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Maximizing genetic gain II

Walter R. Fehr and Walter P. Suza

Readings:

Introduction

The emphasis of the lesson is on the role of genotype x environment interaction on the effectiveness of selection for quantitative traits. The importance of this interaction is highly dependent on the trait under selection. For example, the relative difference among genotypes for the number of days from planting to maturity is much more consistent among environments than the differences among genotypes for seed or forage yield. As a result, the genotype x environment component in the genetic gain equation discussed in the previous chapter is smaller for days to maturity than for yield. Therefore, a breeder can use fewer environments to obtain reliable values for the maturity of a genotype than are necessary to determine the genetic potential of a genotype for yield.

From the farmers’ perspective, performance data in one season is used to select cultivars for planting the next season. When a trait has a low genotype x environment interaction, the relative differences among cultivars in one season will be similar in the coming season. For example, a cultivar that matures 10 days earlier than another in one season would be expected to mature earlier in the coming season, although not necessarily by 10 days. For yield, one cultivar that is higher yielding than a second cultivar in one season may be lower yielding than the second cultivar in the next season.

In conducting research to determine the importance of genotype x environment interaction in a breeding program, a significant interaction may be found in an analysis of variance. Whether or not the interaction is of importance to the breeder depends on which of the types (illustrated in chapter 18 of Principles of Cultivar Development) are involved. The most difficult interaction to deal with is one that results when the best genotypes in one environment perform less well than others in another environment. When this type of interaction occurs, the breeder generally uses less stringent selection in one environment when deciding which genotypes to advance to the next season of testing.

The breeder ultimately relies on test results from multiple locations and years for making the decision on whether a genotype merits release as a clonal or pure-line cultivar or as a component of synthetic or hybrid cultivar. The purpose of Applied Learning Activity 2 is to illustrate the challenge that genotype x environment interaction presents to the breeder for making selections.

 

Applied Learning Activity 2

The following data are from the M.S. thesis of Raechel Baumgartner at Iowa State University in which she evaluated the total tocopherol (Vitamin E) content in the oil of soybean lines. There were 20 lines with mid-oleate content of about 50% grown in a randomized complete-block design with two replications at each of three Iowa locations. The goal of the breeding program is to develop cultivars with high Vitamin E content.

The genotype x environment interaction was significant for population 2, but not for populations 1. Provide answers for each of the following questions and an explanation for each answer.

  1. What are two primary causes or types of genotype x environment interaction? How does each type affected selection of lines by a breeder?
  2. Which of the two types of interactions are responsible for the significant genotype x environment interaction for mid-oleate lines in population 2? It is possible to both types of interactions to be involved in a significant genotype x environment interaction.
  3. Based on the phenotypic correlations among the mean values for the individual lines, do you think that genotype x environment interaction would likely make it more or less difficult for a breeder to select the best lines in population1 than in population 2, even though the analyses of variance for population 1indicated that the interaction was not statistically significant?
  4. Which lines, if any, would you be comfortable selecting in each population based on one environment of data? Give the designation of the lines for each population that you would be comfortable selecting. Keep in mind that any genotype you advance for additional testing will utilize your financial resources that always are limiting in a breeding program.
  5. To minimize the impact of genotype x environment interaction on genetic gain in a breeding program, would it be more important to emphasize the number of environments used to evaluate lines or the number of replications at each environment? Use the genetic gain equation to defend your answer.
  6. How would the heritability of a trait relate to the amount of testing required to determine the genetic potential of an individual for that traits? Compare a trait of your choice that has a relatively low heritability and another that has a relatively high heritability. Use a real example from the literature or your own experience, not a hypothetical example.

 

Table 1. Phenotypic correlation coefficients for total tocopherol content among locations for mid-oleate lines from three populations.
Population
Location Carlisle Rippey
1 Ames 0.20ns† 0.41ns
1 Carlisle

0.25ns
2 Ames 0.86** 0.85**
2 Carlisle

0.94**
  • * significant at the 0.05 probability level
  • ** significant at the 0.01 probability level
  • † ns = not significant at the 0.05 probability level
Table 2. Means and ranks for total tocopherol content of 20 mid-oleate lines for Population 1 at three Iowa locations.
Entry Mean (Ames), mg kg-1 Rank (Ames) Mean (Carlisle), mg kg-1 Rank (Carlisle) Mean (Rippey), mg kg-1 Rank (Rippey) Overall Mean Rank
418001 1646 20 1799 14 1820 12 1755 17
418002 1786 16 1924 8 1648 17 1786 16
418003 1824 13 1474 20 1111 20 1469 20
418004 2078 1 2010 3 1737 14 1942 8
418005 1677 19 1945 6 2019 4 1880 9
418007 2041 4 1952 4 1993 8 1995 3
418008 1843 12 1877 10 1789 13 1836 12
418010 1925 11 1805 12 1633 18 1787 15
418011 1949 9 1950 5 1980 9 1960 6
418012 1798 14 1788 15 1927 10 1837 11
418014 1948 10 1926 7 1652 16 1842 10
418016 1681 18 1640 17 1702 15 1674 18
418017 1949 8 2012 2 2044 2 2002 2
418018 2035 5 1820 11 2008 6 1954 7
418019 1978 7 1485 19 1993 7 1819 14
418020 1727 17 1726 16 1489 19 1647 19
418022 2046 3 1533 18 2324 1 1968 5
418024 1995 6 1909 9 2032 3 1979 4
418026 1788 15 1803 13 1865 11 1819 13
418027 2053 2 2017 1 2008 5 2026 1
Table 2 Appendix
Mean, Ames Mean, Carlisle Mean (Rippey) Overall Mean
LSD 0.05 220 777 642 340
LSD 0.01 295 1040 859 451
Table 3. Means and ranks for total tocopherol content of 20 mid-oleate lines for Population 2 at three Iowa locations.
Entry Mean (Ames), mg kg-1 Rank (Ames) Mean (Carlisle), mg kg-1 Rank (Carlisle) Mean (Rippey), mg kg-1 Rank (Rippey) Overall Mean Rank
419002 1885 10 1800 10 1992 7 1892 9
419006 1730 19 1621 18 1734 17 1695 19
419007 1917 8 1816 9 2002 6 1912 8
419008 1809 16 1631 17 1689 19 1710 18
419009 1987 4 1898 3 2108 1 1998 3
419010 2006 3 1886 5 2071 4 1988 4
419011 2030 2 1957 1 2075 3 2021 2
419012 1953 7 1865 7 2017 5 1945 5
419013 1862 13 1597 19 1729 18 1729 17
419014 1844 14 1737 13 1857 13 1813 13
419015 1711 20 1580 20 1665 20 1652 20
419017 1954 6 1891 4 1972 9 1939 7
419018 1875 11 1671 16 1795 16 1780 15
419020 2135 1 1924 2 2091 2 2050 1
419021 1785 18 1701 14 1842 15 1776 16
419022 1901 9 1745 11 1943 10 1863 10
419023 1798 17 1686 15 1896 12 1793 14
419025 1979 5 1876 6 1977 8 1944 6
419026 1838 15 1841 8 1908 11 1862 11
419027 1863 12 1738 12 1851 14 1817 12
Table 3. Appendix
Mean, Ames Mean, Carlisle Mean (Rippey) Overall Mean
LSD 0.05 116 88 69 73
LSD 0.01 155 118 93 98

 


References

Fehr, W. R. (ed). 1987. Principles of Cultivar Development. Vol 1. Theory and Technique. McGraw-Hill, Inc., New York.

 

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