Genetic Correlations

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Genetic Correlations
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Genetic Improvement
Breeding efforts to improve a trait are
real, what happens if an unselected change
results in another trait ?
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Correlated Responses
With selection for a phenotype (trait) there are
changes that result in unselected traits. How do
you determine if the changes are related ?
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How do you determine if there is an association
in the response ?
Determine the statistical relationship between
the two traits, correlation coeficient (Pierce
, p 654)
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Correlation (r) Values, range
-1 to 1
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Zero Correlation
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Positive Correlation
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Negative Correlation
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P G x E PXY GXY EXY
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A genetic correlation exists when genes
influence one trait also influence another
trait.
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Examples of Genotypic (rG) and Phenotypic (rP)
Correlations rG rP Pigs
Growth rate food conversion ratio -.80
-.75 Growth rate food intake .40
.45 Growth rate backfat thickness
-.25 -.10 Feed conversion ratio
backfat thick. .30 .20 Domestic chickens
Egg number age at first egg - .35
- .25 Egg number body weight
- .20 .00 Egg number egg
weight - .30 - .05
Age at first egg body weight .10
.00 Age at first egg egg
weight .15 .10 Body weight
egg weight .40 .33


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Examples of genetic correlations and phenotypic
correlations for traits in farm animals
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Are all correlation coefficients (genetic and
phenotypic) real ?
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Real or Significant Correlation
Coefficient Calculated r must gt Table value
with a specified number of df Example Calc. r
.37 with n17 observation, df15 Tabular r _at_
P.05 level .48 Calculated r is not
significant Pgt.05)
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Degrees of Freedom (df) associated with a
correlation coefficient n-2 n number of
observations, note in chi square, df n-1
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Having established that a correlation coefficient
is significant............. What is the
importance of the size of the correlation ?
or is it useful?
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Genoty/Phenoty
Beef cattle BW WW BWG FW YW CMA Birth wt
BW .58 .38 .64 ? Weaning wt
WW .39 .98 .79 .67 ? Birth-wean
gain BWG .23 .97 .73 Final feedlot wt FW
.42 .65 .63 Yearling wt (pasture)
YW .64 Cow maternal ability CMA
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• Its Application in Selection
• Correlation indicates how changes in 2 traits
  will occur - predictive

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• Its Application in Selection
• Allows for the planning of countermeasures

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Its Application in Selection

• Use to select for a hard / difficult trait to
  measure by selecting for its correlate, indirect
  selection. (improving feed efficiency)

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Example of a Correlations Application Merino
Sheep (Reproduction, wool, meat)

• Correlation parasite resistance (Haemonchus
  contortus) reproduction rate
• rG in 2 flocks -. 22 . 64
• L1-reprod. not affected with selection for
  increased parasite resistance
• L2-bred for Hc susceptibility had reduced
  fertility.
• Accuracy of estimates is important when applied
  in breeding

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• Thus far, discussed how to improve a single
  quantitative trait in an animal....
• know the phenotypic trait of interest
• h 2 of the trait
• population size known, SD planned, NE is
  appropriate, aware of rGs
• How often do we select for a single
  phenotypic trait in a population ?

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Typical situation ........ improvement for
multiple traits simultaneously within an
animal Examples ? - nega. gen. correlation
Butterfat and milk Yield in dairy cattle -- cant
have a breed that has the vol. of a Holstein and
the fat of Jersey
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• Three Practical Breeding Methods for Selecting
  for More Than One Trait
• Tandem Selection
• - not recommended too costly in time
• gains decay without contin. sel. press.
• Independent Culling
• - loose superior animals requires large pop. to
  select from uncompromising

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• Three Practical Methods......
• Selection Index
• - Most valued and efficient approach, allows for
  partial weakness in the breeders is used for
  several traits simultaneously.
• I b1X1 b2X2 ............bnXn
• where, Iindex value X trait, and n
  adjustment factor for the trait

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Comparison of Methods for Improving Selection
Response Selection Index gt Independent Culling
gt Tandem Selection
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Reference Text for Procedures Used to Calculate
Genetic Parameters, rG, h 2, ..... Manual of
Quantitative Genetics by Walter A Becker, 5th
ed. 1992
Deceased