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

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

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

Symptoms of gray leaf spot

GLS predisposes maize to stalk rot pathogens

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.

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

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;

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;

Comparison of QTL significance and effects in combined analysis QTL ANOVA GLM Mixed R2 P value R2 P value Vm/Vp P value   NC005 0.61 <.0001 0.63 0.4592 0 0.0049 BNLG182 0.63 <.0001 0.62 0.1470 0 <.0001 BNLG381 0.63 0.0006 0.65 0.6094 0 0.0004 BNLG108 0.61 0.0137 0.61 0.7713 0 0.0389 BNLG371 0.62 <.0001 0.63 0.3937 0 .0056 <.0001 Phi073 0.62 <.0001 0.63 0.1998 0.0080 <.0001 Phi072 0.62 0.4369 0.62 0.8374 0 0.4434 Phi085 0.61 0.4132 0.61 0.8811 0 0.1727

Comparison of QTL at each location for specific adaptation QTL Location 1 Location 2 Location 3 Location 4 P value Vm/Vp P value Vm/Vp P value Vm/Vp P value Vm/Vp NC005 0.8558 0.00 0.9750 0.00 0.4990 0.00 0.0911 0.095 BNLG182 0.0977 0.00 0.1896 0.00 0.3757 0.00 0.2895 0.00 BNLG381 0.5807 0.00 0.8367 0.00 0.2532 0.03 0.4620 0.00 BNLG108 0.8845 0.00 0.9078 0.00 0.7423 0.00 0.4504 0.00 BNLG371 0.8834 0.00 0.3356 0.00 0.6294 0.00 0.2338 0.04 Phi073 0.2290 0.026 0.4263 0.00 0.5096 0.00 0.5110 0.00 Phi072 0.6079 0.00 0.2214 0.005 0.7698 0.00 0.8524 0.00 Phi085 0.7630 0.00 0.9906 0.00 0.8921 0.00 0.1600 0.046

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

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