Look up top associations from GWAS in a GWAS of HRC data

compare_hrc_gwas.sh

awk '{print $1"_"$2, $1"_"$2,$6}' pts_mrsc_mix_am-qc.fam > mrsc.pheno 
awk '{print $1"_"$2, $1"_"$2,$4,$5,$6,$7,$8}' pts_mrsc_mix_am-qc-eur_pca.menv.mds_cov > mrsc.cov
awk '{print $1"_"$2, $1"_"$2,$4,$5,$6,$7,$8}' pts_mrsc_mix_am-qc-aam_pca.menv.mds_cov > mrscA.cov

zcat chr1.dose.vcf.gz | head -n 100000 | gzip > chr1.abbr.dose.vcf.gz
/mnt/sdb/genetics/tiff1/hrc_impute/plink --vcf  chr1.dose.vcf.gz --hide-covar --pheno mrsc.pheno --allow-no-sex --covar mrsc.cov --double-id --logistic --ci 0.95 --out mrsc_chr1

#Compare to gwas results
zcat daner_mrsc_eur_analysis_run3.gz | grep -v NA | awk '{if (NR == 1 || $1 == "1") print}' | sort -g -k 11  > chr1_eur.out
awk '{print $1":"$3,$2,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13}'  chr1_eur.out > chr1_eur.out2
head chr1_eur.out2

head -n1 mrsc_chr1.assoc.logistic
grep 1:55385443   mrsc_chr1.assoc.logistic
grep 1:60352782    mrsc_chr1.assoc.logistic
grep 1:244559373    mrsc_chr1.assoc.logistic
grep 1:7716925 mrsc_chr1.assoc.logistic
grep 1:77016457 mrsc_chr1.assoc.logistic
grep 1:7914835 mrsc_chr1.assoc.logistic

1:77016457 rs4949728 T C 0.6431 0.7190 0.9766 0.6969 0.0919 8.589e-05 0

zcat  chr1.info.gz | head -n1
zgrep 1:244559373 chr1.info.gz
zgrep 1:55385444 chr1.info.gz
zgrep 1:7914835

#It appears like the major differences occur at rarer or worse imputed variants


#What does it look like in AAMS?

/mnt/sdb/genetics/tiff1/hrc_impute/plink --vcf  chr1.dose.vcf.gz --hide-covar --pheno mrsc.pheno --allow-no-sex --covar mrscA.cov --double-id --logistic --ci 0.95 --out mrscA_chr1


#What are the top hits in this analysis?