Download Genetic mapping of quantitative phenotypic traits in Saccharomyces

MINIREVIEW
Genetic mapping of quantitative phenotypic traits in
Saccharomyces cerevisiae
Steve Swinnen1, Johan M Thevelein2,3 & Elke Nevoigt1
1
School of Engineering and Science, Jacobs University gGmbH, Bremen, Germany; 2Laboratory of Molecular Cell Biology, Institute of Botany and
Microbiology, Katholieke Universiteit Leuven, Heverlee, Belgium; 3Department of Molecular Microbiology, VIB, Heverlee, Belgium
Correspondence: Elke Nevoigt, School of
Engineering and Science, Jacobs University
gGmbH, Campus Ring 1, D-28759 Bremen,
Germany. Tel.: +49 421 2003541; fax:
+49 421 2003249;
e-mail: [email protected]
Received 29 September 2011; revised 1
December 2011; accepted 5 December 2011.
Final version published online 24 January
2012.
DOI: 10.1111/j.1567-1364.2011.00777.x
Editor: Jack Pronk
YEAST RESEARCH
Keywords
yeast; Saccharomyces cerevisiae; phenotypic
trait; QTL; genetic mapping; inverse
engineering.
Abstract
Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of
introducing advantageous traits such as stress tolerances. Exploring subspecies
diversity of S. cerevisiae has identified strains that bear industrially relevant
phenotypic traits. Provided that the genetic basis of such phenotypic traits can
be identified inverse engineering allows the targeted modification of production
strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as
quantitative trait loci (QTL). A straightforward approach to identify the genetic
basis of quantitative traits is QTL mapping which aims at the allocation of the
genetic determinants to regions in the genome. The application of high-density
oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire
genome. This review focuses on the basic principle and state of the art of QTL
mapping in S. cerevisiae. Furthermore we discuss several approaches developed
during the last decade that allow down-scaling of the regions identified by
QTL mapping to the gene level. We also emphasize the particular challenges of
QTL mapping in nonlaboratory strains of S. cerevisiae.
Introduction
Saccharomyces cerevisiae has been an important eukaryotic
model organism in fundamental research and at the same
time a robust and versatile production organism in
industrial biotechnology. Pathway-engineered S. cerevisiae
strains for the industrial production of ethanol from lignocellulosic biomass, lactic and succinic acid, butanols,
isoprenoids, and polyketides have been generated or are
under development (Nevoigt, 2008; Krivoruchko et al.,
2011). Although industrial production strains have been
selected for best performance, there is always room for
further improvement, particularly when it comes to tolerance toward the multiple stresses that occur during
industrial processes. This has been recently exemplified in
the fermentation of lignocellulosic biomass (Almeida
et al., 2011) and very-high gravity brewing (Puligundla
et al., 2011). Stress tolerance is a multifactorial trait and
FEMS Yeast Res 12 (2012) 215–227
has therefore been difficult to engineer in a rational way.
Therefore, alternative engineering strategies that do not
require prior knowledge about the phenotype to genotype
relationship such as adaptive evolution/evolutionary engineering (Cakar et al., 2005; Aguilera et al., 2010), global
transcription machinery engineering (Alper et al., 2006),
and breeding (Benjaphokee et al., 2011) have been
applied. These strategies may prove problematic, however,
as they may result in the accumulation of disadvantageous mutations because of the highly focused selection
pressure applied. Additionally, such strategies do not
allow linking of the phenotypic improvement to the
underlying molecular and/or genetic basis and therefore
do not provide any understanding or information for further strain improvement.
In contrast, inverse engineering is a strategy that seeks
to identify the genetic determinants of a phenotypic trait
of interest followed by the targeted genetic improvement
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216
of an industrial production strain (Bailey et al., 2002). By
doing so, this strategy exploits the biodiversity of yeast
for strain optimization, taking advantage of the well-documented natural phenotypic variation within the species
S. cerevisiae as well as between S. cerevisiae and closely
related species (Fay & Benavides, 2005; Carreto et al.,
2008; Kvitek et al., 2008; Liti et al., 2009; Schacherer
et al., 2009; Csoma et al., 2010). It is obvious that this
diversity presents a treasure chest with regard to genetic
determinants of industrially relevant phenotypic traits
that can be used in inverse engineering of yeast. However,
the major challenge in inverse engineering is the identification of the genetic determinants of the phenotypic trait
of interest. A mere global molecular analysis of the strains
by next-generation sequencing or ‘omics’ technologies
can certainly lift a corner of the veil (Blieck et al., 2007;
Duong et al., 2011); however, they cannot distinguish
between trait-relevant and trait-irrelevant molecular differences. In this regard, the integration of traditional
genetic linkage analysis (called genetic mapping) with
genomic technologies is a promising avenue to identify
causative genetic determinants among the usually high
number of genetic variations between different S. cerevisiae
strains. Saccharomyces cerevisiae provides an ideal platform for genetic mapping because of the ease by which
experimental crosses can be performed and the high
recombination rate during meiosis (on average about 90
crossovers per meiosis; Mancera et al., 2008).
This review gives an overview of the state of the art of
genetic mapping of quantitative phenotypic traits in
S. cerevisiae. We will introduce the basic concept of mapping quantitative trait loci (QTL) and demonstrate how
recent advances in global, high-throughput genotyping
technologies have strongly facilitated genetic mapping of
quantitative phenotypic traits in this organism. Challenges
posed to genetic mapping in industrial S. cerevisiae strains
will also be discussed.
Confounding factors in the genetic
dissection of quantitative traits
The phenotypic variation between strains can be categorized as qualitative or quantitative (Falconer & Mackay,
1996). Qualitative traits are often referred to as Mendelian traits because they are controlled by a single locus
that has a discrete effect on the phenotype. Identifying
genes underlying qualitative traits has been fairly straightforward through the application of positional cloning
(Botstein & Risch, 2003). Most traits, however, are quantitative, meaning that they comprise a continuous distribution of a measurable character. Well-known examples
of quantitative traits in S. cerevisiae include varying
types of stress tolerance, such as the industrially relevant
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S. Swinnen et al.
characteristics of heat tolerance (Parts et al., 2011) and
ethanol tolerance (Hu et al., 2007). Quantitative traits are
typically controlled by multiple loci referred to as QTL
(Abiola et al., 2003). A QTL thus refers to an individual
locus that explains a specific part of the phenotypic
expression of a quantitative trait. A QTL can contain a
single gene or a cluster of closely linked genes that contribute to the quantitative trait (Mackay, 2001). We
emphasize here that the distinction between qualitative
and quantitative traits is somewhat artificial, as qualitative
traits can also be considered as extreme cases of quantitative traits. This implies that the strategies for the genetic
dissection of quantitative traits discussed later in this
review can also be applied to qualitative traits.
In general, the identification of genes that contribute to
quantitative traits has proven difficult (Flint & Mott,
2001). This is because of the complex genetic architecture
of quantitative traits, which is dictated by factors such as
variable QTL contribution, epistasis, genetic heterogeneity, and gene–environment interaction. With regard to
the quantitative contribution of QTL, it has long been
thought that quantitative traits are determined by numerous loci each having a small effect on the trait (called
minor QTL) (Barton & Turelli, 1989). However, the successful dissection of quantitative traits in various species
over the last decade suggests that quantitative traits are
instead determined by loci with varying contributions to
the trait, of which some may have a large effect (called
major QTL) (Risch, 2000). For example, it has been
demonstrated in S. cerevisiae that more than 90% of the
difference in sporulation efficiency between the high-efficiency strain SK1 and the low-efficiency strain S288c was
determined by just three major QTL (Deutschbauer &
Davis, 2005). The same number of QTL have been found
to explain 94% of the difference in frequency of petite
formation between the low-frequency strain RM11-1a and
the high-frequency strain BY4716 (Dimitrov et al., 2009).
While the identification of major QTL has been straightforward in most published studies, the identification of
minor QTL has required more sophisticated strategies
and therefore has been a much greater challenge (Brem
et al., 2005; Demogines et al., 2008; Sinha et al., 2008;
Ehrenreich et al., 2010; Parts et al., 2011).
Apart from the low contribution of some QTL, the
genetic dissection of quantitative traits can also be
impaired by epistatic effects (Carlborg & Haley, 2004).
Epistatic effects are interactions between different QTL,
such that the phenotypic expression of the quantitative
trait cannot be predicted simply by summing up the
effects of the individual QTL. A case of synergistic epistasis has been encountered in the above-mentioned
study of Dimitrov et al. (2009). This study identified
three alleles from BY4716 (SAL1, CAT5, and MIP1) that
FEMS Yeast Res 12 (2012) 215–227
QTL mapping in yeast
contributed to its high petite frequency. When the contribution of each individual BY4716 allele to petite frequency was determined in the BY4716 background (i.e.
the two other BY4716 alleles were replaced by their
RM11-1a counterparts), the obtained petite frequencies
were 24% (SAL1), 10% (CAT5), and 14% (MIP1). These
frequencies added up to 48%, which is less than twothirds of the petite frequency of the BY4716 wild type
(79%). An additional factor presenting an obstacle in
the study of quantitative traits is genetic heterogeneity,
in which different combinations of genetic determinants
cause the same, indistinguishable phenotype (Risch,
2000). Finally, gene–environment interactions also influence the genetic study of quantitative traits. In fact, the
effect of a genetic determinant on a quantitative trait
can differ in different environments. One example of
gene–environment interaction in S. cerevisiae has been
given by Smith & Kruglyak (2008). These authors measured transcript abundances of thousands of genes as
quantitative traits in segregants from the cross between
RM11-1a and BY4716 grown in either glucose or ethanol as carbon source and QTL were identified that
exhibited different effects on gene expression in the two
conditions.
The basic principle of QTL mapping
The aforementioned interdependent and complex interactions between QTL make it virtually impossible to identify all relevant QTL when studying each QTL separately.
Hence, the identification of QTL demands a method in
which they can all be collectively identified. A straightforward method is QTL mapping, which aims at the simultaneous genomic localization of all loci determining a
quantitative trait. QTL mapping in S. cerevisiae is typically performed by crossing two strains that differ in the
trait of interest (Fig. 1a). In particular, a haploid parental
strain possessing the trait (referred to as the trait+ parent)
is mated with another haploid parental strain lacking the
trait (referred to as the trait parent). After mating, the
diploid hybrid strain is sporulated to yield segregants that
are genetically different. This genetic diversity is the result
of meiotic recombination events at both the chromosomal and intra-chromosomal level that occur during
meiosis (Mancera et al., 2008). It is associated with variation in the phenotypic expression of the quantitative trait
of interest. Segregants with a phenotypic expression comparable to the trait+ parent will be enriched in the genetic
determinants crucial for the quantitative trait, while
segregants with a lower phenotypic expression will lack
some or all determinants. The selection of the former
segregants is usually the starting point for QTL mapping
as depicted in Fig. 1b and discussed later.
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217
During QTL mapping, the allocation of the genetic
determinants to regions in the genome relies on their
co-segregation with genetic loci of known positions,
which are called genetic markers. The most widely used
genetic markers are DNA polymorphisms such as single
nucleotide polymorphisms (SNPs), which are usually
plentiful in number and thus enable complete genome
coverage. These molecular markers are generally presented
on a map that shows the order and relative distances
between the markers along each chromosome. Before the
availability of molecular markers, genetic mapping was
solely based on morphological markers (alleles encoding
morphological traits) and biochemical markers (alleles
encoding variants of enzymes). Even though both types
of markers have enabled the mapping of several qualitative traits, they were fairly limited in number, cumbersome to detect, and therefore very inefficient for the
mapping of quantitative traits (Tanksley, 1993).
Provided that a minimal number of segregants with a
comparable phenotypic expression as the trait+ parent
have been selected (Fig. 1b), the unknown positions of
the QTL can be inferred from the common presence of
genetic markers in these segregants. This is based on the
principle of meiotic recombination that is responsible for
the relationship between the relative distance between two
loci on a chromosome and their tendency to co-segregate
in a cross. On the one hand, when the loci are located far
away from each other on a single chromosome, there is a
large probability that one or more crossovers will occur
between them and separate them in the cross. In this
case, the recombination frequency between the loci will
approach 50%, which is the same value as obtained for
two loci located on different chromosomes. On the other
hand, when the loci are located close together on a single
chromosome, it is very unlikely that a crossover will
occur between them and hence the loci will tend to segregate together in the cross. As a consequence, the recombination frequency will approach 0% for loci located very
close to one another. This principle of meiotic recombination implies that any enrichment in genetic determinants crucial for the phenotypic trait under study in the
selected segregants can be inferred from the enrichment
of genetic markers that co-segregate with them (Fig. 1b).
The significance of this enrichment must be evaluated by
means of statistical analysis.
The QTL mapping approach described in Fig. 1 is
known as selective genotyping because it involves the
selection of segregants that represent the quantitative
extreme of the phenotypic trait under study (i.e. maximal
expression of the trait). The strength of this approach is
its high mapping power, which means that the chance of
detecting a QTL is generally high (Lander & Botstein,
1989). However, there may be circumstances where it is
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218
S. Swinnen et al.
Fig. 1. Schematic overview of QTL mapping in Saccharomyces cerevisiae. (a) On the one hand, QTL mapping requires the construction of a
whole-genome map for the two haploid parent strains revealing the polymorphisms with regard to the reference sequence (molecular markers).
On the other hand, phenotypic variation for the quantitative trait of interest within the population of segregants is needed. The molecular
markers reflecting the genotype of the trait+ parent (expressing the extreme value of the phenotypic trait of interest) are represented by black
rectangles. White rectangles represent the trait parent’s genotype. For simplicity, only a small chromosomal region is shown. (b) The segregants
selected for the extreme expression of the quantitative trait (indicated by black circles) can be used to infer the location of the unknown QTL.
The genotypes of these segregants are expected to show an overrepresentation of those genetic regions that contain the genetic determinants
of the phenotypic trait under study. Mostly, these regions originate from the trait+ parent. Keeping in mind the basic principle of meiotic
recombination, molecular markers closely located to a QTL will co-segregate in the cross and thus also show a deviation from random
segregation in the selected segregants. The regions with a statistically significant deviation from random segregation are referred to as QTL.
advantageous to use the genotypic and phenotypic information from all segregants independent of their phenotypic expression of the trait, for example when the ratio
of extreme to total segregants is relatively low (implying a
large number of QTL contributing to the phenotypic trait
under study) or when phenotyping is cumbersome to
perform. This second QTL mapping approach implies
that the genotyped population is partitioned into two
groups based on the presence of either the trait+ parent’s
genotype or the trait parent’s genotype for each genetic
marker. A statistical test determines for each marker
whether there is a significant difference between the phenotypic distributions of the two genotypic groups (Broman, 2001).
In contrast to what is generally assumed, phenotypic
variation between the parent strains is not a necessity for
QTL mapping. The phenotypic variation must only reside
in the population of segregants. In fact, each parent strain
may contain several loci of opposite effects on the trait
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that reduce the phenotypic difference between the parent
strains in comparison with the population of segregants in
which the loci may have been separated. This phenomenon
is called transgressive segregation (Rieseberg et al., 1999),
and an extreme case has been encountered in a study mapping the variation in gene expression between a laboratory
strain and a wild isolate of S. cerevisiae during exponential
growth (Brem et al., 2002). In this study, the transcription
level of genes showing differential expression between the
two parent strains and/or the segregants from a cross
between the parent strains were considered quantitative
traits. Linkage analysis between the markers and the transcription levels revealed that 570 transcripts showed linkage with at least one locus, of which almost half were not
different in the parent strains. Transgressive segregation
has also been encountered in the mapping of variation in
morphological traits (Nogami et al., 2007) as well as sensitivity to small-molecule drugs (Perlstein et al., 2007) and
DNA-damaging agents (Demogines et al., 2008).
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QTL mapping in yeast
An alternative approach to identify genetic determinants of quantitative traits is association mapping, in
which the mapping is performed at the level of population
including many strains of a species instead of at the level
of two parent strains. Association mapping relies on the
assumption that phenotypically relevant sequence variants
are likely to be more prevalent in yeast strains that display
the trait of interest than in strains that lack the trait. Association mapping has been extensively used in higher
organisms in which experimental crosses are difficult or
even impossible to perform (Risch, 2000). To our knowledge, there are no published studies describing the genetic
dissection of quantitative traits in S. cerevisiae using association mapping. However, Liti et al. (2009) sequenced
the complete genome of 36 S. cerevisiae strains from a
large variety of sources and locations and compared the
genotypic data with phenotypic data for roughly 200
traits. The results revealed a substantial correlation
between genotypic and phenotypic properties within the
species S. cerevisiae. Although the authors did not focus
on the mapping of the determinants underlying the traits,
the results showed that association mapping might be a
promising route in S. cerevisiae. The primary advantage of
QTL mapping at the population level in comparison with
the level of two single parent strains is the easier identification of the genetic determinants. This results from the
larger diversity in polymorphisms between distinct yeast
isolates as compared to segregants from a single cross
(Schacherer et al., 2009). However, this method does not
consider epistasis and the fact that different genetic elements and/or different combinations thereof may control
the same trait in different strains. In addition, the complexity of the QTL defining a specific trait at the population level can be so high that reliable identification of the
QTL (particularly minor QTL) usually becomes exceedingly difficult. These three obstacles prove to be the most
significant disadvantages of this method.
High-throughput genotyping
technologies for high-resolution QTL
mapping
High-resolution QTL mapping is only possible by the
simultaneous genotyping of thousands of genetic markers,
and thus, this method was impossible for a long time
because of the low number of available genetic markers
and the laboriousness of low-throughput genotyping
methods. It is obvious that the progress in whole-genome
genetic analysis has facilitated QTL mapping in terms of
genome coverage and mapping resolution (Segrè et al.,
2006). Indeed, QTL mapping in S. cerevisiae has received
renewed attention since the introduction of rapid and
cost-effective methods to analyze genetic variation
FEMS Yeast Res 12 (2012) 215–227
between strains. In particular, the introduction of
high-density oligonucleotide arrays and whole-genome
re-sequencing in QTL mapping has allowed the successful
genetic dissection of several quantitative phenotypic traits
in S. cerevisiae.
High-density oligonucleotide arrays
Winzeler et al. (1998) were the first to describe a successful approach of QTL mapping in S. cerevisiae that made
use of a high-throughput genotyping technology. They
hypothesized that the genetic variations between two S.
cerevisiae strains could be detected by hybridizing their
genomic DNA to high-density oligonucleotide arrays. The
authors reasoned that the extent of hybridization of a
sequence to an oligonucleotide probe present on the array
depends on the number and position of mismatches
between the two sequences. Thus, the genetic variations
between two strains are revealed as hybridization differences on an array (measured as fluorescence intensities),
while the actual sequence changes remain unknown. To
test the feasibility of their hypothesis, total genomic DNA
from two different S. cerevisiae strains (YJM789 and S96)
were hybridized on separate Affymetrix gene expression
micro-arrays. Comparison of the hybridization patterns
identified 3714 probes that showed significantly different
signal strengths between the two strains. The hybridization differences unambiguously distinguished both strains
and showed a bimodal distribution across four segregants
from one tetrad of the hybrid YJM789/S96. Thus, the
hybridization differences could serve as markers in a
genetic mapping experiment, and this was experimentally
supported by the simultaneous mapping of five Mendelian traits to their known genomic positions with a resolution of 11–64 kb.
In the following years, many quantitative traits in
S. cerevisiae were mapped using high-density oligonucleotide arrays, leading to the identification of causative QTL,
genes, and even SNPs. Examples include traits such as
high-temperature growth (Steinmetz et al., 2002; Sinha
et al., 2008), gene expression levels (Brem et al., 2002;
Yvert et al., 2003), sporulation efficiency (Deutschbauer
& Davis, 2005; Ben-Ari et al., 2006), sensitivity to smallmolecule drugs (Perlstein et al., 2007) and DNA-damaging agents (Demogines et al., 2008), acetic acid production (Marullo et al., 2007), cell morphology (Nogami
et al., 2007), and frequency of petite formation (Dimitrov
et al., 2009). All of these studies utilized commercially
available gene expression arrays, which typically contain a
few 25-mer oligonucleotide probes for each annotated
open reading frame. This implies that these arrays are
only able to reveal genetic strain-to-strain variation with
regard to protein encoding genes. The marker density
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S. Swinnen et al.
220
generally obtained with such gene expression arrays has
been one marker per 3–4 kb (Brem et al., 2002; Steinmetz
et al., 2002; Deutschbauer & Davis, 2005). To overcome
the limitation of low marker density when using gene
expression arrays, Gresham et al. (2006) have introduced
yeast tiling microarrays in QTL mapping. Yeast tiling
microarrays consist of overlapping 25-mer oligonucleotide
probes spaced on average 5 bp apart to provide complete
and approximately fivefold redundant coverage of the
entire genome. This array design provides several measurements of a given nucleotide’s effect on the extent of
hybridization, which can subsequently be modeled to
derive a statistical measure of the likelihood of a polymorphism at a particular site.
In all of the above-mentioned studies, commercially
available arrays have been used that contain probes
designed from the S288c genome sequence (Cherry et al.,
1997). S288c is a natural isolate maintained under selective laboratory conditions, which has led to certain evolutionary adaptations (Gu et al., 2005). Thus, it has to be
kept in mind that hybridization against probes of this
strain might reveal QTL which are simply based on general genetic differences present in the strains under investigation compared with S288c. One example is the
identification of MKT1 as a common quantitative trait
gene in several QTL mapping experiments performed
against the S288c background (Steinmetz et al., 2002;
Deutschbauer & Davis, 2005; Demogines et al., 2008;
Dimitrov et al., 2009; Kim & Fay, 2009; Ehrenreich et al.,
2010). In fact, the same polymorphism in the Mkt1 protein (D30G) was found to be responsible for at least some
part of the phenotypic difference between the strains
studied by Deutschbauer & Davis (2005) (sporulation
efficiency), Sinha et al. (2006) (high-temperature growth),
and Dimitrov et al. (2009) (frequency of petite formation). It was proposed that the S288c mutation (Mkt1D30) is a loss-of-function mutation (Sinha et al., 2006).
Moreover, Mkt1-D30 seems to be a rare variant, as Mkt130G has been conserved in all non-S288c S. cerevisiae
strains investigated (Deutschbauer & Davis, 2005; Sinha
et al., 2006; Dimitrov et al., 2009). The pleiotropic effect
of Mkt1 on cellular function can most likely be attributed
to its recently established regulatory role in global gene
expression as proposed by Zhu et al. (2008). These
authors combined all available molecular data from segregants of a cross between the laboratory strain BY, an
auxotrophic derivative of S288c (Brachmann et al., 1998),
and the wild isolate RM11-1a and generated networks of
genes that are co-expressed. Several of these networks
were enriched in genes controlled by common genetic
loci [called expression QTL (eQTL) hot spots], and the
genetic dissection of one such eQTL hot spot revealed
Mkt1 as a major regulator of global gene expression.
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The QTL mapping power strongly depends on the
number of genotyped segregants. In this context, the
application of high-density oligonucleotide arrays for
genotyping each individual segregant is laborious and
expensive. An elegant solution has been to pool many segregants expressing the trait of interest and genotype the
pool as a whole. This approach is known as bulk-segregant analysis and was first developed for genotyping
genetic markers in pools of plants (Michelmore et al.,
1991). Bulk-segregant analysis typically relies on the construction of two pools of segregants that are referred to
as the selected pool and the control pool. The selected
pool contains a large number of segregants that express
the trait of interest, while the control pool contains a
similar number of segregants that were not selected for
the trait. Once the pools are made, the genomic DNA
from each pool is extracted and genotyped for each marker. Genetic regions in the pool where genetic markers
originating from one of the parent strains are overrepresented in the selected pool relative to the control pool are
predicted to contribute to the trait of interest. Segrè et al.
(2006) introduced the concept of bulk-segregant analysis
in S. cerevisiae QTL mapping using high-density oligonucleotide arrays. As a proof-of-principle experiment, they
successfully mapped three antibiotic resistance genes
(KANR, HYGR, and NATR) in a cross between W303 and
SK1 to their known chromosomal positions. The selected
pool contained approximately 107 segregants from W303/
SK1 that were selected in liquid medium containing all
three antibiotics (geneticin, hygromycin, and nourseothricin). The control pool containing the same number of
segregants was isolated in rich medium without antibiotics. We highlight that in addition to the reduction in time
and working costs, an important advantage of bulk-segregant analysis is the increased mapping resolution that can
be obtained by examining the numerous recombinations
present in a large pool of segregants.
Whole-genome re-sequencing
Another method for high-throughput genotyping that has
become available for high-resolution QTL mapping is
whole-genome re-sequencing. In recent years, there has
been a shift away from automated Sanger sequencing
toward the development of next-generation sequencing
that parallelizes the sequencing process, thereby yielding
an enormous volume of inexpensive and accurate genome
sequence data (Metzker, 2010). Next-generation sequencing constitutes a number of sequencing technologies that
rely on the physical separation of relatively short single
DNA molecules on a solid surface or support, allowing
thousands to millions of sequencing reactions to be performed simultaneously. This process generates a high
FEMS Yeast Res 12 (2012) 215–227
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QTL mapping in yeast
number of partially overlapping short sequences referred
to as reads. The reads are usually aligned to a known reference sequence to generate the new sequence, a process
that is known as re-sequencing. The reads can also be
assembled de novo; however, this has proven to be a substantial challenge and usually requires larger reads and
read coverage, which is accompanied by higher costs and
more intensive bioinformatic input (Metzker, 2010).
The development of next-generation sequencing provides a new avenue to score large numbers of SNPs as
markers for QTL mapping. The selection of markers
starts by establishing the whole-genome sequence of the
two parent strains, which usually provides a large number
of SNPs. Only SNPs that can be unambiguously scored
can be reliably used as markers for QTL mapping. In
practice, such high-quality SNPs are selected by defining
a minimal required SNP coverage (i.e. the number of
reads covering the nucleotide) and SNP frequency (i.e.
the ratio between the number of reads with the specific
nucleotide and the total number of reads). These conditions have to be well considered: with more stringent
conditions, fewer reliable markers will be available, and
thus, the mapping resolution that can be obtained is
decreased. Under less stringent conditions, more markers
can be selected, which may, however, be less informative.
Applying whole-genome re-sequencing to a large number of individual segregants remains very expensive, similar to the situation with high-density oligonucleotide
arrays used for scoring SNPs in individual segregants.
Therefore, QTL mapping using whole-genome resequencing has thus far been applied exclusively in combination with bulk-segregant analysis. In practice, the
selected pool and the control pool are sequenced and the
reads aligned against one of the parental sequences. The
frequency of each selected SNP is then plotted against its
chromosomal position, and the difference between the
selected and control pool is statistically analyzed. Two
studies have recently applied this approach to map quantitative traits. Ehrenreich et al. (2010) identified loci that
contribute to resistance to 17 diverse chemical agents in a
cross between RM11-1a and BY4716, one of it being the
DNA-damaging agent 4-nitroquinoline (4-NQO). The
authors isolated large pools of resistant segregants by
exposing approximately 107 segregants to the different
agents. The control pool consisted of segregants that were
grown in the absence of any of the agents. The nucleotide
frequency of SNPs detected by Illumina whole-genome
sequence analysis in the DNA from these pools was subsequently applied to map the loci. Parts et al. (2011) used
a similar approach to identify loci contributing to hightemperature growth. They crossed an oak tree bark strain
that grew well at high temperature (YPS128) with a palm
wine strain that grew poorly under identical conditions
FEMS Yeast Res 12 (2012) 215–227
(DVGBP6044). A population of segregants from the
hybrid strain was intercrossed for many generations to
accumulate a high number of recombinations. In such a
cross, only markers close to the genetic determinants
remained linked, which allowed QTL mapping to a higher
resolution. This strategy is known as the advanced intercross line approach (Darvasi & Soller, 1995). Half of the
intercrossed population containing 107–108 segregants
was then grown at 40 °C to generate the selected pool,
while the other half was grown in permissive temperature
(23 °C) to generate the control pool. Whole-genome resequencing of genomic DNA from both pools revealed 21
regions of which the allele frequency in the selected pool
was significantly different from that in the control pool.
Statistical methods in QTL mapping
Any QTL mapping requires statistical inference to evaluate the significance of the obtained data. Such mathematical methods range from simple statistical methods to
identify an association between a marker and a phenotypic trait to very complex and computationally intensive
algorithms to identify a potential QTL. For a detailed
overview, the reader is referred to comprehensive reviews
regarding statistical methods in QTL mapping (Broman,
2001; Wu et al., 2007). Here, we will focus only on
selected aspects that we find most relevant.
In general, the mapping power is determined by the
number of segregants and the number of markers scored.
Statistics can help to determine the minimum number of
segregants and markers to be analyzed to obtain the
desired mapping power. However, such absolute methods
are, to our knowledge, currently not available in the literature. Nonetheless, it is logical that the mapping power
generally increases with an increased number of analyzed
segregants as exemplified by the following comparison.
Demogines et al. (2008) performed linkage analysis on
123 segregants and identified a single locus that contributed to 4-NQO sensitivity. In contrast, Ehrenreich et al.
(2010) selected a much larger number of segregants by
exposing a pool of approximately 107 segregants to the
drug. Linkage analysis on the selected pool identified thirteen additional loci with significant linkage to 4-NQO
sensitivity.
Statistical methods have been commonly used to establish whether the putative linkage between specific markers
and the phenotypic trait under study is significant. In this
regard, two general approaches have been developed. Single-marker tests evaluate whether there is a significant difference between the occurrence of an individual marker
in the selected segregants and in randomly selected segregants. Examples of single-marker tests are analysis of variance, t-tests, and their nonparametric equivalents
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222
(Doerge, 2002). In contrast to single-marker tests, interval
mapping allows more sophisticated methods of QTL analysis, which rely on genetic maps (Lander & Botstein,
1989). Interval mapping is more powerful than singlemarker approaches to detect a QTL because of the structural and additional genotypic data supplied by the
genetic map.
There are also statistical approaches that allow the location of multiple QTL at the same time; however, they are
based upon the identification of all single QTL and their
potential interactions (Kao et al., 1999) and can include
other markers as cofactors (Jansen, 1993; Zeng, 1993).
Such multiple QTL models are computationally very
demanding, and there is no single QTL model that is
superior to the other fitted QTL models.
The primary complication with single-marker tests and
interval mapping is that they fail to account for multiple
testing, consequently increasing the occurrence of false
positives. A technique to control the false discovery rate
has been proposed (Storey & Tibshirani, 2003). However,
this technique must be carefully applied to avoid excluding actual QTL, especially when the power of the QTL
mapping experiment is low.
Thus far, very few methods are available that are specifically developed to analyze data obtained from QTL
mapping using bulk-segregant analysis based on wholegenome re-sequencing. Such methods take into account
the fact that in such analyses, the selected pool does not
exclusively contain segregants possessing the trait+ phenotype (as it is the case when analyzing a number of individually selected segregants) but is only enriched in those
segregants. Moreover, they consider variation introduced
during the sequencing of pools. Ehrenreich et al. (2010)
have described a method that is a combination of standard paired t-tests (single-marker test) and a regressionbased peak finding approach to detect potential QTL.
Other methods have been suggested by Nikolaev et al.
(2009), Li et al. (2009), and Magwene et al. (2011).
Dissection of a QTL to the gene level
QTL mapping is a genuine genetic mapping method,
meaning that it can only allocate the genetic determinants
to intervals in the genome instead of identifying the
determinants themselves. Therefore, once the QTL have
been identified, they still must be down-scaled to the gene
and/or nucleotide level by a combination of traditional
methods such as sequence analysis, candidate gene prediction, and functional complementation (Abiola et al.,
2003).
Depending on the number of markers and segregants
analyzed, a single QTL may range from a few to several
hundreds of kilobases in size. This indicates that the
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S. Swinnen et al.
number of genes localized within a QTL is often too high
to perform the functional evaluation of each individual
gene. Therefore, a QTL may first be reduced in size by an
approach that is known as fine-mapping, which relies on
the analysis of additional markers and/or recombination
events within the respective QTL (Steinmetz et al., 2002;
Deutschbauer & Davis, 2005; Marullo et al., 2007). As
discussed before, not all genetic variations between two
strains can be detected or unambiguously scored by highthroughput genotyping technologies and thus selected as
markers. In the case of gene-expression microarrays, only
genetic variations in protein expressing genes that result
in significant hybridization differences can be selected as
markers. This significance in hybridization difference is
also the prime consideration when applying yeast tiling
microarrays. In the case of whole-genome re-sequencing,
only genetic variations with a minimum coverage and
ratio in the short reads are considered reliable and can be
selected as markers. In most QTL, it is therefore possible
to identify additional markers by performing Sanger
sequencing of the obtained QTL interval in both parent
strains. Establishing linkage between the additional markers and the phenotype under study may reveal markers
with a stronger linkage and thus reduce the QTL size.
Fine-mapping of a QTL may also rely on additional
recombination events within the QTL interval. In fact, a
recombination event in specific segregants may separate
the unknown genetic determinant from closely linked
markers, thus allowing further reduction in the QTL size.
It is obvious that the latter approach usually requires
scoring of a large number of segregants, as genetic markers in one QTL are by nature in very close proximity and
thus usually co-segregate. Only the segregants with a
recombination event within the QTL under study are
informative for fine-mapping. Therefore, to avoid exhaustive phenotyping, it is advisable to first genotype a large
number of segregants for markers flanking the QTL and
subsequently phenotype only those that show a recombination event between these markers (Ronin et al., 2003).
Although fine-mapping reduces the size of a QTL, it
does usually not result in a QTL interval small enough to
contain a single gene. Thus, additional steps are required
to identify the causative gene variant(s) (Glazier et al.,
2002; Abiola et al., 2003). Most studies continue with prioritizing the genes present in the identified QTL (Tabor
et al., 2002). The logical first step is to look for open
reading frames that show polymorphisms likely to have a
consequence on the amino acid sequence of the gene
product, such as missense, nonsense, and frame shift
mutations. Further prioritization can be based on published information of genes involved in the quantitative
trait, for example those genes located in biochemical or
regulatory pathways known to be involved in establishing
FEMS Yeast Res 12 (2012) 215–227
223
QTL mapping in yeast
the trait or from studies that have identified proteins that
interact with components of these pathways. Although
potentially straightforward, such a prioritization does not
consider the fact that polymorphisms in promoters might
also have strong phenotypic consequences. In this context, useful information for prioritization might result
from gene expression analysis or phenotyping the corresponding deletion mutant. Rational evaluation of such
available information might help to identify the crucial
genetic determinants within a QTL; however, previous
studies have shown it to be often insufficient (Deutschbauer & Davis, 2005; Sinha et al., 2006).
Unbiased approaches form an alternative to rational
prioritization. Such approaches are advantageous in that
they do not rely on prior assumptions. One unbiased
approach that has been used in the genetic dissection of
QTL is marker-trait association, which resembles the
above-mentioned association mapping. The main difference between both approaches is that marker-trait association is performed on QTL level, while association
mapping is performed on whole-genome level. Markertrait association has the potential to directly identify the
relevant sequence variant(s); however, in practice, it has
had little success because of the already mentioned drawbacks of association mapping (Steinmetz et al., 2002;
Deutschbauer & Davis, 2005; Sinha et al., 2006).
Another unbiased approach is reciprocal hemizygosity
analysis (RHA) (Steinmetz et al., 2002). This functional
analysis approach evaluates all genes in a QTL for relevance in establishing the trait of interest. It is based on
the construction of two isogenic strains in the hybrid diploid background from both parent strains that differ
genetically only in the alleles of one copy of a specific
candidate gene (Fig. 2). Hence, one strain carries the
allele from the trait+ parent and is deleted for the allele
from the trait parent, while the other strain carries the
allele from the trait parent and is deleted for the allele
from the trait+ parent. By comparing the phenotypes of
the two strains, it will be revealed whether an allele from
one genetic background is advantageous over that from
the other. As RHA analyzes the contribution of each allele
in the hybrid diploid background, it takes into account
the possible requirement for interactions with other
mutant alleles from the parental backgrounds to confer
the phenotype under study. Nevertheless, the diploid
hybrid background used in RHA is different from the
haploid backgrounds used in the QTL mapping experiment, which means that the possible influence of the
ploidy of the strains on the phenotypic expression of the
trait cannot be disregarded.
The most conclusive evidence for a gene’s contribution
to the trait under study resides in the ability of performing allelic replacement in both parent strains. One or
FEMS Yeast Res 12 (2012) 215–227
Fig. 2. Principle of RHA according to Steinmetz et al. (2002). For
each gene in the QTL, two diploid strains are constructed in the
parental hybrid background that carries either the allele from the
trait+ or the trait parent. The reciprocal deletions are usually
engineered using a KanR deletion cassette. If the strains show a
difference in the phenotypic expression of the trait, it implies that
there is a difference in the phenotypic contribution of the parental
alleles of the gene under study.
more parental alleles are replaced with the allele(s) from
the other parent strain and the impact tested on the phenotype. As the phenotypic expression of an allele from
one parent may depend on its interaction with other
alleles from the same background, the transfer of a single
causative allele into the other parent’s genetic background
does not necessarily result in the desired outcome. Hence,
the best solution is to express all the causative alleles
from one parent in the other parent’s background.
QTL mapping in industrial and natural
S. cerevisiae strains
Quantitative traits of S. cerevisiae strains have great
importance for industrial use. Those of high economic
value include fermentation capacity, stress tolerance, flocculation, substrate range, and yield of products or lucrative compounds. Targeted genetic modification by means
of inverse engineering offers the best potential for reliable
and predictable improvement of such traits. However,
this requires knowledge about the genetic basis of these
traits. It has now been convincingly shown that QTL
mapping provides a straightforward approach to identify
genetic determinants of quantitative traits. However, virtually all published QTL mapping studies in S. cerevisiae
have been carried out in laboratory strains, with the
exception of one study mapping acetic acid production in
a cross between two commercial wine strains (Marullo
et al., 2007) and another study mapping several sake
brewing characteristics in a cross between a commercial
sake brewing strain and a laboratory strain (Katou et al.,
2009).
Industrial strains or natural isolates pose a much
greater challenge than laboratory strains for QTL mapping. Industrial strains have been reported to show a
more complex genome, often being diploid, polyploid, or
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S. Swinnen et al.
224
even aneuploid (Benitez et al., 1996). In this regard, the
genetic complexity of industrial strains might strongly
hamper QTL mapping as long as it is difficult to obtain a
true and stable haploid descendant that still shows the
extreme expression of the quantitative trait of interest of
its parent and can serve as the trait+ strain in QTL mapping. In fact, if chromosome copy number is strongly
contributing to the quantitative expression of the trait,
QTL mapping becomes significantly more difficult. In
addition, aneuploidy results in a deviation from 2 : 2 segregation for polymorphisms located on multiplied chromosomes, making them unsuitable as genetic markers in
QTL mapping. The multiplied chromosomes have therefore been excluded from interrogation in the QTL mapping studies in industrial strains (Marullo et al., 2007;
Katou et al., 2009).
Another complication for QTL mapping in industrial
strains and natural isolates of S. cerevisiae is the likely
presence of sequences that are not present in the reference sequence (most often S288c) used for designing the
probes on arrays or aligning the short reads obtained by
next-generation sequencing. For example, molecular
karyotype analysis of an industrial bio-ethanol production
strain has revealed additional chromosomal regions
within subtelomeric regions that were not present in
S288c (Argueso et al., 2009). These regions even differ
between pairs of homologous chromosomes. Generally,
these regions do not contain genes that are essential for
viability but may contribute to fitness in highly specific
environments, such as those used in industry. It is obvious that the identification of genetic determinants localized in genetic regions unique to an industrial strain (e.g.
specific chromosomal insertions) and (partially) responsible for the phenotype of interest requires additional
efforts. One solution would be to perform de novo assembly of the reads obtained by whole-genome re-sequencing
that do not map to the reference sequence. The selected
segregants must then be evaluated for the presence of the
obtained contigs, and linkage analysis must be performed
to confirm that the contigs are significantly linked with
the phenotypic trait of interest (Wenger et al., 2010).
Another common feature of industrial strains and natural isolates is their strong variation in sporulation efficiency and spore viability or the tendency to be
homothallic, that is, the spontaneous switching of mating
type, thus not allowing the generation of stable haploid
strains (Tamai et al., 2001). The latter limitation can be
overcome by converting homothallic strains into heterothallic strains by deleting all HO gene copies present
(Marullo et al., 2007).
Another issue that poses a challenge to QTL mapping
in nonlaboratory S. cerevisiae strains is that most industrially relevant traits, such as fermentation capacity or the
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
heterologous production of a valuable compound, are not
selectable and require a thorough phenotypic evaluation
of each segregant individually. Usually, such phenotypic
screenings are laborious and time consuming. Thus, the
selection of a large number of segregants with the phenotype of interest is much more laborious for most industrially relevant traits. This also implies that bulk-segregant
analyses as performed in the recent studies of Ehrenreich
et al. (2010) and Parts et al. (2011) are not directly applicable for most industrially relevant traits.
Conclusion and outlook
Facilitated by the rapid progress in high-throughput
genotyping, QTL mapping in S. cerevisiae has become
well established in the past 10 years. There have been a
number of studies demonstrating efficient QTL mapping
in this yeast, as well as the further down-scaling of QTL
to the relevant genes and nucleotides. So far, the strategies have been developed and evaluated mostly in laboratory strains and for easily selectable or scorable
phenotypic traits. Nevertheless, there has also been one
published study that used QTL mapping to resolve an
industrially relevant phenotypic trait (i.e. the production
of acetic acid which is relevant in wine making) down
to gene level (Marullo et al., 2007). Another QTL mapping study of an industrial S. cerevisiae strain focusing
on high-ethanol tolerance phenotype will be provided by
the authors of this review (Swinnen & Thevelein) in the
near future. We suggest that QTL mapping could
become a valuable and powerful tool for S. cerevisiae
industrial strain improvement programs in the context
of inverse engineering. In fact, this approach is able to
link interesting phenotypic traits to genetic determinants
and thereby allows the exploitation of the high phenotypic diversity of the species S. cerevisiae for the optimization of industrial strains. This requires that the
challenges of QTL mapping with regard to industrial
strains, which often possess a much more complex
genetic constitution and usually nonselectable industrially
relevant phenotypic traits, have to be overcome.
Acknowledgements
We thank Jürgen Claesen for helpful discussions about
statistical methods in QTL mapping and Thomas R. Beatman and Joseph N. McInnes for English proofreading of
the manuscript. Original research was supported by an
SBO grant (IWT 90043) from the Agency for Innovation
by Science and Technology (IWT-Flanders), the EC 7th
Framework program (NEMO project), IOF-Knowledge
platform (IKP/10/002 ZKC 1836), and BOF-Program
financing (project NATAR) to J.M.T.
FEMS Yeast Res 12 (2012) 215–227
QTL mapping in yeast
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