1. HCS825 Class Project
Identification of source of resistance to Cercospora zeae-maydis in maize using molecular markers
Godfrey Asea

2. Background
Gray leaf spot is an important foliar disease of maize worldwide
Yield losses in excess of 50% have been reported (Lipps, 1987)
Causal agent is Cercospora zeae-maydis which over winters on crop residue left in the field
Maize is unquestionably a very important part of life as we know it today

3. Control methods
Conventional tillage that buries crop residue
Fungicide application
Use of resistant hybrids in production

4. Symptoms of gray leaf spot

5. GLS predisposes maize to stalk rot pathogens

6. Approach
Development of resistant hybrid varieties by backcrossing resistant maize donors to elite germplasm
Linkage of molecular markers to disease resistance loci
144 (F2:3) progenies of Vo613Y x Pa405 were evaluated at Wooster and Cedera (replicated)
Disease score based on based on percentage leaf affected.

7. QTL analysis Model for combined analysis
Y = U+marker+rep(loc)+loc+gen(marker)+loc(marker)+e
F test = marker/gen(marker)

8. Codes data one; infile 'E:GLS.csv ' dlm= ',' firstobs=2;
input gen Rep loc plaa trplaa nc005 BNLG182 BNLG381 BNLG108 BNLG371 Phi073 Phi072 Phi085;
Proc sort;
by BNLG381;
Proc glm;
class gen rep loc BNLG381; model trplaa =BNLG381 rep(loc) loc gen(BNLG381) loc*BNLG381 / SS3;
random rep(loc) loc gen(BNLG381) loc*BNLG381/test;
proc mixed data=one covtest;
class gen rep loc BNLG381;
model trplaa =BNLG381 rep(loc) loc gen(BNLG381) loc*BNLG381/ddfm=satterth;
random BNLG381 rep(loc) loc gen(BNLG381) loc*BNLG381;
lsmeans BNLG381;

9. proc varcomp method=REML;
class gen rep loc BNLG381;
model trplaa =BNLG381 rep(loc) loc gen(BNLG381) loc*BNLG381;
title 'variance component';
quit;
Proc anova;
class gen rep loc Phi085;
model trplaa = gen Phi085 rep(loc) loc gen(Phi085);
test h= Phi085 e= gen(Phi085);
means Phi085/lsd lines;
title'analysis of Phi085';
run;

10. Comparison of QTL significance and effects in combined analysis
QTL ANOVA GLM Mixed
R2 P value R2 P value Vm/Vp P value
NC <
BNLG < <.0001
BNLG
BNLG
BNLG < <.0001
Phi < <.0001
Phi
Phi

11. Comparison of QTL at each location for specific adaptation
QTL Location Location Location Location 4
P value Vm/Vp P value Vm/Vp P value Vm/Vp P value Vm/Vp NC
BNLG
BNLG
BNLG
BNLG
Phi
Phi
Phi

12. Conclusion
For replicated data R2 is not an effective way of representing variation explained
Adjusting for the proper error terms makes P-values non-significant in Proc GLM
Amount of variation in Proc mixed was greatly reduced because of replication
The lack of variance explained suggests that the other effects are more important than the markers

13. Stuart Gordon Dr. D. Francis
Acknowledgement
Stuart Gordon
Dr. D. Francis