Advances in genomics for the improvement of quality in Coffee381677/UQ381677... · 2019-10-09 ·...

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eAdvances in genomics for the improvement of quality in Coffee

Hue T.M. Tranab, L. Slade Leec, Agnelo Furtadoa, Heather Smytha, Robert Henrya*

a Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of

Queensland, Australia

b Western Highlands Agriculture & Forestry Science Institute (WASI), Vietnam

c Southern Cross University, Australia

* Correspondence to: Robert Henry, Queensland Alliance for Agriculture and Food

Innovation (QAAFI), The University of Queensland, St Lucia QLD 4072, Australia, Tel.

+61 7 3346 0551, Email: [email protected]

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/ jsfa.7692

This article is protected by copyright. All rights reserved.

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eAbstract

Coffee is an important crop that provides a livelihood to millions of people living in

developing countries. Production of genotypes with improved coffee quality attributes

is a primary target of coffee genetic improvement programs. Advances in genomics are

providing new tools for analysis of coffee quality at the molecular level. The recent

report of a genomic sequence for robusta coffee, Coffea canephora, is a major

development. However, a reference genome sequence for the genetically more

complex arabica coffee (C. arabica) will also be required to fully define the molecular

determinants controlling quality in coffee produced from this high quality coffee

species. Genes responsible for control of the levels of the major biochemical

components in the coffee bean that are known to be important in determining coffee

quality can now be identified by association analysis. However, the narrow genetic

base of arabica coffee suggests that genomics analysis of the wild relatives of coffee

(Coffea spp.) may be required to find the phenotypic diversity required for effective

association genetic analysis. The genomic resources available for the study of coffee

quality are described and the potential for the application of next generation

sequencing and association genetic analysis to advance coffee quality research are

explored.

Key words: Coffee quality, genetics, genomics, biochemical compounds, next

generation sequencing, association studies.


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eGENERAL INTRODUCTION

Coffee is an important crop and the second most traded commodity in the world (after

petroleum) providing a living to more than 125 million people 1. Coffee belongs to the

Rubiaceae family and the Coffeeae tribe and consists of more than 124 species spread

across two genera Coffea L. and Psilanthus Hook.f, each of which in turn consist of two

sub-genera, Coffea and Baracoffea (J.-F. Leroy) J.-F. Leroy, and Psilanthus and

Afrocoffea (Moens) summarized by Anthony et al. 2 respectively. The grouping

of Coffea and Psilanthus genera has been examined in several studies 3 4 5 6 7.

Commercial coffee production is dominated by only two species belonging to the

Coffea genus: C. arabica and C. canephora (the latter generally referred to as robusta

coffee). All coffee species are diploid (2n=2x=22) and generally self-incompatible,

except for C. arabica which is a self-fertile tetraploid (2n=4x=44) derived from a

spontaneous hybridization between C. canephora (as paternal progenitor) and C.

eugenioides (as maternal progenitor) 8 9.

Although C. arabica is considered to have better cupping quality than C. canephora,

improving the quality of both commercial species remains a target for most coffee

improvement programs. With advances in genomic and sequencing technology, it is

feasible to understand the coffee genome and the molecular inheritance underlying

coffee quality, thereby helping improve the efficiency of breeding programs.

This review will discuss current knowledge regarding the genetics of those biochemical

compounds which are considered quality determinants, for use as a foundation for

improving coffee quality by breeding. Available genomic resources for the study of the

genetics of biochemical compounds that are likely to be playing a role in coffee flavour

will also be discussed. In addition, the potential value of the study of genetics of coffee


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equality using next generation sequencing and association genetic analysis will be

considered.

BIOCHEMICAL CONTROL OF COFFEE QUALITY AND ITS VARIATION

Coffee quality is assessed by evaluating both the physical attributes of the coffee beans

and the organoleptic properties of the coffee. Physical quality reflects moisture content,

defects (e.g. sticks, stones, damaged beans and black beans ), bean size and bean colour;

while organoleptic quality reflects aroma, taste, flavour, body (a feeling of the heaviness or

richness on the tongue), acidity and the preference of tasters 10. A range of biochemical

compounds in coffee beans are important contributors to the quality of the coffee in the

cup.

Biochemical compounds influencing the organoleptic quality of coffee beans include

non-volatile and volatile components. Important non-volatile components of the bean

include carbohydrates and fibre (sucrose, reducing sugars, cell wall polysaccharides,

lignin), nitrogenous compounds (protein, free amino acids, caffeine, trigonelline), lipids

(coffee oil, diterpene esters), minerals (potassium and phosphorus), acids and esters

(total chlorogenic acids, aliphatic acids and quinic acid) 11. All of these biochemical

compounds are considered to play a role in roasting chemistry 12. Proteins and amino

acids, for example, react with reducing sugars to produce aroma precursors.

Chlorogenic acids (CGAs) and caffeine, are responsible for bitterness 13. Among these

components, sucrose, caffeine, trigonelline, lipids and CGAs are major biochemical

compounds that contribute to the flavour of the beverage after the roasting of the

beans 11. While sucrose, trigonelline and lipid have a positive correlation with coffee

cup quality, caffeine and some sub classes of CGAs have a negative correlation with

coffee cup quality (often referred to simply as ‘cup’). There are about three hundred

volatiles detected so far in green coffee bean 14 of which twenty one volatiles,


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eincluding 3-isobutyl-2-methoxypyrazine and 2-methoxy-3,5-dimethylpyrazine, have

been identified as key compounds for coffee cup 14. In the roasted coffee bean, there

are more than a thousand volatiles which can be grouped into twenty key aroma

compound-groups 15. The aroma of the brew is different from that of roasted coffee.

Several notes in roasted coffee become more intensive in the brew such as caramel and

phenolic odour. This change in aroma profile of coffee is not caused by the formation of

new odorants, but by a shift in the concentrations of existing ones as summarised by

Grosch 16.

Coffee flavour and its formation is extremely complex; however, numerous studies on

coffee flavour and its constituents have been reported. How the volatile and non-

volatile compounds contribute to coffee quality and relationships between sensory

properties and the composition of coffee has been thoroughly reviewed by Flament 12

and more recently by Sunarharum et al.17. The reactions involved in the formation of

coffee aroma and its mechanisms have been reviewed by Buffo & Cardelli-Freire 18.

However, the change in bean composition, especially in aroma profile, from green to

roasted bean is complicated and not all formation pathways are fully understood

under coffee roasting conditions 19. The use of green bean or roasted bean in the study

of coffee quality genetics therefore should be considered carefully since aroma in

roasted bean and brew is of customer's interest, while the green bean attributes are

more directly affected by genetics. This is a challenge in the study of genetics of bean

composition, that is, ascribing links to coffee quality. However, with the progress in

functional genomic approaches, the identification of molecular determinants of coffee

quality characteristics is feasible and it is possible to select coffee varieties with

superior beverage quality 20.


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eThe bean chemical composition and organoleptic characteristics are significantly

different between species and to a lesser extend within species 10. A number of studies

on the variation in the biochemical composition of the bean are summarised in Table 1

21-24.

Fatty acid and CGAs were used as indicators to differentiate arabica varieties 25 26.

Similarly, Tessema et al. 27 also found that the quality traits (organoleptic quality) and

biochemical constituents (sucrose, fat, crude protein and minerals) were diverse in the

arabica germplasm collections from Ethiopia. This is an encouraging result for the

study of variation of quality traits in C. arabica for association mapping. In general,

compounds that have a positive correlation with quality such as sucrose, trigonelline

and lipids are at a higher concentration in C. arabica than in C. canephora and some

other Coffea species.

Significant differences in flavour between different coffee types have also been reported.

Robusta coffee beans have a bitter, full bodied taste, but low acidity while those of arabica

coffee are more aromatic with more perceptible acidity but less body. Within the robusta

coffees, Moschetto et al 28 found important differences in cup quality between the two

main genetic groups studied (Congolese and Guinean), for preference, aroma, acidity,

body and bitterness. Within the arabica coffees, different varieties can be associated with

specific flavour profiles. For example, genotype SL28 produces a milder brew than Kent,

genotypes Blue Mountain and Bourbon produce finer coffees than Catimor, while

genotype Liberica yields coffee that is bitter and without finesse 29.

The composition of the beans that determines the properties and quality of coffee in

the cup depends not only on genetic factors (species, varieties) but also on non-genetic

factors such as the cultivation conditions (soil, air temperature, altitude, sun exposure


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eand rainfall), plant condition (plant age, bean maturity), cultivation practices (shade

management, fertilisation and irrigation) and harvest and processing methods. In

general, volcanic soils, low temperature, high altitude and growing under shade have

positive correlation to coffee compositions which relates to coffee quality 30-34. Post-

harvest techniques, roasting and the preparation of the beverage also have a strong

influence on coffee quality 30 35. Understanding the influence of each factor and their

interaction would help with defining realistic breeding strategies. Since there have

been many reports presented in detail on these issues 26 33 29 36 37 38 20, especially the

most recent reviews of Joet et al. 39 on the relationships between coffee quality, bean

chemical composition and environmental effects, or Santos et al. 40 on climate change

impacts on bean quality, or Sunarharum et al. 17 on the impacts of processing, roasting

and preparation methods to aroma profile, they are not reviewed in this paper.

Another factor that may influence coffee quality is epigenetic effects. Epigenetics is

modified gene expression not caused by alteration in the gene sequence or DNA code.

This change is due to factors that control how and when each gene is expressed.

Epigenetics is extremely complex and is not yet fully understood. Although there have

been several studies on factors affecting coffee quality, there has not been research on

epigenetics and coffee quality. Only a very few studies have reported on epigenetics in

relation to size and shape of the tree 41, and epigenetic mechanisms (especially in

phenolic compounds) in somatic embryo regulation in arabica coffee species 42 43.

Being an allotetraploid species formed by a recent natural hybridization between two

diploid ancestors, C. canephora and C. eugenioides, and which may have gone through

a single allopolyploidization event 44, arabica may have genetic redundancy allowing

for sequence divergence resulting in development of functional variations between

duplicated genes (homoelogues). Proteins with different properties may be encoded


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eby homoelogues leading to new genetic diversity in breeding populations. However,

homoelogues are also subject to extensive epigenetic control and are therefore not

always equally expressed 45. Landey et al. 43confirmed the very limited genetic and

epigenetic alternations in somatic embryogenesis-derived plants during coffee somatic

embryogenesis while embryogenic suspensions have a high risk of genome and

epigenome instability. However, Vidal et al. 46 found homoeologous genes displaying

differential expression for arabica using Single Nucleotide Polymorphisms (SNPs) from

Expressed Sequence Tags (ESTs) and gene expression from transcript redundancy

between C. arabica and C. canephora. The divergence of function in homoeologous

genes may result in phenotypic novelty with useful agronomic traits. Homoeologous

gene expression in C. arabica grown in two contrasted conditions was also used to

compare with those of its progenitors and to examine its ability to adapt to

environmental variations 47. Recently, Lashermes et al. 44 confirmed that the genomic

rearrangements involving homoeologous exchanges as well as gene silencing,

elimination and conversion appear to have occurred in C. arabica and to be a major

source of genetic diversity. This on one hand will make a potent new source of genetic

diversity for different traits, but on the other hand make it difficult to conduct

association genetics in coffee.

GENETICS OF BIOCHEMICAL COMPOUNDS KNOWN TO BE RELATED TO COFFEE

QUALITY

Improving both yield and quality is the target for coffee breeding programs. In a study

by Montagnon et al 48, genetic correlations between coffee yield and quality

determinants and cup tasting components measured in two groups of C. canephora

were not significant, indicating that the coffee quality is not negatively associated

with yield in C. canephora. Understanding the genetic inheritance of quality traits can


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ebenefit breeding programs, but there appears little research that has focussed on

understanding the genetic control of key biochemical compounds that relate to coffee

beverage quality. Known inheritance mode and heritability values of the key

biochemical compounds determining coffee quality in intraspecific and interspecific

hybrids are detailed in Table 2 48-56.

Male/female additive inheritance in sucrose and trigonelline was contrasted in two

studies using intraspecific hybrids 51, 56. The heritability measured in interspecific

hybrids was different from that of intraspecific hybrids due to the differences among

species in relation to their fixation during evolution of specific alleles of some genes

relating to the variability of quality components. As a result, the genetic variation in

quality at the interspecific level is not similar to that in intraspecific hybrids 10. The high

narrow-sense heritability of caffeine and fat content in intraspecific hybrids implies that

these traits can be improved at the intraspecific level. The negative correlation between

fat content and sucrose content indicates that care should be taken in selecting for a

combination of fat and sucrose content 48. Both major and minor genes with additive

gene effects appear to be involved in the variation of caffeine content in the seeds 57

implying the complexity of this trait.

The inheritance of coffee cup quality was also reported by Leroy et al 51 where for

bitterness of robusta coffee, female additive effects were predominant, while for

acidity, only a male additive effect was observed 51. This information will assist the

breeding (e.g. parent selection) for robusta coffee quality improvement. The

inheritance of quality characters in introgressed lines, which resulted during arabica

improvement to introduce the disease resistant genes from Timor hybrid to arabica,

was mentioned by Bertrand et al. 58 31 and also reviewed by Leroy et al. 10.


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eIn summary, highly heritable traits like fat or caffeine content could be improved

effectively by crossing between parents of different favourable values. For traits with

lower heritability like trigonelline, CGAs or sucrose, Marker Assisted Selection (MAS)

would be necessary 4. With of the advances in sequencing technology and marker

development coupled with trait phenotyping, it should be possible to easily identify

trait-linked markers for use in MAS.

For coffee genetic improvement, most of the past and recent research has focused on

the improvement of canephora quality due its known lower quality compared to

arabica. The focus of arabica improvement is more on transfer of disease resistance

from Timor hybrid 10 or climate change adaptation 59; quality improvement is the

second priority in arabica as its quality is considered superior. However, this does not

mean that arabica quality improvement is unnecessary. Quality determinants are not

the same between arabica and canephora. For canephora, apart from caffeine

reduction, the bean composition of galactomannans 60or other compounds (other

polysaccharides) which influence the extraction during the industrial process of

lyophilisation (i.e. instant coffee processing) is probably the most important target.

Fresh brewing is popular for arabica, rather than instant coffee processing – although it

may be applied to arabica in the future. For arabica, the increasing consumer demand

for speciality coffee has intensified research aimed at pinpointing small differences in

quality factors as well as to quantify these organoleptic features 61. For more detail on

the trend and strategies of canephora and arabica improvement breeding, there are

excellent reviews by Leroy et al. 10 and Vossen et al. 20.


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eCOFFEE GENOMIC RESOURCES

Coffee genetic diversity in C. arabica and C. canephora

A number of works on the assessment of arabica diversity have been done with

differing results. In general, among three main types of material (cultivar/varieties;

accessions/introgression/hybrids; and spontaneous/sub-spontaneous) almost all

studies show a very low genetic variation amongst arabica cultivars using different

marker systems (RAPD, AFLP, SSR, ISSR, SRAP, TRAP) and coffee collected from

different regions (Brazil, Yemen, Indian, Australia and Vietnam, Tanzania, Hawaii) 62-69.

The second group (accessions/introgression/hybrids) shows low to significant

variation, more diverse in the introgressed lines 70 27 71-74. The last group, in which one

may expect to see the most diversity, has diversity ranging from low 75 72 76 to

moderate 77 to high 78. The correlation of genetics with geography/origin clearly shows

in some studies 77-79 but not in the others 75 72 70. In the most recent work presented in

the World Coffee Research annual report 59, genetic diversity assessment of 800

arabica coffee accessions from the arabica collection at CATIE, Costa Rica shows the

least genetic diversity of this species compared to other major crops. This study also

found that cultivated coffee varieties contain approximately 45% of the genetic

diversity found in the 800 aforementioned accessions (while only 2% of tomato

diversity found in cultivated tomato) indicating the limitation of variability for breeding

programs. This also is problematic for association mapping work in arabica for

breeding improvement, and thus the selection of appropriate germplasm for

association mapping is very critical. Therefore it is essential to understand the

population structure and its genetic diversity.

In contrast to C. arabica, C. canephora possess a high genetic diversity due to its origin,

reproduction method and dissemination 80, and it is structured in two main groups


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e(Guinean and Congolese) with six subgroups 81-83. The linkage disequilibrium assessed

in C. canephora breeding populations was variable, indicating the possibility of

applying association studies within C. canephora species and using it to improve C.

arabica due to its parental relationship to this species 84. Understanding the genetic

diversity of the two dominant species is critical for any genetic improvement scheme

for any traits in coffee.

Coffee whole genome and synteny

The genome size of coffee has been estimated to be 1.3 Gb for C. arabica 85 and 710Mb

for C. canephora 86. Using flow cytometry, the DNA content of arabica was estimated as

2.47 pg (equivalent to 1.2 Gb) and canephora as 1.43 pg (equivalent to 700 Mb), while

other diploid coffee species varied from 0.96 pg (469 Mb) (C. mauritiana) to 1.84 pg (900

Mb) (C. humilis) 87 88. Despite its economic importance, the first high-quality draft genome

of robusta coffee has just been completed and reported in 2014 86. To generate a whole

genome sequence of C. canephora, Roche 454 single and mate-pair reads and Sanger

BAC-end reads were used, representing 29.5X coverage of the genome size (710 Mb).

In addition, Illumina sequence data, corresponding to 60X of coverage of the Coffee

genome (710 Mb), was also used to correct sequencing errors and fill gaps. The

resulting genome assembly consists of 25,216 contigs and 13,345 scaffolds with a total

length of 568.6 Mb (Table 3). Using a high-density genetic map, a total of 349 scaffolds

covering approximately 364 Mb were anchored to the 11 C. canephora chromosomes,

among which 139 were both anchored and oriented. Coffee displays the most

conservative chromosomal gene order among asterid angiosperms. Transposable

elements (TEs) in the C. canephora genome were identified and classified accounting

for almost half (49.2%) of the reference genome length. Organelle to nuclear genome

transfers was also analysed with a typical amount of chloroplast-derived fragments


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e(2,014 insertions, accounting for 0.16% of the draft genome) and an unusually large

750kb mitochondrion-derived fragment more than 100 Kb longer than those known

from other plant genomes. In total, 25,574 protein-coding gene models were obtained

in which several gene functions involved in secondary compound biosynthesis and

coffee flavour and aroma were characterized. Species-specific gene family expansions

were examined such as N-methyltransferases (NMTs), defence-related genes, and

alkaloid and flavonoid enzymes involved in secondary compound synthesis. This is the

most valuable genomics resource for further genomics study in coffee, especially as all

of the genomics data, transciptomic data, SNP polymorphisms and genotyping data,

gene families and metabolic pathways are publicity available via the Coffee Genome

Hub 89.

The whole chloroplast genome sequence of Coffea arabica L. was first reported by Samson

et al. 90 (Table 3) and this also provides an important reference for studying other species

and helps build up the phylogeny among members of the Coffeeae tribe which is still

questionable. Several research groups are working on an C. arabica genome sequence

using different biological materials and sequencing platforms 91 92 93 94. This genomic

resource is extremely important for the study of genetics and genomics of the high quality

coffee species, C. arabica.

Several studies 95-97 have compared genomes between coffee and other crops. The

most recent study reported that coffee chromosomal regions showed unique one-to-

one correspondences with grape chromosomes, proving the lack of any events

changing ploidy intervening in their separate histories. Coffee and grape genomes

show one-to-three correspondence with the tomato genome 86.

Among coffee species, comparison of genomes showed a high gene synteny between

C. arabica (EaEaCaCa) and C. canephora (CC) and C. arabica (EaEaCaCa), C. eugenioides


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e(EE) and C. liberica (LL) 98-100, but numerous chromosomal rearrangements were

detected 98. The two sub-genomes of C. arabica (Ca and Ea) indicated sufficient

sequence differences, and thus obtaining a whole-genome sequence would be an

important resource for the allotetraploid genome of C. arabica. Results also revealed

that C. eugenioides was more closely related to C. arabica than to C. liberica, which

was in agreement with the ancestral history of the allotetraploid C. arabica 100.

Other genomic resources

Molecular markers

As for other crops, determination of molecular predictors for coffee quality traits

would help reduce the length of breeding selection cycles and thus phenotypic

evaluation cost. However, the use of DNA technology in coffee quality improvement is

still in its infancy and therefore limited. Pot et al. 101 used polymorphisms generated

from SNPs, INDELs (insertions and deletions) and SSRs (Simple Sequence Repeats) to

identify the nucleotide diversity of four sucrose metabolism enzymes in C. canephora

genotypes using direct sequencing. The variation of these genes was also analysed

between different Coffea species to allow the identification of more polymorphic sites

using parallel in silico analysis of EST resources 101. AFLP (Amplified Fragment Length

Polymorphism) and SSR markers were used to construct a genetic map of an F2

population between C. arabica and C. canephora (artificial tetraploid). The number of

markers associated with quality traits identified was nineteen for sugar content, eight

for caffeine, eight for CGAs and one for caffeine and CGAs 102. These markers need to

be validated in other genotypes for consistency before they can be used in marker

assisted selection in coffee breeding. Recently, a total of 33,239 SNPs specific to C.

arabica and 87,271 SNPs specific to C. canephora were developed using targeted

genome capture strategies and next-generation sequencing, and were evaluated on 72


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esamples from C. canephora and 72 from C. arabica. These genomic resources will

support for genome assemblies, accelerate breeding of interested traits as well as

manage genetic diversity in coffee species 103.

Bacterial artificial chromosome (BAC) libraries.

Three BAC libraries were developed for each species of C. canephora and C. arabica in

relation to sucrose metabolism 104, genome composition and evolution 105, nematode

and leaf rust resistant 106, bean size and cup quality 107 and mannose-6-phosphate

reductase gene (CaM6PR) 108 (Table 4). These available BAC libraries of Coffea are an

important resource to support genomic studies such as comparative genomics and the

identification of genes and regulatory elements controlling important traits in coffee.

Expressed sequence tags (ESTs)

The development of large EST sequencing projects has supported the identification of

potential genes contributing to quality traits and their interplay with other genes

through essential biochemical pathways. ESTs facilitate the development of whole

transcriptome analysis and the identification of the biosynthetic pathways associated

with the expression of quality and the genes within these pathways 10. The EST

resources available in coffee have been reviewed by Kochko et al. 85 and are

summarised in Table 5.

A total of 246,500 ESTs have been reported with 69,801 for C. canephora, 166,133 for C.

arabica and 10,566 for C. racemosa using different organs of the plant (leaves, flower,

fruits and roots) at different stages of development and maturation 109-115. However, not

all these resources are published or available which limits their use. A number of studies

have used the available resource of ESTs for further research such as microsatellite marker

discovery 116, analysis of transcriptome divergence and analysis of genes involved in the

biosynthesis pathways of lipids and main storage proteins 117, gene structure prediction


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eand functional annotation with the detection of a total of 345 pathways in C. arabica and

300 pathways in C. canephora 118. This genomic resource will be very important for further

research on coffee quality improvement.

QTL identification and genetic mapping

The construction of genetic maps is often the first step for molecular dissection of

complex traits (yield components, quality and disease). Genetic maps enable the

mapping of quantitative trait loci (QTLs) controlling these traits and thus provide a

framework for isolation of candidate genes followed by manipulation of genes for yield

increase and quality improvement 119. In many cases, markers linked to QTLs are

directly used in MAS without the need of understanding gene functionality underlying

the QTLs.

Linkage mapping in coffee in general requires more effort and cost than in annual crops

due to its long life cycle, low polymorphism (in the case of C. arabica – with the exception

of Ethiopian cultivars and germplasm), and the absence of a large collection of DNA

markers as well as genomic resources 119. The first genetic maps were constructed in the

diploid C. canephora by Paillard et al. 120 followed by several others constructed for

different coffee populations 51, 56, 96, 102, 120-129 130 (Table 6).

To date, a number of QTLs analyses relating to quality compounds and cup have been

performed on C. canephora and other species, but none has been reported for C.

arabica as indicated in Table 7 50, 51, 56, 131, 132

Co-location of some QTLs and genes involved in different metabolic pathways related

to coffee quality were noted 51. Using such candidate genes in association mapping

may determine the allelic variation of coffee quality 133. The co-localisation between

QTLs relating to organoleptic traits and genes associating with caffeine or CGA


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ebiosynthesis may explain the fact that both CGA and caffeine are involved in conferring

bitterness in the coffee cup 51.

Gene identification

The identification of genes relating to quality is one of the main objectives of several

coffee research groups around the world. Thanks to the concerted efforts on coffee

genomics, a number of coffee candidate genes have been identified and some of them

have been cloned and characterised. These results are useful to the coffee genetics

community, especially those on genes encoding the enzymes of key metabolic

processes. These genes are candidate genes which may control the variability of coffee

quality 10. Genes regulating the main chemical components that are thought to be

involved in the flavour and sensory quality of coffee are listed in Table 8 13, 51, 86, 104, 117,

131, 134-141. Recently, 36,935 unigenes from leaves and fruits of C. eugenioides, one of C.

arabica’s ancestors, were identified by Yuyama et al. 142. A sub-set of these genes

related to sugar metabolism and fruit ripening were examined for their expression.

This will be a valuable resource for molecular and genetics studies of commercial

coffee species. Currently there are works on transcriptomics to understand the

transcriptional regulations during seed development 39 13 or more specifically for

caffeine accumulation 143 and chlorogenic acids 144 or galactomannan 60. These could

facilitate the development of robust genomics/metabolomics fingerprints of coffee

bean quality and ultimately be useful in breeding programs for coffee quality.

Although several genes encoding the biosynthesis of biochemical compounds in coffee

have been identified in C. arabica and C. canephora, there are no genes identified for

trigonelline synthesis and no studies on allelic variation (e.g., SNPs) associated with

low and high levels of the key biochemical compounds which can be utilised in MAS.

However, sequences from the genes that are known can serve as useful references in


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ere-sequencing to detect polymorphisms for genetic mapping of the control of

biochemical compounds in C. arabica.

ASSOCIATION GENOMICS APPROACH USING NEXT GENERATION SEQUENCING (NGS)

AND PERSPECTIVES FOR THE STUDY OF GENETICS OF COFFEE QUALITY

Research on genetics and genomics for coffee, especially in relation to quality is

relatively limited compared to other crops and thus belies its potential and economic

contribution. The reasons could possibly be due to the lack of funding – most coffee

growing regions being developing countries – the complexity of the quality traits and

the limitations of the technology used. More studies have been conducted for robusta

coffee than arabica coffee due to its lower ploidy level and greater genetic diversity.

Previous studies showed considerable genetic variation in compounds defining coffee

quality among coffee species, especially in diploids. In addition, a number of genes

relating to the synthesis of sucrose, caffeine, several sub-groups of CGAs and lipids

have also been identified. These studies provided gene sequences via ESTs that will

facilitate further studies on bean composition relating to coffee quality. However,

there has been no research focusing on allelic variation of these genes in natural

populations except bi-parental mapping of QTLs for trigonelline and CGA contents.

A number of previous studies involving genome-wide association studies (GWAS) using

next generation sequencing (NGS) have used these genetic approaches to detect

candidate genes/QTLs of complex traits such as physical wood properties, resistant genes,

drought tolerance, cold hardiness and timing of bud set summarised by Sexton et al. 145

and successfully applied in various crops including annuals (rice, maize, soybean, wheat) or

perennial crops and forestry trees (pine, cottonwood) summarised by Hall et al. 146. Based

on knowledge of biochemical compounds (and their reference gene sequences) that are

thought to be involved in the flavour and sensory quality, it should be possible to apply


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eNGS and GWAS combined with bi-parental QTL mapping to detect valuable haplotypes

from natural populations for use in breeding coffee varieties with better quality.

Application of NGS in plant genomics studies has been extensively reviewed by a

number of authors 147-150. NGS lowers the cost of sequencing and generates a large

amount of data in a single sequencing run, making it feasible to study genetics at the

whole genome level. The steps involved in the NGS strategies (shearing of DNA, ligation,

library preparation) as well as different sequencing platforms (Illumina, Life Technologies,

PacBio) have been described and their advantages and disadvantages have been reviewed

by several authors 151, 152. NGS can be used via an approach of whole genome sequencing or

targeted sequencing.

Two methods of whole genome sequencing can be used including (1) de novo

sequencing - applied for species that do not have pre-existing sequence data, followed

by the use of bioinformatics tools to assemble the sequences and obtain the genomic

map for that species; and (2) re-sequencing performed on individuals of a species that

has already known genome sequence152.

Targeted sequencing includes two approaches, amplicon sequencing and target

enrichment. Amplicon sequencing will sequence amplified regions (PCR amplicons) of

small and selected regions of the genome with lengths of hundreds of base pairs and

with a high depth of coverage to identify common and rare sequence variations.

Sequencing PCR amplicons of sets of candidate genes from DNA bulks will help to

identify the available variation in these genes exploited in a population 148. Target

enrichment is quite similar to amplicon sequencing in terms of using only selected

regions or genes enriched in the library from genomic DNA. However, target

enrichment allows for larger DNA insert sizes and enables a greater amount of total

DNA to be sequenced per sample. For this method, regions of interest in the genome


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ecan be targeted, making it an ideal approach for examining specific gene pathways, or

as a follow-up analysis to GWAS 153.

When making decisions on the approaches of candidate gene or of whole-genome,

one of the most critical factors is the extent of linkage disequilibrium (LD) in the

organism of interest. In genome-wide studies, the extent of LD determines the

mapping resolution that is achieved and the numbers of markers covered 154. Genetic,

biochemical, physiological and phenotypic information must be used to support the

selection of candidate genes 155. Where a developmental or biochemical pathway is

well-understood, candidate gene selection is straightforward; but the researcher is

typically limited to the field of known genes, which presents the risk of overlooking

causal gene mutations not previously identified 146

NGS technology has been used to assemble the whole genome sequence of C.

canephora 86 and to examine genetic change in allopolyploidisation of arabica 44. To

obtain the genomic sequence of C. arabica, as only the draft genome of C. canephora

is available, a de novo approach can be applied. The other approach for obtaining the

genomic sequence of C. arabica is whole genome re-sequencing as the available draft

genomic sequence of C. canephora can be used as reference since it is one of

progenitors of C. arabica. The whole genome sequence will be a key resource of data

to identify gene sequences and SNP markers for most genes including novel and

published genes. EST and BAC sequences can be used as reference sequences to

assemble sequence reads from NGS data to identify genes/alleles and their positions

on the chromosome. In addition, whole-genome re-sequencing and SNP genotyping

data can be used to generate a dense SNP genetic map of the population for QTL

mapping.


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eAmplicon sequencing in a larger set of samples could be an appropriate approach to genetic

association study of quality traits, since the biosynthesis pathways, QTLs and candidate

genes of sucrose, caffeine and several subgroups of CGAs are known. This will help to

confirm or validate the candidate genes from the literature and identify the alleles that are

useful for breeding. For those compounds for which candidate genes have never been

identified or very few identified, for example trigonelline and some compounds

belonging to the lipid group, the approach could be to use whole genome sequencing

via NGS to detect SNPs, then using an association genomics approach to detect the link

between SNPs and the traits of interest.

Association studies differ from quantitative trait locus mapping by utilising samples

from genetically diverse populations that contain short stretches of linkage

disequilibrium due to generations of recombination 145. Such studies thus enable

relationships between phenotypic traits and underlying DNA sequence variation to be

understood. Association studies may be suitable for perennial species such as coffee

because they can be carried out on pre-existing populations, in collections or in

selection trials. This approach does not require specific populations created by

controlled crossing (like conventional genetic mapping approaches) which is very

difficult for perennial crops 155. Association or linkage disequilibrium mapping has

become a very popular method for dissecting the genetic basis of complex traits in

plants 146. For the study on volatile and non-volatile compounds in coffee and that are

likely to be playing a role in coffee flavour, once the data on biochemical compounds

and SNPs variation are available, association genomics will be applied to identify key

genes associating coffee quality. The integration of NGS technologies and association

study to analyse complex traits will be a powerful strategy complementing the

traditional method of parent-hybrid map construction 152.


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eCONCLUSIONS

While whole genome technology will provide a powerful resource, a good start has

been made with AFLP and SSR markers associated with quality traits identified for

sugar content, caffeine and CGAs in C. arabica. These are already helpful in studies

aimed at genetic improvement of coffee quality, but importantly, they will provide

useful reference points as the genomic approach is developed. Existing coffee BAC

libraries are beneficial in comparative genomics, the identification of genes and

regulatory elements controlling important traits in coffee, and will play an important role

in validating new C. arabica genomic data as it emerges. With the current knowledge of

biochemical compounds that govern coffee bean composition that relates to beverage

quality and available genetic resources, it is possible to apply an association genomics

approach using both whole genome sequencing and targeted sequencing to detect the link

between SNPs and the traits of quality determinants from natural populations for use in

quality improvement via breeding programs. However, caution should be taken when

using this approach for C. arabica since this species has a narrow genetic base, lacks

a reference genome, is a polyploid (4n) and lacks populations from controlled crosses

for analysis of specific traits and the validation of markers or genes once detected. The

aforementioned difficulties can be overcome by the use of wild arabica accessions

from Ethiopia, development of a reference genome sequence for C. arabica, and the

complementary use of the two approaches of targeted re-sequencing and whole

genome re-sequencing in variant discovery respectively. There is no doubt that the

availability of whole genome sequences will provide the greatest stimulus yet to the

understanding of the genetic basis of coffee quality traits, and indeed of many other

aspects of the plant’s biology.


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e

ACKNOWLEDGEMENTS

This research was supported under Australian Research Council's Linkage Projects

(project number LP130100376). We acknowledge Australia Awards Scholarship (AAS)

and QAAFI/UQ for their ongoing support of this research.

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Table 1. Variation of the main biochemical compounds determining quality in coffee

Compound Level C. arabica

(dmb – g kg-1)

C. canephora

(dmb – g kg-1)

Other species

(dmb – g kg-1)

Sucrose Lowest 7.40 4.05 3.80

Highest 11.10 7.05 10.70

Caffeine Lowest 0.96 1.51 0.00

Highest 1.62 3.30 2.64

Trigonelline Lowest 0.88 0.75 0.39

Highest 2.90 1.24 1.77

Lipids Ave. 15 10 8-30

CGAs Lowest 4.34 7 0.8

Highest 4.80 14.4 11.9

Source: (15-18)


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Table 2: Inheritance mode and heritability of coffee quality determinants

Trait Intraspecific hybrids Interspecific hybrids

Sucrose content Polygenic inheritance

NSH = 0.11

Male & female additive &

dominant effects

Polygenic & additive inheritance

Higher heritability value

Caffeine Additive inheritance

NSH = 0.33; BSH = 0.76

NSH = 0.80

Female additive effects

Polygenic & additive inheritance

One major gene with two alleles

No caffeine = recessive gene

(caf1)

High BSH

Trigonelline Male & female additive and

dominant effects

NSH = 0.38

Not additive, maternal

inheritance

heritability = 0.71

Fat content NSH = 0.74

Male additive

CGAs NSH = 0.36

Female additive effects

Nuclear inheritance

3-FQA isomer controlled by one

major gene

NSH = narrow-sense heritability; BSH = broad-sense heritability

Source: (41-49)


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Table 3. Genome sequence data available for the two main domesticated coffee

species

Whole genome sequence of C. canephora

Genome

size

(Mb)

Coverage

No of

contigs

No of

scaffolds

No of

genes

MicroRNA

precursors

Organellar-to-

nuclear genome

transfers

Est. 710 30 x 25,216 13,345 25,574 92 2,573

Chloroplast genome sequence of C. arabica

Genome

size (bp)

IRs No of

genes

Coding region

protein

genes

tRNA

genes

rRNA

genes

genes containing

introns

155,189 25,943 130 79 29 4 18


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Table 4: Bacterial Artificial Chromosome libraries of two main coffee species

Genotypes No of

clones

Ave. size of

clones (kb)

Coverage Authors

C. canephora

IF 126 55,296 135 10.6 x Leroy et al., 2005

IF 200 DH 36,864 166 8.6 x Dereeper et al., 2013

IF 200 DH 36,864 121 6.3 x Dereeper et al., 2013

C. arabica

IAPAR 59 80,813 130 8 x Noir et al., 2004

Hybrid (Mokka x Catimor) 52,416 94 4 x Jones et al., 2006

Timor Hybrid CIFC 832/2 56,832 118 5-6 x Cacao et al., 2013


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Table 5: Expressed Sequence Tags from the three main coffee species

Project/

organisation

Tissues

ESTs of species Unigenes/

genes C.

arabica

C.

canephora

C.

racemosa

Fernandez et al. (98) leaves 527

Nestle and Cornell

Uni

Lin et al. (99)

seeds

47,000 13,175

Brazilian Genome

Vieira et al. (100)

leaves and

fruits

130,792 12,381 10,566 33,000

CENICAFE

Montoya et al. (101)

leaf, flower

and fruit

32,961 10,799

De Nardi et al. (102) leaves and

roots

1,587

French IRD

Poncet et al. (103)

leaves and

fruits

10,420 5,534

Salmona et al. (104) seeds 266

Total (246,500) 166,133 69,801 10,566 62,508


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Table 6. Genetic linkage maps of coffee

Markers Population used Length

(cM)

No of

linkage

groups

Traits/

purpose

Authors

C. arabica

AFLP pseudo-F2

population

(Mokka hybrid x

Catimor)

1,802.8 31 source-sink traits (110)

AFLP F2 of Tall Mokka and

Catimor

1,042.4 40 cupping quality

and morphology

(111)

AFLP and SSR F2 of C. arabica x C.

canephora

1,011 37 quality and

productivity

(92)

RAPD F2 of (Mundo Nov x

Hybrido de Timor) x

Hybrido de Timor

540.6 8 Partial linkage

map

(118)

SSR F2 and F3 of Caturra

x CCC1046

3800 22 yield, plant height and fruit size

(119)

C. canephora

RFLP and

RAPD

doubled haploid

(IF200)

1,402 15 QTL analysis (109)

RFLP and SSR doubled haploid and 1,041 11 segregation (112)


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eTC (IF200 x DH) distortion

RFLP and SSR (C. heterocalyx x C.

canephora) x C.

canephora

1,360 15 QTL analysis (113)

RFLP and SSR BP409 x Q121 1,258 11 yield and vigour (114)

AFLP and

ISSR

(C.canephora x C.

liberica) x C. liberica

1,502 16 morphological

traits

(115)

Conserved

Ortholog Set

(COS)

Intraspecific hybrid

of C. canephora

1,331 11 synteny maps

between coffee

and tomato

(86)

SSR pseudo-backcross of

C. canephora

1,290 11 yield and quality (44)

SSR 2 populations of

intraspecific hybrid

of C. canephora

1,201 11 yield and quality (49)

Other species

AFLP and

RFLP

(C.

pseudozanguebariae

x C. liberica var.

dewevrei (DEW)) x

DEW

1,144 14 biochemical

traits (caffeine,

CGAs, sucrose)

(116)

SSR C. liberica x C.

eugenioides

798.68 11 QTL analysis (117)


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Table 7: Quantitative Trait Loci and Linkage Groups in coffee

Traits No of QTLs and LGs

C. canephora Other species

Sucrose 9 QTLs/A, E, I, J

Caffeine 14 QTLs/A, C, E, I, K 2 QTLs/A, G

Trigonelline 5 QTLs/F, G, I, K 1 QTL/G

Lipids 7 QTLs/B, C, E, G, I

CGAs 23 QTLs/A, B, D, E, F, I, J, K 1 QTL/A

Cup quality 14 QTLs/A, B, D, E, G, H, I

Source: (43, 44, 49, 120, 121)


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eTable 8: Genes controlling biochemical compounds determining quality traits in coffee

Species Sucrose Caffeine Trigonelline Fatty acids CGAs

C. arabica CaSUS1 &

CaSUS2,

CaInv3

CmXRS1, CTS2, CCS1, CaXMT1-2, CaMXMT1-2, and CaDXMT1-2

CTgS1 &

CTgS2

OLE2,

DGAT

4CL8, HQT,

F5H1 & POD

C.

canephora

CcSUS1,

CcSUS2

& 8 new

genes

CmXRS1, CTS2,

CCS1 &

23 genes

relating to

alkaloid

catalytic

N/A CcOLE1-5,

CcSTO1

& 15 other

genes

Many genes

involved in

phenylpropa

-noid

pathway

Source: (7, 44, 79, 93, 106, 120, 123-130)


Advances in genomics for the improvement of quality in Coffee381677/UQ381677... · 2019-10-09 ·...

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