Datasets:

Genentech
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alzheimers-variant-tutorial-data

Tasks:

Tabular Regression

Size:

1K<n<10K

Tags:

biology

genomics

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test · 1k rows

snpid stringlengths 13 15	chrom stringclasses 2 values	pos int64 1.56M 170M	alt stringclasses 6 values	ref stringclasses 6 values	rsid stringlengths 8 11	pval float64 0 0	beta float64 -0.4 0.6	se float64 0 0.13
6:32630634_G_A	chr6	32,630,634	G	A	6:32630634	0.000071	0.025194	0.006339
6:32630797_A_G	chr6	32,630,797	A	G	6:32630797	0.000053	0.024866	0.006155
6:32630824_T_C	chr6	32,630,824	T	C	6:32630824	0.000088	0.02663	0.00679
6:32630829_G_A	chr6	32,630,829	G	A	6:32630829	0.000052	0.027463	0.00679
6:32630925_T_A	chr6	32,630,925	T	A	6:32630925	0.000081	0.025407	0.006444
6:32630949_G_A	chr6	32,630,949	G	A	6:32630949	0.000083	0.025586	0.0065
6:32631252_C_T	chr6	32,631,252	C	T	6:32631252	0.000024	0.029619	0.007016
6:32631313_A_G	chr6	32,631,313	A	G	6:32631313	0.000032	0.026472	0.006369
6:32634263_T_G	chr6	32,634,263	T	G	rs66957194	0.000083	0.026303	0.006683
6:32634264_T_C	chr6	32,634,264	T	C	rs72844399	0.000002	0.013217	0.002799
6:32634313_G_C	chr6	32,634,313	G	C	rs1049059	0.000003	0.011748	0.002497
6:32634369_A_C	chr6	32,634,369	A	C	rs1049056	0.000003	0.012602	0.002715
6:32634426_A_G	chr6	32,634,426	A	G	rs67147537	0.000004	0.012537	0.00273
6:32634870_G_A	chr6	32,634,870	G	A	rs9274554	0.000033	-0.021934	0.005285
6:32635205_C_T	chr6	32,635,205	C	T	rs9274573	0.000083	0.020837	0.005294
6:32635420_A_G	chr6	32,635,420	A	G	rs75351515	0.000042	-0.021851	0.005332
6:32635754_G_T	chr6	32,635,754	G	T	rs3828789	0.000087	0.008513	0.002169
6:32635809_T_C	chr6	32,635,809	T	C	rs3828790	0.000079	0.008531	0.002161
6:32635883_T_C	chr6	32,635,883	T	C	rs3828793	0.000002	0.013166	0.002773
6:32636392_A_T	chr6	32,636,392	A	T	rs34984116	0	0.01679	0.003247
6:32636866_A_T	chr6	32,636,866	A	T	rs3134996	0.000098	0.008841	0.00227
6:32638371_G_A	chr6	32,638,371	G	A	rs9274761	0	-0.013175	0.002545
6:32639163_T_C	chr6	32,639,163	T	C	rs9274791	0.000015	-0.011605	0.00268
6:32639287_A_G	chr6	32,639,287	A	G	6:32639287	0.000081	0.029227	0.007416
6:32639836_A_C	chr6	32,639,836	A	C	6:32639836	0.000037	-0.034949	0.008466
6:32639931_T_G	chr6	32,639,931	T	G	6:32639931	0.000032	-0.035072	0.008432
6:32639972_T_C	chr6	32,639,972	T	C	6:32639972	0.000005	-0.03868	0.008466
6:32640549_G_T	chr6	32,640,549	G	T	rs115145510	0.000026	0.1233	0.0293
6:32640863_G_A	chr6	32,640,863	G	A	6:32640863	0.000055	-0.021901	0.005429
6:32640864_G_A	chr6	32,640,864	G	A	6:32640864	0.000063	-0.024361	0.006088
6:32640877_G_A	chr6	32,640,877	G	A	6:32640877	0.000063	-0.021609	0.005402
6:32640889_G_C	chr6	32,640,889	G	C	6:32640889	0.000029	-0.022527	0.005391
6:32640918_G_C	chr6	32,640,918	G	C	6:32640918	0.000098	-0.022644	0.005814
6:32641231_C_T	chr6	32,641,231	C	T	6:32641231	0.000092	0.027637	0.007066
6:32641403_T_C	chr6	32,641,403	T	C	6:32641403	0.000026	-0.024632	0.005862
6:32641411_T_C	chr6	32,641,411	T	C	6:32641411	0.000036	-0.024794	0.006004
6:32641416_C_T	chr6	32,641,416	C	T	6:32641416	0.000054	-0.021913	0.005427
6:32641419_C_T	chr6	32,641,419	C	T	6:32641419	0.000054	-0.021915	0.005427
6:32641625_T_C	chr6	32,641,625	T	C	6:32641625	0.000015	-0.025379	0.005862
6:32641673_C_T	chr6	32,641,673	C	T	6:32641673	0.000019	-0.025738	0.006027
6:32641747_T_G	chr6	32,641,747	T	G	6:32641747	0	0.015757	0.003076
6:32641796_C_T	chr6	32,641,796	C	T	6:32641796	0.000032	-0.026526	0.00638
6:32641890_G_A	chr6	32,641,890	G	A	rs9274955	0.000072	-0.021433	0.0054
6:32641942_C_T	chr6	32,641,942	C	T	6:32641942	0.000079	-0.02562	0.006489
6:32642187_G_T	chr6	32,642,187	G	T	rs9274975	0	-0.014247	0.002607
6:32642378_C_T	chr6	32,642,378	C	T	6:32642378	0.000043	-0.02597	0.006349
6:32642379_A_G	chr6	32,642,379	A	G	6:32642379	0.000043	-0.025965	0.006349
6:32642542_T_C	chr6	32,642,542	T	C	rs116743563	0.000094	-0.1031	0.0264
6:32642554_G_C	chr6	32,642,554	G	C	rs114120805	0.000085	-0.1045	0.0266
6:32643351_T_C	chr6	32,643,351	T	C	rs9275030	0.000031	-0.024629	0.005907
6:32644523_C_T	chr6	32,644,523	C	T	6:32644523	0.000091	0.025609	0.006546
6:32646830_G_T	chr6	32,646,830	G	T	6:32646830	0.000093	-0.022261	0.005696
6:32646837_C_G	chr6	32,646,837	C	G	6:32646837	0.000073	-0.022554	0.005686
6:32647379_A_C	chr6	32,647,379	A	C	6:32647379	0.000092	-0.032852	0.008399
6:32647433_C_T	chr6	32,647,433	C	T	6:32647433	0.000096	-0.032497	0.008334
6:32647846_C_T	chr6	32,647,846	C	T	rs28371238	0.000068	-0.023071	0.005791
6:32647854_C_G	chr6	32,647,854	C	G	rs28371239	0.000087	-0.0227	0.005786
6:32647975_A_G	chr6	32,647,975	A	G	rs28371244	0.000017	-0.1078	0.0251
6:32648039_T_C	chr6	32,648,039	T	C	rs28371250	0.000032	-0.022382	0.005386
6:32648189_A_C	chr6	32,648,189	A	C	rs116341596	0.000064	-0.046689	0.01168
6:32648285_G_A	chr6	32,648,285	G	A	rs115597494	0.000036	-0.164062	0.039726
6:32648312_T_C	chr6	32,648,312	T	C	rs115258523	0.000072	-0.1033	0.026
6:32648354_A_C	chr6	32,648,354	A	C	rs114783120	0.000093	-0.0998	0.0255
6:32648381_T_C	chr6	32,648,381	T	C	rs9275064	0.000089	-0.024386	0.006225
6:32648401_C_T	chr6	32,648,401	C	T	rs9275065	0.000071	-0.1833	0.0461
6:32648414_C_G	chr6	32,648,414	C	G	6:32648414	0.000025	-0.026356	0.006252
6:32648418_C_T	chr6	32,648,418	C	T	6:32648418	0.000046	-0.02827	0.006938
6:32648502_T_C	chr6	32,648,502	T	C	rs9275072	0.000022	-0.125155	0.029526
6:32648522_C_T	chr6	32,648,522	C	T	rs9275074	0.000023	-0.1094	0.0258
6:32648550_A_G	chr6	32,648,550	A	G	rs9275075	0.000019	-0.120435	0.028125
6:32648553_G_C	chr6	32,648,553	G	C	rs9275076	0.000019	-0.105137	0.024562
6:32648565_C_T	chr6	32,648,565	C	T	rs9275077	0.000027	-0.025538	0.006088
6:32648614_C_A	chr6	32,648,614	C	A	6:32648614	0.000079	-0.023947	0.006065
6:32648627_T_C	chr6	32,648,627	T	C	6:32648627	0.000079	-0.02394	0.006065
6:32648694_C_T	chr6	32,648,694	C	T	6:32648694	0.000079	-0.023905	0.006057
6:32648987_T_C	chr6	32,648,987	T	C	rs9275092	0	-0.019712	0.00387
6:32649088_G_C	chr6	32,649,088	G	C	rs9275095	0	-0.023116	0.003659
6:32649126_G_A	chr6	32,649,126	G	A	rs9275097	0	-0.02293	0.003629
6:32649161_T_C	chr6	32,649,161	T	C	rs9275098	0	-0.02283	0.003611
6:32649308_G_A	chr6	32,649,308	G	A	rs9275100	0	-0.013787	0.00256
6:32649508_G_A	chr6	32,649,508	G	A	rs9275107	0	-0.013442	0.00256
6:32649609_A_G	chr6	32,649,609	A	G	rs9275108	0.000001	-0.012781	0.00256
6:32649723_C_T	chr6	32,649,723	C	T	rs9275110	0	-0.013796	0.00256
6:32649735_T_G	chr6	32,649,735	T	G	rs9275111	0	-0.013801	0.002559
6:32650185_G_A	chr6	32,650,185	G	A	rs9275121	0.000009	-0.012083	0.002719
6:32650233_C_T	chr6	32,650,233	C	T	rs9275122	0	-0.01376	0.002561
6:32650268_C_T	chr6	32,650,268	C	T	rs2516006	0	-0.014714	0.002743
6:32650500_A_G	chr6	32,650,500	A	G	rs9275132	0	-0.014004	0.00256
6:32650612_G_C	chr6	32,650,612	G	C	rs9275134	0	-0.013921	0.002559
6:32650897_A_C	chr6	32,650,897	A	C	rs3998180	0	0.01725	0.003255
6:32651018_A_G	chr6	32,651,018	A	G	rs9275140	0	-0.013039	0.002444
6:32651218_A_T	chr6	32,651,218	A	T	rs9275144	0.000011	-0.011178	0.002542
6:32651616_T_C	chr6	32,651,616	T	C	rs9275149	0	-0.013521	0.002443
6:32651617_G_A	chr6	32,651,617	G	A	rs9275150	0	-0.013663	0.002444
6:32651741_T_C	chr6	32,651,741	T	C	rs3021062	0	-0.013751	0.00255
6:32651839_G_C	chr6	32,651,839	G	C	rs3021061	0	-0.013763	0.002557
6:32651846_C_G	chr6	32,651,846	C	G	rs3021060	0	-0.013758	0.002557
6:32651900_G_T	chr6	32,651,900	G	T	rs6457614	0	0.017279	0.003246
6:32651964_C_T	chr6	32,651,964	C	T	rs6457615	0	0.016954	0.003163
6:32652196_C_T	chr6	32,652,196	C	T	rs9275152	0	-0.022706	0.00364

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alzheimer's-variant-tutorial-data

Dataset Summary

This dataset contains summary statistics for 1,000 genomic variants. Each row represents a single-nucleotide polymorphism (SNP) mapped to the hg19 reference genome.

Dataset Structure

Data Fields

Based on the header of variants.csv:

Column	Type	Description
`snpid`	string	Unique identifier in `chr:pos_ref_alt` format
`chrom`	string	Chromosome (e.g., `chr6`)
`pos`	int	Genomic position (hg19)
`alt`	string	Alternate allele (effect allele)
`ref`	string	Reference allele (non-effect allele)
`rsid`	string	Reference SNP cluster ID
`pval`	float	P-value of the association
`beta`	float	Regression coefficient (effect size)
`se`	float	Standard error of the beta

Usage

from datasets import load_dataset
dataset = load_dataset("Genentech/alzheimers-variant-tutorial-data", split="test")
df = dataset.to_pandas()
print(df.head())

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Size of downloaded dataset files:

99 kB

Size of the auto-converted Parquet files:

56.5 kB

Number of rows:

1,000

Collection including Genentech/alzheimers-variant-tutorial-data

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Zoo of models and datasets associated with https://github.com/Genentech/gReLU/ • 2 items • Updated about 2 hours ago