Analysis of malaria parasite phenotypes using experimental genetic crosses of Plasmodium falciparum.
ABSTRACT We review the principles of linkage analysis of experimental genetic crosses and their application to Plasmodium falciparum. Three experimental genetic crosses have been performed using the human malaria parasite P. falciparum. Linkage analysis of the progeny of these crosses has been used to identify parasite genes important in phenotypes such as drug resistance, parasite growth and virulence, and transmission to mosquitoes. The construction and analysis of genetic maps has been used to characterise recombination rates across the parasite genome and to identify hotspots of recombination.
Analysis of malaria parasite phenotypes using experimental genetic crosses
of Plasmodium falciparum
Lisa C. Ranford-Cartwright⇑, Jonathan M. Mwangi
Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Sir Graeme Davies Building, 120 University Place,
Glasgow G12 8TA, Scotland, UK
a r t i c l ei n f o
Received 21 December 2011
Received in revised form 8 March 2012
Accepted 10 March 2012
Available online 29 March 2012
Quantitative trait loci
a b s t r a c t
We review the principles of linkage analysis of experimental genetic crosses and their application to Plas-
modium falciparum. Three experimental genetic crosses have been performed using the human malaria
parasite P. falciparum. Linkage analysis of the progeny of these crosses has been used to identify parasite
genes important in phenotypes such as drug resistance, parasite growth and virulence, and transmission
to mosquitoes. The construction and analysis of genetic maps has been used to characterise recombina-
tion rates across the parasite genome and to identify hotspots of recombination.
? 2012 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.
1. Introduction; recombination in the laboratory and in nature
Malaria parasites undergo recombination during meiosis, in a
similar way to other eukaryotes. The processes of independent seg-
regation of the 14 chromosomes and crossing over between homol-
ogous chromosomes both occur during meiosis in the diploid
zygote stage of the parasite (Sinden and Hartley, 1985; Walliker
et al., 1987), which is formed after fertilisation of the female gamete
by the male in the mosquito stomach. The zygote then develops
into an oocyst on the mosquito midgut wall, within which there
are multiple rounds of mitosis (see Baton and Ranford-Cartwright,
2005 for a review of the parasite stages in the mosquito).
fore possess combinations of chromosomes, and genes along those
binants). Meiosis produces recombinants only following fertilisa-
tion events between genotypically distinct gametes; fertilisation
between a male and female gamete from the same parasite geno-
type (self-fertilisation) would result in a zygote that was totally
homozygous at all loci, and chromosomal segregation and crossing
over would produce progeny that were identical to the original ga-
In the laboratory, cross- and self-fertilisation occur between ga-
metes in the blood meal of a mosquito feeding on an artificial mix-
ture of gametocytes of two different genotypes, at frequencies
determined by random associations between gametes, i.e. there
does not appear to be a favouring of self- or cross-fertilisation
(Ranford-Cartwright et al., 1993). A similar situation of random
mating appears to occur in mosquitoes feeding on naturally in-
fected individuals (Anderson et al., 2000); the occurrence of
cross-fertilisation requires the presence of genetically distinct
gametocytes within the blood of the person bitten by the mos-
quito, but this appears to be common, at least in high transmission
areas (Babiker et al., 1994, 1999; Paul et al., 1995; Paganotti et al.,
2006; Mzilahowa et al., 2007).
Laboratory crossing experiments are easier to analyse than
those occurring during natural transmission, because the geno-
types of the gametocytes are known and defined, and the maxi-
mum number of alleles at any locus is two. The recombinant
progeny can be cloned and analysed for their inheritance of the
two parental alleles (Walliker et al., 1987). The frequency of
recombination events can then be established and the regions of
the genome inherited from each parent described for each progeny
clone, thus establishing a genetic map.
Three experimental genetic crosses have been performed to
date for the human malaria parasite Plasmodium falciparum (Wall-
iker et al., 1987; Wellems et al., 1990; Hayton et al., 2008), using
five different clones of the parasite (Table 1). The major limitation
on performing further experimental genetic crosses is that there is
0020-7519/$36.00 ? 2012 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.
⇑Corresponding author. Tel.: +44 141 330 2639; fax: +44 141 330 4600.
International Journal for Parasitology 42 (2012) 529–534
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currently no efficient in vitro system for the liver stages of P. falci-
parum; all crosses to date have involved infecting a chimpanzee
with sporozoites, and collection of infected erythrocytes for culture
from the chimpanzee blood.
2. Linkage analysis studies in P. falciparum
Analysis of genetic crosses is an especially useful technique to
identify genomic loci influencing parasite phenotypes for which
there are no obvious candidate genes, for example, drug resistance
where the mechanism of action of the drug is unknown. A major
advantage of the analysis of crosses over population association
studies is that there are only two alleles (maximum) within the
progeny clones in the former – those existing in the two parents
used for the cross – whereas there could be very many within a
population sample in the latter studies. Therefore, loci can be iden-
tified by analysing smaller numbers of parasite clones from an
experimental cross, and there are less likely to be confounding ef-
fects from multiple forms of multiple genes.
If variation in a parasite phenotype is controlled by polymor-
phisms within a single gene, each haploid progeny clone will in-
herit the allele from either one parent or the other, and the
progeny will all exhibit one of two variants of the phenotype –
two parental-like groups – with no intermediate phenotypes.
Any variation in phenotype will be due to environmental differ-
ences or to experimental noise. The gene controlling the pheno-
typic variation of interest can be identified by examining the
genomes of the progeny clones, and finding the region(s) of the
genome that are shared among the parent and progeny that exhibit
one phenotypic state. This is the principle of linkage analysis.
In cases where multiple genes contribute to the phenotypic var-
iation, non-parental phenotypes would be observed, which could
fall between the two parental phenotypes, or be greater or smaller
than either parent. These multigenic or complex traits are analysed
using a statistical approach known as quantitative trait locus (QTL)
analysis (Gelderman, 1975).
A linkage analysis study of an experimental genetic cross in-
volves three steps: (i) the phenotype of interest is examined in
the progeny of a genetic cross; (ii) a genetic map of each of the
progeny clones is generated, based on the inheritance of polymor-
phic markers, to identify the regions of each chromosome inherited
from each of the two parents in the cross; and (iii) the phenotype
and genotype data are linked using statistical methods such as
maximum likelihood (ML) analysis and Log of Odds (LOD) scores
(Fisher, 1922a, b; Haldane and Smith, 1947; Barnard, 1949).
2.1. Constructing a genetic map
To perform any linkage analysis, it is necessary to identify
polymorphic markers throughout the genome and ‘‘map’’ their
positions and relative genetic distances along each chromosome
(Sturtevant, 1913). The polymorphic markers are usually based
on differences in the DNA such as substitutions (single nucleotide
polymorphisms or SNPs), rearrangements such as insertions or
deletions, or errors in replication of repetitive regions of DNA
(e.g. microsatellites) (Botstein et al., 1980; Spierer et al., 1983; We-
ber and May, 1989; Kruglyak, 1997; Collard et al., 2005). More ra-
pid typing of multiple markers within a parasite clone can be
achieved using high throughput methods such as microarrays
(Jiang et al., 2008).
Initial analysis of experimental crosses of P. falciparum used ge-
netic maps based on restriction fragment length polymorphism
(RFLP) markers (Walker-Jonah et al., 1992). Denser maps were con-
structed using hundreds of polymorphic microsatellite markers (Su
and Wellems, 1996; Su et al., 1999), and the most recent maps
have utilised thousands of SNPs (Jiang et al., 2011), on platforms
such as Affymetrix Molecular Inversion probes and chips (Mu
et al., 2010) and Nimblegen (Tan et al., 2010). The density of mark-
ers within a map must, however, be carefully considered: more
markers do not necessarily improve a map. The algorithms used
in linkage analysis assume that alleles at each marker are indepen-
dent of one another; densely distributed markers (such as SNPs)
are more likely to be in linkage disequilibrium, where adjacent
SNPs are associated with one another and are not inherited inde-
pendently. This problem can be overcome using one of several
methods proposed to select SNP markers that are in linkage equi-
librium (Abecasis et al., 2002; Allen-Brady et al., 2007; Purcell
et al., 2007; Thomas, 2007).
2.2. Linking phenotype and genotype
Linkage analysis examines the co-segregation of a chromosomal
region and a trait of interest. Basically, for each marker in the map,
the progeny clones are sorted into those with one parental allele or
the other and the phenotype difference between the two groups is
examined (Fig. 1). A significant difference in phenotype between
the two marker groups indicates the marker locus being used is
linked (near to) the gene controlling the trait. A marker that is
not linked to the gene controlling the trait will be randomly inher-
ited with respect to the trait gene, and there will be no significant
difference between the mean phenotype of the two marker groups.
Linkage between markers and the trait is usually expressed as a
LOD score (log of odds), which represents the likelihood of linkage
versus no linkage (Morton, 1955). A LOD score of 3 indicates that
linkage is 1,000 times more likely than no linkage i.e. odds of link-
age of 1,000 to 1. A LOD score is calculated for each of the markers
on the genetic map and LOD scores can also be calculated for two
or more markers interacting either in an additive fashion or epi-
statically. There are several statistical packages to analyse linkage
both of simple traits (single locus) or more complex traits involving
Experimental genetic crosses of Plasmodium falciparum.
Cross dateParent clones (origin)No. progeny clones (% non-parental) No. independent recombinant clones available Reference
19853D7 (The Netherlands)a
113 (89%)55 Walliker et al. (1987)
35Wellems et al. (1990)
2008 >200 (>86%)33Hayton et al. (2008), Sa et al. (2009)
a3D7 is a clone (Walliker et al., 1987) of isolate NF54 (Delemarre and van der Kaay, 1979; Ponnudurai et al., 1981).
bHB3 is a clone of isolate H1 (Trager et al., 1981; Bhasin and Trager, 1984).
cDd2 is a clone of the W2-Mef line (Wellems et al., 1988; Oduola et al., 1988a), which was selected from the W2 clone of the Indochina III isolate originally derived from a
Laotian patient who failed chloroquine therapy (Campbell et al., 1982; Oduola et al., 1988b).
d7G8 is a clone of Brazilian isolate IMTM22 (Burkot et al., 1984).
eGB4 is a clone of isolate Ghana III/CDC (Sullivan et al., 2003).
fAs reported in Wellems et al. (1990); subsequent cloning produced more recombinant progeny.
L.C. Ranford-Cartwright, J.M. Mwangi/International Journal for Parasitology 42 (2012) 529–534
multiple contributing loci (QTL) (Lander and Botstein, 1989; Lynch
and Walsh, 1997; Zou and Zeng, 2008).
Plasmodium crosses have been analysed for single-locus as well
as QTL, which will be discussed in Section 4. Analysis is made
simpler because the parasite stages undergoing phenotyping are
haploid; for a single-locus study there are no heterozygotes and
parasite clones are treated as being fully homozygous at all loci.
Analysis of experimental genetic crosses has allowed the identifi-
cation of parasite loci controlling both simple and complex traits,
including resistance to antimalarial drugs such as chloroquine
and quinine (Wellems et al., 1991; Ferdig et al., 2004), the ability
to invade erythrocytes of different primate species (Hayton et al.,
2008), and the ability to infect mosquitoes (Mwangi and Ran-
ford-Cartwright, unpublished data).
3. Recombination rates and hotspots of recombination
Genetic maps define the relative order of loci along a chromo-
some in terms of genetic distance, which measures the likelihood
of crossover events between those two loci. Crossover events are
more likely to occur between markers further apart on a chromo-
some than between those close together. Crossover events are ini-
tiated by the formation of a double strand break (Keeney et al.,
1997), which is then repaired using the homologous chromosome
as a template. While the genetic map is usually similar to the phys-
ical map (where sequence data define the precise position of the
loci on the chromosome), there can be differences; for example
the genetic distance between two loci will appear greater than
the physical distance if there is high recombination activity (a
The genome-wide rate of recombination varies widely between
different organisms, but seems to be high in apicomplexan para-
sites compared with other eukaryotes e.g. 0.8 Mb/cM for humans,
1.8 Mb/cM for rat (Jensen-Seaman et al., 2004), 4.5 kb/cM for Thei-
leria parva (Katzer et al., 2011), 10–56 kb/cM for Cryptosporidium
parvum (Tanriverdi et al., 2007). The most recent SNP-based genet-
ic map for P. falciparum used over 3,000 SNPs (Jiang et al., 2011) to
genotype 32 progeny clones from an experimental cross between
parasite clones GB4 and 7G8 (Hayton et al., 2008). The map gener-
ated allowed an estimate of recombination rate, with a map unit
size of 9.6 kb/cM (including the highly recombinogenic subtelo-
meric regions) or 12.8 kb/cM (excluding the subtelomeric regions)
(Jiang et al., 2011), smaller than the previous estimate of 36 kb/cM
obtained using 285 microsatellite markers (Hayton et al., 2008).
Estimates from other P. falciparum genetic crosses are similar:
12.1 kb/cM for the HB3 ? Dd2 cross (Su et al., 1999) and ?11 kb/
cM for the 3D7 ? HB3 cross (Ranford-Cartwright and Mwangi,
Crossover/gene conversion events are not randomly distributed
throughout the genome. In human and yeast genomes, ‘‘hotspots’’
have been identified, which are highly localised short regions
(70–250 bp) with relatively high frequencies of crossover and gene
Fig. 1. The principle of linkage analysis. Examples of two markers, A and B, and their linkage to a phenotype. The marker type gives the allelic variant of each marker in the
progeny clones. Ten progeny clones are shown to illustrate the principles. QTL, quantitative trait locus analysis.
L.C. Ranford-Cartwright, J.M. Mwangi/International Journal for Parasitology 42 (2012) 529–534
conversion events (Gerton et al., 2000; Myers et al., 2005). In con-
trast to bacterial recombination, which often occurs at a ‘‘chi’’ se-
quence (Cross-over Hot-spot Instigator) (Smith et al., 1995), there
appears to be no consensus sequence for eukaryotic hotspots of
recombination in humans. In P. falciparum, crossover/gene conver-
sion events are more common in the subtelomeric regions of the
chromosome (Mu et al., 2005; Jiang et al., 2011), possibly due to
the presence of multi-copy gene families such as var, stevor and ri-
fin. However, recombination during mitosis may contribute to the
high frequencies seen for the telomeric var (Freitas-Junior et al.,
2000). Analysis of non-subtelomeric hotspots identified a 12 bp
GC-rich motif with a 3 bp G periodicity (Jiang et al., 2011), which
is similar to a motif found in human recombination hotspots that
may interact with zinc-finger DNA-binding proteins (McVean,
The ability to identify candidate genes is dependent on the loca-
tion of the informative recombination events and an adequate
number of markers to capture these recombination events to de-
fine the locus. Therefore, the relatively high recombination fre-
quency of P. falciparum reduces the number of progeny clones
that need to be phenotyped and genotyped to identify QTL to an
experimentally manageable number. For example, the Pfmdv1 lo-
cus underlying a male gametocyte defect was initially mapped
using just 11 progeny clones (Vaidya et al., 1995).
4. Identification of parasite loci through linkage analysis
4.1. Genes contributing to drug resistance
Many linkage analysis studies in P. falciparum have been per-
formed to identify parasite genes that contribute to drug resis-
tance, starting with chloroquine resistance. The Pfcrt gene was
mapped initially, using 16 progeny clones from the cross between
the chloroquine-sensitive parasite HB3 and the chloroquine-resis-
tant clone Dd2, as a 400 kb locus on chromosome 7 (Wellems et al.,
1991). Isolation and analysis of a further 19 progeny clones nar-
rowed the locus to a 36 kb segment containing nine open reading
frames (ORFs) (Su et al., 1997). A highly fragmented gene, denoted
Pfcrt, was subsequently discovered within the 36 kb locus; a spe-
cific mutation causing a lysine to threonine change at position 76
(K76T) was found to be required for resistance (Fidock et al.,
2000; Sidhu et al., 2002). Analysis of a cross between two chloro-
quine-resistant parasites, GB4 and 7G8 (Hayton et al., 2008), re-
vealed that an additional gene, Pfmdr1, contributed to the level of
chloroquine resistance (Sa et al., 2009). Further analysis of the
HB3 ? Dd2 cross showed that Pfmdr1 also influenced the degree
of resistance in chloroquine-resistant progeny of the Dd2 ? HB3
cross (Patel et al., 2010).
Analysis of low level quinine resistance in the HB3 ? Dd2 cross
using QTL techniques revealed contributions from loci on six sep-
arate chromosomes (Ferdig et al., 2004). Loci on chromosomes 5,
7 and 11 were found to interact in an additive fashion; these loci
contain the Pfcrt, Pfmdr and the sodium-proton exchanger Pfnhe
genes, respectively. Further studies confirmed that changes in the
level of Pfnhe expression affected the response to quinine and that
this effect was strain-dependent, which suggested the involvement
of other polymorphic genetic loci making up different genetic
backgrounds (Nkrumah et al., 2009). There were additional inter-
active effects between two loci on chromosomes 9 and 6 and the
two QTL on chromosomes 13 and 7 (Ferdig et al., 2004), which
have not yet been investigated further.
More recently, a high throughput approach was used to screen
for growth inhibitors in a library of over 1,000 bioactive chemicals
(Yuan et al., 2009). Three chemicals demonstrating differential
activity against parasite clones GB4 and 7G8 were analysed further
using the progeny clones from the cross between these two clones
to identify potential target genes. Parasite sensitivity to trimetho-
prim (a DHFR inhibitor used to treat urinary tract infections) and
triamterene (a Na+channel blocker) mapped to pfdhfr, while para-
site response to dihydroergotamine methanesulfonate (DHMS, a
serotonin receptor antagonist), mapped to pfmdr1 (Yuan et al.,
4.2. Genes contributing to intraerythrocytic growth and invasion
The genes contributing to the ability of P. falciparum to invade
erythrocytes of the squirrel monkey, Aotus nancymaae, were
mapped using a QTL approach with the progeny of the GB4 ? 7G8
cross (Hayton et al., 2008). The GB4 parasite is highly virulent in A.
nancymaae monkeys, whereas the 7G8 clone is unable to invade A.
nancymaae erythrocytes. The invasion trait mapped to a 14 kb re-
gion on chromosome 4 containing two genes, Pfrh4 and Pfrh5,
and allelic exchange showed that a single polymorphism in the
Pfrh5 gene, which resulted in an amino acid change from isoleucine
to lysine at codon 204, conferred the ability to invade A. nancymaae
Parasite growth rates within the erythrocytes also appear to be
under genetic control. Analysis of the HB3 ? Dd2 cross revealed
that 75% of the variation seen in the time taken for a parasite to
complete the intraerythrocytic cycle was explained by a major
QTL on chromosome 4, two additive loci on chromosome 14, and
with additional contributions from two loci on chromosome 4
and 13 that acted epistatically (Reilly et al., 2007; Reilly Ayala
et al., 2010).
4.3. Genes contributing to parasite transmission
The parasite Dd2 infects mosquitoes poorly, despite producing
morphologically normal gametocytes; analysis of 11 progeny from
the HB3 ? Dd2 cross indicated that this failure to infect mosqui-
toes was linked to an 800 kb locus on chromosome 12 (Vaidya
et al., 1995). Further mapping using an additional eight progeny
clones narrowed the locus to an 82 kb region containing 29 pre-
dicted genes (Furuya et al., 2005). Comparison of gene expression
between the defective Dd2 clone and its non-defective W2 parent
clone revealed down-regulation of one gene, denoted pfmdv-1 (P.
falciparum male gametocyte development gene 1). Disruption of
the pvmdv-1 gene resulted in a reduction of mature gametocytes
and developmental arrest at stage I. Although this was not the ex-
act phenotype seen in the cross, the study identified Pfmdv-1 as a
key contributor to gametocyte development.
QTL analysis has also been carried out to investigate parasite
genes contributing to parasite development within the mosquito
(Ranford-Cartwright and Mwangi, unpublished data). QTL analysis
of progeny from the 3D7 ? HB3 cross revealed a major locus on
chromosome 12 which accounted for 94% of the variation in infec-
tion intensity (number of oocysts per mosquito midgut) and 44% of
the variation in infection prevalence (proportion of mosquitoes
4.4. Genetic loci affecting gene expression
Although linkage analysis has primarily been used to identify
polymorphisms within protein-coding genes that affect phenotype,
transcriptional variation can also be measured and mapped using
genetic crosses, thus identifying expression QTL (eQTL). Analysis
of a P. falciparum genetic cross revealed that expression levels of
many transcripts during the asexual parasite lifecycle were herita-
ble and could be mapped (Gonzales et al., 2008). Approximately
18% of genes expressed in late ring stage/early trophozoites were
regulated by a significant eQTL and both cis- and trans-regulatory
L.C. Ranford-Cartwright, J.M. Mwangi/International Journal for Parasitology 42 (2012) 529–534
loci were identified, including a regulatory hotspot associated with
a copy number variant on chromosome 5 that affected expression
levels of 269 transcripts within the genome. eQTL analyses there-
fore can provide additional information on variation in regulatory
loci including promoter or transcription factors, but also epigenetic
components, copy number variation, non-coding RNAs or signal-
ling cascade variations that are capable of changing transcript
Analysis of experimental crosses (linkage analysis) remains an
important tool in malaria research, allowing unbiased identifica-
tion of gene(s) affecting a phenotype, rather than a candidate gene
approach. Modern genotyping methods and computing power al-
low more rapid identification of genetic regions controlling a trait
and thus have significantly strengthened the power of linkage
mapping approaches in recent years. The approach allows the
identification of multiple genes contributing to a phenotype,
including loci interacting epistatically and additively, and it is pos-
sible to estimate the relative contribution of a genetic locus to a
Analysis of experimental genetic crosses of P. falciparum has al-
lowed the discovery of genetic mechanisms behind a variety of
phenotypic traits for which there were no obvious candidate genes.
Experimental crosses have advantages over studies using field iso-
lates with diverse genetic backgrounds, for example (i) the limited
genetic variation in the progeny (derived from only two parents)
avoids possible confounding effects of multiple forms of multiple
genes, and (ii) the ability to phenotype laboratory clones repeat-
edly under controlled conditions can reduce environmental varia-
tion (biological and measurement noise).
Progeny clones from the three experimental crosses performed
to date, coupled with the high density genetic maps now available
for each cross, provide a useful community resource for future
work to understand the influence of parasite genetic polymor-
phism on biological phenotypic variation. Further refinement of
the genetic linkage maps, including complete sequence informa-
tion for the progeny clones, will reveal new crossovers, assist in
identifying precise points of recombination (chromosome break-
points) and hot spots of recombination, and will facilitate posi-
tional mapping of candidate genes.
The work in our laboratory is supported by the Wellcome Trust,
UK (Ref. No. 078749, 091791) and the European Union (MALSIG,
TM-REST, and EviMalaR). We thank Karen Hayton and Mike Ferdig,
and members of the Ferdig laboratory, for many stimulating
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