Get started: understanding the data format

GWAS template

Chr	Pos	RSID	Allele1	Allele2	Freq1	Effect	StdErr	P-value	n_total_sum
1	10539	rs537182016	a	c	0.0013	-5.1213	20.0173	0.7981	7043
1	11008	rs575272151	c	g	0.9215	0.1766	0.2610	0.4985	7042.99
1	11012	rs544419019	c	g	0.9215	0.1766	0.2610	0.4985	7042.99
1	14674	rs561913721	a	g	0.0032	0.8040	0.8364	0.3364	7043

GeneAtlas template

SNP	ALLELE	NBETA-selfReported_n_1526	NSE-selfReported_n_1526	PV-selfReported_n_1526
rs1110052	T	-0.00032386	0.000269	0.2286
rs112164716	T	6.5121e-05	0.001126	0.95388
rs11240779	A	-0.00022416	0.00028937	0.43855
rs11260596	C	0.00025168	0.00024248	0.29931

How to choose model template?

-	chr info in the file name	chr info not in the file name
chr info in the header	gene_atlas_model	gwas_model
chr info absent in the header	gene_atlas_model	gene_atlas_model

Before step1:

Please unzip your .gz file first.

Step1: Unify the data format

After knowing the data format, users can choose the model (gwas or geneatlas) to unify the data format and filter out SNPs(optional). :heavy_exclamation_mark: SNPs are extract out by RSID not chromosome position

Function: gprs geneatlas-filter-data

Filter GeneAtlas csv file by P-value and unify the data format as following order: SNPID, ALLELE, BETA, StdErr, Pvalue

How to use it?

Shell:

$ gprs geneatlas-filter-data --ref [str] --data_dir [str] --result_dir [str] --snp_id_header [str] --allele_header [str] --beta_header [str] --se_header [str] --pvalue_header [str] --pvalue [float/scientific notation] --output_name [str]  
$ gprs gwas-filter-data --ref [str] --data_dir [str] --result_dir [str] --snp_id_header [str] --allele_header  [str] --beta_header [str] --se_header [str] --pvalue_header [str] --pvalue [float/scientific notation] --output_name [str]  

Python:

from gprs.gene_atlas_model import GeneAtlasModel
if __name__ == '__main__':
    geneatlas = GeneAtlasModel( ref='1000genomes/hg19',
                    data_dir='data/2014_GWAS_Height' )

    geneatlas.filter_data( snp_id_header='MarkerName',
                            allele_header='Allele1',
                            beta_header='b',
                            se_header ='SE',
                            pvalue_header='p',
                            output_name='2014height')
   
from gprs.gwas_model import GwasModel
if __name__ == '__main__':
    gwas = GwasModel( ref='/home1/ylo40816/1000genomes/hg19',
                 data_dir='/home1/ylo40816/Projects/GPRS/data/2019_GCST008970')

    gwas.filter_data( snp_id_header='RSID',
                   allele_header='Allele1',
                   beta_header='Effect',
                   se_header='StdErr',
                   pvalue_header='P-value',
                   output_name='GCST008970',
                   file_name='gout_chr1_22_LQ_IQ06_mac10_all_201_rsid.csv')

output files

• *.QC.csv (QC files )

• *.csv (snplist)