SWEET CORN BREEDING FOR FLORIDA'S FRESH MARKET

By

UTSAV KUMAR

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2019

© 2019 Utsav Kumar

To my family for constantly supporting me throughout my program

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ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Marcio Resende for his invaluable guidance in

conceptualizing the research problem, planning and setting out the experiment and helping me

continuously monitor and improve the manuscript. I would also like to thank the other members

of my committee Dr. German Sandoya, Dr. Esteban Rios and Dr. Curt Hannah for their support

and assistance throughout this program.

I want to express my gratitude to the staff of the Plant Science Research and Education

Unit, Citra and Everglades Research, and Education Center, Belle Glade for their kind support in

providing every facility required for my field experiment.

I want to further thank our lab manager Dr. Kristen Leach for helping in the field and

laboratory. I’m grateful to her for always being ready to help and advice with any questions both

on and off the topic of my thesis. I also want to thank our biochemist Dr. Susan Boehlein for

helping and showing patience in teaching me biochemistry as a part of my second objective. I

would also like to thank Dr. Mark Settles (UF) for letting me use single kernel NIR for the sugar

analysis experiment.

I also want to thank other lab members Juan M. Gonzalez, Ying Hu, and Christina

Finegan for helping with planting, phenotyping, suggestions on my project, presentations, etc. I

give special thanks to Juan for his wonderful friendship inside and outside of the lab and for

helpful advice on every topic.

Finally, I'm indebted to my family members for supporting me throughout my master's

program.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF FIGURES .........................................................................................................................8

LIST OF ABBREVIATIONS ..........................................................................................................9

ABSTRACT ...................................................................................................................................10

CHAPTER

1 GENERAL INTRODUCTION ..............................................................................................12

Sweet Corn vs Other Corn ......................................................................................................12 Su1 (sugary 1) ..................................................................................................................13 Se (sugar enhancer) .........................................................................................................13

Sh2 (shrunken 2) ..............................................................................................................14 Double Mutants (Synergistic) ..........................................................................................14

Economic Value ......................................................................................................................16 Sweet Corn Production in Florida ..........................................................................................17 Fresh Market Requirements ....................................................................................................19

Breeding Methods ...................................................................................................................20 Pedigree Breeding ...........................................................................................................21

Backcross Breeding .........................................................................................................22

Recurrent Selection .........................................................................................................22

Bulk Method ....................................................................................................................23 Phenotypic Mass Selection ..............................................................................................23

Selection Based on Breeding Values ...............................................................................24 Objectives ...............................................................................................................................24

2 ESTIMATION OF GENETIC PARAMETERS IN SWEET CORN .....................................25

Material and Methods .............................................................................................................28 Plant Material ..................................................................................................................28

Field Experiment .............................................................................................................28 Data Collection ................................................................................................................30

Data Analysis ...................................................................................................................30 G x E interaction (Type B genetic correlation) ........................................................32 Type A correlation ...................................................................................................32 Trait genetic correlation ...........................................................................................33 Estimated genetic gain .............................................................................................33

Results and Discussion ...........................................................................................................33 Heritability .......................................................................................................................34 G x E Interaction .............................................................................................................37

Trait Genetic Correlation .................................................................................................39

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Estimated Genetic Gain ...................................................................................................40 Conclusion ..............................................................................................................................45

3 ANALYSIS OF SUGAR CONTENT USING NEAR-INFRARED SPECTROSCOPY

ON A FRESH KERNEL OF SWEET CORN ........................................................................47

Introduction .............................................................................................................................47 Material and Methods .............................................................................................................50

Extraction of Soluble Sugars ...........................................................................................51 Near-infrared Spectroscopy (NIRS) ................................................................................53

Result and Discussion .............................................................................................................55 Enzymatic Quantification Using Megazyme Assay Kit ..................................................55 Enzymatic Method vs. NIRS ...........................................................................................57

Conclusion ..............................................................................................................................59

4 SUMMARY AND CONCLUSION .......................................................................................61

APPENDIX: SUPPLEMENTARY MATERIAL .........................................................................64

LIST OF REFERENCES ...............................................................................................................67

BIOGRAPHICAL SKETCH .........................................................................................................73

7

LIST OF TABLES

Table page

1-1 Commercially used genes in sweet corn breeding programs.. ...........................................15

2-1 Selected traits and their measurements ..............................................................................30

2-2 ANOVA of RCBD for six marketable traits in sweet corn in Belle Glade .......................34

2-3 ANOVA of RCBD for six marketable traits in sweet corn in Citra ..................................34

2-4 Estimates of narrow-sense heritability (h2) and type B & type A GxE interaction

from two different environments .......................................................................................39

2-5 Genetic correlation coefficients among traits measured in Belle Glade (South

Florida) ...............................................................................................................................41

2-6 Genetic correlation coefficients among traits measured in Citra (Central Florida) ...........41

2-7 Direct (bold values) and indirect genetic gain as a percent of mean in Belle Glade. ........43

3-1 Analysis of variance for three sugars in sweet corn by Enzymatic method ......................55

3-2 Concentration (%) of sucrose, glucose, and fructose in 14 sweet corn hybrids

determined by Enzymatic method .....................................................................................56

3-3 Statistics for sugar samples using PLS regression models (n = 56) ..................................58

3-4 NIRS predicted concentration (%) of sucrose, glucose, and fructose in 14 sweet corn

hybrids with respect to the enzymatic method ...................................................................58

A-1 List of cultivars used in the study in Chapter 2 .................................................................64

A-2 List of hybrids used in the study in Chapter 3 ...................................................................66

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LIST OF FIGURES

Figure page

1-1 Acres harvested of fresh market sweet corn by state (Source: USDA, NASS, 2012) .......18

1-2 Fresh market crop production and their rank in Florida (NASS-USDA, 2017) ................19

2-1 Phenotypic distribution of six selected traits in Belle Glade (South Florida)....................36

2-2 Phenotypic distribution of selected traits in Citra (Central Florida) ..................................37

3-1 Single kernel Near-infrared Spectroscopy .........................................................................53

3-2 Sucrose content grouped by mutant genes using the enzymatic method ...........................57

3-3 NIRS predicted versus reference values ............................................................................60

9

LIST OF ABBREVIATIONS

ae1 amylose extender 1

ANOVA Analysis of Variance

bt1 brittle 1

bt2 brittle 2

DAP Days After Pollination

EH Ear Height

EL Ear Length

EW Ear Width

G x E Genotype-by-Environment Interaction

KRN Kernel-Row Number

NASS National Agricultural Statistics Service

NIRS Near-Infrared Spectroscopy

PH Plant Height

PLSR Partial least square regression

RCBD Randomized Complete Block Design

S% Selection Percentage

se Sugar enhancer

sh2 Shrunken 2

su1 Sugary 1

TL Tassel Length

UF University of Florida

USDA United State Agriculture Department

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Abstract of Thesis Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

SWEET CORN BREEDING FOR FLORIDA'S FRESH MARKET

By

Utsav Kumar

August 2019

Chair: Marcio Resende

Major: Horticultural Sciences

Florida is the largest producer of fresh market sweet corn (Zea mays) and sustained

breeding efforts are needed to support this industry. It is important to understand genetic

parameters to identify which traits have the potential to be improved and to develop a breeding

scheme across multiple locations. The first objective of this study was to estimate heritability,

Genotype x Environment (GxE) interaction and genetic gain for sweet corn traits in two Florida

environments. A population of 76 genotypes was planted in 2 locations in spring and summer ‘18

in a randomized complete block design with three replications. Narrow sense heritability for

plant height, tassel length, primary ear height, ear length, ear width, and kernel row number

ranged from 0.09 to 0.29. The type A and B GxE between South Florida and Central Florida

suggest that ear height, tassel length, plant height, and kernel row number can be selected in

Central Florida to accelerate the rate of genetic gain. We also analyzed trait correlations with the

objective of assessing the potential for indirect selection. Genetic correlation between many pairs

of traits was low.

The second objective validated NIR spectroscopy for sugar quantification as an

alternative method to enzymatic quantification. NIR calibration curves were established by

partial least squares regression against enzymatic quantification. Spectral range and the number

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of PLS factors were optimized for the lowest root mean square error and prediction accuracy.

The correlation between NIR and enzymatic quantification was comparatively better for sucrose

(0.83) and fructose (0.44) but the low predictive ability was obtained for glucose (0.04).

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CHAPTER 1

GENERAL INTRODUCTION

Sweet corn (Zea mays L.) was first reported by European settlers in 1779 (Boyer &

Shannon, 1983). It is one of the most popular vegetables in the United States and Canada and has

observed enormous growth in consumption in most parts of Europe, Eastern Asia, and South

America (William F Tracy, 1997; W.F. Tracy, 2001). Sweet corn has become extremely popular

that when the word ‘corn’ is used, it is commonly assumed as sweet corn, even though field corn

production is 50 – 60 times higher than that of sweet corn.

Sweet Corn vs Other Corn

Sweet corn belongs to the same species of other types of corn (Z. mays), such as popcorn

and field corn, the grain that is most commonly produced in the world. The most distinguishing

characteristic, when compared to any of the other corn types, is the result of naturally occurring

recessive mutations that control the conversion of sugar into starch inside the endosperm

(William F Tracy, 1997). While the increased sugar content in the endosperm is what defines

sweet corn, elite materials have historically been selected for the presence of additional traits that

are relevant for the sweet corn market, such as aesthetics of ears, plants, and grain traits such as

flavor, tenderness, and texture (Brewbaker & Martin, 2015). In the case of corn planted for the

fresh market, most of the harvest is manual, which demands plants with low stature and

biomass(W.F. Tracy, 2001). Furthermore, most sweet corn markets currently do not tolerate

genetic engineering traits, therefore imposing additional selection pressures to enhance the plant

tolerance to pests. Altogether, the considerable differences are highly noticeable between elite

sweet corn hybrids and materials that are commercialized for feedstock and ethanol production.

Sweet corn and field corn can be intercrossed, and this germplasm can be a good source of

genetic diversity into a sweet corn breeding program (W. F. Tracy, 1990). However, all the

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starch-deficient genes that are commercially used in sweet corn are recessive. Therefore, kernels

of sweet corn must have two copies of each gene, for a kernel to express the sweetness trait

(Mannering, 2008). Currently, the most popular and widely used genes (Table 1-1) are:

Su1 (sugary 1)

Sugary 1 was the only commercially used sweet corn mutant after the discovery by East

and Hayes (1911). Su1 encodes a starch debranching isoamylase. The gene is expressed later in

the starch biosynthesis pathway, and the mutant allele reduces starch production while increasing

the sugar content. Su1 knock out alleles which results in around 15% of sugar at the immature

milky phase of endosperm development (~20 days after pollination). Furthermore, sugary1 lines

increase the concentration of phytoglycogen, providing a smooth and creamy texture to the grain

(Creech, 1968). In the dry state, its kernels are translucent and have a glassy appearance.

Regardless of its acclaimed taste, the use of sugary 1 mutants in the fresh market decreased due

to the fast reduction in sugar content once the ears were harvested, thereby severely lowering the

product’s shelf life (Coe, Neuffer, & Hoisington, 1988; William F Tracy, 1997).

Se (sugar enhancer)

Sugar enhancer is a feature that was discovered by A.M. Rhodes in 1970s and improves

the sweet phenotype in su1 backgrounds (Ferguson, Rhodes, & Dickinson, 1978). The gene

cannot be used independently though since it doesn’t have any effect on its own. The exact

mechanism, enzyme, and role in the pathway for the se gene are still unknown. However, it is

conventionally introduced into su1 parents, which results in an increase in sugar content by 10 to

25% in the hybrids (Gonzales, Rhodes, & Dickinson, 1974). The se varieties have tender

pericarp, lighter kernel color, and creamy texture like sugary1 lines. However, due to the higher

sugar content, the hybrids also have a higher shelf life than su1 lines.

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Sh2 (shrunken 2)

The Shrunken 2 gene encodes the large subunit of the ADP – glucose pyrophosphorylase

enzyme. This enzyme is the first committed step in starch biosynthesis. At present, this gene is

the most widely mutant used commercially, as the knock-out allele inhibits starch synthesis at

the beginning of the starch biosynthesis pathway which leads to ~35% of sugar accumulation and

higher shelf life (Creech, 1968). The hybrids are generally referred to as “supersweets”, and the

dry seed has a shriveled texture and opaque appearance (A. R. Hallauer, 2000). The increased

sugar content, however, comes with its negative consequences. The shrunken seeds have a

significantly reduced amount of starch in the grain, which in turn creates germination problems

(Wilson Jr & Mohan, 1998). Hence, sh2 hybrids did not become commercially available until

breeders were able to select materials that improved the germination rate (W.F. Tracy, 2001;

Wolf, 1962).

Double Mutants (Synergistic)

Sugar content and shelf life are two of the main traits that sweet corn breeders are

regularly improving. One way to achieve this goal without compromising germination rate is to

combine starch deficiency genes in different dosages (W.F. Tracy, 2001). Synergistic sweet corn

hybrids are typically homozygous for su1 (and possibly se) while having Sh2sh2 at the

heterozygous state (Mannering, 2008). This combination results in tender kernels with higher

sugar content. This happens because 25% of the resulting kernels will be “supersweet”, while the

remaining will have high sugar content from the su1-se genotype and creamy texture from the

presence of phytoglycogen. The other major advantage of this type of corn is the fact that only

one inbred line is shrunken2 (typically the male line). Hence, seed production is not as expensive

since one of the inbred lines has fewer issues with germination. Finally, the hybrid seed that is

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Table 1-1. Commercially used genes in sweet corn breeding programs. Chromosome location in the maize genome, the type of

enzyme produced and characteristics of the phenotype. Mutant Type Gene Chromosome Enzyme Phenotype

sugary 1 su1 4 Starch debranching isoamylase

Translucent, wrinkled, vigorous seed,

no shelf life

sugary enhancer1 se1 2 Unknown Creamy, tender, color varies with the background,

slow drying, short shelf-life

shrunken 2 sh2 3 ADP glucose pyrophosphorylase Translucent, shriveled, sweet, crisp, long shelf-life,

low seed vigor

Synergistic

NA NA Crisp, vigorous seed, longer shelf life,

mixed kernel types on the ear

Miscellaneous

genes

amylose extender

1

ae1 5 Starch branching enzyme IIB

Tarnished, glassy, high amylose content

brittle 1 bt1 5 Starch granule-bound phospho-

oligosaccharide synthase

Angular often translucent, brittle,

mature kernel collapsed

brittle 2 bt2 4 ADP glucose pyrophosphorylase Transparent, shriveled, sweet kernels collapse

on drying becoming angular and brittle

Source:(Boyer & Shannon, 1983; Coe et al., 1988; Lertrat & Pulam, 2007; William F Tracy, 1997)

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planted also has a Sh2sh2 genotype, resulting in fewer issues with germination (Lertrat & Pulam,

2007).

Economic Value

In the United States, the popularity of sweet corn rose in the 1950s when Dr. John

Laughnan, a corn geneticist discovered Supersweet sweet corn. He later released it through

Illinois Foundation Seeds Inc. (IFSI) which became famous as “Illini Chief”, a shrunken2

conversion of the “Iochief” line. IFSI later developed the three-way hybrid “Illini Xtra Sweet”

because of germination issues in “Illini Chef” which revolutionized US sweet corn production

(A. R. Hallauer, 2000; Lertrat & Pulam, 2007; William F Tracy, 1997). At present, the United

States is the world’s largest sweet corn producer, followed by Canada and Japan. Sweet corn’s

demand and popularity have increased considerably throughout the world in the past two

decades. At present, it is also grown in Taiwan, France, Hungry, Australia, New Zealand, China,

India, and South America which were previously importers from US (Lertrat & Pulam, 2007;

William F Tracy, 1997).

The sweet corn industry in the US has also increased since 1960 (NASS, 2010). Total

annual acreage fluctuated between 76,000 and 93,000 hectare, but production rose by more than

30% from 942,400 tons in 1993 to 1,444,950 tons in 2008 (NASS, 2010). From 1984 to 1996,

the increase was explained by the expansion on the export of canned corn, which rose from

57,000 tons to 177,000 tons and for frozen corn from 33,000 tons to 57,000 tons (NASS, 2010).

In the last 2 decades, the processing market, however, has been decreasing in value, while the

fresh market is increasing throughout the country. This increase is associated with the

improvement on quality and taste along with the increase in shelf life (W.F. Tracy, 2001).

Out of $1.1 billion crop value of sweet corn, 74% (~842.3 million) of the revenue is

generated from the fresh market (Hansen, 2017). It is evident from these numbers that fresh

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market is the winner in terms of sales although, in terms of total production, processing market is

higher with an annual production of approximately 2.5 million tons when compared to fresh

market which is around 1.49 million tons (Hansen, 2017).

Sweet Corn Production in Florida

Florida was one of the first states to adopt and realize the importance of Supersweet

sweet corn in the fresh market because of its prolonged shelf life and high sugar content (Wolf,

1962). It was the effort of Professor Emil A. Wolf, a sweet corn breeder at Everglades Research

and Education Center (University of Florida), Belle Glade who revolutionized the Florida sweet

corn industry. Wolf released a few hybrids using sh2, first one being ‘Florida Sweet’ which

faced issues with germination and was also susceptible to seed and seedling rots (Wolf, 1978).

Later, he improved this variety to increase fungal resistance to Helminthosporium turcicum (Ht)

and collaborated with Crookham Seed Company to improve seed vigor and germination. This

effort resulted in the release of ‘Florida Staysweet’ in 1978. By 1980s the superiority of

supersweet sweet corn was clearly visible to Florida growers which resulted in a shift from su1

to sh2 in just a few years (Watson & Ramstad, 1987). Today, the entire fresh market industry in

Florida is composed of sh2 mutants or synergistic combinations.

At present, a majority of the fresh market sweet corn produced in the US comes from

Florida, California, and Georgia. Among these, Florida has always been the highest producer in

the fresh market by contributing approximately 27 to 34% of the total value. Sweet corn is

ranked fifth in terms of dollar generated in the state of Florida, only behind oranges,

strawberries, tomatoes, and peppers (NASS-USDA, 2017). All of Florida’s production is

targeted for the fresh market. The contribution of sweetcorn is absolute when viewed in terms of

the proportion of its individual value to the value of the total crops produced in the state. The

acreage of sweet corn planted and harvested in the state in 2017 was only 17,000 ha or 2.6% of

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the total area for all crops. Nonetheless, the industry was valued at ~ $190 million or 6.3% of the

total value of all crops (NASS-USDA, 2017).

Figure 1-1. Acres harvested of fresh market sweet corn by state (Source: USDA, NASS, 2012)

Sweet corn production in Florida has been important because just like other fruits and

vegetables, the early production enables the commercialization at a premium price, which

increases the farmer profit margins. South Florida, (mainly Palm Beach and Miami-Dade

counties) the region where the bulk of the state’s production occurs, is an area that generally has

fall and winter temperature that is conducive to healthy crop growth. The production in Central

Florida and North Florida is still incipient and currently not a major crop (Mossler, 2008).

19

Figure 1-2. Fresh market crop production and their rank in Florida (NASS-USDA, 2017)

Fresh Market Requirements

In terms of consumption, sweet corn can be classified into 3 major categories- fresh,

frozen or processed. In terms of production, there are broadly two types- fresh market hybrids

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and processing hybrids (William F Tracy, 1997). Both of which have very different market

requirements. Fresh market sweet corn must follow very strict norms before it reaches the

market. In the majority of the cases, only the top (primary) ear is harvested from the plant.

Hence, a large focus is put towards improving the top ear. The ear should be between 16 – 20

cms long and have dark green husk with long leaves projecting from the tip of the husks

enclosing the ear. The husks are also used to protect the ear tip since fresh kernels are very

tender. After de-husking the ears, aesthetic traits are important. The ear is expected to have

straight 16 -18 rows of narrow kernels with good tip fill. Yield in fresh market sweet corn is

represented by marketable yield, and ears that do not set seed completely are often discarded.

Fresh market consumers in the U.S. also have a color preference. The majority of fresh market

consumers want bi-color (mix of yellow and white kernels) whereas other market demands

mono-color either white or yellow. Fresh market corn is mostly harvested by hand. Hence, one

very important operational trait is the easy removal/detachment of the ear from the plant for

harvest. Finally, plant height and ear height are also important traits that affect the easiness of

manual harvest (William F Tracy, 1997; W.F. Tracy, 2001).

Whereas for processing market majority of the focus is on making ears with uniform

thickness and uniform maturity to be able to machine harvest, de-husk, and shell because in

processing shelled kernels and depth of kernels produced by ear per hectare are only counted

towards yield. Unlike fresh market corn, less importance is given to kernel appearance, flavor,

and tenderness (William F Tracy, 1997).

Breeding Methods

In order to keep up with market demands and traits requirements, sweet corn needs

constant breeding support. The basic framework in sweet corn breeding programs is similar to

field corn and has taken advantage of the experiences learned in field corn breeding. However,

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sweet corn breeding programs tend to have a much narrower genetic basis, given the strict

requirements for the presence of recessive starch mutants and the demand for many additional

traits involved in aesthetically appealing ears. At any rate, the main goal of the breeding

program, just like in any other species, is to develop a hybrid which is better than existing ones,

in some way or another.

The task of a corn breeder is generally to develop elite inbred lines that have the good

combining ability. These inbred lines are tested against a known good inbred (tester) in a method

that is known as a test cross. The selection occurs based on the performance of the hybrid

combination. To create these inbred lines, there are also multiple breeding methods that are

available to a breeder. The main breeding and selection methods are highlighted below:

Pedigree Breeding

In pedigree breeding, a detailed record for selection from segregating generations of

crosses of self-pollinated crops is maintained. Since a detailed record is maintained of every

generation, if needed, a plant can be traced back to its F2 plants where it originated. Usually

crosses between most elite germplasm or available material are used to get started to improve the

specific weakness of a variety. However, if germplasm for specific objectives is not available,

exotic sources are used. For example, sources of disease resistance are often not available. In

such cases, sources are explored in field corn for sweet corn (A. R. Hallauer, 2000).

If non-sweet germplasm is used to improve some specific trait, then taste testing for

flavor and tenderness becomes necessary at some point during inbreeding. In sweet corn, test

crosses are made usually at later generation (F6 – F7) because uniformity is of utmost importance.

Hybrids made from early generation crosses often segregate for many required traits, which

hinders the evaluation process. (A. R. Hallauer, 2000; Singh, 2015). The main advantage of

pedigree breeding is the fact that selection is performed early on, reducing the number of inferior

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genotypes that are carried throughout the process. On the other hand, the process, in its strictest

sense, cannot be used in non-target environments, such as off-season nurseries. Moreover, the

process is labor-intensive, since every generation requires a lot of record-keeping (Singh, 2015).

Backcross Breeding

In backcross breeding, the hybrid and the offspring in subsequent generations are

repeatedly backcrossed to one of the parents of the F1. As a result, the genotype of the backcross

progeny becomes increasingly like its elite parent from which the backcross was made and by

the end of 6-8 generations, the progeny would be almost identical to its parent (Singh, 2015).

The motive behind this method is to improve one or a few traits controlled by a major effect

gene. The characteristics lacking in the elite variety is then obtained from a donor parent without

changing the remaining genotype of this elite variety. Thus, the result is a well-adapted variety

with one or two improved traits (Lee & Tracy, 2009).

Backcross breeding was the first method to be used after the discovery of supersweet

sweet corn. At the time, the sh2 allele was backcrossed into elite su1 inbreds. The shrunken

phenotype was selected, without selection pressure on the su1 locus. Backcrossing is simple

when the trait trying to be improved is monogenically controlled. In sweet corn, the method was

unsuccessful to improve organoleptic traits (William F Tracy, 1997).

Recurrent Selection

In this method, the selection is made generation after generation by isolating and

intermating superior selected progenies to produce population for the next cycle. The main

objective of this method is to increase the mean of the population in a favorable direction

(Darrah, McMullen, & Zuber, 2019). This method is conventionally used to improve quantitative

traits and populations. By increasing the frequency of favorable alleles, the breeder enhances the

probability of obtaining superior cultivars (Hall, Booker, Siloto, Jhala, & Weselake, 2016; Singh,

23

2015). One example of recurrent selection applied to sweet corn breeding was published to

improve the resistance to common rust (Puccinia sorghi schw.) (Abendon & Tracy, 1998).

Bulk Method

In the bulk method, the segregating F2 or subsequent generations are harvested in bulk to

be grown in the next generation. This method is mainly used for three reasons: i) in

inbred/hybrid development programs, the optimum unit of selection is the hybrid, which can

only be evaluated in late stages of development, ii) The bulk method allows selection to be

performed in off-season nurseries, and iii) The method is considerably simpler and less labor-

intensive than the pedigree method (Singh, 2015).

Phenotypic Mass Selection

Mass selection is not a breeding method per se, but a selection strategy. The method

relies solely on the selection based on the phenotypic appearance of the plant. This method is the

oldest selection strategy available and has been used since farmers have been saving seeds from

plants with desired properties. Hence, it was directly involved in the domestication and early

breeding of many crops. Today, this method is often used in small scale breeding programs with

limited resources to establish and analyze experimental designs. The main advantages are its

simplicity and low implementation cost. However, the method is only very effective in traits with

high heritability, when the phenotype accurately reflects the genotype and when a trait can be

easily measured (Sleper & Poehlman, 2006). The rate of genetic gain from mass selection, for

traits that are quantitative is significantly reduced compared to other approaches (Cornelius,

1994; Daetwyler, Villanueva, Bijma, & Woolliams, 2007; Duvick, 2005). In corn, the

effectiveness of the method depends on the trait being improved, the degree of isolation and the

precision of experiment techniques used (A. Hallauer & Miranda, 1981).

24

Selection Based on Breeding Values

The inefficiency from phenotypic mass selection comes from the fact that the breeder is

performing indirect selection where the target of improvement is the breeding values, but the

selection is performed on the phenotypic values. One way to address this issue is to use

quantitative genetics to estimate the breeding values prior to selection. This method was initially

proposed and well established in animal breeding and was only later adopted in plant breeding

(Entringer, Vettorazzi, Santos, Pereira, & Viana, 2016; Piepho, Möhring, Melchinger, & Büchse,

2008). The breeding values are typically estimated using linear mixed models that adjust for

covariates associated with the experimental design and estimate the individual contribution to the

phenotype that is due to the breeding value. The mixed model framework allows the use of

pedigree records, which are associated into the model through variance-covariance matrices

associated with the breeding value term. The method, proposed in 1975, utilizes two

mathematical approaches: Restricted Maximum likelihood (REML) which is used to estimate

variances and Best Linear Unbiased Predictor (BLUP), which is used to estimate the breeding

values (Henderson, 1975).

Objectives

The goal of this study was to develop the information necessary to outline breeding

strategies of fresh market sweet corn for Florida. Specifically, two major objectives were

defined: 1) determine the genetic parameters (heritability, GxE interaction and genetic gain) for 6

marketable traits (ear length, ear width, kernel row number/ear, ear height, plant height and tassel

length) in two environments in Florida to identify where selection for particular traits need to be

performed for our main market, and 2) develop high throughput sugar characterization method.

25

CHAPTER 2

ESTIMATION OF GENETIC PARAMETERS IN SWEET CORN

The United States is the largest producer of sweet corn in the world with a crop value of

$1.1 billion. The US is also the largest exporter of fresh, frozen and canned sweet corn. In 2018,

the US exported value of the US $157 million sweet corn to Canada, Japan, Mexico,

Netherlands, and other European countries. According to Economic Research Services of the

USDA, a growth of $300,000 to $500,000 has been observed every year (ERS-USDA, 2019).

The domestic fresh market of sweet corn is around $842.3 million which represents the largest

fraction of the crop total value (Hansen, 2017). In fresh market production, Florida is the largest

fresh market sweet corn producer in the US. The crop is among the top 5 economically important

crops in Florida. According to USDA National Agriculture Statistics Services’ Florida’s fresh

market sweet corn production comprises 22% of the U.S. total fresh market’s value (NASS-

USDA, 2017).

Sweetcorn breeding program at the University of Florida initially started in 1948.

However, to support the States' current demand, a new sweet corn breeding program was

proposed. For this, the evaluation of the potential of quantitative genetics for sweet corn breeding

was essential since no studies have been reported in sweet corn’s quantitively inherited trait

using quantitative genetics. The few reported ones have been done on the population which had

already undergone selection, which violates the assumption of randomness and in turn biases

their estimation and predictions (Henderson, 1975). Usually, trait data are collected subjectively

in sweet corn. Therefore, there is need of use of quantitative genetics which uses mathematical

approaches like BLUP and REML to remove those bias by assuming that genetic effects are

random and also by incorporating pedigree of the genotypes which borrows the information from

26

related genotypes which helps in predicting the genetic merits of relatives, even if the phenotypic

data wasn’t collected for them (Lynch & Walsh, 1997; Piepho et al., 2008).

Quantitative genetics is a well-established field which could be used in plant breeding

programs by breeders to help make better decisions in several aspects (Piepho et al., 2008). In

sweet corn, many of the traits are polygenic. Therefore, it is important to analyze these traits

using quantitative genetics. Generally, this happens by generating a ranking of the superior

individuals based on estimates of breeding values or genetic values, compared to traditional

visual phenotypic evaluation and mass selection (A. Hallauer & Miranda, 1981). Furthermore,

quantitative genetics can provide an assessment of the breeding potential of the program by

estimating population genetic parameters, such as heritability and genetic gain. The estimate of

genetic parameters in quantitative traits of interest is important for several reasons. For example,

i) trait heritabilities are population-specific parameters that can indicate the amount of genetic

variability in the germplasm and aid in trait prioritization; ii) estimates of genetic gain support

the establishment of short, medium and long term breeding plan; iii) Estimates of indirect

selection can guide the decision based on which traits or environment to be selected, potentially

reducing the cost of breeding and selecting for traits and environments that are not needed.

Indirect selection is selection applied to one trait based on measurements made on the basis of a

second trait (A. Hallauer & Miranda, 1981). This kind of selection becomes effective only when

the secondary trait has higher heritability than the primary trait, and genetic correlation between

the two traits is high (Falconer, 1989). The use of indirect selection is advantageous when the

desired trait is difficult or expensive to measure. Indirect selection may shorten the variety

development period if several traits that are correlated to the secondary trait may be improved

upon simultaneously (Garwood, McArdle, Vanderslice, & SF Shannon, 1976). Altogether, a

27

series of practical decisions can result from the application of quantitative genetics into plant

breeding programs

In addition to improving the genetic gains using quantitative genetics, corn breeding

programs can be accelerated by utilizing off-season nurseries to advance the development of

inbred lines. This is particularly relevant in the case of sweet corn, since double haploid

protocols are not well established, mainly due to the inhibition of the color markers which

hinders the detection of haploid induction (Yu & Birchler, 2016). The use of off-season nurseries

is typically associated with a bulk breeding scheme, where the off-season nurseries are only used

to advance generations, with no or limited selection. However, quantitative genetics can also

point to traits that show low genotype by environment interaction and can be selected throughout

the entire year in all the seasons planted. This process accelerates the progress since the genetic

gain is also obtained off-season. The University of Florida breeding program has historically

focused much of its program to develop cultivars adapted to South Florida, specifically Palm

Beach County which represents the bulk of the Florida market. The agricultural area in this

county is characterized by the muck soil, which is rich in organic matter. Palm Beach County has

a perfect agro-climatic condition. It receives an average annual precipitation of 155 cm, most of

which is received in the peak cropping season from May to September. Their annual average

temperature ranges from 15°C to 34°C and has a short cool season from December to March

with a low possibility of frost and also has broad daylight ranging from 10 -14 hours around the

year. All these conditions allow early planting of a crop which in return can be sold at premium

prices.

However, the program has recently expanded to include an off-season nursery in Citra,

located in Central/North Florida. While this site has a sandy soil, which is significantly different

28

from the muck, the area is geographically close to Palm Beach County and has similar climatic

conditions during the planting season. Hence, quantifying genotyping by environment

interactions among these two sites will help to define the best breeding strategy to maximize

genetic gains. With these goals in mind, this study seeks to accomplish the following objectives:

1. Evaluate marketable traits in sweet corn for Florida’s fresh market.

2. Estimate heritability for each of these traits.

3. Calculate the trait correlation and the potential for indirect selection.

4. Estimate genotype by environment interaction between Central and South Florida.

Material and Methods

Plant Material

For this work, the plant material used consisted of 57 hybrids with the shrunken 2 alleles

which were obtained from the sweet corn breeding program at Everglades Research & Education

Center (UF), Belle Glade, Florida (Table A-1).

Field Experiment

All 57 hybrids were planted in Summer 2017 at the Plant Science Research & Education

Unit (University of Florida), Citra, Florida (29.4119° N, 82.1098° W). The location has a

subtropical climate with annual average precipitation of ~120 cm. The field was planted in April

of 2017 and harvested in July. During these months, the temperature varied from 22 to 36 oC.

The soil at the experiment site was sandy loam with low water holding capacity and pH of 6.2.

The experiment was laid out in randomized complete block design (RCBD) with three

replications. In each plot, 8 corn seeds were planted manually via hand planter at a depth of 4 – 5

cm with the plant to plant spacing of 18 cm and row to row spacing of 90 cm. Plants were

fertilized with 270 kg N, 60 kg P, 250 kg K per hectare during the growing season. Water was

applied as per the requirement using central pivot irrigation to supplement rainfall and weeds

29

were controlled by Prowl H2O (BASF, Germany) at 1.16 l/ha, Dual magnum (Syngenta,

Switzerland) at 876 ml/ha and Atrazine (Drexel company co., US) at 11 l/ha. All other

intercultural operations (eg. Ploughing, earthing up) were carried out uniformly to raise the crop.

All the hybrids which reached silking and flowering stage were selfed via controlled

pollination. For that from each row randomly two plants were picked. After harvest, drying, and

shelling, the seeds extracted from the two ears within a row were bulked together. At this stage,

we recovered 47 F2 seeds. The 47 genotypes were planted following the same experimental

design and agricultural practices in Fall 2017, to advance a generation. From this season, we

were able to recover 30 different F3 seeds. The difference in the number of F1 to F2 and F2 to F3

was either because of germination issue or nicking.

The resulting population was combined with the F2 and the genotypes (47 F2 + 29 F3 =

76) consisted of the evaluation set used in this study. The 76 genotypes were planted in 2

locations: 1) In the Spring of 2018, we planted a field in the Everglades Research & Education

Center (University of Florida), located in Belle Glade, Florida (26.667862° N, -80.632349° W).

The location is under subtropical weather, with temperature ranging from 12 to 34°C in the

growing season. The annual average rainfall was 130 cm. The soil at the field was muck soil

(rich in organic matter) with good water holding capacity and pH of 7.4. 2) The second location

where this population was planted was the Plant Science Research & Education Unit (University

of Florida) in the summer of 2018. In both the locations, the experiment was laid out in

randomized complete block design (RCBD) with three replications. Plots were established with

12 corn seeds per row, planted manually via hand planter at depth of 3 – 4 cm with the plant to

plant spacing of 18 cm and row to row spacing of 90 cm. The management practices in both

locations were carried according to standard practices regularly used in each site. Irrigation was

30

applied when necessary to avoid water stress. Pests, diseases, and weeds were controlled to

obtain optimum growth of the crop in both locations.

Data Collection

The same data collection (Table 2-1) process was carried out in both fields. Plant height,

tassel length, and ear height were measured in the field, later in the development of the plant and

after pollination. In each plot, open-pollinated ears from four random plants were collected for

ear trait measurements. All plants in the plot were measured in cases where low germination

resulted in less than four plants per row. The ear traits measured were ear length, ear width and

kernel row number (KRN) after the ears were dried.

Table 2-1. Selected traits and their measurements

Traits Measurement

Ear height Length from the surface of the soil to the top ear's node

Tassel Length Length from the flag leaf node to the tip of the tassel

Plant Height Length from the surface of the soil to the tip of the tassel

Kernel-Row Number Number of kernel rows in an ear

Ear Length Length of the dehusked ear from tip to base

Ear Width The diameter of the dehusked ear

Data Analysis

All the data were analyzed in Asreml-R package (Gilmour, Gogel, Cullis, Thompson, &

Butler, 2009). We estimated the following genetic parameters, as described in more details

below: Trait heritability, Type A correlation, Type B genetic correlation, genetic trait by trait

correlation and genetic gain.

Trait heritability was estimated within each environment using the additive linear mixed

model

𝑌 = 𝜇 + 𝑟 + 𝑔 + 𝜖

Where, Y represents the response variable with the phenotypic measurement for each trait, 𝜇 is

the fixed effect of the overall mean, 𝑟 represents the fixed effect of replicates, g is the random

31

effect of genotype, and 𝜖 is the residual error. The genotype effect was modeled under the

assumption that g ~N (0, A𝜎𝑔2), where A is the numerator relationship matrix calculated from the

pedigree records for the population. The residual was modeled as 𝜖 ~ N (0, I𝜎𝜖2). Following this

model, variance components were estimated for g and 𝜖. Heritability is the proportion of the

phenotyping variance in a given population that is due to genetic variation. This can range

between 0 i.e: no genetic contribution to 1 i.e. full genetic contribution. From the estimates of

variance component, the narrow-sense heritability was calculated as:

ℎ̂2 =𝑉𝐴

𝑉𝑃=

�̂�𝑔2

�̂�𝑔2+�̂�𝜖

2

Where, ℎ̂2 is the estimate of narrow Sense Heritability, �̂�𝐴 is the estimate of additive genetic

variance and �̂�𝑃 is the estimate of phenotypic variance which can be decomposed as the sum of

σ𝑔2 and, σ𝑒

2.

The multi-environment joint analysis was carried to estimate the genotype by

environment interactions. The following model was used:

𝑌 = 𝜇 + 𝑋1𝑠 + 𝑋2𝑟(𝑠) + 𝑍1𝑔 + 𝑍2(𝑔 𝑥 𝑠) + 𝜖

Where, Y is the response variable of the trait being analyzed (i.e. tassel length, plant height, ear

height, ear length, ear width or kernel row number), 𝜇 is the fixed effect of the overall mean, s is

the fixed effect of site, 𝑟(𝑠) is the fixed effect of replicate within site, g is the random effect of

genotype assuming g ~N(0, A𝜎𝑔2), g x s is the random effect of the genotype x environment; (g x

s) ~ N(0,A⊗ Iσ2gxs), 𝜖 is the random residual effect; 𝜖 ~ N(0, D𝜎𝜖2), X and Z are incidence

matrices for location, rep, genotype and genotype by location interaction, I is an identity matrix,

D is a block diagonal matrix and ⊗ represents the Kronecker product. The genotype by

32

environment was calculated in two different ways, as proposed by (Baltunis, Gapare, & Wu,

2010; Yamada, 1962)

G x E interaction (Type B genetic correlation)

Type B correlation was specifically proposed to estimate G x E interaction in the multi-

environment analysis. The method quantifies the proportion of the total genetic variance

compared to the sum of variances due to genetics and G x E interaction. Values close to 1

represent a small amount of interaction, which would indicate a similar ranking of the breeding

values across locations. Small values of type B correlation represent that the majority of the

genetic variance is associated with the interaction. Hence, the component can be estimated as:

Type B (CorB,C) = 𝜎𝑔2

𝜎𝑔2+𝜎𝑔𝑥𝑠

2

Where, 𝜎𝑔2 is additive genetic variance and 𝜎𝑔𝑥𝑠

2 is the variance associated with the genotype by

environment interaction, as shown above in the multi-environment model.

Type A correlation

The second measure of genotype by environment is the type A correlation. It is calculated

as the correlation between

Type A (CorB,C) = 𝜎𝐵,𝐶

√𝜎𝐵2 .𝜎𝐶

2

Where, 𝜎𝐵,𝐶 is the additive genetic covariance between Belle Glade and Citra, 𝜎𝐵2 is the additive

genetic variance estimated in Belle Glade, and 𝜎𝐶2 represents the additive genetic variance from

the environment in Citra. The estimate measures the strength of the relationship between the

performance of one trait in one location and the performance of the same trait in another

location. It was calculated from a bivariate analysis in order to estimate the covariance between

breeding value estimates.

33

Trait genetic correlation

Trait genetic correlation was computed to know the impact of the selection of one trait on

another by indirect selection. The model was similar to the one used for type A genetic

correlation under GxE. The bivariate was used to estimate the covariance among a pair of traits.

Trait genetic correlation was calculated as:

(𝜌X, Y) = 𝜎𝑋,𝑌

√𝜎𝑋2 .𝜎𝑌

2

Where, 𝜎𝑋,𝑌 is the covariance between trait X and Y, 𝜎𝑋2 is the genetic variance of trait X, and

𝜎𝑌2 is the genetic variance of trait Y.

Estimated genetic gain

Estimated genetic gains were computed to estimate the rate of improvement predicted

from the difference between the selected individuals and the base population. The direct and

indirect genetic gain was calculated as:

- Selection gain: SG = 𝑆𝐷 . ℎ2. 𝜌𝑔𝑥,𝑦

- Selection difference: SD = x̅ 𝑠 − x̅ 0

- Genetic gain as a percent of mean: 𝐺𝐺

x0̅ x 100

Where, 𝜌𝑔𝑥,𝑦 is the genetic correlation between traits or between environments. This parameter

was assumed to be 1 for the direct genetic gain calculation; x̅ 𝑠 represents the phenotypic mean of

the selected individuals, and x̅ 0 represents the base population mean.

Results and Discussion

In this work, we have evaluated the performance of 76 sweet corn lines for different

commercially relevant traits. This analysis allows us to estimate quantitative genetic parameters

for this population, and to generate a ranking of the breeding values for all the individuals and

34

for each trait. The distribution of the phenotypes for each trait and each location can be seen in

Figures 2-1 and 2-2. Generally, the phenotypes had distributions that resembled a normal

distribution, even in the case of kernel row number where the phenotype is multi-categorical.

The analysis of variance (Table 2-2 and 2-3) for each environment showed significant effects for

the fixed effect of genotype, rejecting the null hypothesis that the mean value of each genotype

for each of the traits was all the same. The fixed effect of replicate was also significant for ear

width and ear length in Belle Glade and ear length, plant height and ear height in Citra, further

supporting the importance of the experimental design used.

Table 2-2. ANOVA of RCBD for six marketable traits in sweet corn in Belle Glade

SV EH TL PH KRN EL EW

Replications 36 86.98* 588.3 5.143 14.796*** 336.9***

Genotypes 507*** 115.21*** 2429.5*** 16.6*** 15.631*** 72.3***

Error 78 49.35 233 3.654 2.121 11

DF 2 2 2 2 2 2

*Significance at 0.05, **Significance at 0.01, ***Significance at 0.001, SV = Source of

Variance, EH = Ear height, TL = Tassel Length, PH = Plant Height, KRN = Kernel Row

Number, EL = Ear Length, EW = Ear Width, DF = Degree of Freedom

Table 2-3. ANOVA of RCBD for six marketable traits in sweet corn in Citra

SV EH TL PH KRN EL EW

Replications 180.1* 36.85 1084.5* 2.55 41.74** 28.56

Genotypes 407.7*** 174.36*** 2680.4*** 11.96*** 27.28*** 70.50***

Error 56.6 32.81 329.7 4.625 6.96 25.65

DF 2 2 2 2 2 2

*Significance at 0.05, **Significance at 0.01, ***Significance at 0.001, SV = Source of

Variance, EH = Ear height, TL = Tassel Length, PH = Plant Height, KRN = Kernel Row

Number, EL = Ear Length, EW = Ear Width, DF = Degree of Freedom

Heritability

The experimental design was used to calculate trait heritability. These results are

important because they allow fixing the status and opportunities present in the current

germplasm of a breeding program. The narrow-sense heritability was calculated in each location

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and ranged from 0.09 to 0.29 (Table 2-4). The average heritability across all traits was similar in

both environments, albeit slightly higher in Belle Glade. These results were below heritabilities

previously reported for sweet corn (Niji, Ravikesavan, Ganesan, & Chitdeshwari, 2018; Voichita

& Ioan, 2009). A low heritability would be obtained in a situation of low genetic diversity, or

when bias has been introduced into the phenotyping or experimental design. Given the

consistently low results across different traits and different environments, as well as the non-

significance of the block effect for the majority of the traits, we conclude that this breeding

population likely has low genetic diversity for the traits measured. The highest estimates of

heritability were obtained for plant height (average across locations of 0.275) and ear height

(0.215). While plant height is often reported to have lower heritabilities than ear traits (Abe &

Adelegan, 2019), the University of Florida breeding program has applied stronger selection

pressure to improve ear traits, further suggesting that the low heritability estimates were

obtained, at least in part, due to the low amount of genetic variability observed in this population.

It is important to highlight that the genetic base of sweet corn is already known to be narrow

when compared to field corn germplasm. This happens because a strong selection pressure is

naturally applied given the requirements for starch mutants as well as all the aesthetic

requirements that further impose new bottlenecks (W.F. Tracy, 2001).

The heritabilities of ear length and ear width showed the greatest variation between the

two environments, varying from 0.14 in Citra to 0.24 in Belle Glade for ear length and 0.09 to

0.22 for ear width. This may be due to differences in multiple growing factors in both the

environment such as Belle Glade has organic muck soil whereas Citra has sandy soil. Both the

environments also have other agro-climatic differences which could be the reason apart from

bias in phenotyping being introduced. Voichita et al. (2009) and Kumari et al. (2006) reported

36

higher heritability for ear length and ear width for a single environment for the hybrid population

used by them.

Kernel row number and tassel length had comparatively lowest heritabilities among all

the traits. Both of these traits are shown to be vastly affected by genetic and environment,

location and genotype. Voichita et al. (2009) found higher heritability for kernel row number on

the hybrid population used.

Figure 2-1. Phenotypic distribution of six selected traits in Belle Glade (South Florida)

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Figure 2-2. Phenotypic distribution of selected traits in Citra (Central Florida)

G x E Interaction

The genotypes and the environments interaction estimates using two different metrics:

type A ranged from 0.46 to 0.80 and type B ranged from 0.43 to 0.83 (Table 2-4). Type A

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correlation is an indication of the agreement in the breeding value rankings in each environment.

Type B genetic correlation is a measure of what is the joint breeding potential for both sites, after

adjusting for the presence of interaction. The results were consistent between type A and type B

measurements, indicating that the amount of genotype by environment variance is proportional

to the change in the ranking of the breeding values across sites. The GxE type B estimates were

high for ear length and ear width (0.46 and 0.50, respectively). Similarly, a high correlation was

observed by type A (0.43 and 0.54 respectively) correlation. On the other hand, Solomon et al.

(2012) drew much higher type B estimates on ear length (0.10) and ear width (0.06) on the

combined hybrid and inbred population used by them which further confirmed the unstability of

these two traits due to environmental influences. This result could open new questions regarding

the genetic mechanism of ear development and its interactions with environmental stresses.

These results will also have an applied impact in the breeding program since they suggest that

ear length and width cannot be selected in Citra, if the goal of the program is to improve these

traits for the markets near Belle Glade. Thus, a larger population and multiple years would be

needed to improve these two traits since only single-season selection in Belle Glade would be

possible.

For other traits, medium-low type B correlation was observed for tassel length (0.80), ear

height (0.78), plant height (0.78) and kernel row number/ear (0.76). Similarly, minor influence

on the ranking of breeding value was observed through type A correlation. The contradictory

result was found by Solomon et al. (2012) for plant height (0.13) and ear height (0.29) for their

three environment type B correlation. The results were surprising, as we originally hypothesized

that the soil differences would have a larger effect on plant traits, such as plant height than ear

traits. Nonetheless, the low estimates of GxE for important traits such as ear height, plant height,

39

and kernel row number could represent an opportunity to have two seasons of selection in the

same year, including the off-season nursery of Citra.

Table 2-4. Estimates of narrow-sense heritability (h2) and type B & type A GxE interaction from

two different environments

h2 Belle Glade h2 Citra

Type B

CorBC

Type A

CorBC

Ear height 0.19 0.24 0.76 0.78

Tassel Length 0.13 0.17 0.80 0.83

Plant Height 0.29 0.26 0.73 0.78

KRN 0.14 0.10 0.70 0.76

Ear Length 0.24 0.14 0.46 0.43

Ear Width 0.22 0.09 0.50 0.54

Trait Genetic Correlation

Breeding programs in any crop take into consideration multiple traits and goals. This

process is complex since selecting for a trait of interest may often have negative effects on

another one. Moreover, phenotyping can also be an expensive and labor-intensive process, which

gets worse as the number of phenotyped traits increase. In this experiment, we also aimed at

calculating trait genetic correlations for sweet corn traits (Table 2-5 and 2-6). This knowledge

can be used to prioritize selections, and in indirect selection (A. Hallauer & Miranda, 1981),

potentially reduce the phenotyping costs.

Pairwise genetic correlation among the traits evaluated in Belle Glade ranged from

negative (-0.06) to positive (0.76) values (Table 2-5). The highest correlations were observed

between tassel length and plant height (0.52), and plant height and ear height (0.76). The high

correlation for plant height and ear height is expected, as taller plants can be expected to have the

primary ear developing at higher heights. In previous studies, these high correlation pairs were

also reported in different populations of sweet corn inbred and hybrids (Gonçalves et al., 2018;

Kashiani, Saleh, Abdullah, & Abdullah, 2010).

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Kernel row numbers, however, were estimated to have a negative correlation with ear

length in both environments. This information is important since sweet corn breeders are

normally aiming to improve both traits simultaneously, which may require independent

selections and parallel breeding schemes. A similar negative correlation was reported by Hefny

(2011).

The trait correlations among all pairs were higher in Citra than in Belle Glade. The results

also suggest that the implementation of indirect selection would show more positive results in

Citra than in Belle Glade. While this information is interesting for future studies on the

molecular mechanism of the genetic interaction, its application is limited since Citra is currently

considered an off-site nursery. It was interesting, though, to observe a positive correlation of ear

width with all other traits.

Sweet corn elite hybrids are known to have significantly longer tassel structures when

compared to field corn hybrids. One of the goals of this study was to assess if the longer tassels

were negatively correlated with ear traits, presumably due to higher energy demands. This

hypothesis was not confirmed and the only negative correlation involving tassel length was

estimated in Belle Glade for the trait kernel row number. Interestingly, there was a positive

correlation between tassel length and ear length (0.53). This result may explain why sweet corn

tassels are bigger and longer when compared to field corn, and why the selection for small and

highly productive tassels has never been a focus of sweet corn breeder.

Estimated Genetic Gain

The final outcome of this experiment was to utilize the genetic parameters to estimate the

rate of progress that we can make in the population used in this study. While phenotypic mass

selection has been successful in many crops, the rate of genetic gain is dependent on the amount

of genetic variability, hence a function of heritability. Furthermore, the amount of gain obtained

41

in each generation is dependent on the intensity of selection and the genetic correlation between

traits (if calculating indirect genetic gain). The breeder’s dilemma is how to maximize the

genetic gain without compromising genetic diversity (Entringer et al., 2016).

Table 2-5. Genetic correlation coefficients among traits measured in Belle Glade (South Florida)

EL = Ear Length, EW = Ear Width, EH = Ear height, KRN = Kernel Row Number, PH = Plant Height, TL = Tassel

Length

Table 2-6. Genetic correlation coefficients among traits measured in Citra (Central Florida)

Traits EL EW EH KRN PH TL

EL 1 0.32 0.27 -0.15 0.24 0.23

EW 1 0.46 0.69 0.53 0.33

EH 1 0.21 0.77 0.15

KRN 1 0.29 0.18

PH 1 0.61

TL 1 EL = Ear Length, EW = Ear Width, EH = Ear height, KRN = Kernel Row Number, PH = Plant Height, TL = Tassel

Length

Here, we have calculated direct genetic gains for each trait in each environment,

assuming selection intensities of 5, 10 and 20%. Furthermore, we have also calculated the

indirect gain observed in one given trait when selection is applied on another trait (table 2-7 and

table 2-8) as well as the indirect gain of one trait in one environment when selection is applied on

the same trait in a different environment. The expected direct genetic gain for most of the traits

in Citra was higher than in Belle Glade except kernel row number, ear length and ear width,

which had higher heritabilities in Belle Glade. It was also observed that the selection intensity of

5% showed the highest genetic gain, which was expected. The downside of this approach is the

Traits EL EW EH KRN PH TL

EL 1 0.02 0.38 -0.06 0.41 0.53

EW 1 0.35 0.37 0.29 0.06

EH 1 0.19 0.76 0.02

KRN 1 0.13 -0.08

PH 1 0.52

TL 1

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fact that the selected individuals would trace to fewer parent and reduce even more the genetic

diversity in the population which may lead to inbreeding depression. For instance, in Citra, at

selection intensity of 5% for ear height, the top 4 genotype selected had 3 common parents of

N12-1, plant height had 2 common sets of parents of 9EC and N129, tassel length had 2 common

parents of 9EC, kernel row number had 2 common parents of 9EC, ear length had 2 common

parents of N12-1, ear width had 2 common sets of parents of N7-1 and 9EC. Similarly, in Belle

Glade at selection intensity of 5% for ear height the top 4 genotype selected had 2 common

parents of N124-1 and 3 common parents of M90, tassel length had 2 common parents of N120

and 3 common parents of M90, kernel row number had 2 common parents of N12-1, ear length

had 2 common parents of N129 and 2 common parents of N7-1, ear width had 2 common parents

of M104-1. If short term genetic gain is needed, a selection intensity of 5% would have to be

coupled with a parallel introgression of novel sources of genetic diversity. Conversely, a

selection intensity of 10% and 20% would be better to minimize the decay in genetic diversity

while improving the population.

The estimated direct genetic gain per cycle as a percentage of mean obtained in Belle

Glade at 5, 10 and 20% of selection for ear height was 7.61, 6.81 and 4.17%; for plant height was

6.33, 5.17 and 3.92%; for tassel length, 2.32, 2.16 and 1.81%; for kernels row number was 2.73,

2.38 and 1.87%; for ear length was 4.62, 4.00 and 3.13%; for ear width was 3.01, 2.67 and

2.15% respectively. In Citra, the estimated direct genetic gain per cycle at 5, 10 and 20% of

selection for ear height was 11.21, 9.36 and 7.51%; for plant height was 6.75, 5.61 and 4.25%;

for tassel length was 4.08, 3.46 and 2.72%; for kernels row number was 1.27, 1.1 and 0.86%; for

ear length was 3.56, 3.09 and 2.79%; for ear width was 1.56, 1.35 and 1.09% respectively. The

low estimates of heritability reflect the low genetic gains obtained. These results are concerning

43

as they suggest that very minimal gains can be obtained for some of these traits, such as kernel

row number in Belle Glade. Previous studies, in different sweet corn populations, found higher

estimates of genetic gain in their population (Alan et al., 2013; Bello et al., 2012; Hefny, 2011).

Table 2-7. Direct (bold values) and indirect genetic gain as a percent of mean in Belle Glade.

The selection was performed in the traits listed in the columns, and the result of

indirect selection is expressed for the traits in the rows

Traits S% EH PH TL KRN EL EW

EH 5 7.61 5.78 0.15 1.45 2.89 2.66

10 6.81 5.18 0.14 1.29 2.59 2.39

20 4.17 3.17 0.08 0.79 1.59 1.46

PH 5 4.81 6.33 3.29 0.82 2.59 1.83

10 3.93 5.17 2.69 0.67 2.12 1.5

20 2.98 3.92 2.04 0.51 1.61 1.14

TL 5 0.05 1.21 2.32 -0.19 1.23 0.14

10 0.04 1.12 2.16 -0.17 1.15 0.13

20 0.04 0.94 1.81 -0.14 0.96 0.11

KRN 5 0.52 0.36 -0.22 2.73 -0.16 1.01

10 0.45 0.31 -0.19 2.38 -0.14 0.88

20 0.36 0.24 -0.15 1.87 -0.11 0.69

EL 5 1.76 1.89 2.45 -0.28 4.62 0.09

10 1.52 1.64 2.12 -0.24 4 0.08

20 1.19 1.28 1.66 -0.19 3.13 0.06

EW 5 1.05 0.87 0.18 1.11 0.06 3.01

10 0.93 0.77 0.16 0.99 0.05 2.67 20 0.75 0.62 0.13 0.79 0.04 2.15

S% = Selection Percentage, EH = Ear height, PH = Plant Height, TL = Tassel Length, KRN = Kernel Row Number, EL = Ear

Length, EW = Ear Width

Another challenge faced by the breeders of any crop is the fact that different traits of

interest may have different genetic mechanisms and opposite trends. Hence, improving the crop

for one trait could have a negative effect on the second trait of interest (Brancourt-Hulmel et al.,

2005). In our experiment, we observed this situation if the selection was to be performed to

improve kernel row number, which would lead to a negative selection gain on ear length. Future

research could evaluate if these trends are causal and mechanistic. For breeding applications, a -

44

possible solution would be to carry parallel selections for the traits of interest or to utilize a

selection index approach that will allow putting weights on traits (and others) in consideration.

Table 2-8. Direct (bold values) and indirect genetic gain as a percent of mean in Citra. The

selection was performed in the traits listed in the columns, and the result of indirect selection is

expressed for the traits in the rows

Traits S% EH PH TL KRN EL EW

EH 5 11.21 8.63 1.68 2.35 3.03 5.16

10 9.36 7.21 1.4 1.97 2.53 4.31

20 7.51 5.78 1.13 1.58 2.03 3.45

PH 5 5.19 6.75 4.12 1.96 1.62 3.58

10 4.32 5.61 3.42 1.63 1.35 2.97

20 3.28 4.25 2.59 1.23 1.02 2.54

TL 5 0.61 2.49 4.08 0.73 0.94 1.35

10 0.52 2.11 3.46 0.62 0.8 1.14

20 0.38 1.66 2.72 0.49 0.63 0.9

KRN 5 0.27 0.37 0.23 1.27 -0.19 0.88

10 0.23 0.32 0.2 1.1 -0.17 0.76

20 0.18 0.25 0.15 0.86 -0.13 0.59

EL 5 0.96 0.85 0.82 -0.53 3.56 1.14

10 0.83 0.74 0.71 -0.46 3.09 0.99

20 0.75 0.67 0.64 -0.42 2.79 0.89

EW 5 0.72 0.83 0.51 1.07 0.5 1.56

10 0.62 0.72 0.45 0.93 0.43 1.35 20 0.5 0.58 0.36 0.75 0.35 1.09

S% = Selection Percentage, EH = Ear Height, PH = Plant Height, TL = Tassel Length, KRN = Kernel Row Number,

EL = Ear Length, EW = Ear Width

Genetic gains for plant traits were generally higher in Citra than in Belle Glade, while ear

traits are predicted to have more gain in Belle Glade. The reduced gains are a consequence of

lower heritability estimates, likely due to noise/bias in the phenotyping which increased the

residual variance during modeling. In addition to infusing novel sources of genetic diversity to

increase the heritabilities, an extra genetic gain can be obtained by improving the phenotyping

methods and tools which will in turn also result in higher heritability estimates.

The genetic gain for the same trait in different environments was also predicted (Table 2-

9). For ear height and tassel length, the indirect selection from Citra Belle Glade is predicted

45

to result in higher genetic gains than the direct selection from Belle Glade Belle Glade. Future

studies with more repetitions of the same environments are needed to address whether this was

simply due to more accurate phenotyping in Citra, or due to the fact that more sources of

variation in Belle Glade (e.g. micro environmental noise) consistently increase the residual

variance and reduce the heritability. Although a breeder would never stop setting field trials in its

main market, these results are encouraging, once again, suggesting that the breeding program can

implement two seasons of selection per year (in Citra and Belle Glade).

Table 2-9. Direct and indirect genetic gain of selecting a trait in different environments

Traits S% BG C Traits S% C BG

EHBG 5 7.61 5.78 EHC 5 11.21 8.52

10 6.81 5.18 10 9.36 7.11

20 5.26 4 20 7.51 5.71

PHBG 5 6.33 4.62 PHC 5 6.65 4.92

10 5.17 3.78 10 5.61 4.09

20 3.92 2.86 20 4.25 3.11

TLBG 5 2.32 1.86 TLC 5 4.08 3.27

10 2.16 1.73 10 3.46 2.77

20 1.81 1.45 20 2.72 2.18

ELBG 5 4.62 2.13 ELC 5 3.56 1.64

10 4 1.84 10 3.09 1.42

20 3.13 1.44 20 2.51 1.15

EWBG 5 3.01 1.51 EWC 5 1.56 0.78

10 2.67 1.33 10 1.35 0.68

20 2.15 1.07 20 1.09 0.55 BG = Belle Glade, C = Citra, S% = Selection Percentage, EH = Ear Height, PH = Plant Height, TL = Tassel Length,

KRN = Kernel Row Number, EL = Ear Length, EW = Ear Width

Conclusion

The replicated study in two environments with 76 genotypes was conducted and

phenotypic variance in the population shows that selected economically important traits can be

46

improved. The narrow-sense heritability (h2) obtained ranged from 0.09 to 0.29 which is within

the expected range of corn but in the lower end of what has typically been observed in other

studies. The genotype by environment interaction (Type B) showed lower interaction with the

environment for ear height, plant height, tassel length, and kernel row number but on the other

hand, it showed high interaction with the environment for ear length and ear width. Hence,

breeding for ear traits requires selection to be performed in South Florida (Belle Glade). Type A

correlation too confirmed the influence of the environment over the ranking of the superior

genotypes on ear length and width. The trait genetic correlation between many traits was low

except for plant height to tassel length (~0.56) and plant height to ear height (~0.76) in both the

environments. Therefore, indicating that strategic selection needs to be applied so that other traits

are not adversely affected. Future research could include the evaluation of different selection

index methods, to identify the best way to select for multiple traits. The estimates of indirect

selection showed positive genetic gain in many of the cases. This strategy will now be expanded

in the breeding program to multiple traits, to identify which ones need or not to be phenotyped.

Stringent selection intensity of 5% gives the highest genetic gain, as expected, but it also limits,

even more, the genetic diversity. Therefore, the selection of 10 or 20 % is recommended. The

inclusion of new sources of genetic diversity will also limit the gain in a short-term but will

guarantee the sustainability of the program.

47

CHAPTER 3

ANALYSIS OF SUGAR CONTENT USING NEAR-INFRARED SPECTROSCOPY ON A

FRESH KERNEL OF SWEET CORN

Introduction

In sweet corn, kernel sweetness is an important quality for consumer satisfaction

(Evensen & Boyer, 1986; Wann, Brown, & Hills, 1971). However, sugar starts deteriorating

after the ear is harvested, therefore becoming an important post-harvest issue. Sweetness is

affected by the rate of cellular respiration as well as the levels that sugar gets converted to starch

in the endosperm. Temperature and time are two parameters that play a major role in sugar loss

(Garwood et al., 1976). According to Boyette et al. (1990), respiration rate in sweet corn is

reported to be one of the highest among fruits and vegetables. At the time of harvest, kernel pulp

temperature is usually above 32 °C, thus rapid removal of field heat is essential to slow the high

respiration rate that leads to the breakdown of sugars, starches, and other complex molecules

(Kader & Saltveit, 2003). Because of these two traits of interest – flavor, and shelf-life - the

selection of sweet corn varieties with the highest amount of sugar content have always been a

target for genetic improvement. The main genes that disrupt the starch biosynthesis pathway and

increase sugar content are known and have been reviewed in Chapter 1. The main ones explored

in sweet corn breeding are shrunken2 (sh2), brittle1 (bt), brittle2 (bt2), sugary1 (su1), and sugary

enhancer1 (se) (Boyer & Shannon, 1983).

The shrunken2, brittle1 and brittle2 genes, decrease significantly the starch content while

increasing the sugar content and higher sugar content were classified as Class 1 mutants (Boyer

& Shannon, 1983) and are located on chromosome 3, 4 and 5 respectively. The dried mature

kernels of both mutants are often angular, translucent, collapsed and brittle. Sugary1 and Sugary

enhancer1 are known to have lower sugar content, higher starch accumulation and also known to

have a reduced shelf life when compared to sh2, bt1, and bt2. Both of these mutants are

48

considered as class 2 mutants (Boyer & Shannon, 1983) and are located on chromosome 4 and 2

respectively. Dry mature kernels of su1 are often wrinkled and translucent while kernels of se are

often light-colored, opaque, collapsed, brittle with its color, at times, varying with the

background (Lertrat & Pulam, 2007; William F Tracy, 1997).

While these are the main genes explored in sweet corn, sugar content is a quantitative

trait, and other genetic mechanisms are involved leading to a range of phenotypes even within a

given mutant class. The amount of total soluble sugars in sweet corn kernels varies among

mutants but it can range between 22% to 80% at fresh harvest on a dry weight basis. Sucrose

comprises approximately 90% of the soluble sugars in sweet corn (Creech, 1965). The se gene

increases the sugar content of sugary backgrounds, which can vary from 14% to 35% (William F

Tracy, 1997). Genetic background of the shrunken2 gene can also show large variability in sugar

content, centered at around 30% (Lertrat & Pulam, 2007). Hence, phenotyping the varieties for

sugar content is a very important trait, and breeders can explore a combination of mutants as well

as additional unknown genetic mechanism commonly referred to as ‘modifiers’.

Breeding of fruits and vegetables typically include secondary selections for flavor and

appearance attributes. In sweet corn, the flavor selection is carried by the breeder in the field, a

process generally referred as ‘bite test’ (Solomon et al., 2012; Voisey & Nuttall, 1965). The

process is the preferred choice because large scale sensory panels are extremely low-throughput

and expensive. Nonetheless, the ‘bite-test’ still has limited throughput and is potentially biased

by the fact that the sensory perception of the breeder may change throughout the day after biting

many different varieties. Given that sugar is the main driver of consumer liking in sweet corn,

one approach to enable the characterization of large amounts of inbred and hybrids is the indirect

phenotyping of sugar content. Several methods to determine sugar content have been reported,

49

however, they all had drawbacks which are typically related to the process which can be too

variable, tedious, time-consuming and difficult to perform. Approaches previously reported were

the use of refractometer (Hale, Hassell, & Phillips, 2005) to measure total soluble sugars (TSS),

paper chromatography (Shallenberger & Moores, 1957) used for qualitative analysis, liquid

chromatography (Kurilich & Juvik, 1999) used for both qualitative and quantitative analysis.

These methods either showed low reliability, low-throughput, were laborious or their results are

difficult to quantify.

Colorimetric quantification based on enzymatic assays has been the golden standard of

sugar quantification in many breeding programs. This was the method of choice because of

simple sample preparation and fact that it doesn’t require any expensive instrumentation. Hence

it is a good alternative for analyzing a small number of samples. Maughan et al. (2000) used this

method to quantify sucrose in 149 soybean varieties, Reyes (1982) used for sugar quantification

in strawberries at three stages of maturity, Viola and Davies (1992) had success using this

enzyme reaction system to quantify sugar in potato tubers. However, even in this case, the

throughput is still not high enough to phenotype all the stages of a sweet corn breeding program.

In this context, we decided to explore solutions that could be applied to the operational

sweet corn breeding program from the University of Florida. Near-infrared spectroscopy (NIRS)

is a high throughput phenotyping method which uses an electromagnetic spectrum of 700nm to

2500nm to provide complex information related to vibration behavior of the combination of

bonds which is generated by several light sources and is invisible to human eyes (Qu et al.,

2015). NIRS has been shown to be accurate, reliable and allows the quantification of a solid

sample with little or no sample preparation (Osborne, 2006; Qu et al., 2015). It can speed up the

sugar phenotyping by cutting down complex and time-consuming methods used by

50

electrochemical, chromatographic and enzymatic quantification. Sugar quantification using

NIRS has already been reported to quantify sucrose in satsuma orange (Kawano, Sato, &

Iwamoto, 1992), sugar quantification in fruit juices (Lanza & Li, 1984), individual sugar

quantification in aqueous mixtures (Giangiacomo & Dull, 1986) and to quantify sugars in tomato

products (Pedro & Ferreira, 2007). In corn, NIRS is also routinely used to quantify starch

(Albrecht, Marten, Halgerson, & Wedin, 1987; Hattey, Sabbe, Baten, & Blakeney, 1994; Jiang et

al., 2007; Wei, Yan, & Dai, 2004), protein content (Jiang et al., 2007; Velasco & Möllers, 2002;

Wei et al., 2004) and oil content (Elfadl, Reinbrecht, & Claupein, 2012; Jiang et al., 2007;

Velasco, Möllers, & Becker, 1999).

To our knowledge, there are currently no methods developed to quantify sugar content in

fresh sweet corn kernels. While sample preparation would still require freezing and subsequently

drying the kernels, we expect the process to be significantly simplified and faster compared with

current methodologies. Hence, the main aim of this chapter was to inspect the enzymatic

quantification and compare it with near-infrared spectroscopy (NIRS) for sucrose, glucose, and

fructose in fresh sweet corn kernels.

Material and Methods

For this experiment, 14 genotypes, a mix of single, double and triple mutants, were

donated by Harris Moran Clause (California, US) hybrid trial at Belle Glade, Florida. These

genotypes were planted in 4 replicates, for a total of 56 plots. These 56 genotypes were self-

pollinated and harvested 20 days after pollination. Soon after harvesting they were transported to

the Everglades Research and Education Center, Belle Glade, Florida. There, the samples were

de-husked, and flash-frozen with liquid nitrogen upon arrival. The kernels from flash-frozen ears

were popped out and stored in 50 ml falcon tube at -80 C.

51

Once all the genotyped had been harvested, 15 kernels were aliquoted and lyophilized

until kernels were dried (~ 4 days). Lyophilized kernels were ground in a Geno-grinder with

metal beads for 5 minutes or until they became a fine powder. This powder was used for the

enzymatic quantification using the ‘Megazyme K-SUFRG 04/18’ (Megazyme Inc., Wicklow,

Ireland) assay kit with minor modifications as described below.

Extraction of Soluble Sugars

All powdered samples were aliquoted in separate 1.5ml Eppendorf tube followed by

adding 1ml of cold d-H2O (4oC) which was vortexed and put on a heated water bath at 80oC for

30 minutes. Later it was centrifuged for 10 minutes to separate supernatant which was pipetted

out to a new 1.5ml Eppendorf tube. At this point, two dilutions were made for each of the

samples, one for glucose/fructose (1:10) and the other for sucrose (1:50). Diluted samples were

put in 96 well plates in 2 volumes (25 and 50 𝜇l) for glucose/fructose and in a separate column

for sucrose.

The principle of the assay relies on the determination of D-glucose concentration by

indirectly measuring an increase in absorbance associated with reduced nicotinamide adenine

dinucleotide phosphate (NADPH) produced during the oxidation of glucose-6-phosphate. The

process is catalyzed by the enzyme hexokinase and glucose-6-phosphate dehydrogenase (G6P-

DH), as indicated below

Glucose/Fructose Column:

(1) D-Glucose + ATP 𝐻𝑒𝑥𝑜𝑘𝑖𝑛𝑎𝑠𝑒 → G-6-P + ADP

(2) G-6-P + NADP+ 𝐺6𝑃−𝐷𝐻 → gluconate-6-phosphate + NADPH + H+

52

The increase in absorbance produced by the increase of NADPH is proportionally

equivalent to the concentration of D-Glucose and can be calculated based on a standard solution

containing known concentrations of glucose.

Fructose is also quantified in a similar method, but with an intermediate step involving

the addition of the enzyme phosphoglucose isomerase (PGI), which converts fructose-6-

phosphate to glucose-6-phosphate as follows:

(3) D-Fructose + ATP 𝐻𝑒𝑥𝑜𝑘𝑖𝑛𝑎𝑠𝑒 → F-6-P + ADP

The F-6-P is subsequently converted to G-6-P by PGI

(4) F-6-P (𝑃𝐺𝐼) ↔ G-6-P

The resulting G-6-P is subsequently treated with G6P-DH and the quantification happens

in the same way as the glucose samples. Finally, the quantification of sucrose also utilizes the

same principle, but depends on the addition of 𝛽 − 𝑓𝑟𝑢𝑐𝑡𝑜𝑠𝑖𝑑𝑎𝑠𝑒 (20 𝜇l) to hydrolyze sucrose

producing D-glucose and D-fructose as follows:

(5) Sucrose + H2O 𝛽−𝑓𝑟𝑢𝑐𝑡𝑜𝑠𝑖𝑑𝑎𝑠𝑒 → D-glucose + D- fructose

The absorbance was measured using a spectrophotometer (Bio Tek Instruments Inc.,

Vermont, USA) and sucrose quantification was based on quantifying D-glucose concentration

before and after the treatment with𝛽 − 𝑓𝑟𝑢𝑐𝑡𝑜𝑠𝑖𝑑𝑎𝑠𝑒. In each tube, 3 readings were recorded.

A1 was recorded before any enzyme was added to be used as a base level, A2 was recorded after

the addition of glucose-6-phosphate dehydrogenase (G6P-DH). In the tube used to quantify

glucose/fructose, a third measurement was taken (A3) after the addition of PGI/G6P-DH, to

quantify the amount of fructose.

Calculation. The amount of glucose was calculated by subtracting A1 from A2 from

glucose/fructose column. In the reaction (2) NADPH was measured by the increased absorbance

53

at 340nm and the amount of NADPH formed is equal to glucose. Similarly, fructose was

calculated by subtracting A3 from A2 from glucose/fructose column. In the reaction (4) PGI

converts G-6-P to F-6-P which again repeats the reaction (2) and leading the further rise in

absorbance from A2 to A3. The difference in absorbance is equal to the amount of fructose. The

amount of sucrose was calculated by subtracting A1 from A2 from sucrose column and further

subtracting the initial glucose concentration.

Near-infrared Spectroscopy (NIRS)

The NIRS used in this study was a custom-built single kernel NIR (Figure 3-1A)

available in the laboratory of Dr. Mark Settles (UF). It was built for measurement of corn,

soybeans or similar size seeds attribute, for determining variability, sorting, etc. Single kernel

NIRS (SkNIRS) was chosen because seed availability was limited and below the general

requirements of other instruments that use ground tissue. The detailed description of the

operation and construction of the assembly is provided in the paper by (Armstrong, 2006).

Figure 3-1. Single kernel Near-infrared Spectroscopy A) Image of the custom-built single kernel

NIR used for the study. B) Single Kernel Near Infrared Instrument schematic diagram

(Credit: Dr. Jeffrey L. Gustin)

54

For the NIR data collection, a different set of kernels harvested from the same ear was

lyophilized but not ground. Individual kernels were manually dropped from the single kernel

NIR feeder onto a microbalance which is programmed to record seed weight. This feature was

currently not used in this study, but it is part of the process. The software after recording seed

weight triggers an air valve to blow kernel into a glass tube. The kernel passes through a glass

tube (12mm X 60mm) illuminated by multiple halogen light bulbs (Figure 3-1B). The reflected

light was collected through two 400-micron fiber optic cables attached at the top and bottom

ends of the glass tube. The fiber optics cables were attached to an In GaAs array-based

spectrometer (NIR-256-1.7TI., Control Development, South Bend, IN). The acquired spectrum

data was sent to the computer where reflectance values were recorded at 1 nm intervals between

906 to 1687 nm and absorbance values were computed as log (1/R) where R is the reflectance.

NIR spectra were collected on 20 kernels per genotype to capture the varying degree of

sweetness from a single ear and were averaged for model development.

Calibration and validation. To utilize the NIR spectra data to quantify sugars in the

kernel, we developed a statistical model based on regression against each of the enzymatic

quantifications of sucrose, glucose, and fructose. The regression was developed using partial

least square regression (De Oliveira, De Castilhos, Renard, & Bureau, 2014). Cross-validation

was used to estimate the prediction error. In summary, the cross-validation method splits all the

samples into two groups i.e. testing set (20%) and training set (80%) and the process was

repeated until all the samples had been once in the training/validation set (De Oliveira et al.,

2014; Xie, Ye, Liu, & Ying, 2009). To evaluate the results, the correlation between predicted and

observed as well as the root mean square error of prediction (RMSEP) was calculated.

55

Result and Discussion

Enzymatic Quantification Using Megazyme Assay Kit

The sugars quantified using Megazyme (K-SUFRG 04/18) enzymatic assay procedure for

14 genotypes ranged from 40 - 65% for sucrose, 3 - 5.6 % for glucose, and 2.3 – 4.8 % for

fructose (Table 3-2). The results are higher than previously reported sucrose by Tracy, 1997,

likely because this analysis was carried on genotypes that were synergistic (hybrids carrying su1,

one or two copies of se and one copy of sh2 gene) and augmented varieties (hybrids carrying su1,

two copies of se and two copy of sh2 gene). Genotype 1495, which is an sh2 commercial hybrid

resulted in sugar concentrations that were in line with previous findings (Wong, Juvik, Breeden,

& Swiader, 1994). While it is possible that technical variability in the method, as well as heat

index variation on plants grown for 20 days after pollination in different environments, led to this

variability, one additional alternative is the presence of additional genetic regulators that affect

sugar content in the same mutant background.

Table 3-1. Analysis of variance for three sugars in sweet corn by Enzymatic method

Source DF Sucrose Glucose Fructose

Genotypes 13 12026.2*** 81.59** 44.19*

Replications 3 1413 18.05 6.89

Error 39 2018.2 23.18 19.46

*Significance at 0.05, **Significance at 0.01, ***Significance at 0.001

The results of the analysis of variance evidenced a significant genotype effect for all three

traits. The replicates were grown in the same environment in a small area and did not show a

significant effect. The constitution of each sample and the partition between sucrose, glucose,

and fructose were very similar and in the majority of the genotypes represented by 88%, 7% and

5% of sucrose, glucose, and fructose, respectively. These results are evidence that the different

56

starch mutants that accompany sh2 in this test set (su1, se1) are involved in the increase of sugar

content but not in creating a different partition of the sugars in the sample.

Table 3-2. Concentration (%) of sucrose, glucose, and fructose in 14 sweet corn hybrids

determined by Enzymatic method

Variety Mutant Sucrose Glucose Fructose

HMX59BS603 - 51.61 ± 4.87 4.19 ± 0.95 3.49 ± 0.72

HMX59BS605 sh2sh2sese x sh2sh2sese 55.4 ± 5.03 4.18 ± 0.64 3.56 ± 0.46

Raquel sh2sh2sese x sh2sh2sese 47.91 ± 3.85 4.66 ± 0.33 3.53 ± 0.32

Rosie

sh2sh2sese x

sh2sh2su1su1sese 60.82 ± 5.09 4.94 ± 0.33 4.25 ± 0.46

Fantastic - 51.48 ± 4 4.3 ± 0.5 3.28 ± 0.6

Kate

sh2sh2sese x

sh2sh2su1su1sese 62.45 ± 2.82 4.1 ± 0.55 3.39 ± 0.54

SV7143 - 54.18 ± 5.58 3.95 ± 0.1 2.99 ± 0.07

Vision - 57.76 ± 8.05 4.5 ± 0.35 3.37 ± 0.36

Cindy

sh2sh2sese x

sh2sh2su1su1sese 60.05 ± 2.43 3.9 ± 0.23 3.13 ± 0.12

Candice

sh2sh2sese x

sh2sh2su1su1sese 60.03 ± 3.24 4.09 ± 0.73 3.28 ± 0.67

Elle

sh2sh2sese x

sh2sh2su1su1sese 61.18 ± 1.23 3.49 ± 0.11 3.21 ± 0.11

Driver sh2sh2 x sh2sh2 43.96 ± 3.05 5.2 ± 0.49 3.79 ± 0.42

Natalie sh2sh2sese x sh2sh2sese 52.62 ± 1.36 4.75 ± 0.32 3.62 ± 0.24

HMX608 sh2sh2sese x sh2sh2sese 57.38 ± 6.16 4.42 ± 0.23 3.91 ± 0.25

The mean and standard deviation of four measurements of each hybrid

The sucrose quantification using the enzymatic method can be grouped based on the

different mutant genotypes (Figure 3 - 2). As expected, the hybrids that contained a triple mutant

in one of the parents (sh2sh2su1su1sese) had the sweetest kernel, followed by hybrids with

double mutant parents (sh2sh2sese) and the single hybrid which had a shrunken2 genotype. The

genotype class for the varieties Fantastic, Vision, SV7143, and HMX59BS605 were unknown.

However, based on the clustering of sucrose contents, we could hypothesize that these varieties,

also had the sh2sh2sese genotype, like others with similar amounts of sucrose. DNA information

from these lines is not available to us to validate this hypothesis. Nonetheless, this observation

57

could lead to future research to develop a method to characterize the commercially explored

starch mutants based on their sucrose concentration.

Figure 3-2. Sucrose content grouped by mutant genes using the enzymatic method

Enzymatic Method vs. NIRS

We utilized all the NIR spectra to regress against the enzymatic concentration and

establish a calibration curve for sucrose prediction. The predictive ability, calculated as the

correlation between observed and predicted, and the mean square error can be seen in Table 3-3.

The correlation for sucrose between enzymatic method and NIRS was 83%. This result is

positive since it evidences a positive trend on the predictions from NIR spectra with the well-

established enzymatic methods. However, further improvements would be needed to actually

implement NIR as a phenotyping tool. Similarly, for fructose, the correlation obtained was 44%

and for glucose, the correlation was virtually 0%. The results per variety can be observed in

Table 3-4.

The results show good potential for sucrose quantification but are yet limiting for

quantification of the other sugars. It is possible that the low predictabilities of sucrose and

58

fructose are associated with the fact that there is a small amount of these sugars in the

endosperm.

Table 3-3. Statistics for sugar samples using PLS regression models (n = 56)

Correlation Mean Square Error

Sucrose 0.83 877.39

Glucose 0.04 19.43

Fructose 0.44 8.49

Table 3-4. NIRS predicted concentration (%) of sucrose, glucose, and fructose in 14 sweet corn

hybrids with respect to the enzymatic method

Variety Mutant Sucrose Glucose Fructose

HMX59BS603 - 51.05 ± 5.01 4.37 ± 0.34 3.38 ± 0.25

HMX59BS605 sh2sh2sese x sh2sh2sese 55.87 ± 4.69 4.36 ± 0.36 3.61 ± 0.42

Raquel sh2sh2sese x sh2sh2sese 60.81 ± 1.7 4.22 ± 0.2 3.61 ± 0.48

Rosie sh2sh2sese x sh2sh2su1su1sese 53.14 ± 7.65 4.39 ± 0.24 3.43 ± 0.15

Fantastic - 57.19 ± 4.1 4.19 ± 0.22 3.41 ± 0.32

Kate sh2sh2sese x sh2sh2su1su1sese 54.16 ± 4.17 4.38 ± 0.1 3.21 ± 0.34

SV7143 - 54.5 ± 0.67 4.43 ± 0.08 3.39 ± 0.18

Vision - 54.21 ± 3.58 4.28 ± 0.08 3.49 ± 0.28

Cindy sh2sh2sese x sh2sh2su1su1sese 57.4 ± 1.41 4.34 ± 0.14 3.61 ± 0.2

Candice sh2sh2sese x sh2sh2su1su1sese 60.41 ± 3.08 4.32 ± 0.03 3.69 ± 0.28

Elle sh2sh2sese x sh2sh2su1su1sese 54.79 ± 1.21 4.41 ± 0.21 3.39 ± 0.28

Driver sh2sh2 x sh2sh2 53.93 ± 7.44 4.39 ± 0.29 3.55 ± 0.35

Natalie sh2sh2sese x sh2sh2sese 57.63 ± 2.76 4.31 ± 0.22 3.56 ± 0.2

HMX608 sh2sh2sese x sh2sh2sese 61.77 ± 2.38 4.51 ± 0.2 3.83 ± 0.27

The mean and standard deviation of four measurements of each hybrid

Moreover, concentrations of sucrose, glucose, and fructose were previously predicted

using the NIR in apples and bayberry juice ((Liu, Ying, Yu, & Fu, 2006; Xie et al., 2009). These

authors used a larger population size to develop their prediction models and consequently

generated more accurate prediction models. Future research involves expanding the population

sizes, to validate whether the prediction of fructose and glucose can be improved. Interestingly,

59

the prediction of total sugars, calculated as the correlation between the sum of glucose, fructose,

and glucose using each method was 0.85.

Conclusion

This study was designed to evaluate the potential of single kernel NIR as a high-

throughput phenotyping method to determine sugar (sucrose, glucose, and fructose)

concentrations. We conclude that the spectral data certainly capture information from the

endosperm that is associated with sugar concentration. The experiment also suggested that partial

least square regression (PLSR) is capable of predicting sucrose content, although it is not clear

yet if the same can be seen for glucose and fructose. Therefore, the versatility of NIR

Spectroscopy and PLSR methods hasn’t yet proven to quantify all the sugars. But it has proven

its ability to reduce the time required to quantify sucrose and total sugar quantification by

manifold.

60

Figure 3-3. NIRS predicted versus reference values A) sucrose, B) glucose, C) fructose, and D)

total sugars using enzymatic method

A) Sucrose B) Fructose

C) Glucose D) Total Sugars

61

CHAPTER 4

SUMMARY AND CONCLUSION

In Chapter 2, we evaluated a sweet corn population and estimated the quantitative genetic

parameters that can guide practical decisions regarding the sweet corn breeding program at the

University of Florida. The data collected on 6 important marketable traits (ear length, ear width,

kernel row number, plant height, ear height, and tassel length) showed that existing population

was normally distributed and has variability to be exploited. Hence, narrow-sense heritability

was estimated using a single-site model. Heritability obtained showed that the population used

had less genetic diversity, but the estimates were within the expected range of corn and had the

potential for breeding and improvement. Out of two locations where the current population was

planted, Belle Glade (South Florida) is our main market and where the majority of growers are

and Citra (Central Florida) is where our research station is and to be used as off-season nursery.

It was found that heritabilities for Citra were better than Belle Glade. It could be because of

better control of the environment. However, our breeding is really focused on Belle Glade

because that’s where the majority of our market is.

As Citra had shown to have better heritabilities potential of trait correlation across the

environments, it was necessary to know if there was a similar trend in Belle Glade or if top

genotype in Citra would be the top line in Belle Glade then breeder can accelerate the breeding.

For this, type B and type A correlation was computed. It was observed that plant traits had low

type B GxE but high GxE was observed on-ear traits. Type A corroborated the similar trend.

One of the objectives was also to find out how traits are genetically correlated among

each other. A high correlation between one trait to other can save breeders time and resources by

only selecting one of them which showed that highest correlation was observed between plant

62

height to ear height (~0.76) and tassel length (~0.56) in both the environments. Therefore, for all

other traits, strategic selection need to be applied so that other traits are not adversely affected.

Lastly, expected direct and indirect genetic gain was also estimated at selection intensity

of 5, 10 and 20%. It was observed that stringent selection of 5% gave higher gain but the

downside of this was these genotypes would trace back to fewer parents which would reduce the

genetic diversity in the population.

The expected direct gain for most of the traits in Citra was higher than in Belle Glade

except kernel row number, ear length and ear width. One possibility of this could be because of

higher heritability obtained for these traits in Belle Glade.

The indirect genetic gain was found to give negative gain for kernel row number to ear

length in both the environment. It was also observed that indirect genetic gain for plant traits was

higher in Citra than in Belle Glade and for ear traits, Belle Glade was higher than Citra.

The genetic gain for the selection of the same trait in a different environment was also

predicted and it was observed that indirect selection Citra to Belle Glade have higher genetic

gain than the direct selection of the same trait in Belle Glade. Again, it could be because of

heritability or microenvironment noise in Belle Glade.

Altogether the results are promising as they suggest that Central Florida can be utilized as

an off-season with the additional advantage of a low genotype by environment interaction for

many commercially relevant traits. The results also suggest an opportunity to introduce novel

genetic diversity into the program, given the small estimates of genetic variance.

In Chapter 3, we have used high throughput method of quantifying sugar using NIR

spectra. For which we used enzymatic quantification as a reference method (wet lab). For this

objective we used single kernel NIR because of limited seed availability to collect spectra data

63

which was regressed against each of the sugars (sucrose, glucose, and fructose) using partial least

square regression (PLSR) and cross-validation was done to predict the error. It was observed that

spectral data is able to capture information associated with sugar concentration from sweet corn

endosperm. It was also observed that PLSR is capable of predicting sucrose content, although it

is not clear yet if the same can be seen for glucose and fructose. However, it was interesting to

note that time difference between sugar quantification through NIR and enzymatic quantification

was large but the application of NIR would significantly accelerate the phenotyping process.

64

APPENDIX

SUPPLEMENTARY MATERIAL

Table A-1. List of cultivars used in the study in Chapter 2

S.No. Genotype ID Female Male Type Source

1 I466-F3 N12-1 M90 Shrunken 2 EREC

2 I481-F3 2EDBT2 L208 Shrunken 2 EREC

3 I472-F2 N129 M104-1 Shrunken 2 EREC

4 I456-F3 M104-1 N71 Shrunken 2 EREC

5 I448-F2 2EDBT2 COES40 Shrunken 2 EREC

6 I483-F2 N129 L212 Shrunken 2 EREC

7 I482-F3 N12-1 L212 Shrunken 2 EREC

8 I456-F2 M104-1 N71 Shrunken 2 EREC

9 I464-F2 2EDBT2 M63 Shrunken 2 EREC

10 I463-F2 N129 M63 Shrunken 2 EREC

11 I428-F3 N129 10DB Shrunken 2 EREC

12 I434-F2 9EC/N124 9EC Shrunken 2 EREC

13 I486-F3 N46 12ED/N7-1 Shrunken 2 EREC

14 I474-F2 N24-1 M111 Shrunken 2 EREC

15 I468-F3 N124-1 M90 Shrunken 2 EREC

16 I461-F3 N12-1 M63 Shrunken 2 EREC

17 I428-F2 N129 10DB Shrunken 2 EREC

18 I443-F3 N51-1 COEP6 Shrunken 2 EREC

19 I481-F2 2EDBT2 L208 Shrunken 2 EREC

20 I430-F3 N124 10DB Shrunken 2 EREC

21 I452-F3 N24-1 N12-1 Shrunken 2 EREC

22 I479-F2 N12-1 L176 Shrunken 2 EREC

23 I432-F2 9EC/N12-1 9EC Shrunken 2 EREC

24 I487-F2 N12-1 9EC/N12-1 Shrunken 2 EREC

25 I472-F3 N129 M104-1 Shrunken 2 EREC

26 I491-F3 9EC 11ED/N129 Shrunken 2 EREC

27 I471-F2 N24-1 M104-1 Shrunken 2 EREC

28 I468-F2 N124-1 M90 Shrunken 2 EREC

29 I455-F3 N12-1 N71 Shrunken 2 EREC

30 I450-F3 N12-1 COES56 Shrunken 2 EREC

31 I467-F2 N120 M90 Shrunken 2 EREC

32 I557-F2 9EC M17 Shrunken 2 EREC

33 I452-F2 N24-1 N12-1 Shrunken 2 EREC

34 I486-F2 N46 12ED/N7-1 Shrunken 2 EREC

35 I475-F2 N120 L39 Shrunken 2 EREC

36 I437-F2 9EC/2ED 2EDBT1 Shrunken 2 EREC

37 I478-F2 N129 L176 Shrunken 2 EREC

65

Table A-1. Continued

S.No. Genotype ID Female Male Type Source

38 I438-F2 2EDBT/N7-1 2EDBT1 Shrunken 2 EREC

39 I491-F2 9EC 11ED/N129 Shrunken 2 EREC

40 I444-F2 2EDBT2 COEP6 Shrunken 2 EREC

41 I476-F2 M104-1 L138 Shrunken 2 EREC

42 I483-F3 N129 L212 Shrunken 2 EREC

43 I461-F2 N12-1 M63 Shrunken 2 EREC

44 I453-F2 N7-1/COEP12 N12-1 Shrunken 2 EREC

45 I444-F3 2EDBT2 COEP6 Shrunken 2 EREC

46 I490-F2 2EDBT2 7DA/N129 Shrunken 2 EREC

47 I450-F2 N12-1 COES56 Shrunken 2 EREC

48 I460-F3 2EDBT2 M17 Shrunken 2 EREC

49 I490-F3 2EDBT2 7DA/N129 Shrunken 2 EREC

50 I435-F3 9EC/N12-1 9EC Shrunken 2 EREC

51 I476-F3 M104-1 L138 Shrunken 2 EREC

52 I537-F2 N7-1 ZCSH2 Shrunken 2 EREC

53 I482-F2 N12-1 L212 Shrunken 2 EREC

54 I459-F2 N24-1 M15 Shrunken 2 EREC

55 I475-F3 N120 L39 Shrunken 2 EREC

56 I433-F2 9EC/N7-1 9EC Shrunken 2 EREC

57 I480-F2 N12-1 L176 Shrunken 2 EREC

58 I485-F2 N12-1 2EDBT/N7-1 Shrunken 2 EREC

59 I431-F2 M126 10DB Shrunken 2 EREC

60 I455-F2 N12-1 N71 Shrunken 2 EREC

61 I473-F2 N124-1 M104-1 Shrunken 2 EREC

62 I557-F3 9EC M17 Shrunken 2 EREC

63 I462-F3 2EDBT2 M31 Shrunken 2 EREC

64 I479-F3 N12-1 L176 Shrunken 2 EREC

65 I435-F2 9EC/N12-1 9EC Shrunken 2 EREC

66 I431-F3 M126 10DB Shrunken 2 EREC

67 I473-F3 N124-1 M104-1 Shrunken 2 EREC

68 I443-F2 N51-1 COEP6 Shrunken 2 EREC

69 I430-F2 N124 10DB Shrunken 2 EREC

70 I460-F2 2EDBT2 M17 Shrunken 2 EREC

71 I429-F2 N132 10DB Shrunken 2 EREC

72 I465-F2 N12-1 M63 Shrunken 2 EREC

73 I462-F2 2EDBT2 M31 Shrunken 2 EREC

74 I466-F2 N12-1 M90 Shrunken 2 EREC

75 I467-F3 N120 M90 Shrunken 2 EREC

76 I459-F3 N24-1 M15 Shrunken 2 EREC

66

Table A-2. List of hybrids used in the study in Chapter 3

S.No. Genotype.ID Varieties Type Source

1 1301 Raquel sh2sh2sese x sh2sh2sese HM Clause

2 1305 Rosie sh2sh2sese x sh2sh2su1su1sese HM Clause

3 1309 Fantastic - HM Clause

4 1361 Kate sh2sh2sese x sh2sh2su1su1sese HM Clause

5 1420 SV7143 - HM Clause

6 1435 Vision - HM Clause

7 1448 Cindy sh2sh2sese x sh2sh2su1su1sese HM Clause

8 1471 Candice sh2sh2sese x sh2sh2su1su1sese HM Clause

9 1485 Elle sh2sh2sese x sh2sh2su1su1sese HM Clause

10 1495 Driver sh2sh2 x sh2sh2 HM Clause

11 1513 Natalie sh2sh2sese x sh2sh2sese HM Clause

12 1554 HMX608 sh2sh2sese x sh2sh2sese HM Clause

13 603 HMX59BS603 - HM Clause

14 605 HMX59BS605 sh2sh2sese x sh2sh2sese HM Clause

67

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73

BIOGRAPHICAL SKETCH

Utsav Kumar moved from Hyderabad, India to Florida to pursue a master's degree in

horticultural sciences with an emphasis in plant breeding at the University of Florida. It was

during his bachelor’s study from Allahabad Agriculture University, that he developed an interest

in plant breeding. To cultivate this further, he took up a 6-month internship in Harris Moran

Clause R&D, Bangalore, India where he had worked on “Screening of tomato germplasm against

White Fly”. After the internship, he got the inspiration to do his master’s for which he got into

the UF. He studied under the sponsorship of Harris Moran Clause and his master’s research

focuses on Sweet corn breeding for Florida's fresh market. He is being mentored by

Dr. Marcio Resende (Sweet Corn Genomics & breeding, Horticultural Science) and his

committee members are Dr. Curt Hannah (Corn molecular genetics & physiology, Horticulture

Science Department), Dr. Esteban Rios (Forage Breeder & Genetics, Agronomy Department)

and Dr. German Sandoya (Lettuce Breeder, EREC).