SNP genotype matrix for GWAS and Machine Learning analyses

SNP datasets used for GWAS and Machine Learning analyses

All datasets come from the easyGWAS website: https://easygwas.ethz.ch/down/1/

=== Horton et al. 2012 ===

1307 Arabidopsis genotypes x 214,057 SNPs

1) In the form of a genotype matrix

The file is called Horton2012.raw

https://www.nature.com/articles/ng.1042

Preview of the first lines and columns:

FID    Chr1_657_T    Chr1_3102_G    Chr1_4648_A    Chr1_4880_T    Chr1_5975_G    Chr1_6063_T    Chr1_6449_C
9381    2    2    2    0    0    0    0
9380    0    0    0    0    0    0    2
9378    2    2    2    0    0    0    0
9371    2    2    2    0    0    0    0
9367    0    0    0    2    0    0    0
9363    2    2    2    0    0    0    0
9356    0    2    2    0    0    0    0
9355    2    2    2    0    0    0    0
9354    2    2    2    0    0    0    0

...etc...

PLINK 1.9 was used to convert the .ped and .map file to a .raw format with: 

plink --file original_data/genotype --recodeA --tab

Genotypes are encoded as 0, 1 or 2 with:

     SNP       SNP_A   
     ---       -----    
     A A   ->    0     
     A C   ->    1     
     C C   ->    2     
     0 0   ->   NA     

Then only the Family ID was kept (same as individual ID) and other columns (Paternal ID, Maternal ID, Sex, Phenotype) were removed.

The corresponding PLINK manual page used is here: https://zzz.bwh.harvard.edu/plink/dataman.shtml#recode

1) In the form of set of files compatible with PLINK out of the box

The archive file is called AtPolyDB_call_method_75_Horton2012.tar.gz and contains three files:

• genotype.ped: pedigree information from the 1307 ecotypes
• genotype.map: the SNP positions on the genome
• phenotypes.pheno: the phenotype value of the 1307 ecotypes