Closed shafiqnoa closed 4 years ago
I ran mvLMM for two traits (x and y) adjusting for four additional traits (including the intercept). However, I can not distinguish which one is the estimate for genetic correlation between x and y.
here is the log:
0.69833 0.0375916 0.065652
0.0321777 0.0108328 0.0072974
0.273633 -0.00751629 0.120799
0.0184782 0.00813352 0.00677273
0.69775 0.0375614 0.0375614 0.0655758
0.0321219 0.0108154 0.00728674
0.273107 -0.00751642 -0.00751642 0.120644
0.018433 0.008118 0.0067622
0.0940317 -0.0878004 0.166091 -0.0150725 0.406611 -0.120677 0.0680489 -0.0198323
0.0752651 0.0684905 0.0199194 0.0139538 0.0320747 0.0288643 0.00833621 0.00586676
Vg is the genetic covariance. You can obtain genetic correlation based on this covariance estimate.
I ran mvLMM for two traits (x and y) adjusting for four additional traits (including the intercept). However, I can not distinguish which one is the estimate for genetic correlation between x and y.
here is the log:
GEMMA Version = 0.98.1 (2018-12-10)
Build profile =
GCC version = 8.2.0
GSL Version = 2.5
Eigen Version = 3.3.5
OpenBlas = OpenBLAS 0.3.2 - DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=6
arch = Haswell
threads = 1
parallel type = threaded
Command Line Input = /trinity/home/rahmanms/softwares/gemma_0.98.1_linux_static -g /trinity/home/DTR_Shared/DTR_genotypes/HRC_IMP_2017/HRC_r1.1_2MZ/GEMMA_2MZ/HRC_r1.1_TUK_chr22_2MZ_GEMMA.gen.gz -a /trinity/home/DTR_Shared/DTR_genotypes/HRC_IMP_2017/HRC_r1.1_2MZ/GEMMA_2MZ/1000G_P3V5_TUK_chr22_2MZ_GEMMA.annot -k /trinity/home/DTR_Shared/DTR_genotypes/1000G_IMP_2017/1000G_P3V5_2MZ/GEMMA_2MZ/T123_1000g_V8_2MZ_kinship_0941.sXX.txt -c /trinity/home/rahmanms/CWP/IgG_CWP/GEMMA/INPUT_FILES/gemma_covar.txt -lmm 4 -n 9 77 -p /trinity/home/rahmanms/CWP/IgG_CWP/GEMMA/INPUT_FILES/gemma_igg_cwp_all.txt -maf 0.01 -miss 0 -o cwp_igg10_chr22_gwas
Date = Thu Nov 12 15:02:42 2020
Summary Statistics:
number of total individuals = 6921
number of analyzed individuals = 3203
number of covariates = 4
number of phenotypes = 2
number of total SNPs/var = 524544
number of analyzed SNPs/var = 106465
REMLE log-likelihood in the null model = -6081.36
MLE log-likelihood in the null model = -6084.94
REMLE estimate for Vg in the null model:
0.69833 0.0375916 0.065652
se(Vg):
0.0321777 0.0108328 0.0072974
REMLE estimate for Ve in the null model:
0.273633 -0.00751629 0.120799
se(Ve):
0.0184782 0.00813352 0.00677273
MLE estimate for Vg in the null model:
0.69775 0.0375614 0.0375614 0.0655758
se(Vg):
0.0321219 0.0108154 0.00728674
MLE estimate for Ve in the null model:
0.273107 -0.00751642 -0.00751642 0.120644
se(Ve):
0.018433 0.008118 0.0067622
estimate for B (d by c) in the null model (columns correspond to the covariates provided in the file):
0.0940317 -0.0878004 0.166091 -0.0150725 0.406611 -0.120677 0.0680489 -0.0198323
se(B):
0.0752651 0.0684905 0.0199194 0.0139538 0.0320747 0.0288643 0.00833621 0.00586676
Computation Time:
total computation time = 24.7901 min
computation time break down:
time on eigen-decomposition = 0.133633 min
time on calculating UtX = 1.10756 min
time on optimization = 18.718 min