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