sorghum_data

The q command below is http://harelba.github.io/q/

Get the season6 phenotype csv

wget https://raw.githubusercontent.com/genophenoenvo/JAGS-logistic-growth/main/data_clean/mac_growth_rate_modeled_season6.csv

Get the vcf file

wget https://storage.googleapis.com/gpe-sorghum/all_combined_Genotyped_lines.filtered.season4.recode.sorted.fmissing_030.maf_001.contig-filtered.imputed.plink-prune-098.vcf.gz

Get the vcf file slimmed down to the right subset of samples

q -d, "select distinct genotype from mac_growth_rate_modeled_season6.csv" | sort > season_6_samples.txt

comm -12 <(sort season_4_samples.txt) <(sort season_6_samples.txt) > season_4_and_6_samples.txt

bcftools query -l all_combined_Genotyped_lines.filtered.season4.recode.sorted.fmissing_030.maf_001.contig-filtered.imputed.plink-prune-098.vcf.gz | sort > vcf_samples.txt

comm -12 season_4_and_6_samples.txt vcf_samples.txt > season_4_and_6_vcf_samples.txt

bcftools view --samples-file season_4_and_6_vcf_samples.txt all_combined_Genotyped_lines.filtered.season4.recode.sorted.fmissing_030.maf_001.contig-filtered.imputed.plink-prune-098.vcf.gz > sorghum.filtered.season4.season6.vcf

modify the phenotype file, less columns, tab separated

q -d, -T -O "select genotype, max_height_cm, max_growth_cm_gdd from mac_growth_rate_modeled_season6.csv" > phenotype/mac_growth_rate_modeled_season6.
tsv

Run GWAS and filter by pvalue

Followed by filtering the vcf by sites identified by GWAS at differing pvalue cutoffs

Finally, [matrices are produced] for each line of each vcf file by comparing Genotype values. Partial matches (Cultivar A: 0|1 vs Cultivar B:1|0) will correlate at 0.5.