Planning rice breeding programs for impact

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Planning rice breeding programs
for impact

• Correlated response to selection

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Introduction
Question Why are breeders concerned with genetic
correlations?

• undesired changes in traits that are important
  but that are not under direct selection
• May be more effective to conduct indirect
  selection for a low-H trait by selecting for a
  correlated high-H trait
• Selection in SE for performance in TPE is a form
  of indirect selection. Response in the TPE to
  selection in the SE is a correlated response

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Learning objectives

• Genetic and env. correlations will be defined for
  traits measured on the same plot, and an
  estimation method presented
• Genetic and environmental correlations will be
  defined for traits measured in different
  environments, and an estimation method presented
• Models for predicting correlated response to
  selection will be presented
• ?Examples of use of correlated response methods
  to answer practical breeding questions

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Basic statistics

• The product-moment correlation
• For 2 variables, A and B, the product-moment
  correlation is
• r sAB/( sA sB) 9.1
•  
• The variance of a sum
•  If Y A B, then
• s2Y s2A s2B 2 sAB 9.2

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Genetic covariances and correlations for traits
measured on the same plot

• For 2 traits, A and B, measured on the same plot
• YA mA GA eA
• YB mB GB eB
•   
• sG(AB)
• rG(AB)
• v (s2G(A) s2G(B) )

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Genetic covariances and correlations for traits
measured on the same plot

• For 2 traits, A and B, measured on the same plot
• YA mA GA eA
• YB mB GB eB
•   
• se(AB)
• re(AB)
• v (s2e(A) s2e(B) )

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Phenotypic correlation (correlation of line
means)
sP(AB) rPAB v (s2P(A) s2P(B)
)   sG(AB) sE(AB)/r v
(s2G(A) s2E(A)/r ) v(s2G(B) s2E(B)/r )
As r increases, the phenotypic correlation
approaches the genotypic correlation!
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1. Estimating rG for traits measured on the same
plot
Remember s2Gy s2GA s2GB 2 sGAB 9.2
Therefore, sG(AB) s2GY (s2GA s2GB
)/2 9.5
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Estimating rG for traits measured on the same
plot

• Method
• Add measurements A and B for each plot, to make a
  new combined variable a new name (say Y). Poss.
  with Excel
• Perform ANOVA on the new combined variable, then
  estimate the genetic variance component using the
  method described in Unit 8
• Use Equation 9.5
• sG(AB) s2GY (s2GA s2GB )/2

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Example Calculating rG for GY HI in 40 lines
For each plot, add HI to GY ? Call new variable
GYHI
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Example Calculating rG for GY HI in 40 lines
Do ANOVA, then calculate variance components
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2. Estimating rG for traits measured in
different environments
Not correlated
YA mA GA eA YB mB GB eB
Correlated
Therefore, rP across environments has no
environmental covariance covP covG
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When means for same trait are estimated in
different trials

• ? the phenotypic covariance is due to genetic
  causes only

   sG(AB) rP(AB) v (s2P(A) s2P(B)
)
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Estimating rG for traits measured in different
environments
SO
rG rP /v( HA x HB) 9.6
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Example Calculating rG for short-season and
long-season sites in the eastern Indian shuttle
network OYT

rP 0.36 Hshort 0.51 Hlong 0.65 rG
0.36/(0.510.65).5
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Question Why do we want to predict correlated
response?

• To find out if we could make more gains by
  selecting for a correlated trait with higher H
• To find out if selection done in our SE will
  result in gains in target environment

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Predicting correlated response
For 2 traits, A and B, OR for same trait in 2
environments, A and B Correlated response (CR)
in A to selection for B is CRA k rG v HB
sG(A) Where k is selection intensity in
phenotypic standard deviation units

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Any questions or comments?
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Summary

• rP is corr. of line means for different traits,
  or for same trait in different environments
• rG is corr. of genotypic effects free from
  confounding with the effect of plots or pots
• 2 main kinds of genetic correlation (corr. for 1
  trait in 2 envs versus corr. for 2 traits in 1
  env.) have to be estimated differently

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Summary

• Main reason to estimate rG is to predict
  correlated response
• rG in combination with H, can be used to evaluate
  different selection strategies by predicting CR
  in the TPE to selection in different SE