1. Review and Complete QTL analysis from Monday (lecture 15).

2. “Guidelines” for breeding – Take home Messages
Focus on the important traits
Learn how to measure the traits
Understand the genetic basis of the phenotype
Design a selection strategy based on the heritability of the trait
Monitor progress

3. Focus on the important traits
Define the important traits
Survey
Talk to growers and end users
Experience

4. Learn how to measure the traits
Repeatability
Maximize ability to measure genetic component
Efficient use of resources

5. Understand the genetics of the trait
Heritability
Number of genes and map position
(of secondary importance)

6. Design a selection strategy based on the heritability of the trait
High heritability
Large populations
Single plant selection
Late generation
Low heritability
Early generation selection
Replication
Family mean basis

7. Monitor progress
Are you improving your populations for the important traits?
Does your selection criteria match reality?

8. An example: overview of breeding strategy for color and lycopene in tomatoes for processing
Strategy to measure VG for color
Selection strategy for color and lycopene
Genetic basis for color
Efficiency of selection
Monitor Progress

9. Objective measure of color

11. Use the results from regional trials to learn about the repeatability of trait measurement and the heritability of the trait.
OH, IN, MI, PENN, NJ

13. What is the biological interpretation of the estimates from the variance partition ?
Genotype (5%-29%)
Between fruit within plot (2%-15%)
Within fruit (31%)

14. Power test to design sampling strategy
Formula for confidence interval about a sample mean:
I = 2 * t * /n1/2
Variance of a genotype's mean is:
2G = 2gy/Y + 2gby/BY + 2f/FBY + 2e/SFBY
(I/2)2 = t2*2gy/Y+z2*2gby/BY+z2*2f/FBY+z2*2e/FSBY
Solve for term (no. of fruit in this e.g.)
F=[(z2/SBY)*(2e + S2fgby)] / [(I/2)2-(z2*2gby/BY-t2* 2gy/Y)]
S=samples per fruit; F = fruit number; B = plot; Y= year

16. So, what’s the point?
We can be (and need to be) precise when measuring quantitative traits
We can rationally design experiments to measure quantitative traits

17. What more can we learn about the genetic basis of fruit color?
An application for genetic mapping
Work within populations that favor breeding progress
AB and IBC populations
Populations of varieties
The “variety” approach is based on assumptions
the population is in linkage equilibrium
there are few alleles at each locus.
Results must be verified in segregating populations
the breeding population is small (n=36-72)
Only 25% of “linkages” were verified in study of oat quality traits

18. The inheritance of color in AB and IBC populations and populations of varieties raises issues that should be accounted for in our models.
Issues:
unbalanced data
sample size
Df for F test {Mj/Gk(Mj)}
fixed and Random effects

19. Some issues can only be satisfactorily resolved with confirming generations. We may also learn more about the trait in subsequent crosses. (IBC2349 x Ohio 8245)  F2 F3 F4
8245
2349

20. Evidence for positive interaction between loci

21. The interaction (epistasis) between genes from cultivated (chrom 4, 6, & 11) and wild species (chrom 7) of tomato produce fruit with better color and higher lycopene content. There is no single gene “fix”.

22. Proportion of Phenotypic variation explained by markers associated with color

23. Phenotypic variance explained by genotype and by marker
Trait Approach %Vp
Hue Genotype
L Genotype
Hue Markers
L Markers

24. Conclusions from color measurement:
Sample size and replication are important for repeatability
Color is a low to moderately heritable trait
Selection strategy must therefore reflect need for replication of families
Markers may help us improve efficiency of selection
ogc & loci on chromosome 4, 7, 11
epistasis

25. The next step:
Use marker data to help select parents
for hybrids
Select for epistatic interactions
Compare to trait-based selection
gain under selection
efficiency
time
cost

26. Tomato Sauces, Rich in Lycopene, May Reduce Risk of Prostate Cancer
Diets rich in tomato sauces appear to reduce the risk of prostate cancer, according to a Harvard Medical School study. The most likely reason appears to be tomatoes' high content of lycopene, a red carotenoid related to beta-carotene….
New Products based on lycopene?

27. Variance components for (one location, two years)
Comparing the efficiency of selection for correlated traits: objective measurements of color and lycopene.
Variance components for (one location, two years)

28. Lycopene content vs L value for ripe fruit

29. Relative efficiency of selection can be expressed as follows
= r(gen){Hcolor/Hlycopene}
Hcolor from variance components
Hlycopene from variance components
r(gen) Genetic covariance

30. Relative efficiency of selection for correlated traits

31. Generalizations for selction strategy
Heritability Selection Intensity (k)
High > 0.5 Early generation, Single plant Stringent
Moderate 0.2 to 0.5
Low < 0.2 Replicated family performance, Relaxed
multiple generations stringency

32. “realized heritability”
Stingency of selection implies numbers as a percent of the population and can be expressed in standard deviation units for normally distributed data.
G = (k)( p)(h2)
Where G is gain under selection, H is heritability,  p is the phenotypic variance of the population, and K is the selection index in standard deviation units
The equation can be
rearranged:
(h2) = G / (k)( p)
“realized heritability”

33. The index k takes into account the mean phenotypic value of the selected families or lines, the mean phenotypic value for the population, the standard deviation of the population (p) 2 , and the stringency of selection q/n (q is the number of selected lines and n is the population size). Expressing k in standard deviation units (based on the normal curve) means that k varies only with selection intensity. A selection coefficient can also be defined for marker or trait.

34. Selection Index (from Allard)
  Percent Directional
k of population Selection
% 0.5%
% 1%
% 2.5%
% 5%
% 10%
% 15%

35. Breeding Strategies (both traditional and MAS) should be designed using all available information
 Heritability
Stringency of selection
Gain under selection
Coefficient of relatedness for the population
CI/2 for the population
Number of alleles in a population
Variation explained by QTL or marker

36. Are we making progress? O9242 13.38 A ogc/ogc C2/C2 O9241 13.03 A
H AB
ogc/ogc
O AB
C2/C2
PS B
wt/wt
OX B
O B

37. How does our evaluation strategy compare to processor grades?
Data for 8 varieties, 2 years, 552 loads

38. Monitoring Progress:
Common checks over locations and years to control for environmental differences.
Populations that change year to year.
Commitment to presenting data in an objective manner that facilitates evaluation.
Periodic re-evaluation of selection strategy for major and minor traits.

39.  Populations that change year to year
 1 (XYABCDEFGH)
  2 (XYAGHIJKLMN)
  3 (XYGHLNOPQRS)

42. “Guidelines” for breeding – Take home Messages
Focus on the important traits
Learn how to measure the traits
Understand the genetic basis of the phenotype
Design a selection strategy based on the heritability of the trait
Monitor progress

43. Recommendations Define goals
Which traits are most important?
What trait values do you want to see in a variety?
Set performance targets
Be realistic!
 Determine optimal strategy for important traits
Find out what is known
Use data as a guide
Avoid fooling yourself
Evaluate progress

44. Recommendations (re-stated)
Identify and prioritize the traits that a new variety should have.
Set quantitative goals for the important traits and define the probability of success you wish to accept.
Adjust the selection criteria and population sizes to fit the high priority traits and quantitative goals.
Set a reasonable time frame for achieving goals.