**5.1 Study sites and populations**

Our study sites include three family-based cohorts that have been followed for over ten years. Two of the cohorts occur in Senegal but differ by an order of magnitude in transmission intensity; in addition to *P. falciparum*, both *P. malariae* and *P. ovale* co-circulate. The third cohort is a large Karen community in Thailand with low transmission intensity but equal incidence of *P. vivax* and *P. falciparum*.

The Dielmo and Ndiop longitudinal surveys in Senegal have been described in detail elsewhere (Rogier et al., 1999; Sakuntabhai et al., 2008; Trape et al., 1994). Briefly, a longitudinal cohort study of malaria has been carried out since 1990 in Dielmo and 1993 in Ndiop. In Dielmo there are 594 individuals forming 190 nuclear families and in Ndiop 653 in 208 nuclear families. In each cohort, the majority of individuals form one large complex family: 453 individuals in Dielmo and 503 in Ndiop. Overall there are 10 completely independent families in Dielmo and 21 in Ndiop. The ethnic composition differs between the two cohorts. In Dielmo, the ethnic groups consisted of 79% Serere, 11% Mandinka and 10% miscellaneous, whereas in Ndiop, there were 76% Wolof, 19% Fulani and 5% miscellaneous. Family structures were constructed by using a questionnaire, interviewing each individual or key representatives of the household to obtain both demographic information such as birth date, age, sex and genetic relationships between children, their parents, and sometimes their grandparents or non-relatives in the same household, and other households. Previous typing with microsatellites enabled the construction of a pedigree based on Identity-by-Descent (IBD) using MERLIN (Abecasis et al., 2001; Sakuntabhai et al., 2008), thereby enabling constitution of the genetic relatedness matrix.

Malaria transmission is perennial in Dielmo, where a river maintains larval breeding sites for the mosquitoes even in the dry season. The number of infective bites per person per year (Entomological Inoculation Rate, EIR) is of the order of 200. By contrast, malaria transmission is strictly seasonal in Ndiop and dependent upon the rainy season that occurs from July-September and the EIR is approximately 20. Such differing transmission has marked consequences on the epidemiology of malaria in the villages. This is most evident in the higher *P. falciparum* prevalence rates of infection in Dielmo (80%) compared to the seasonal rates in Ndiop that change from 20% in the dry season to 70% in the rainy season (Sakuntabhai et al., 2008). Peak incidence of clinical *P. falciparum* infections occurs in the 1-4 year old age group in Dielmo and in the 8-12 year old age group in Ndiop.

In Thailand, a community-based cohort study has been on-going since 1998 (Phimpraphi et al., 2008a), situated in a mountainous area of Ratchaburi province, Thailand near the Thai-Myanmar border. Suan Phung has a total population of 5,368 living in 7 hamlets. The ethnic

link function between E(*variable | covariates*). For binary outcome variables (e.g. presence or absence of gametocytes), GLMM are fitted with a Binomial distribution with a *logit* link function. A maximal model with all covariates is fitted and a minimal adequate model including only significant covariates obtained. The effect of each covariate on the outcome variable is estimated taking into account inbreeding, via the genetic relatedness matrix integrated in GLIMMIX using the LDATA option, repeated measures and house effects.

**5. Heritability analyses of malaria phenotypes in longitudinal family-based** 

Our study sites include three family-based cohorts that have been followed for over ten years. Two of the cohorts occur in Senegal but differ by an order of magnitude in transmission intensity; in addition to *P. falciparum*, both *P. malariae* and *P. ovale* co-circulate. The third cohort is a large Karen community in Thailand with low transmission intensity

The Dielmo and Ndiop longitudinal surveys in Senegal have been described in detail elsewhere (Rogier et al., 1999; Sakuntabhai et al., 2008; Trape et al., 1994). Briefly, a longitudinal cohort study of malaria has been carried out since 1990 in Dielmo and 1993 in Ndiop. In Dielmo there are 594 individuals forming 190 nuclear families and in Ndiop 653 in 208 nuclear families. In each cohort, the majority of individuals form one large complex family: 453 individuals in Dielmo and 503 in Ndiop. Overall there are 10 completely independent families in Dielmo and 21 in Ndiop. The ethnic composition differs between the two cohorts. In Dielmo, the ethnic groups consisted of 79% Serere, 11% Mandinka and 10% miscellaneous, whereas in Ndiop, there were 76% Wolof, 19% Fulani and 5% miscellaneous. Family structures were constructed by using a questionnaire, interviewing each individual or key representatives of the household to obtain both demographic information such as birth date, age, sex and genetic relationships between children, their parents, and sometimes their grandparents or non-relatives in the same household, and other households. Previous typing with microsatellites enabled the construction of a pedigree based on Identity-by-Descent (IBD) using MERLIN (Abecasis et al., 2001; Sakuntabhai et al., 2008), thereby enabling constitution of the genetic relatedness matrix.

Malaria transmission is perennial in Dielmo, where a river maintains larval breeding sites for the mosquitoes even in the dry season. The number of infective bites per person per year (Entomological Inoculation Rate, EIR) is of the order of 200. By contrast, malaria transmission is strictly seasonal in Ndiop and dependent upon the rainy season that occurs from July-September and the EIR is approximately 20. Such differing transmission has marked consequences on the epidemiology of malaria in the villages. This is most evident in the higher *P. falciparum* prevalence rates of infection in Dielmo (80%) compared to the seasonal rates in Ndiop that change from 20% in the dry season to 70% in the rainy season (Sakuntabhai et al., 2008). Peak incidence of clinical *P. falciparum* infections occurs in the 1-4

In Thailand, a community-based cohort study has been on-going since 1998 (Phimpraphi et al., 2008a), situated in a mountainous area of Ratchaburi province, Thailand near the Thai-Myanmar border. Suan Phung has a total population of 5,368 living in 7 hamlets. The ethnic

year old age group in Dielmo and in the 8-12 year old age group in Ndiop.

**cohorts** 

**5.1 Study sites and populations** 

but equal incidence of *P. vivax* and *P. falciparum*.

composition of this community is majoritarily Karen (85%), with Thai (14%) and the rest are Mon and Burmese (1%). The total pedigrees are comprised of 2,427 individuals, including absent or deceased relatives. There are 238 independent families containing 603 nuclear families; the majority are 2 generation-families with family size range from 3 to 958. The epidemiology of malaria has been described elsewhere (Phimpraphi et al., 2008a). Briefly, the incidence of malaria is highly seasonal with annual peaks in May-June. Incidence was low, peaking at 141 episodes of *P. falciparum* per 1000 person-years and 70 for *P. vivax* over the 6-year intense study period. In this site, virtually all infections lead to febrile episodes and thus there is no information on asymptomatic infections. Peak incidence occurs in an earlier age group (5-9 years old) for *P. vivax* than for *P. falciparum* (10-15 years old). Parasite densities of either species peak in the <10 years old age group. Microsatellite genotyping again enabled the construction of a pedigree based on IBD (Phimpraphi et al., 2008b).

#### **5.2 Heritability of malaria-related phenotypes with differing transmission intensity**

Heritability analyses were conducted on several clinical and biological malaria-related phenotypes. The major non-genetic factors included age and variables concerned with differential extent of exposure on both a temporal (season, year) and spatial scale (hamlet, house). Figure 1 summarises the differential impact of the non-genetic and genetic factors on the number of clinical episodes (*P. falciparum* and *P. vivax*), the asexual parasite density

Fig. 1. Percentage of variance in malaria-related phenotypes explained by additive genetics (red), house (orange), hamlet (light blue), age (dark blue), date (turquoise), days in village (mauve), asexual parasite density (dark grey) and unknown (light grey). Malaria-related phenotypes include the prevalence of asymptomatic and clinical episodes and the mean and maximum asexual parasite density (trophozoite) during asymptomatic and clinical episodes.

Human Genetic Contribution to the Outcome of Infection with Malaria Parasites 279

genetic relatedness and house matrices will enable both effects to be estimated, even though they are clearly confounded. Parasite density phenotypes during clinical episodes were not influenced by house in any site, consistent with the hypothesis that although house can contribute to the tendency to become infected, it does not impact upon the parasite once the

Examination of the genetic contribution among the study sites reveals evidence for a strong genetic contribution to the number of *P. falciparum* clinical attacks: 12%, 16% and 22% with increasing transmission intensity. Interestingly, excluding household effects, results in contributions of approximately 20% in all 3 sites. The similarity of these values is remarkable given the very differing transmission intensities observed and the differing ethnicities. The marginal decrease in heritability with the decrease in transmission intensity likely reflects the increased heterogeneity in exposure at lower transmission intensities. The difference in the contribution of genetics is, however, small given the very large difference in transmission intensity. Moreover, the heritability of the number of clinical episodes is quite conserved irrespective of the phenotypic definitions used, the geographical region and even the *Plasmodium* species implicated. Similar values were observed in a study in Kenya (20%; Mackinnon et al., 2005) and a study in Sri Lanka (15%; Mackinnon et al., 2000) and for

A striking feature is the extensive differences in the impact of genetics on the mean *vs*. maximum parasite density. Parasite density has been recognised for a long time to be under human genetic control (Garcia et al., 1998; Rihet et al., 1998a), but the biological meaning of measures of parasite density requires contextual interpretation. The maximum parasite densities in both clinical and asymptomatic infections clearly indicate the extent to which the parasite can be controlled during and prior to the onset of a clinical episode. However, whilst the latter intuitively pinpoints the density at which an individual will clinically express the infection, a run-away symptomatic infection may attain exaggerated densities that are poorly informative of the biological pathways involved. The mean parasite density provides at least some replicated measure of how the individual controls a single infection, being less reliant on the extreme values. Although inherited blood disorders might result in generally lower parasite densities, the major pathways influencing parasite density will certainly be those of the immune system. Age has often been used as a proxy for the acquisition of immunity and accounting for age should reduce the contribution of that arm of the immune system to the genetic effect. It should be noted, however, that there may be a genetic contribution to acquired immunity (Stirnadel et al., 1999) and there is now some evidence that the inherited blood disorders operate interactively with the acquired immune response (Amaratunga et al., 2011). Nevertheless, our analyses would suggest that there is a

*P. vivax* (19% in Thailand (Phimpraphi et al., 2008b) and 15% in Sri Lanka).

contribution to the genetic effect from the innate arm of the immune system.

Gametocyte carriage has been associated with a worsening blood environment for the parasite (e.g. fever responses, anaemia) (Nacher et al., 2002; Price et al., 1999). However, such cues are only associated with symptomatic episodes of malaria and it is now well established that asymptomatic infections can also generate gametocytes and infect mosquitoes (Bousema & Drakeley, 2011). Differences in the tendency of sympatric ethnic groups to carry gametocytes have been noted for a long time (Perry 1914; Paganotti et al.

infection has started.

**5.2.2 Impact of genetic factors** 

during both clinical and asymptomatic infections and gametocyte prevalence and density, but only during asymptomatic infections; there was no significant heritability for gametocyte phenotypes during symptomatic infections. Non-malaria fever is included in the Thai study despite an absence of any genetic contribution, because of its significant correlation with malaria phenotypes.

#### **5.2.1 Impact of non-genetic factors**

Overall, it can be seen that age is more important in the highest transmission cohort (Dielmo) and decreases in importance as transmission intensity drops. This reflects the acquisition of clinical and anti-parasite immunity with exposure. A finer measure of exposure is given by the number of days present in the cohort, which has a significant impact on the number of clinical episodes. This effect not only reflects the probability of being infected, but also the well-recognised short-lived nature of clinical immunity: despite a history of exposure, absence from a malaria endemic setting can reduce the extent of clinical immunity developed. Temporal variation in the number of clinical episodes and in parasite density during both clinical and asymptomatic parasite density is notable for the Senegal cohort of highly seasonal transmission (Ndiop). Whilst seasonal variation in clinical episodes is expected, a seasonal effect on parasite density is less easily explained, especially for asymptomatic infections. This same effect is present for the production of gametocytes and we have recently shown that variation in mosquito biting rate has an impact on both asexual parasite density and gametocyte production (unpubl. data). This effect is seemingly linked to immune status of the individual, notably the levels of IgE.

Spatial variation is evident among the seven hamlets in the Thai site, but has little impact at the finer scale of house. In zones of high transmission intensity, where there is effectively saturating transmission and thus homogeneous exposure, fine scale spatial differences may not exist. The relatively small effect of house, even at lower transmission intensities, may be the result of the biting behaviour of the mosquitoes. In the Thai study site, the two mosquito vector species are *Anopheles minimus* and *Anopheles maculatus*, which are predominantly exophilic, thereby explaining the lack of a house level effect (Somboon et al., 1994). In Ndiop, Senegal, the major vector is *Anopheles arabiensis*, which will bite both indoors and outdoors and will also bite cattle. This non-specialist host choice may result in a biting distribution that is less house-oriented, being also affected by the distribution of animal enclosures. Although the occurrence of a small house effect in all three sites for several phenotypes, including parasite density, might reflect very local scale variability in biting rate, the confounded relationship of genetic relatedness and house may not be sufficiently resolved. Analysis of the heritability in the Thai study site with and without taking into account house led to estimates of the genetic contribution of 12% (with house) *vs*. 20% (without house) for the number of clinical episodes. The absence of any house effect for clinical *P. vivax* cases but the presence of such an effect for *P. falciparum* in the Thai site is likely because of relapse rather than differential biting habits of different mosquito vectors, although there may be differential transmission of the two species by different mosquito spp. (Somboon et al., 1994). There is no analytical approach to enable complete differentiation of the genetic and share house effects, especially in the case where there are no relatives in the cohort living in different houses. In our case, the cohorts all contain relatives living in different houses and non-relatives sharing households and thus the genetic relatedness and house matrices will enable both effects to be estimated, even though they are clearly confounded. Parasite density phenotypes during clinical episodes were not influenced by house in any site, consistent with the hypothesis that although house can contribute to the tendency to become infected, it does not impact upon the parasite once the infection has started.

#### **5.2.2 Impact of genetic factors**

278 Malaria Parasites

during both clinical and asymptomatic infections and gametocyte prevalence and density, but only during asymptomatic infections; there was no significant heritability for gametocyte phenotypes during symptomatic infections. Non-malaria fever is included in the Thai study despite an absence of any genetic contribution, because of its significant

Overall, it can be seen that age is more important in the highest transmission cohort (Dielmo) and decreases in importance as transmission intensity drops. This reflects the acquisition of clinical and anti-parasite immunity with exposure. A finer measure of exposure is given by the number of days present in the cohort, which has a significant impact on the number of clinical episodes. This effect not only reflects the probability of being infected, but also the well-recognised short-lived nature of clinical immunity: despite a history of exposure, absence from a malaria endemic setting can reduce the extent of clinical immunity developed. Temporal variation in the number of clinical episodes and in parasite density during both clinical and asymptomatic parasite density is notable for the Senegal cohort of highly seasonal transmission (Ndiop). Whilst seasonal variation in clinical episodes is expected, a seasonal effect on parasite density is less easily explained, especially for asymptomatic infections. This same effect is present for the production of gametocytes and we have recently shown that variation in mosquito biting rate has an impact on both asexual parasite density and gametocyte production (unpubl. data). This effect is seemingly

Spatial variation is evident among the seven hamlets in the Thai site, but has little impact at the finer scale of house. In zones of high transmission intensity, where there is effectively saturating transmission and thus homogeneous exposure, fine scale spatial differences may not exist. The relatively small effect of house, even at lower transmission intensities, may be the result of the biting behaviour of the mosquitoes. In the Thai study site, the two mosquito vector species are *Anopheles minimus* and *Anopheles maculatus*, which are predominantly exophilic, thereby explaining the lack of a house level effect (Somboon et al., 1994). In Ndiop, Senegal, the major vector is *Anopheles arabiensis*, which will bite both indoors and outdoors and will also bite cattle. This non-specialist host choice may result in a biting distribution that is less house-oriented, being also affected by the distribution of animal enclosures. Although the occurrence of a small house effect in all three sites for several phenotypes, including parasite density, might reflect very local scale variability in biting rate, the confounded relationship of genetic relatedness and house may not be sufficiently resolved. Analysis of the heritability in the Thai study site with and without taking into account house led to estimates of the genetic contribution of 12% (with house) *vs*. 20% (without house) for the number of clinical episodes. The absence of any house effect for clinical *P. vivax* cases but the presence of such an effect for *P. falciparum* in the Thai site is likely because of relapse rather than differential biting habits of different mosquito vectors, although there may be differential transmission of the two species by different mosquito spp. (Somboon et al., 1994). There is no analytical approach to enable complete differentiation of the genetic and share house effects, especially in the case where there are no relatives in the cohort living in different houses. In our case, the cohorts all contain relatives living in different houses and non-relatives sharing households and thus the

linked to immune status of the individual, notably the levels of IgE.

correlation with malaria phenotypes.

**5.2.1 Impact of non-genetic factors** 

Examination of the genetic contribution among the study sites reveals evidence for a strong genetic contribution to the number of *P. falciparum* clinical attacks: 12%, 16% and 22% with increasing transmission intensity. Interestingly, excluding household effects, results in contributions of approximately 20% in all 3 sites. The similarity of these values is remarkable given the very differing transmission intensities observed and the differing ethnicities. The marginal decrease in heritability with the decrease in transmission intensity likely reflects the increased heterogeneity in exposure at lower transmission intensities. The difference in the contribution of genetics is, however, small given the very large difference in transmission intensity. Moreover, the heritability of the number of clinical episodes is quite conserved irrespective of the phenotypic definitions used, the geographical region and even the *Plasmodium* species implicated. Similar values were observed in a study in Kenya (20%; Mackinnon et al., 2005) and a study in Sri Lanka (15%; Mackinnon et al., 2000) and for *P. vivax* (19% in Thailand (Phimpraphi et al., 2008b) and 15% in Sri Lanka).

A striking feature is the extensive differences in the impact of genetics on the mean *vs*. maximum parasite density. Parasite density has been recognised for a long time to be under human genetic control (Garcia et al., 1998; Rihet et al., 1998a), but the biological meaning of measures of parasite density requires contextual interpretation. The maximum parasite densities in both clinical and asymptomatic infections clearly indicate the extent to which the parasite can be controlled during and prior to the onset of a clinical episode. However, whilst the latter intuitively pinpoints the density at which an individual will clinically express the infection, a run-away symptomatic infection may attain exaggerated densities that are poorly informative of the biological pathways involved. The mean parasite density provides at least some replicated measure of how the individual controls a single infection, being less reliant on the extreme values. Although inherited blood disorders might result in generally lower parasite densities, the major pathways influencing parasite density will certainly be those of the immune system. Age has often been used as a proxy for the acquisition of immunity and accounting for age should reduce the contribution of that arm of the immune system to the genetic effect. It should be noted, however, that there may be a genetic contribution to acquired immunity (Stirnadel et al., 1999) and there is now some evidence that the inherited blood disorders operate interactively with the acquired immune response (Amaratunga et al., 2011). Nevertheless, our analyses would suggest that there is a contribution to the genetic effect from the innate arm of the immune system.

Gametocyte carriage has been associated with a worsening blood environment for the parasite (e.g. fever responses, anaemia) (Nacher et al., 2002; Price et al., 1999). However, such cues are only associated with symptomatic episodes of malaria and it is now well established that asymptomatic infections can also generate gametocytes and infect mosquitoes (Bousema & Drakeley, 2011). Differences in the tendency of sympatric ethnic groups to carry gametocytes have been noted for a long time (Perry 1914; Paganotti et al.

Human Genetic Contribution to the Outcome of Infection with Malaria Parasites 281

suggesting that common mechanisms are involved in determining these phenotypes. Individuals previously infected with *P. vivax* during the early years of study (1998-2000) had higher maximum *P. falciparum* parasite densities in 2001-2004. They also had a greater number of *P. vivax* episodes, but did not have significantly different maximum *P. vivax* parasite densities from those not previously infected with *P. vivax*. This suggests that common mechanisms do govern susceptibility to infection by *P. vivax* and control of *P. falciparum* density but not control of *P. vivax* density. Given the differences in red blood cell tropism, although blood disorders may be implicated, the involvement of shared immunological mechanisms is likely, supporting the hypothesis that there is significant

**6. Linkage analyses of malaria phenotypes identifies regions associated with** 

Linkage analyses are the classical statistical genetic analyses testing for co-segregation between a chromosomal region and a phenotype of interest. Prior to the high density genotyping now available for genome wide association, linkage analysis was the preferred method for gene discovery, enabling chromosomal regions of interest to be identified with

Our genome wide linkage analysis in the two Senegalese cohorts identified three novel regions of linkage in addition to the 5q31 region that has been previously reported to be linked to asymptomatic parasite density (Flori et al., 2003; Garcia et al., 1998; Rihet et al., 1998b). All of the regions have been previously found to be linked to asthma/atopic disease or related phenotypes, such as IgE titres (Iyengar et al., 2001; Jang et al., 2005; Kurz et al., 2006; Xu et al., 2001; Zhang et al., 2003). The novel regions of linkage were chromosome 5p15-p13 and 13q13-q22 linked with the number of *P. falciparum* clinical malaria attacks in Dielmo, and chromosome 12q21-q23 with the maximum parasite density during asymptomatic carriage in Ndiop. The linkage results differ for the two cohorts, likely reflecting the important differences both in the ethnic backgrounds and in the prevailing transmission conditions. Interestingly, the 5q31 locus has been linked to several immune related disorders, including asthma/atopy (Meyers et al., 1994), inflammatory bowel disease (Lee et al., 2002), Crohn disease (Peltekova et al., 2004), Celiac disease (Latiano et al., 2007) and psoriasis (Friberg et al., 2006). Moreover, genes within the 5q31 locus have been suggested to regulate delayed-type hypersensitivity responses associated with Leishmania chagasi infection (Jeronimo et al., 2007). This region contains a cluster of cytokines, among which IL12B may play a critical role since it has been associated with some immune-related diseases. An insertion/deletion polymorphism in the promoter region of IL12B has been reported to be associated with psoriasis (Cargill et al., 2007) and cerebral malaria (Morahan et al., 2002a) while two intronic SNPs were associated with asthma (Morahan et al., 2002b;

With the exception of the β-globin locus, there was no overlap of the regions of linkage that we detected and the location of the genes that have been previously reported to be associated with severe/cerebral malaria. This apparent discordance between genes

cross-species protective immunity (Loucoubar et al., 2011b).

**6.1 Genome wide linkage analysis of** *P. falciparum* **in Senegal** 

relatively low genotyping coverage.

Randolph et al., 2004).

**atopy** 

2006). We found a significant human genetic contribution associated with gametocyte prevalence in asymptomatic *P. falciparum* infections. By contrast, there was no heritability associated with the production of gametocytes for *P. falciparum* or *P. vivax* symptomatic infections. Increased gametocyte carriage has been observed in individuals with HbC (Gouagna et al., 2010) and HbS, although its contribution was small (Lawaly et al., 2010). Clearly there are other genes implicated in eliciting gametocyte production by the parasite. Thus, a proportion of the population is susceptible to carry gametocytes and be superspreaders. Targeting such individuals with specific intervention methods is an intriguing option.

#### **5.3 Correlation among phenotypes**

Examining the correlations among phenotypes can be extremely informative in characterising the outcome of infection. The number of clinical episodes and maximum parasite density were positively associated with each other in the Senegalese cohorts (Dielmo r=0.54; Ndiop r=0.34) but not in Thailand. The correlation was strongest in the cohort of highest transmission intensity (Dielmo). In Thailand, there was comparatively little variation in the number of clinical episodes experienced by any individual (1-13). The positive correlation between these two phenotypes is consistent with the interpretation that individuals experiencing many *P. falciparum* clinical attacks have a poor capacity to control parasite density, which thus frequently reaches the high threshold density necessary to elicit a clinical attack (Rogier et al., 1996). As immunity develops, the human will be increasingly capable of controlling the proliferation of the parasite and keep the densities at sub-clinical levels. However, there was a negative correlation between the ability to harbour high parasite loads without symptoms and the number of clinical malaria attacks. Our results point to a genetic influence on the control of parasite density governing the occurrence of clinical attacks. In other words, higher asymptomatic parasite loads seemingly protect against occurrence of clinical episodes. One possible mechanism is through maintenance of an efficient concomitant clinical immunity reducing the risk of developing clinical malaria despite the presence of a relatively elevated parasite load. This is the first indication for a genetic basis to premunition acquired by individuals living in endemic areas (Sergent & Parrot, 1935). The immunological basis of such premunition remains elusive. Interestingly, high parasite specific IgE levels in asymptomatic infected individuals have been shown to be reduce the odds of a clinical episode (Bereczky et al., 2004), thus suggesting the Th1/Th2 balance and hence the relative cytophilic IgG1/3 and IgE titres produced in response to parasite density determine the progression to clinical disease.

The Thai analyses demonstrated a strong negative correlation between the number of nonmalaria fevers and *P. falciparum* cases, suggesting that illness due to non-malaria pathogens may protect from *P. falciparum* infection or disease and vice versa. That there was no genetic contribution to non-malarial fever would suggest that there is no genetic trade-off generating susceptibility to *P. falciparum* with protection from non-malaria fever. However, the non-malaria fever phenotype is very imprecise and thus absence of a genetic effect may be a phenotype problem. Nevertheless, the negative correlation underlines the importance of considering malaria infections in context of other circulating pathogens. In contrast, there was evidence of between *Plasmodium* spp. interactions. There was a positive correlation between the number of *P. vivax* episodes and maximum *P. falciparum* parasite density,

2006). We found a significant human genetic contribution associated with gametocyte prevalence in asymptomatic *P. falciparum* infections. By contrast, there was no heritability associated with the production of gametocytes for *P. falciparum* or *P. vivax* symptomatic infections. Increased gametocyte carriage has been observed in individuals with HbC (Gouagna et al., 2010) and HbS, although its contribution was small (Lawaly et al., 2010). Clearly there are other genes implicated in eliciting gametocyte production by the parasite. Thus, a proportion of the population is susceptible to carry gametocytes and be superspreaders. Targeting such individuals with specific intervention methods is an intriguing

Examining the correlations among phenotypes can be extremely informative in characterising the outcome of infection. The number of clinical episodes and maximum parasite density were positively associated with each other in the Senegalese cohorts (Dielmo r=0.54; Ndiop r=0.34) but not in Thailand. The correlation was strongest in the cohort of highest transmission intensity (Dielmo). In Thailand, there was comparatively little variation in the number of clinical episodes experienced by any individual (1-13). The positive correlation between these two phenotypes is consistent with the interpretation that individuals experiencing many *P. falciparum* clinical attacks have a poor capacity to control parasite density, which thus frequently reaches the high threshold density necessary to elicit a clinical attack (Rogier et al., 1996). As immunity develops, the human will be increasingly capable of controlling the proliferation of the parasite and keep the densities at sub-clinical levels. However, there was a negative correlation between the ability to harbour high parasite loads without symptoms and the number of clinical malaria attacks. Our results point to a genetic influence on the control of parasite density governing the occurrence of clinical attacks. In other words, higher asymptomatic parasite loads seemingly protect against occurrence of clinical episodes. One possible mechanism is through maintenance of an efficient concomitant clinical immunity reducing the risk of developing clinical malaria despite the presence of a relatively elevated parasite load. This is the first indication for a genetic basis to premunition acquired by individuals living in endemic areas (Sergent & Parrot, 1935). The immunological basis of such premunition remains elusive. Interestingly, high parasite specific IgE levels in asymptomatic infected individuals have been shown to be reduce the odds of a clinical episode (Bereczky et al., 2004), thus suggesting the Th1/Th2 balance and hence the relative cytophilic IgG1/3 and IgE titres produced in response to

The Thai analyses demonstrated a strong negative correlation between the number of nonmalaria fevers and *P. falciparum* cases, suggesting that illness due to non-malaria pathogens may protect from *P. falciparum* infection or disease and vice versa. That there was no genetic contribution to non-malarial fever would suggest that there is no genetic trade-off generating susceptibility to *P. falciparum* with protection from non-malaria fever. However, the non-malaria fever phenotype is very imprecise and thus absence of a genetic effect may be a phenotype problem. Nevertheless, the negative correlation underlines the importance of considering malaria infections in context of other circulating pathogens. In contrast, there was evidence of between *Plasmodium* spp. interactions. There was a positive correlation between the number of *P. vivax* episodes and maximum *P. falciparum* parasite density,

option.

**5.3 Correlation among phenotypes** 

parasite density determine the progression to clinical disease.

suggesting that common mechanisms are involved in determining these phenotypes. Individuals previously infected with *P. vivax* during the early years of study (1998-2000) had higher maximum *P. falciparum* parasite densities in 2001-2004. They also had a greater number of *P. vivax* episodes, but did not have significantly different maximum *P. vivax* parasite densities from those not previously infected with *P. vivax*. This suggests that common mechanisms do govern susceptibility to infection by *P. vivax* and control of *P. falciparum* density but not control of *P. vivax* density. Given the differences in red blood cell tropism, although blood disorders may be implicated, the involvement of shared immunological mechanisms is likely, supporting the hypothesis that there is significant cross-species protective immunity (Loucoubar et al., 2011b).
