Post by Jian Yang on Oct 15, 2015 7:30:44 GMT
1. Creating a GRM using SNP data
2. REML analysis with the --reml-pred-rand option to output the BLUP solutions of the individuals (i.e. estimate of total genetic value of each individual)
From the analysis above, you will have a output file test.indi.blp. There is no header line. Columns are family ID, individual ID, an intermediate variable, the total genetic value, another intermediate variable and the residual. If there are multiple GRMs fitted in the REML analysis, each GRM will insert additional two columns, i.e. an intermediate variable and a total genetic value, in front of the last two columns.
For a mixed linear model y = g + e, the BLUP estimates of genetic values (ug) and residuals (ue) are calculated using the two equations below (Lynch and Walsh 1996, page 749)
ghat = VgA V-1y and ehat = VeV-1y
where Vg is the genetic variance, Ve is the residual variance, A is the GRM, and y is the phenotype vector.
3. BLUP solutions for the SNP effects
The result will be saved in a file test.snp.blp. Columns are SNP ID, reference allele and BLUP of SNP effect. If there are multiple GRMs, each GRM will add an additional column to the file. You can alway ignore the last column.
4. You may then use PLINK --score option using the test.snp.blp as input to predict the polygenic profiles of new samples.
gcta64 --bfile test --make-grm test --out test
2. REML analysis with the --reml-pred-rand option to output the BLUP solutions of the individuals (i.e. estimate of total genetic value of each individual)
gcta64 --reml --grm test --pheno test.phen --reml-pred-rand --out test
From the analysis above, you will have a output file test.indi.blp. There is no header line. Columns are family ID, individual ID, an intermediate variable, the total genetic value, another intermediate variable and the residual. If there are multiple GRMs fitted in the REML analysis, each GRM will insert additional two columns, i.e. an intermediate variable and a total genetic value, in front of the last two columns.
01 0101 -0.012 -0.014 -0.010 -0.035
02 0203 0.021 0.031 -0.027 -0.031
03 0305 0.097 0.102 -0.026 -0.041
……
For a mixed linear model y = g + e, the BLUP estimates of genetic values (ug) and residuals (ue) are calculated using the two equations below (Lynch and Walsh 1996, page 749)
ghat = VgA V-1y and ehat = VeV-1y
where Vg is the genetic variance, Ve is the residual variance, A is the GRM, and y is the phenotype vector.
3. BLUP solutions for the SNP effects
gcta64 --bfile test --blup-snp test.indi.blp --out test
The result will be saved in a file test.snp.blp. Columns are SNP ID, reference allele and BLUP of SNP effect. If there are multiple GRMs, each GRM will add an additional column to the file. You can alway ignore the last column.
rs103645 A 0.00312 0.00451
rs175292 G -0.00021 0.00139
……
4. You may then use PLINK --score option using the test.snp.blp as input to predict the polygenic profiles of new samples.