Healthcare
21 columns
150 rows
8 formats

Whole Genome Sequencing (WGS) Variant Data

This dataset provides comprehensive whole genome sequencing variant data, including detailed records of SNPs, indels, structural variants, and copy number variations, along with rich annotation, pathogenicity predictions, and population frequency metrics. It is ideal for precision medicine, enabling genetic analysis, clinical interpretation, and population studies. The schema supports robust data integration for research, diagnostics, and personalized healthcare applications.

last updated 2 Nov 2025
by GoMask

The brief that made it

Clinical interpretation of genetic variants for personalized medicine

Sample rows

preview · 8 of 150 rows · all 21 columns

sample_idstring	clinical_significancestring	pathogenicity_scorefloat	chromosomestring	variant_idstring	positioninteger	reference_allelestring	alternate_allelestring	variant_typestring	zygositystring	gene_symbolstring	transcript_idstring	variant_consequencestring	population_frequencyfloat	dbsnp_idstring	inheritance_modestring	sample_sexstring	sample_ageinteger	sequencing_platformstring	quality_scorefloat	date_sequenceddate
PT-001ADULT	pathogenic	0.96	1	chr1:123456_A>T	123456	A	T	SNP	heterozygous	BRCA1	ENST00000357654	missense	0.004	rs1234567	autosomal dominant	female	42	Illumina	98.3	2023-04-15
FAM-002CHILD	likely pathogenic	0.88	2	chr2:789654_G>C	789654	G	C	SNP	homozygous	TP53	ENST00000269305	nonsense	0.001	rs2345678	autosomal recessive	male	8	Illumina	91.7	2023-09-20
ZX-003EDGE	uncertain significance	0.31	10	chr10:543210_C>G	543210	C	G	indel	heterozygous	CFTR	ENST00000003084	frameshift	0.012	rs3456789	autosomal recessive	female	34	PacBio	81.2	2022-07-11
RSQ-004EXTREME	pathogenic	0.99	X	chrX:100120_T>C	100120	T	C	SNP	hemizygous	DMD	ENST00000357033	nonsense	0.0007	rs4567890	X-linked	male	20	Illumina	97.5	2024-02-01
TK-005YOUNG	not provided	blank	Y	chrY:90000_G>A	90000	G	A	SNP	hemizygous	blank	blank	intergenic	0.05	blank	unknown	male	2	Oxford Nanopore	29.4	2019-11-18
GB-006MIDDLE	likely pathogenic	0.87	3	chr3:789321_T>G	789321	T	G	SNP	heterozygous	EGFR	ENST00000275493	missense	0.007	rs5678901	autosomal dominant	female	53	Illumina	93.2	2024-05-07
MIN-007INFANT	pathogenic	0.92	5	chr5:456123_C>T	456123	C	T	SNP	heterozygous	HBB	ENST00000335295	missense	0.002	rs6789012	autosomal recessive	male	3	Illumina	96.6	2023-12-21
BIGPOP-008ADULT	benign	blank	7	chr7:654321_A>C	654321	A	C	SNP	heterozygous	blank	blank	intergenic	0.78	rs7890123	unknown	female	60	Illumina	84.7	2022-03-10
MN-009ELDER	uncertain significance	0.11	12	chr12:321654_G>T	321654	G	T	indel	homozygous	APOE	ENST00000252486	frameshift	0.06	blank	unknown	female	77	PacBio	74.1	2020-06-16
SP-010TEEN	likely pathogenic	0.81	6	chr6:987654_T>A	987654	T	A	SNP	heterozygous	MTHFR	ENST00000361789	missense	0.009	rs8901234	autosomal dominant	female	15	Illumina	87.9	2022-08-11
NOSYMBOL-011UNKNOWN	uncertain significance	blank	MT	chrMT:16000_A>G	16000	A	G	structural	unknown	blank	blank	other	0.13	blank	mitochondrial	unknown	blank	PacBio	15.3	2018-02-25
Q-012YOUNG	pathogenic	0.94	9	chr9:654789_G>C	654789	G	C	SNP	heterozygous	F5	ENST00000349036	missense	0.008	rs9012345	autosomal dominant	male	6	Illumina	97.1	2024-03-23
INTER-013LATE	uncertain significance	blank	14	chr14:987123_T>C	987123	T	C	CNV	unknown	blank	blank	regulatory	0.21	blank	unknown	unknown	blank	Oxford Nanopore	32.7	2018-06-14
EXTREME-014ADULT	likely pathogenic	0.89	11	chr11:110000_A>C	110000	A	C	SNP	heterozygous	G6PD	ENST00000393585	missense	0.002	rs1234501	X-linked	male	32	Illumina	94.2	2023-06-29
YOUNG-015TEEN	benign	blank	8	chr8:123321_C>T	123321	C	T	SNP	homozygous	PAH	ENST00000265434	synonymous	0.34	rs1234512	autosomal recessive	female	16	Illumina	78.5	2022-11-07
OLDEST-016ELDER	likely benign	blank	20	chr20:876543_T>A	876543	T	A	indel	heterozygous	blank	blank	intron	0.45	blank	unknown	male	92	PacBio	88.6	2021-04-18
PT-017ADULT2	likely pathogenic	0.84	4	chr4:345987_G>T	345987	G	T	SNP	heterozygous	JAK2	ENST00000220998	missense	0.003	rs1234523	autosomal dominant	male	45	Illumina	95.8	2024-05-01
TK-018INFANT	pathogenic	0.93	13	chr13:231654_A>C	231654	A	C	SNP	homozygous	GJB2	ENST00000382823	missense	0.005	rs1234534	autosomal recessive	female	2	Illumina	99.1	2023-10-10
Q-019YADULT	benign	blank	17	chr17:567890_G>A	567890	G	A	SNP	heterozygous	blank	blank	intergenic	0.82	rs1234545	unknown	female	37	Illumina	86.3	2021-09-03
FAM-020CHILD2	likely pathogenic	0.91	19	chr19:876543_T>C	876543	T	C	SNP	homozygous	ABCA4	ENST00000296241	missense	0.002	rs1234556	autosomal recessive	male	9	Illumina	95.2	2023-11-05

What the 150 rows show

from the 150-row sample

Uncertain significance (clinical significance) stands out: mean pathogenicity_score is 0.20, against 0.91 for the rest.

0.89median pathogenicity_score
3sample sexes
3sequencing platforms
4variant types
4zygosities
5inheritance modes

Mean pathogenicity_score by clinical_significance90 rows

pathogenicity_score90 rows, in bands of 0.1

Median 0.89, from 0.02 to 0.99.

chromosome150 rows · top 10 of 25 values

MT11
210
59
77
127
87
177
16
106
36

21 columns by typefrom the column list below

string 15
integer 2
float 3
date 1

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 15 columns
`sample_id`	string	Unique identifier for the sequenced sample (e.g., patient or specimen)unique	PT-001ADULT
`variant_id`	string	Unique identifier for the genomic variant (e.g., chr1:123456_A>T)unique	chr1:123456_A>T
`chromosome`	string	Chromosome where the variant is located (e.g., '1', 'X', 'MT')25 values	1
`reference_allele`	string	Reference allele at the variant position	A
`alternate_allele`	string	Alternate allele observed at the variant position12 alleles	T
`variant_type`	string	Type of variant (e.g., SNP, indel, structural, CNV)SNP · indel · structural · CNV	SNP
`zygosity`	string	Zygosity of the variant in the sample (e.g., homozygous, heterozygous, hemizygous)homozygous · heterozygous · hemizygous · unknown	heterozygous
`gene_symbol`	string	HGNC gene symbol affected by the variant (if applicable)optional	BRCA1
`transcript_id`	string	Transcript identifier (e.g., ENST00000367770) if variant affects a transcriptoptional	ENST00000357654
`variant_consequence`	string	Predicted functional consequence of the variant (e.g., missense, nonsense, synonymous, frameshift)9 values · optional	missense
`clinical_significance`	string	Clinical significance annotation (e.g., pathogenic, likely pathogenic, benign, uncertain)6 values · optional	pathogenic
`dbsnp_id`	string	dbSNP identifier (e.g., rs123456) if availableoptional	rs1234567
`inheritance_mode`	string	Mode of inheritance if known (e.g., autosomal dominant, autosomal recessive, X-linked)5 values · optional	autosomal dominant
`sample_sex`	string	Biological sex of the sample donormale · female · unknown · optional	female
`sequencing_platform`	string	Platform used for sequencing (e.g., Illumina, PacBio, Oxford Nanopore)3 platforms · optional	Illumina
Numbers 5 columns
`position`	integer	Genomic position (base pair) of the variant on the chromosome1 or more	123456
`pathogenicity_score`	float	Numeric score predicting pathogenicity (e.g., CADD, PolyPhen, SIFT)0 to 1 · optional	0.96
`population_frequency`	float	Frequency of the variant in the general population (e.g., allele frequency from gnomAD)0 to 1 · optional	0.004
`sample_age`	integer	Age of the sample donor at time of sequencing (in years)0 to 120 · optional	42
`quality_score`	float	Variant call quality score (e.g., Phred score)0 or more · optional	98.3
Dates and times 1 column
`date_sequenced`	date	Date when the sample was sequencedoptional	2023-04-15

Use it for

A healthcare dashboard
Pathogenicity_score by clinical_significance and a breakdown of chromosome. Excel, Power BI or Tableau.
Why do the 22 uncertain significance rows have a mean pathogenicity_score of 0.20?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Samples150
PT-001ADULT0.96pathogen…
FAM-002CHILD0.88likely p…
ZX-003EDGE0.31uncertai…
A software demo
Believable samples with variant_id, chromosome and position to fill a screen in front of a buyer.

This dataset150 rows21 columns

Yours1,000 rows21 columns

blueprint · whole-genome-sequencing-wgs-variant-data

Behind this dataset

Same schema. As many rows as you need.

These 150 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Sample ID unique per patient or specimen
Sequencing platform: Illumina, PacBio, Oxford Nanopore
Coverage depth: 30x for clinical WGS
Reference genome: hg38 or hg19
Variant type: SNP (single nucleotide polymorphism), indel, CNV, SV
Chromosomal location: chromosome and position
Reference allele and alternate allele
Zygosity: homozygous, heterozygous

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: whole-genome-sequencing-wgs-variant-data

What should your data show?

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Sample rows

What the 150 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?