Advanced Plant Breeding CSS 650 - Download as DOC by 907R84


									Advanced Plant Breeding CSS 650                               Name
Take-Home Final Exam, Fall 2011
Due 9:00 am on Friday, December 9, 2011

Part 1 – Recurrent Selection in Radish

        You (and your classmates) have been hired as consultants for a local seed company that
specializes in production of organic vegetable seeds. They would like you to design a population
improvement scheme that they could use to improve a genetically diverse radish population.
Radish seed is an important cash crop in the Willamette Valley, so one of your objectives is to
ensure that the population is well-suited for seed production in the area. The population must
also have desired characteristics for production of organic radishes for the fresh market in the
Willamette Valley, as well as for home gardens in other regions of the US.
        The seed company has the capability to implement the breeding plan using three testing
environments in the Willamette Valley. All of the sites can be irrigated. Growth chambers and
greenhouse facilities can be utilized if needed. A plant pathologist is employed by the company
and is available for assistance on this project. The company is interested in improving the
population on a continuing basis. They understand the need to maintain genetic diversity in the
population and do not expect that each cycle of selection should be suitable for immediate
release as a commercial variety. However, they would like to be able to extract good varieties
from the population in a reasonable time frame, using procedures described in the article
published by the Organic Seed Alliance (2007) entitled “Principles and Practices of Organic
Radish Seed Production in the Pacific Northwest”.
        Additional resources available to you include the lecture presentation on Recurrent
Selection (Module 10), your textbook (Chapter 10), and Chapter 6 from Hallauer and Miranda
(1988). You are welcome to consult other references, but this is not intended as a research
project. If there is information that you think you need that is not in the references provided, you
can make a reasonable assumption and indicate that in your write-up. If any of you have access
to additional information about radish that you think would be relevant for this exercise, please
share that with your classmates on the discussion board in Blackboard. Each of you must submit
your own breeding plan, but you are welcome to exchange ideas with your classmates.

Based on the Terms of Reference for your consultancy, you must respond to the following

1) What intrapopulation selection schemes can be considered for this radish population? Explain
your reasons for excluding other types of recurrent selection systems.

2) Provide an outline of the selection scheme that you will recommend. Indicate how each stage
of selection will fit into prevailing environments in the Willamette Valley, and the traits that will
be selected at each stage of the process. Include approximate numbers of families or individuals
that will be evaluated and selected at each stage.

3) Justify your choice of selection schemes (this should be a fairly comprehensive discussion –
refer to your lecture notes for ideas and examples). Be sure to include a discussion about the
expected gain from selection for your scheme compared to other potential selection strategies.

Advanced Plant Breeding CSS 650                                Name
Take-Home Final Exam, Fall 2011
Due 9:00 am on Friday, December 9, 2011

Part 2 – Selection for multiple traits in meadowfoam

A breeder wants to improve meadowfoam as an oilseed crop in Oregon. In 2011 she evaluated
270 half-sib families from a breeding population in a yield trial at a single location using a lattice
design with two complete replications (blocks). For the purposes of this exercise, data for seed
weight/plot (seedwt in g), thousand seed weight (TSW in g), and oil content (percent by weight
at ~10% moisture) will be analyzed for a subset of 159 families, ignoring the incomplete
blocking structure in the experiment.

1) To assist in developing a selection index, we will first calculate the genetic variance and
covariance matrix for the three traits (on a family mean basis). Use the following SAS code to
generate univariate analyses for all traits as well as an analysis of covariance among traits. You
will first need to load the datafile ‘mf’ into SAS (the data is in FinalPart2_data.xls).

       proc glm data=mf;
       class rep entry;
       model seedwt TSW oil=rep entry;
       manova h=entry/printh printe;
       random rep entry/test;

2) You will use the ‘E = Error SSCP Matrix’ and ‘H = Type III SSCP Matrix for Entry’ to do
your calculations.
     Copy the results from your output into Excel.
     Divide each matrix by the appropriate degrees of freedom to obtain the corresponding
       matrices for Mean Squares and Mean Cross Products (you should have one matrix for
       Error and another for Entries).
     Generate a new matrix of genetic variances and covariances for these three traits, using
       the same approach that you used in HW4 to estimate the Genetic Variance among
     Attach a hard copy of your Excel spreadsheet to your exam (you do not need to submit
       your SAS output).

3) Use the genetic variance-covariance matrix to calculate genetic correlations among the three
traits. Note that you could use the same process to obtain environmental correlations (from the
Error MCP matrix) and phenotypic correlations (from the MCP matrix for Entries), but you are
not required to do this. The environmental correlations should have been provided in your
computer output (Partial Correlation Coefficients from the Error SSCP Matrix). Are the
environmental correlations and genetic correlations similar in magnitude and direction?

4) Calculation of genetic correlations using MANOVA will give the same results as a mixed
model analysis when the data are balanced. To illustrate the use of mixed models, run the
following SAS code to obtain the genetic correlation between TSW and oil. You will first need
to input the dataset ‘correl’ into SAS (also in FinalPart2_data.xls). This is the same data that you
used for the MANOVA, but the format is different. How do the data sets differ?

You will need to include a statement in your data step to remove the variable ‘seedwt’ from the
analysis, because we only wish to obtain a correlation for TSW and oil.

if trait='seedwt' then delete;

Once the data have been input into SAS, run the SAS program (adapted from the article by
Piepho and Möhring, 2011, Crop Sci. 51:1-6):

proc mixed data=correl;
class rep entry plot trait;
model Y=trait trait*rep;
random trait /subject=entry type=unr;
repeated trait / subject=plot type=unr;

The analysis is computer intensive, and will take a few minutes to run. A similar analysis could
be done for seedwt and TSW, and seedwt and oil content (but you don’t need to run these
programs). The scale is quite different for seedwt than for the other traits, so I found that I
needed to transform seedwt ( I divided by 10) in order to obtain a solution. The correlations
among traits were not affected by the transformation.

Paste the covariance estimates from SAS below. Explain the meaning of the variance and
correlations that you obtained.

5) Use the output that you have already obtained to calculate heritability for TSW and oil content
(there are several ways to do this - refer to HW4 if necessary).

6) If the breeder selects the best 10% of the families for oil content, what would be the expected
response to selection after those families are intermated?

7) If the breeder selects the highest 10% of the families for TSW, what change in oil content
would be expected in the next generation?

8) Meadowfoam growers are paid for seed produced by the pound, but processors value
meadowfoam with high oil content. The breeder decides to give equal weight to these two traits
in a selection index. She includes TSW in the index because she knows it is correlated with seed
yield and oil content.

Show how you would set up the matrices for the selection index, using economic weights of +1
for seedwt and oil content, and 0 for TSW.

Using either Excel or R, solve for the values of the coefficients. Refer to the updated handout on
regression using matrices for examples of both methods.

9) For entry 11, average seedwt = 304.5 g, TSW = 10.09 g, and oil content = 26.785%. What
would the index value be for this entry? How would you use this information in your selection?


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