Troubleshooting: MA-GBLUP

Published

February 25, 2026

For MA-GBLUP: SS=summary statistics; MetaPAs= using SS of PYT-S and AYT-S; MetaPAo= using SS of PYT-S and AYT-O; MetaAAs= using SS of AYT-O and AYT-S

BM

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("BM \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4,
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1682, 812, 7774)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	0.5164847	0.3200712	1682
AYT-S	0.2572273	0.3603862	812
PYT-S	0.3266281	0.4321116	7774
^a n= number of lines

GYD

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("GYD \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4,
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1679, 813, 7759)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	95958.31	113799.9	1679
AYT-S	95474.06	112204.1	813
PYT-S	115717.96	95800.4	7759
^a n= number of lines

MT

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("MT \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4, 
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1702, 851, 7822)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	40.14131	43.52643	1702
AYT-S	20.48739	44.97834	851
PYT-S	46.03035	33.59257	7822
^a n= number of lines

NDVI

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("NDVI \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4,
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1701, 849, 7819)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	0.0014004	0.0010380	1701
AYT-S	0.0024375	0.0009781	849
PYT-S	0.0008862	0.0017456	7819
^a n= number of lines

PH

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("PH \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4,
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1702, 851, 7822)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	0.9740531	1.1405037	1702
AYT-S	0.7472458	1.1458741	851
PYT-S	1.1832437	0.8984803	7822
^a n= number of lines

TKW

Code

par(mfrow= c(1, 3))
par(mar=c(5,7,6,0))
plot(Gh_AYT_O, mt_AYT_O, xlab = expression(hat(g)[AYT_O]), 
     ylab = expression(hat(g)[MetaPAs]), cex.lab= 1.5, col="orangered3")
plot(Gh_AYT_S, mt_AYT_S, xlab = expression(hat(g)[AYT_S]), 
     ylab = expression(hat(g)[MetaPAo]), cex.lab= 1.5, col="gold3")
plot(Gh_PYT_S, mt_PYT_S, xlab = expression(hat(g)[PYT_S]), 
     ylab = expression(hat(g)[MetaAAs]), cex.lab= 1.5, col="forestgreen")
mtext("TKW \n Within Estimated SNP effect vs Meta-SNP effect", side =3, line= -4,
      outer = TRUE)

Code

df_var <- data.frame(Within=c(GP_AYT_O, GP_AYT_S, GP_PYT_S), Meta = c(mt2_AYT_O,
                                                                      mt2_AYT_S, mt2_PYT_S),
                     n = c(1698, 850, 7800)) %>%
  `row.names<-`(c("AYT-O", "AYT-S", "PYT-S"))

kable(df_var,caption="Estimated genetic variance") %>% 
  kable_styling(latex_options = "scale_down")%>% 
  add_footnote(c("n= number of lines"))

Estimated genetic variance
	Within	Meta	n
AYT-O	11.90081	19.95556	1698
AYT-S	10.47922	19.36336	850
PYT-S	20.98716	11.42676	7800
^a n= number of lines