Welcome to GPRS documentation!

About gprs package

This package aims to generate the PRS model from GWAS summary statistics. It is designed to deal with the data format based on the GWAS catalog and GeneATLAS database GWAS summary statistics data.

Understanding the workflow of this package:

Filter GWAS summary statistics files (remove duplicate SNPID and select significant SNPs by P-value)
Generate bfiles by Plink1.9
Do clumping by Plink1.9
Generate PRS model by Plink2.0
Calculate statistic value of PRS model

Environment setup

Setup virtualenv

$ python3 -m venv venv

Activate virtualenv

$ source ./venv/bin/activate

Install this package

$ pip install -e .

Twelve commands in gprs:

geneatlas-filter-data
gwas-filter-data
generate-plink-bfiles
clump
select-clump-snps
build-prs
combine-prs
prs-statistics
combine-prs-stat
transfer_atcg (optional)
sub-setpop (optional)
generate_plink_bfiles_w_individual_info (optional)
random_draw_samples_from_fam (optional)
subset_vcf_w_random_sample (optional)

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)

Setp 2: Generate Plink bfiles (.bim, .bam, .fam)

After filtering SNPs in GWAS summary statistics data. Users have to generate plink files for generate PRS model.

Function: `gprs generate-plink-bfiles`

This option encodes plink1.9 make-bed function

plink --vcf [ref] --extract [snplists after qc] --make-bed --out [bfile folder/output_name]

How to use it?

Shell:

$ gprs generate-plink-bfiles --ref [str] --snplist_name [str] --symbol [str] --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.generate_plink_bfiles(snplist_name='2014height_MEC', output_name='2014height_hg38',extra_commands="--vcf-half-call r" ,symbol='_GRCh38.genotypes')

output files

*.bim
*.bed
*.fam

Setp 3-1: Clumping (remove linked SNPs)

Function: `gprs clump`

This option encodes plink1.9 clump function

plink --bfile [bfiles] --clump [qc snpslists] --clump-p1  --clump-p2  --clump-r2  --clump-kb  --clump-field  --clump-snp-field  --out 

The plink_bfiles_dir, qc snpslists and clump_output_dir will automatically be filled in the script. Users have to indicate the options below.

How to use it?

Shell:

$ gprs clump --ref [str] --data_dir [str] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_p2 [float/scientific notation] --clump_r2 [float] --clump_field [str] --clump_snp_field [str] --plink_bfile_name [str] --qc_file_name [str] --output_name [output name]

Python:

from gprs.gene_atlas_model import GeneAtlasModel

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

    geneatlas.clump(output_name='2014height',
                    clump_kb='250',
                    clump_p1='0.02', clump_p2='0.02',
                    qc_file_name='2014height',
                    plink_bfile_name='2014height')

output files

*.clump

CHR	F	SNP	BP	P	TOTAL	NSIG	S05	S01	S001	S0001	SP2
1	1	rs1967017	145723645	3.72e-16	19	0	2	6	3	8	rs11590105(1),rs17352281(1),rs9728345(1),rs11587821(1)
1	1	rs760077	155178782	7.45e-10	12	0	2	2	1	7	rs11589479(1),rs3766918(1),rs4625273(1),rs4745(1),rs12904(1)

Setp 3-2: Filter SNPs depends on `.clump`

After clumping, we have to filter SNPs again, to remove linked SNPs. In this step, we will have new SNPs list, and use it for generate PRS model.

Function: `gprs select-clump-snps`

How to use it?

Shell:

$ gprs select-clump-snps --result_dir [str] --qc_file_name [str] --clumpfolder_name [str] --clump_file_name [str] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float] --output_name [output name]

Python:

from gprs.gene_atlas_model import GeneAtlasModel

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

    geneatlas.select_clump_snps(output_name='2014height',clump_file_name='2014height',
                           qc_file_name='2014height',clumpfolder_name='',clump_kb='250',
                    clump_p1='0.02', clump_r2='0.02')

output files

*.qc_clump_snpslist.csv

With Chromosome information:

CHR	SNP	Allele	Beta	SE	Pvalue
1	rs1967017	A	-0.0736	0.0315	0.01938
1	rs760077	T	-0.1603	0.0543	0.003139

Without Chromosome information:

SNPID	Allele	Beta	SE	Pvalue
9:98316375:A:G	A	-0.0736	0.0315	0.01938
9:105570921:T:C	T	-0.1603	0.0543	0.003139

Setp 4-1: Generate PRS model

Generate PRS model by using Dosage by plink2.0

Function: `gprs build-prs`

plink2 --vcf [vcf input] dosage=DS --score [snplists afte clumped and qc]  --out 

The clumped qc snpslists and prs_output_dir will automatically be filled in the script. Users have to indicate the options below.

How to use it?

Shell:

$ gprs build-prs --vcf_input [str] --qc_clump_snplist_foldername [str] --symbol [str/int] --columns [int] --plink_modifier [str] --memory [int] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float] --output_name [output name]

Python:

from gprs.gene_atlas_model import GeneAtlasModel

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

    geneatlas.build_prs( vcf_input= '1000genomes/hg19',
                          output_name ='2014height', memory='1000',clump_kb='250',
                    clump_p1='0.02', clump_r2='0.02', qc_clump_snplist_foldername='2014height')

output files

*.sscore

IID	ALLELE_CT	NAMED_ALLELE_DOSAGE_SUM
HG00096	130	116
HG00097	130	114
HG00099	130	119
HG00100	130	110

Setp 4-2: Combined PRS model

Combined PRS model (python script create by Soyoung Jeon; update by Ying-Chu Lo))

Function:`gprs combine-prs`

Combine-prs will combine all .sscore files as one .sscore file. And calculate score average and sum per individual.

How to use it?

Shell:

$ gprs combine-prs --ref [str] --result_dur [str] 

Python:

from gprs.gene_atlas_model import GeneAtlasModel

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

    geneatlas.combine_prs(filename="2014height",clump_r2="0.5",clump_kb="250",clump_p1="0.02")

output files

*.sscore

id	ALLELE_CT	SCORE_AVG	SCORE_SUM
HG03270	1872.0	-0.00109666512273	-2.2826939078
HG03271	1872.0	-0.00111419935	-2.2831272058
NA19670	1872.0	-0.00117191923182	-2.4014961794
HG03279	1872.0	-0.00115016386364	-2.3057819

Setp 5-1: Calculate statistics value of PRS model

(Rscript create by Soyoung Jeon; update by Ying-Chu Lo)

Function: `prs-statistics`

After obtained combined sscore file, prs-statistics calculates BETA, AIC, AUC, PseudoR2 and OR ratio

How to use it?

Shell:

$ gprs prs-statistics --score_file [str] --pheno_file [str] --data_set_name [str] --prs_stats_R [str] --r_command [str] --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.prs_statistics(output_name='2014height', score_file = "/home1/ylo40816/Projects/GPRS/tmp/2014height_250_0.02_0.1.sscore",
        pheno_file = "Projects/GPRS/tmp/result/plink/prs/2014height_pheno.csv",
        r_command='/spack/apps/linux-centos7-x86_64/gcc-8.3.0/r-4.0.0-jfy3icn4kexk7kyabcoxuio2iyyww3o7/bin/Rscript',
        prs_stats_R="Projects/GPRS/gprs/prs_stats_quantitative_phenotype.R", data_set_name="2014height",
                             clump_kb='250',
                             clump_p1='0.02',
                             clump_r2='0.1'
                             )

output files

*_stat.txt

data	filter_condition	snps_nb	P	BETA	Degree_of_freedom	PseudoR2	OR_top1_to_middle20	OR_top2_to_middle20	OR_top5_to_middle20	OR_top10_to_middle20
2014height	250_0.02_0.1	143	0.5632	0.0019	1007	0.0002	0.5396	1.3899	0.9408	1.2972

Setp 5-2: Combined PRS statistics results (Optional)

Function:`combine-prs-stat`

If you have more than one trained PRS model, combine-prs-stat function is designed to combine statistics results. For instance: the first PRS model was filtered with P < 0.05, the second PRS model was filtered with P < 0.0005. You will have DATA_0.05_stat.txt/DATA_0.0005_stat.txt Combining two statistic tables allows users easy to compare between PRS models

How to use it?

Shell:

$ gprs combine-prs-stat --data_set_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.combine_prs_stat(data_set_name='2014height')

output files

*_combined_stat.txt

data	filter_conditions	P	BETA	PseudoR2	OR_top1_to_middle20	OR_top2_to_middle20	OR_top5_to_middle20	OR_top10_to_middle20
2014height	250_0.02_0.1	0.5632	0.0019	0.0002	0.5396	1.3899	0.9408	1.2972
2014height	250_0.02_0.1	0.5632	0.0019	0.0002	0.5396	1.3899	0.9408	1.2972

Example of giant height from Wood et al 2014

This is an example from Wood et al. The example aimed to use the GPRS package to replicate the Fig4A in Wood et al paper.

Get started: 2014 height data structure

The GWAS summary statistics were downloaded from the GIANT database.

Data name: GIANT_HEIGHT_Wood_et_al_2014_publicrelease_HapMapCeuFreq.txt

The data structure:

MarkerName	Allele1	Allele2	Freq.Allele1.HapMapCEU	b	SE	p	N
rs4747841	A	G	0.551	-0.0011	0.0029	0.70	253213
rs4749917	T	C	0.436	0.0011	0.0029	0.70	253213
rs737656	A	G	0.367	-0.0062	0.0030	0.042	253116
rs737657	A	G	0.358	-0.0062	0.0030	0.041	252156
rs7086391	T	C	0.12	-0.0087	0.0038	0.024	248425

Which template to use?

Before starting to process the data, we have to choose which template to use. Please use the table below to select the 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

The chromosome information is absent in 2014 height data, and 2014 height data also has no header with Chromosome information. Thus, I choose gene_atlas_model as a template

Output folders

In the GPRS package, the result folder will automatically generate under the execution directory. i.e. The user run GPRS package in /home/user/ then the default result directory is /home/user/result

The structure of output folders are: result/plink result/qc result/snplists result/stat result/plink/bfiles result/plink/clump result/plink/prs result/plink/qc_and_clump_snpslist

Step1 preparing data set - unified the data format

In step one, the filter_data function will filter raw data with p-value, the default is 1 and gives you three output files: snplist, qc'd file and summary After preparing the data, the snplist: result/snplists/[OUTPUT_NAME].csv will be used to generate plink files.

$ 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]  

In real use:

$ gprs geneatlas-filter-data --data_dir data/2014_GWAS_Height --result_dir [str] --snp_id_header MarkerName --allele_header Allele1 --beta_header b --se_header SE --pvalue_header p --pvalue 1 --output_name 2014height

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')   

After preparing the data-set three files obtained

The output folder will automatically generate and named as result

result/snplists/2014height.csv

SNPID
rs737656
rs737657
rs7086391

result/qc/2014height.QC.csv

SNPID	Allele	Beta	SE	Pvalue
rs737656	A	-0.0062	0.003	0.042
rs737657	A	-0.0062	0.003	0.041
rs7086391	T	-0.0087	0.0038	0.024

summary file: /result/qc/[output_name].filteredSNP.withPvalue0.05.summary.txt

no header
2014height: Total SNP BEFORE FILTERING: 2157028
2014height: Total SNP AFTER FILTERING: 395061

Step2 use qc’d SNPs list to obtain Plink bfiles(.bed, .bim, .fam)

Step2 uses the SNP list from step1 to generate plink bfiles.

$ gprs generate-plink-bfiles --ref [str] --snplist_name [str] --output_name [str] --symbol [str] --extra_commands [str] 

In real use:

$ gprs generate-plink-bfiles --ref 1000genomes/hg19 --snplist_name 2014height --symbol . --output_name 2014height

from gprs.gene_atlas_model import GeneAtlasModel

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

    geneatlas.generate_plink_bfiles(snplist_name='2014height', output_name='2014height',extra_commands="--vcf-half-call r" ,symbol='.genotypes')

chr1-chr22 bfiles obtained

result/plink/bfiles/chr(1-22)_2014height.bed
result/plink/bfiles/chr(1-22)_2014height.bim
result/plink/bfiles/chr(1-22)_2014height.fam

Step3.1 remove linked SNPs

In step3, we are trying to find the linked SNPs and remove them for further analysis. The package uses the plink clump function to find the linked SNPs. The r2 value in the clump function is 0.1; if the user wants to apply another value, it should be specified in the command. The function --clump_field is asking the user to enter the column name, the default here is Pvalue (from the Step1 qc'd output )

$ gprs clump --plink_bfile_name [str] --output_name [str] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_p2 [float/scientific notation] --clump_r2 [float] --clump_field [str] --qc_file_name [str] --clump_snp_field [str]   

In real use:

$ gprs clump --data_dir data/2014_GWAS_Height --clump_kb 250 --clump_p1 0.02 --clump_p2 0.02 --clump_r2 0.1 --clump_field Pvalue --clump_snp_field 2014height --plink_bfile_name 2014height --qc_file_name 2014height --output_name 2014height

from gprs.gene_atlas_model import GeneAtlasModel

if __name__ == '__main__':
    geneatlas = GeneAtlasModel( ref='1000genomes/hg19',
                    data_dir='data/2014_GWAS_Height' )
    
    geneatlas.clump(output_name='2014height',plink_bfile_name='2014height',
                    qc_file_name='2014height',clump_kb='250',
                    clump_p1='0.02',clump_p2='0.02', clump_r2='0.02')

chr1-chr22 clumped files obtained

result/plink/clump/*.clumped

CHR	F	SNP	BP	P	TOTAL	NSIG	S05	S01	S001	S0001	SP2
1	1	rs1967017	145723645	3.72e-16	19	0	2	6	3	8	rs11590105(1),rs17352281(1),rs9728345(1),rs11587821(1)
1	1	rs760077	155178782	7.45e-10	12	0	2	2	1	7	rs11589479(1),rs3766918(1),rs4625273(1),rs4745(1),rs12904(1)

Step3.2 select clumped SNPs

After clump, we will receive a list of SNPs, and we filter out the original qc’d file to generate a new SNPs list. From this step, users have to provide C+T (clumping + threshold) conditions as a marker in the output file name.

$ gprs select-clump-snps --qc_file_name [str] --clump_file_name [str] --output_name [output name] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float] --clumpfolder_name [str]

In real use:

$ gprs select-clump-snps --qc_file_name 2014height --clump_file_name 2014height --clump_kb 250 --clump_p1 0.02 --clump_r2 0.1 --clumpfolder_name 2014height --output_name 2014height

from gprs.gene_atlas_model import GeneAtlasModel

if __name__ == '__main__':
    geneatlas = GeneAtlasModel( ref='1000genomes/hg19',
                    data_dir='data/2014_GWAS_Height' )
    
    geneatlas.select_clump_snps(output_name='2014height',
                                clump_file_name='2014height',
                                qc_file_name='2014height',
                                clump_kb='250',
                                clump_p1='0.02',
                                clump_r2='0.5',clumpfolder_name='2014height')

new snps list obtained

result/plink/clump/*.qc_clump_snpslist.csv

CHR	SNP
1	rs1967017
1	rs760077

Step4.1 Generate PRS model

In step4 the function build-prs is built on Plink2.0 dosage.

$ gprs build-prs --vcf_input [str] --output_name [str] --qc_clump_snplist_foldername [str] --memory [int] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float] --symbol [str/int] --columns [int] --plink_modifier [str]  

In real use:

$ gprs build-prs --vcf_input 1000genomes/hg19 --symbol . --qc_file_name 2014height --columns 1 2 3 --memory 1000 --clump_kb 250 --clump_p1 0.02 --clump_r2 0.1 --output_name 2014height 

from gprs.gene_atlas_model import GeneAtlasModel

if __name__ == '__main__':
    geneatlas = GeneAtlasModel( ref='1000genomes/hg19',
                    data_dir='data/2014_GWAS_Height' )
    
    geneatlas.build_prs( vcf_input= 'home/1000genomes/hg19',
                         output_name ='2014height', memory='1000',clump_kb='250',
                         clump_p1='0.02', clump_r2='0.02', qc_clump_snplist_foldername='2014height')

chr1-chr22 sscore files obtained

result/plink/prs/*.sscore

IID	NMISS_ALLELE_CT	NAMED_ALLELE_DOSAGE_SUM
HG00096	1912	889
HG00097	1912	884
HG00099	1912	875

Step4.2 Combined PRS model

From step 4.1 we will have 1-22 chromosomes sscore files, in this step we are going to combine these files as one output.

$ gprs build-prs --vcf_input [str] --symbol [str/int] --qc_file_name [str] --columns [int] --plink_modifier [str] --memory [int] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float] --output_name [output name] --clump_kb [int] --clump_p1 [float/scientific notation] --clump_r2 [float]

In real use:

$ gprs build-prs --vcf_input 1000genomes/hg19 --symbol . --qc_file_name 2014height --columns 1 2 3 --memory 1000 --clump_kb 250 --clump_p1 0.02 --clump_r2 0.1 --output_name 2014height 

from gprs.gene_atlas_model import GeneAtlasModel

if __name__ == '__main__':
    geneatlas = GeneAtlasModel( ref='1000genomes/hg19',
                    data_dir='data/2014_GWAS_Height' )
    
    geneatlas.combine_prs(filename="2014height",
                          clump_r2="0.5",clump_kb="250",clump_p1="0.02")

one combined sscore files obtained

result/plink/prs/*.sscore

id	ALLELE_CT	SCORE_AVG	SCORE_SUM
HG00096	59636.0	0.0009896786363636364	60.676823334000005
HG00097	59636.0	0.0009901211363636362	60.46440781399999
HG00099	59636.0	0.0010224948181818182	63.233378824

Visualize distribution of PRS in each population

# Libraries
library(ggplot2)
library(dplyr)

data <- read.csv("combine_profil_w_pop.txt", sep=" ")

data$SCORE_Z <- (data$SCORE-mean(data$SCORE))/sd(data$SCORE) 

ggplot(data, aes(x=data$SCORE_Z, group=data$super_pop, fill=data$super_pop)) +
  geom_density(adjust=1.25, alpha=.7) +
  scale_fill_manual(values=c("brown2","springgreen3","mediumorchid","deepskyblue3","orange"))+
  labs(x = "Polygenic Score", y = "Density", fill = "Super population")+
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

Welcome to GPRS documentation!

About gprs package

Environment setup

Get started: understanding the data format

GWAS template

GeneAtlas template

How to choose model template?

Before step1:

Step1: Unify the data format

Function: gprs geneatlas-filter-data

How to use it?

output files

Setp 2: Generate Plink bfiles (.bim, .bam, .fam)

Function: gprs generate-plink-bfiles

How to use it?

output files

Setp 3-1: Clumping (remove linked SNPs)

Function: gprs clump

How to use it?

output files

Setp 3-2: Filter SNPs depends on .clump

Function: gprs select-clump-snps

How to use it?

output files

With Chromosome information:

Without Chromosome information:

Setp 4-1: Generate PRS model

Function: gprs build-prs

How to use it?

output files

Setp 4-2: Combined PRS model

Function:gprs combine-prs

How to use it?

output files

Setp 5-1: Calculate statistics value of PRS model

Function: prs-statistics

How to use it?

output files

Setp 5-2: Combined PRS statistics results (Optional)

Function:combine-prs-stat

How to use it?

output files

Example of giant height from Wood et al 2014

Get started: 2014 height data structure

The data structure:

Which template to use?

Output folders

Step1 preparing data set - unified the data format

After preparing the data-set three files obtained

Step2 use qc’d SNPs list to obtain Plink bfiles(.bed, .bim, .fam)

Step3.1 remove linked SNPs

Step3.2 select clumped SNPs

Step4.1 Generate PRS model

Step4.2 Combined PRS model

Visualize distribution of PRS in each population

Example of MEC American Africans

GPRS

GeneAtlasModel

GwasModel

Indices and tables

Function: `gprs geneatlas-filter-data`

Function: `gprs generate-plink-bfiles`

Function: `gprs clump`

Setp 3-2: Filter SNPs depends on `.clump`

Function: `gprs select-clump-snps`

Function: `gprs build-prs`

Function:`gprs combine-prs`

Function: `prs-statistics`

Function:`combine-prs-stat`