Breeding for Yield

PLS 664Spring 2007

Issues

• How do we build yield potential into a cross?

• How do we select for yield in the generations prior to yield testing?

• How do we select for yield in the first generations of yield testing?

Yield Potential - where does it come from?

• Good x good• Dandan’s data• tanksley

Yield Potential - how to breed for it in very early generations

• Evauating vigor

Microplots

• Hill plots• Honeycomb design• Single row plots

The first yield test - 1 location, 1 replication

• How meaningful can it be?

Test Plot Line Entry Yield (bu/a) Testwt (lb/bu)PT2HAL06 1001 McCormick 1 85.4 60.7PT2HAL06 1002 KY00C-2025-13 2 98.5 59.1PT2HAL06 1003 KY00C-2025-14 3 81.6 57.9PT2HAL06 1004 KY00C-2025-15 4 76.9 57.9PT2HAL06 1005 KY00C-2025-16 5 82.9 60.2PT2HAL06 1006 KY00C-2025-17 6 84.9 58.3PT2HAL06 1007 KY00C-2025-18 7 84.6 59.6PT2HAL06 1008 KY00C-2025-19 8 80.0 58.1PT2HAL06 1009 KY00C-2025-20 9 87.9 57.9PT2HAL06 1010 Cumberland 10 85.1 59.0PT2HAL06 1011 KY00C-2025-21 11 67.4 58.8PT2HAL06 1012 KY00C-2025-22 12 86.0 58.3PT2HAL06 1013 KY00C-2025-23 13 87.0 57.8PT2HAL06 1014 KY00C-2025-24 14 72.0 56.1PT2HAL06 1015 KY00C-2036-07 15 53.2 54.8PT2HAL06 1016 KY00C-2036-08 16 77.3 58.4PT2HAL06 1017 KY00C-2036-09 17 57.5 61.2PT2HAL06 1018 KY00C-2036-10 18 82.1 57.1PT2HAL06 1019 KY00C-2039-11 19 73.4 59.2PT2HAL06 1020 Truman 20 80.1 59.4PT2HAL06 1021 KY00C-2039-12 21 66.3 58.1PT2HAL06 1022 KY00C-2039-13 22 53.8 57.6PT2HAL06 1023 KY00C-2039-14 23 73.8 58.3PT2HAL06 1024 KY00C-2045-03 24 79.9 60.5PT2HAL06 1025 KY00C-2045-04 25 79.8 60.9PT2HAL06 1026 KY00C-2045-05 26 82.1 60.7

Preliminary Wheat TestLogan Co. 2006

Next Step

• Multiple Environments• Why?• G x E

Genotype x Environment Interaction

• Two sources– Differences in scale of genetic variance– Genotype rank changes

• Genotype rank changes are the real problem

Env. 1 Env. 2

V 1

V 2Yie

ld

Genotype Rank Change - The BaneOf The Plant Breeder’s Existence

Yield Potential vs. Yield Stability

1 2 3 4 5 6 7 8 9

Locations

Yie

ld

Yield Potential vs. Yield Stability

1 2 3 4 5 6 7 8 9

Locations

Yie

ld

Yield Potential vs. Yield Stability

1 2 3 4 5 6 7 8 9

Locations

Yie

ld

We want it all!

Heritability: Proportion of phenotypic varianceThat can be explained by genetic effects

€

σG2

σ P2 = h2

The most important function of heritability is its predictive role.

Traits with high heritability values can be improved with rapidity and with less intensive evaluation than traits with low heritability (Nyquist, 1991).

For example, we evaluate tens of thousands of F3:4, F4:5,

and F5:6 lines in short 4-foot rows annually. We are

successful in identifying lines with appropriate plant height and maturity in these unreplicated plots because of the high heritability of these two traits.

Selection for grain yield, on the other hand, a low heritability trait, begins with F5:7 lines, grown in 55

square-foot plots in replicated tests at several locations. This intensive evaluation continues over several years for each new set of F5:7 lines.

Definitions of Heritability Nyquist (1991) pointed out two contexts within which a trait is hereditary:

1) the context of the trait being determined by the genotype of the individuals being observed, This context gives rise to what we call Broad-Sense Heritability (H), which is that portion of the phenotypic variance that is genetic in origin,

=

σ

σG

P

2

2

σ

σ σ σG

G GE

2

2 2 2+ +=

It reflects the correspondence between phenotypic values and genotypic values. If the proportion is high, the genotype plays a large role in determining the observed phenotype; if the proportion is low, then the alternative is true

The second context gives rise to what we call Narrow-Sense Heritability (h2), which is that portion of the phenotypic variance that is due to variance in breeding values among the individuals in a population, or the ratio:

€

σA2

σ P2

.

σ

σ σ σA

G GE

2

2 2 2+ +=

In this equation refers to additive genetic varianceOr the variance of breeding values.

€

σA2

Using our own breeding program as an example, narrow-sense heritability for plant height and maturity must be high in our F3:4 wheat lines, because we find that the short, early

maturing lines we select will generally produce short, early maturing F4:5 progeny.

Narrow-sense heritability gets to the core of the predictive role of heritability. It expresses the degree to which the phenotype is determined by the alleles transmitted from parent to offspring--i.e., is what we see in this generation what we will get in the following generation? It gives a practical interpretation to heritability. It is an estimate of the phenotypic difference between lines which one expects to recover in the progeny.

We can estimate two forms of broad-sense heritability based on the data that this experiment has provided: 

1. Per-plot basis

2. Entry mean basis

Heritability on a Per-Plot Basis 

This experiment involved evaluation of genotypes in a two-rep experiment grown at two locations in two years- common scenario.

In order to have some common base-line of comparison, the heritability can be reported on a per-plot basis--the plot being the lowest unit of observation, or selection.

H (per-plot basis) =

σ

σ σ σ σ σG

G GL GY GLY

2

2 2 2 2 2+ + + +

50

50 20 32 50 100

50

2520198

+ + + += = .= (19.8%)

Heritability on an Entry Mean Basis

While the per-plot estimate of heritability described previously has value as a common baseline comparison, the phenotypic means that are available to us from the experiment analyzed in Table 10.2 were based on data averaged over eight plots (2 replications at 2 locations in each of 2 years).

Such data would be far more precise than single plot data not only because we have two replications at each location, but because σ2

GY, σ2GL and σ2

GLY were all

significant sources of variation in the analysis of variance.of variation also.

Having done the work, we can estimate the heritability of the trait based on the Entry Means over the eight values for each line. The formula is 

H (entry mean basis)

lyrlyyl

22GLY

2GY

2GL2

G

2G

σ+

σ+

σ+

σ+σ

σ=

100

50

8

100

4

50

2

32

2

2050

50=

++++

= 0.495 (or 49.5%).

=

Thus, having exposed our material to the vagaries of σ2GL,

σ2GY, σ2

GLY, and σ2, the phenotypic means we have

calculated are closer approximations to the true genotypic value than the single plot estimates.

As one scans over the yields of the lines based on entry mean data, it can be inferred that for every one unit difference in observed phenotypic means, 49.5% of the difference is due to differences at the DNA level. The remaining 50.5% is due to the blurring of the true genotypic value by environmental and experimental error effects.As we would expect, the performance of the progeny of a line selected on the basis of its entry mean would likely be a closer approximation to the eventual performance of the progeny than the performance of the progeny of a line selected on a per-plot basis.

Heritability on a Single Plant Basis

Single Plants Within a Plot or Grid Pattern

Selection on a single plant basis can be conducted on plants within plots of, say, S0:1 lines or half-sib families. Alternatively, one could

grow a heterogeneous population on a large area of the nursery and subdivide the area into grids or plots. The superior plant(s) within each grid would be selected without consideration of plants in other grids.

Variance among plots

VarianceWithinplots

Heritability on a single plant basis would be estimated as:

σ

σ σ σ σ σG

G GL GY GLY w

2

2 2 2 2 2+ + + +

For convenience we utilize the variances from the previous example. Heritability on a per plant basis is estimated as:

 

h2 =

= 0.12 (or 12%).

50

50 20 32 50 250+ + + +

σ

σ σ σ σ σ σG

G GL GY GLY w Bp

2

2 2 2 2 2 2+ + + + +

50

50 20 32 50 250 750105

+ + + + += .

Under reasonable management conditions, we would assume that plot-to-plot variation measured over a large number of plants per plot would be less than

plant-to-plant variation within plots (σ2w). Heritability

on a per-plant basis is estimated as:

Or 11 percent.