1. Prepare principal component results

GCTA requires the phenotypic input file to be a plain text file and have the family and UUID in the first two columns and it does not recognise headers. I converted the csv file to text file, removed the headers, and added a family ID column.

2. Apply loop to perform function for estimating heritability

for i in {1..9}
do  ./gcta64 --reml --grm gcta_output/merged_nies_grm --pheno pheno_pca_coord.txt --mpheno $i --reml-pred-rand          --qcovar gcta_output/merged_nies_pca.eigenvec --out gcta_output/$i.pc.est

    head gcta_output/$i.pc.est.hsq >> pc.heritability.gcta.txt
done

read.csv('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gcta_output/pc_heritability_gcta.csv')

##   PC       h2    LRT     pval
## 1  1 0.622134 19.655 4.64e-06
## 2  2 0.219030  2.022 7.75e-02
## 3  3 0.253975  4.680 1.52e-02
## 4  4 0.477213 13.662 1.09e-04
## 5  5 0.000000  0.000 5.00e-01
## 6  6 0.137624  1.287 1.28e-01
## 7  7 0.279968  4.734 1.48e-02
## 8  8 0.226968  5.234 1.11e-02
## 9  9 0.000000  0.000 5.00e-01

The results indicate that five principal components are heritable (p-val < 0.05) and the most heritable principal component is 1.

3. List significantly heritable traits in order of estimates

read.csv('C:/Users/Martha/Documents/Honours/Project/honours.project/Data/gcta_output/all_heritability_gcta.csv')

##             Trait       h2    LRT     Pval
## 1       R K-val V 0.756885 28.886 3.84e-08
## 2    L Pachimetry 0.667679 23.898 5.08e-07
## 3               1 0.622134 19.655 4.64e-06
## 4    R Pachimetry 0.600582 19.815 4.27e-06
## 5           R CDR 0.596660 18.296 9.45e-06
## 6       L K-val V 0.593822 16.975 1.89e-05
## 7           R Cyl 0.559755 17.911 1.16e-05
## 8           L CDR 0.540132 14.500 7.01e-05
## 9       R K-val H 0.518472 14.657 6.45e-05
## 10              4 0.477213 13.662 1.09e-04
## 11     Total UVAF 0.458766 10.262 6.79e-04
## 12 R Axial Length 0.370540  7.356 3.34e-03
## 13 L Axial Length 0.353147  6.614 5.06e-03
## 14          R ACD 0.312516  6.960 4.17e-03
## 15      L K-val H 0.294400  4.182 2.04e-02
## 16          L ACD 0.293333  6.386 5.75e-03
## 17              7 0.279968  4.734 1.48e-02
## 18          R IOP 0.265188  4.139 2.09e-02
## 19              3 0.253975  4.680 1.52e-02
## 20              8 0.226968  5.234 1.11e-02
## 21          L IOP 0.216258  3.149 3.80e-02

Listing the traits by heritability estimates will allow me to prioritise the traits for GWAS.