Description="Total Allele Count">
##ALT=<ID=DEL,Description="Deletion">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DS,Number=1,Type=Float,Description="Genotype dosage from MaCH/Thunder">
##FORMAT=<ID=GL,Number=.,Type=Float,Description="Genotype Likelihoods">
##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele, ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/pilot_data/technical/reference/ancestral_alignments/README">
##INFO=<ID=AF,Number=1,Type=Float,Description="Global Allele Frequency based on AC/AN">
##INFO=<ID=AMR_AF,Number=1,Type=Float,Description="Allele Frequency for samples from AMR based on AC/AN">
##INFO=<ID=ASN_AF,Number=1,Type=Float,Description="Allele Frequency for samples from ASN based on AC/AN">
##INFO=<ID=AFR_AF,Number=1,Type=Float,Description="Allele Frequency for samples from AFR based on AC/AN">
##INFO=<ID=EUR_AF,Number=1,Type=Float,Description="Allele Frequency for samples from EUR based on AC/AN">
##INFO=<ID=VT,Number=1,Type=String,Description="indicates what type of variant the line represents">
##INFO=<ID=SNPSOURCE,Number=.,Type=String,Description="indicates if a snp was called when analysing the low coverage or exome alignment data">
##reference=GRCh37
##reference=GRCh37
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO    FORMAT  HG00096 HG00097 HG00099 HG00100 HG00101 HG00102 HG00103 HG00104 HG00106 HG00108 HG00109 HG00110 HG00111 HG00112 HG00113 HG00114 HG00116 HG00117 HG00118 HG00119 HG00120 HG00121 HG00122 HG00123 HG00124 HG00125 HG00126 HG00127 HG00128 HG00129 HG00130 HG00131 HG00133 HG00134 HG00135 HG00136 HG00137 HG00138 HG00139 HG00140 HG00141 HG00142 HG00143 HG00146 HG00148 HG00149 HG00150 HG00151 HG00152 HG00154 HG00155 HG00156 HG00158 HG00159 HG00160 HG00171 HG00173 HG00174 HG00176 HG00177 HG00178 HG00179 HG00180 HG00182 HG00183 HG00185 HG00186 HG00187 HG00188 HG00189 HG00190 HG00231 HG00232 HG00233 HG00234 HG00235 HG00236 HG00237 HG00238 HG00239 HG00240 HG00242 HG00243 HG00244 HG00245 HG00246 HG00247 HG00249 HG00250 HG00251 HG00252 HG00253 HG00254 HG00255 HG00256 HG00257 HG00258 HG00259 HG00260 HG00261 HG00262 HG00263 HG00264 HG00265 HG00266 HG00267 HG00268 HG00269 HG00270 HG00271 HG00272 HG00273 HG00274 HG00275 HG00276 HG00277 HG00278 HG00280 HG00281 HG00282 HG00284 HG00285 HG00306 HG00309 HG00310 HG00311 HG00312 HG00313 HG00315 HG00318 HG00319 HG00320 HG00321 HG00323 HG00324 HG00325 HG00326 HG00327 HG00328 HG00329 HG00330 HG00331 HG00332 HG00334 HG00335 HG00336 HG00337 HG00338 HG00339 HG00341 HG00342 HG00343 HG00344 HG00345 HG00346 HG00349 HG00350 HG00351 HG00353 HG00355 HG00356 HG00357 HG00358 HG00359 HG00360 HG00361 HG00362 HG00364 HG00366 HG00367 HG00369 HG00372 HG00373 HG00375 HG00376 HG00377 HG00378 HG00381 HG00382 HG00383 HG00384 HG00403 HG00404 HG00406 HG00407 HG00418 HG00419 HG00421 HG00422 HG00427 HG00428
22  20000086    rs138720731 T   C   100 PASS    AC=7;RSQ=0.8454;AVGPOST=0.9983;AA=T;AN=2184;LDAF=0.0040;THETA=0.0001;VT=SNP;SNPSOURCE=LOWCOV;ERATE=0.0003;AF=0.0032;AFR_AF=0.01 GT:DS:GL    0/0:0.000:-0.03,-1.19,-5.00 0/0:0.000:-0.04,-1.05,-5.00 0/0:0.000:-0.07,-0.85,-5.00 0/0:0.000:-0.03,-1.18,-5.00 0/0:0.000:-0.06,-0.87,-5.00 0/0:0.000:-0.03,-1.14,-5.00 0/0:0.000:-0.06,-0.90,-5.00 0/0:0.000:-0.23,-0.45,-1.28 0/0:0.000:-0.03,-1.20,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.11,-0.65,-4.40 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.06,-0.91,-5.00 0/0:0.000:-0.18,-0.47,-2.54 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.06,-0.90,-5.00 0/0:0.000:-0.01,-1.74,-5.00 0/0:0.000:-0.00,-3.66,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.00,-2.53,-5.00 0/0:0.000:-0.09,-0.73,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.00,-3.11,-5.00 0/0:0.000:-0.06,-0.89,-5.00 0/0:0.000:-0.09,-0.71,-4.10 0/0:0.000:-0.11,-0.65,-4.40 0/0:0.000:-0.18,-0.47,-2.34 0/0:0.000:-0.22,-0.45,-1.32 0/0:0.000:-0.02,-1.29,-5.00 0/0:0.000:-0.03,-1.15,-5.00 0/0:0.000:-0.02,-1.45,-5.00 0/0:0.000:-0.00,-3.34,-5.00 0/0:0.000:-0.12,-0.61,-3.19 0/0:0.000:-0.11,-0.67,-4.40 0/0:0.000:-0.05,-0.99,-5.00 0/0:0.000:-0.18,-0.48,-2.15 0/0:0.000:-0.01,-1.47,-5.00 0/0:0.000:-0.10,-0.67,-3.62 0/0:0.000:-0.03,-1.14,-5.00 0/0:0.000:-0.09,-0.73,-4.40 0/0:0.000:-0.07,-0.84,-4.40 0/0:0.000:-0.18,-0.48,-2.46 0/0:0.000:-0.0292813,-1.18575,-5    0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.01,-1.67,-5.00 0/0:0.000:-0.18,-0.47,-2.40 0/0:0.000:-0.03,-1.25,-5.00 0/0:0.000:-0.11,-0.66,-3.44 0/0:0.000:-0.09,-0.73,-4.70 0/0:0.000:-0.0418663,-1.03687,-4.39794  0/0:0.000:-0.08,-0.79,-3.14 0/0:0.000:-0.00,-2.30,-5.00 0/0:0.000:-0.00,-2.54,-5.00 0/0:0.000:-0.03,-1.21,-5.00 0/0:0.000:-0.06,-0.86,-5.00 0/0:0.000:-0.09,-0.71,-4.70 0/0:0.000:-0.01,-1.49,-5.00 0/0:0.000:-0.01,-1.88,-5.00 0/0:0.000:-0.09,-0.71,-4.70 0/0:0.000:-0.03,-1.19,-5.00 0/0:0.000:-0.10,-0.67,-4.40 0/0:0.000:-0.01,-1.51,-5.00 0/0:0.000:-0.02,-1.40,-5.00 0/0:0.000:-0.03,-1.17,-5.00 0/0:0.000:-0.05,-0.93,-5.00 0/0:0.000:-0.01,-1.48,-5.00 0/0:0.000:-0.00,-2.02,-5.00 0/0:0.000:-0.03,-1.18,-5.00 0/0:0.000:-0.02,-1.46,-5.00 0/0:0.000:-0.03,-1.17,-5.00 0/0:0.050:-0.18,-0.47,-2.73 0/0:0.000:-0.17,-0.49,-2.97 0/0:0.000:-0.10,-0.68,-4.40 0/0:0.000:-0.05,-0.99,-5.00 0/0:0.000:-0.12,-0.62,-3.38 0/0:0.000:-0.00,-2.06,-5.00 0/0:0.000:-0.16,-0.51,-2.66 0/0:0.000:-0.11,-0.64,-4.22 0/0:0.000:-0.03,-1.22,-5.00 0/0:0.000:-0.01,-1.64,-5.00 0/0:0.000:-0.00,-2.85,-5.00 0/0:0.000:-0.02,-1.38,-5.00 0/0:0.000:-0.05,-0.94,-5.00 0/0:0.000:-0.0311436,-1.15989,-5    0/0:0.000:-0.36,-0.42,-0.73 0/0:0.000:-0.01,-1.88,-5.00 0/0:0.000:-0.05,-0.92,-5.00 0/0:0.000:-0.03,-1.16,-5.00 0/0:0.000:-0.04,-1.04,-5.00 0/0:0.000:-0.13,-0.59,-5.00 0/0:0.000:-0.02,-1.36,-5.00 0/0:0.000:-0.16,-0.51,-2.36 0/0:0.000:-0.02,-1.31,-5.00 0/0:0.000:-0.03,-1.19,-5.00 0/0:0.000:-0.03,-1.17,-5.00 0/0:0.000:-0.00,-4.40,-5.00 0/0:0.000:-0.03,-1.16,-5.00 0/0:0.000:-0.09,-0.73,-3.70 0/0:0.000:-0.19,-0.47,-1.77 0/0:0.000:-0.00,-3.32,-5.00 0/0:0.000:-0.17,-0.51,-2.00 0/0:0.000:-0.00,-2.17,-5.00 0/0:0.000:-0.00,-2.91,-5.00 0/0:0.000:-0.10,-0.71,-4.10 0/0:0.000:-0.03,-1.12,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.01,-1.67,-5.00 0/0:0.000:-0.00,-2.09,-5.00 0/0:0.000:-0.04,-1.09,-5.00 0/0:0.000:-0.01,-1.48,-5.00 0/0:0.000:-0.02,-1.41,-5.00 0/0:0.000:-0.10,-0.69,-3.80 0/0:0.000:-0.01,-1.54,-5.00 0/0:0.000:-0.03,-1.16,-5.00 0/0:0.000:-0.09,-0.73,-4.70 0/0:0.000:-0.09,-0.74,-4.70 0/0:0.000:-0.06,-0.92,-5.00 0/0:0.000:-0.05,-0.97,-5.00 0/0:0.000:-0.08,-0.78,-5.00 0/0:0.000:-0.06,-0.92,-5.00 0/0:0.000:-0.10,-0.67,-4.40 0/0:0.000:-0.01,-1.71,-5.00 0/0:0.000:-0.03,-1.20,-5.00 0/0:0.000:-0.02,-1.26,-5.00 0/0:0.000:-0.04,-1.10,-5.00 0/0:0.000:-0.02,-1.27,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.01,-1.47,-5.00 0/0:0.000:-0.00,-2.00,-5.00 0/0:0.000:-0.10,-0.67,-4.22 0/0:0.050:-0.18,-0.47,-2.34 0/0:0.000:-0.05,-1.00,-5.00 0/0:0.000:-0.11,-0.65,-3.85 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0/0:0.000:-0.01,-1.51,-5.00 0/0:0.000:-0.01,-1.67,-5.00 0/0:0.000:-0.08,-0.75,-4.40 0/0:0.000:-0.03,-1.23,-5.00 0/0:0.000:-0.10,-0.69,-4.40 0/0:0.000:-0.12,-0.63,-3.92 0/0:0.000:-0.01,-1.74,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.19,-0.46,-2.60 0/0:0.000:-0.19,-0.46,-2.62 0/0:0.000:-0.11,-0.65,-4.70 0/0:0.000:-0.11,-0.66,-4.70 0/0:0.050:-0.18,-0.49,-2.04 0/0:0.050:-0.10,-0.67,-4.40 0/0:0.000:-0.01,-1.62,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.23,-0.41,-1.51 0/0:0.000:-0.18,-0.48,-2.18 0/0:0.000:-0.03,-1.17,-5.00 0/0:0.000:-0.10,-0.68,-4.10 0/0:0.000:-0.03,-1.24,-5.00 0/0:0.000:-0.18,-0.48,-2.14
22  20000146    rs73387790  G   A   100 PASS    LDAF=0.0169;RSQ=0.9482;THETA=0.0004;AA=G;AN=2184;AVGPOST=0.9972;VT=SNP;SNPSOURCE=LOWCOV;AC=36;ERATE=0.0003;AF=0.02;AFR_AF=0.07;EUR_AF=0.0013    GT:DS:GL    0/0:0.000:-0.00,-2.68,-5.00 0/0:0.000:-0.07,-0.82,-5.00 0/0:0.000:-0.13,-0.60,-3.05 0/0:0.000:-0.03,-1.24,-5.00 0/0:0.000:-0.18,-0.47,-3.08 0/0:0.000:-0.06,-0.90,-5.00 0/0:0.000:-0.01,-1.48,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.16,-0.51,-2.63 0/0:0.000:-0.01,-1.76,-5.00 0/0:0.000:-0.10,-0.67,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.11,-0.66,-4.40 0/0:0.000:-0.10,-0.69,-4.70 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.18,-0.47,-2.30 0/0:0.000:-0.00,-2.80,-5.00 0/0:0.000:-0.00,-2.02,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.01,-1.49,-5.00 0/0:0.000:-0.01,-1.76,-5.00 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.48,-0.48,-0.48 0/0:0.000:-0.02,-1.40,-5.00 0/0:0.000:-0.00,-2.09,-5.00 0/0:0.000:-0.10,-0.70,-4.10 0/0:0.000:-0.22,-0.46,-1.27 0/0:0.000:-0.18,-0.48,-2.39 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22  20000428    rs55902548  G   T   100 PASS    AC=323;AVGPOST=0.9983;AA=G;AN=2184;VT=SNP;RSQ=0.9949;LDAF=0.1473;SNPSOURCE=LOWCOV;ERATE=0.0003;THETA=0.0003;AF=0.15;ASN_AF=0.0017;AMR_AF=0.15;AFR_AF=0.31;EUR_AF=0.15   GT:DS:GL    1/0:1.000:-5.00,0.00,-5.00  0/0:0.000:-0.35,-0.43,-0.73 0/1:1.000:-1.81,-0.01,-2.95 0/0:0.000:-0.01,-1.79,-5.00 0/0:0.000:-0.06,-0.86,-5.00 1/0:1.000:-0.19,-0.46,-2.18 0/0:0.000:-0.10,-0.68,-5.00 0/1:1.000:-4.40,-0.03,-1.12 0/1:1.000:-5.00,-0.69,-0.10 0/0:0.000:-0.10,-0.69,-4.70 0/0:0.000:-0.48,-0.48,-0.48 0/1:1.000:-5.00,-0.01,-1.77 0/0:0.000:-0.18,-0.48,-2.57 0/0:0.000:-0.02,-1.31,-5.00 0/0:0.000:-0.11,-0.65,-4.70 0/0:0.000:-0.10,-0.68,-4.70 0/0:0.000:-0.01,-1.72,-5.00 1/0:1.000:-5.00,0.00,-5.00  1/0:1.000:-1.38,-0.02,-2.61 0/1:1.000:-5.00,-1.40,-0.02 0/0:0.000:-0.00,-2.97,-5.00 0/0:0.000:-0.19,-0.47,-2.15 0/0:0.000:-0.44,-0.46,-0.54 0/0:0.000:-0.00,-2.52,-5.00 0/0:0.000:-0.05,-0.93,-5.00 0/0:0.000:-0.01,-1.77,-5.00 0/1:0.750:-0.22,-0.46,-1.26 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We'd like to convert the genotype data (GT field) into the matrix of minor alleles.

Genetic model

There are differnt SNP models. The additive SNP model essentially counts the number of minor allele (0, 1 or 2) per genotype. Other SNP models are dominant and recessive, both in terms of the minor allele. When ALT allele is the minor allele, genotypes are translated to real number according to

Genotype VCF GT model=:additive model=:dominant model=:recessive
ALT,ALT 0/0, 0|0 2 1 1
REF,ALT 0/1, 0|1 1 1 0
REF,REF 1/1, 1|1 0 0 0
missing . Null Null Null

When REF allele is the minor allele, genotypes are translated according to

Genotype VCF GT model=:additive model=:dominant model=:recessive
ALT,ALT 0/0, 0|0 0 0 0
REF,ALT 0/1, 0|1, 1/0, 1|0 1 1 0
REF,REF 1/1, 1|1 2 1 1
missing . Null Null Null

To properly record the missing genotypes, VCFTools convert VCF GT data to NullableArrays. Each element of a nullable array A is of type Nullable{T}. isnull(A[i, j]) == true indicates that A[i, j] is a missing genotype. See the NullableArrays.jl package for detailed documentation.

Convert GT data into a numeric matrix

Convert GT data in VCF file test.08Jun17.d8b.vcf.gz to a Nullable{Int8} array.

@time A = convert_gt(Int8, "test.08Jun17.d8b.vcf.gz"; model = :additive, impute = false, center = false, scale = false)
  0.164331 seconds (1.59 M allocations: 150.705 MiB, 17.38% gc time)





191×1356 NullableArrays.NullableArray{Int8,2}:
 0  0  0  0  1  0  0  0  0  0  0  0  0  …  0  0  0  0  1  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  0  0  1  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  1  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  1  0  0  0  0  0  0  0  0  …  0  0  0  0  1  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  1  1  0
 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  0  0  1  0  0  0  0  0  0  0
 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  1  0  1  0  0  0  0  1  1  0
 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  1  1  0
 ⋮              ⋮              ⋮        ⋱     ⋮              ⋮              ⋮
 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  1  1  0
 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  0  0  1  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0

Convert GT data in VCF file test.08Jun17.d8b.vcf.gz to a Nullable{Float64} array. Impute the missing genotypes according to allele frequency, center the dosages around 2MAF, and scale the dosages by sqrt(2MAF*(1-MAF)).

@time A = convert_gt(Float64, "test.08Jun17.d8b.vcf.gz"; model = :additive, impute = true, center = true, scale = true)
  0.150267 seconds (1.59 M allocations: 152.434 MiB, 14.09% gc time)





191×1356 NullableArrays.NullableArray{Float64,2}:
 0.0  0.0  0.0  0.0  1.41301    0.0  …  0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  1.41301    0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  1.41301    0.0  …  0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  2.36899    2.36899    0.0
 0.0  0.0  0.0  0.0  1.41301    0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  1.41301    0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0  …  0.0  0.0  2.36899    2.36899    0.0
 0.0  0.0  0.0  0.0  1.41301    0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  2.36899    2.36899    0.0
 ⋮                              ⋮    ⋱                                  ⋮  
 0.0  0.0  0.0  0.0  1.41301    0.0     0.0  0.0  2.36899    2.36899    0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0  …  0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0  …  0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0     0.0  0.0  -0.390016  -0.390016  0.0
 0.0  0.0  0.0  0.0  -0.586138  0.0  …  0.0  0.0  -0.390016  -0.390016  0.0

Extract GT data marker-by-maker or window-by-window

Large VCF files easily generate numeric arrays that cannot fit into computer memory. Many analyses only need to loop over markers or sets of markers. This can be achieved by the copy_gt! function.

  • To loop over all markers in the VCF file test.08Jun17.d8b.vcf.gz:
using GeneticVariation, NullableArrays, VCFTools

# initialize VCF reader
people, snps = nsamples("test.08Jun17.d8b.vcf.gz"), nrecords("test.08Jun17.d8b.vcf.gz")
reader = VCF.Reader(openvcf("test.08Jun17.d8b.vcf.gz"))
# pre-allocate vector for marker data
g = NullableArray(zeros(people))
for j = 1:snps
    copy_gt!(g, reader; model = :additive, impute = true, center = true, scale = true)
    # do statistical anlaysis
end
close(reader)
  • To loop over markers in windows of size 25:
using GeneticVariation, NullableArrays, VCFTools

# initialize VCF reader
people, snps = nsamples("test.08Jun17.d8b.vcf.gz"), nrecords("test.08Jun17.d8b.vcf.gz")
reader = VCF.Reader(openvcf("test.08Jun17.d8b.vcf.gz"))
# pre-allocate matrix for marker data
windowsize = 25
g = NullableArray(zeros(people, windowsize))
nwindows = ceil(Int, snps / windowsize)
for j = 1:nwindows
    copy_gt!(g, reader; model = :additive, impute = true, center = true, scale = true)
    # do statistical anlaysis
end
close(reader)
WARNING: Only 7 records left in reader; columns 8-25 are set to missing values

As the warning suggests, the last window has less than 25 markers. The remaining columns in the matrix g are set to missing values.