• View in gallery

    Age profile of infection prevalence (shaded bars) and geometric mean intensity (solid line) of Schistosoma haematobium infection in the five villages participating in the study.

  • View in gallery

    Age profile for the prevalence of different traits related to Schistosoma haematobium infection within the study population. A, Heavy S. haematobium infection (≥ 400 eggs per 10 mL of urine); B, moderate-severe hydronephrosis; C, Bladder abnormality (significant thickening irregularity, and/ or polyp formation, as defined by World Health Organization guidelines).22

  • 1

    WHO, 1993. The Control of Schistosomiasis: Second Report of the WHO Expert Committee. Geneva: World Health Organization.

  • 2

    Sturrock RF, 2001. The schistosomes and their intermediate hosts. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 7–83.

  • 3

    el Kholy H, Arap Siongok TK, Koech D, Sturrock RF, Houser H, King CH, Mahmoud AA, 1989. Effects of borehole wells on water utilization in Schistosoma haematobium endemic communities in Coast Province, Kenya. Am J Trop Med Hyg 41 :212–219.

    • Search Google Scholar
    • Export Citation
  • 4

    Prata A, 2001. Disease in schistosomiasis mansoni in Brazil. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 297–332.

  • 5

    Ouma JH, El-Khoby T, Fenwick A, Blanton RE, 2001. Disease in schistosomiasis mansoni in Africa. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 333–360.

  • 6

    Smith JH, Christie JD, 1986. The pathobiology of Schistosoma haematobium infection in humans. Hum Pathol 17 :333–345.

  • 7

    Abel L, Demenais F, Prata A, Souza AE, Dessein A, 1991. Evidence for the segregation of a major gene in human susceptibility/resistance to infection by Schistosoma mansoni. Am J Hum Genet 48 :959–970.

    • Search Google Scholar
    • Export Citation
  • 8

    Marquet S, Abel L, Hillaire D, Dessein H, Kalil J, Feingold J, Weissenbach J, Dessein AJ, 1996. Genetic localization of a locus controlling the intensity of infection by Schistosoma mansoni on chromosome 5q31–q33. Nat Genet 14 :181–184.

    • Search Google Scholar
    • Export Citation
  • 9

    Marquet S, Abel L, Hillaire D, Dessein A, 1999. Full results of the genome-wide scan which localises a locus controlling the intensity of infection by Schistosoma mansoni on chromosome 5q31–q33. Eur J Hum Genet 7 :88–97.

    • Search Google Scholar
    • Export Citation
  • 10

    Muller-Myhsok B, Stelma FF, Guisse-Sow F, Muntau B, Thye T, Burchard GD, Gryseels B, Horstmann RD, 1997. Further evidence suggesting the presence of a locus, on human chromosome 5q31–q33, influencing the intensity of infection with Schistosoma mansoni. Am J Hum Genet 61 :452–454.

    • Search Google Scholar
    • Export Citation
  • 11

    Dessein AJ, Hillaire D, Elwali NE, Marquet S, Mohamed-Ali Q, Mirghani A, Henri S, Abdelhameed AA, Saeed OK, Magzoub MM, Abel L, 1999. Severe hepatic fibrosis in Schistosoma mansoni infection is controlled by a major locus that is closely linked to the interferon-gamma receptor gene. Am J Hum Genet 65 :709–721.

    • Search Google Scholar
    • Export Citation
  • 12

    Rodrigues V Jr, Piper K, Couissinier-Paris P, Bacelar O, Dessein H, Dessein AJ, 1999. Genetic control of schistosome infections by the SM1 locus of the 5q31–q33 region is linked to differentiation of type 2 helper T lymphocytes. Infect Immun 67 :4689–4692.

    • Search Google Scholar
    • Export Citation
  • 13

    Bethony J, Gazzinelli A, Lopes A, Pereira W, Alves-Oliveira L, Willams-Blangero S, Blangero J, Loverde P, Correa-Oliveira R, 2001. Genetic epidemiology of fecal egg excretion during Schistosoma mansoni infection in an endemic area in Minas Gerais, Brazil. Mem Inst Oswaldo Cruz 96 (Suppl):49–55.

    • Search Google Scholar
    • Export Citation
  • 14

    Bethony J, Williams JT, Blangero J, Kloos H, Gazzinelli A, Soares-Filho B, Coelho L, Alves-Fraga L, Williams-Blangero S, Loverde PT, Correa-Oliveira R, 2002. Additive host genetic factors influence fecal egg excretion rates during Schistosoma mansoni infection in a rural area in Brazil. Am J Trop Med Hyg 67 :336–343.

    • Search Google Scholar
    • Export Citation
  • 15

    Williams-Blangero S, Blangero J, Bradley M, 1997. Quantitative genetic analysis of susceptibility to hookworm infection in a population from rural Zimbabwe. Hum Biol 69 :201–208.

    • Search Google Scholar
    • Export Citation
  • 16

    Williams-Blangero S, Subedi J, Upadhayay RP, Manral DB, Rai DR, Jha B, Robinson ES, Blangero J, 1999. Genetic analysis of susceptibility to infection with Ascaris lumbricoides. Am J Trop Med Hyg 60 :921–926.

    • Search Google Scholar
    • Export Citation
  • 17

    Williams-Blangero S, McGarvey ST, Subedi J, Wiest PM, Upadhayay RP, Rai DR, Jha B, Olds GR, Guanling W, Blangero J, 2002. Genetic component to susceptibility to Trichuris trichiura: evidence from two Asian populations. Genet Epidemiol 22 :254–264.

    • Search Google Scholar
    • Export Citation
  • 18

    Muchiri EM, Ouma JH, King CH, 1996. Dynamics and control of Schistosoma haematobium transmission in Kenya: an overview of the Msambweni Project. Am J Trop Med Hyg 55 :127–134.

    • Search Google Scholar
    • Export Citation
  • 19

    Gomm R, 1972. Harlots and bachelors: marital instability among the coastal Digo of Kenya. Man 7 :95–113.

  • 20

    Peters PAS, Kazura JW, 1987. Update on diagnostic methods for schistosomiasis. Mahmoud AAF, ed. Bailliere’s Clinical Tropical Medicine and Communicable Diseases, Schistosomiasis. London: Bailliere Tindall, 419–433.

  • 21

    Savioli S, Hatz C, Dixon H, Kisumku UM, Mott KE, 1990. Control of morbidity due to Schistosoma haematobium on Pemba Island: egg excretion and hematuria as indicators of infection. Am J Trop Med Hyg 43 :289–295.

    • Search Google Scholar
    • Export Citation
  • 22

    Richter J, Hatz C, Campagne G, Bergquist NR, Jenkins JM, 2000. Ultrasound in Schistosomiasis: A Practical Guide to the Standardized Use of Ultrasonography for the Assessment of Schistosomiasis-Related Morbidity. Geneva: World Health Organization.

  • 23

    King CH, 2002. Ultrasound monitoring of structural urinary tract disease in S. haematobium infection. Mem Inst Oswaldo Cruz 97 (Suppl 1):149–152.

    • Search Google Scholar
    • Export Citation
  • 24

    Dyke B, 1989. PEDSYS, PGL Tech. Report No. 2. San Antonio, TX: Southwest Foundation for Biomedical Research.

  • 25

    Almasy L, Blangero J, 1998. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62 :1198–1211.

  • 26

    Williams-Blangero S, Vandeberg JL, Blangero J, Teixeira AR, 1997. Genetic epidemiology of seropositivity for Trypanosoma cruzi infection in rural Goias, Brazil. Am J Trop Med Hyg 57 :538–543.

    • Search Google Scholar
    • Export Citation
  • 27

    Woolhouse MEJ, 1998. Patterns in parasite epidemiology: The peak shift. Parasitol Today 14: 428–434.

  • 28

    Zinn-Justin A, Marquet S, Hillaire D, Dessein A, Abel L, 2001. Genome search for additional human loci controlling infection levels by Schistosoma mansoni. Am J Trop Med Hyg 65 :754–758.

    • Search Google Scholar
    • Export Citation
  • 29

    Chan L, Bundy DA, Kan SP, 1994. Aggregation and predisposition to Ascaris lumbricoides and Trichuris trichiura at the familial level. Trans R Soc Trop Med Hyg 88 :46–48.

    • Search Google Scholar
    • Export Citation
  • 30

    Chan L, Bundy DA, Kan SP, 1994. Genetic relatedness as a determinant of predisposition to Ascaris lumbricoides and Trichuris trichiura infection. Parasitology 108 :77–80.

    • Search Google Scholar
    • Export Citation
  • 31

    King CL, Malhotra I, Mungai P, Wamachi A, Kioko J, Muchiri E, Ouma JH, 2001. Schistosoma haematobium-induced urinary tract morbidity correlates with increased tumor necrosis factor-alpha and diminished interleukin-10 production. J Infect Dis 184 :1176–1182.

    • Search Google Scholar
    • Export Citation
  • 32

    Kariuki HC, Mbugua G, Magak P, Bailey JA, Muchiri EM, Thiongo FW, King CH, Butterworth AE, Ouma JH, Blanton RE, 2001. Prevalence and familial aggregation of schistosomal liver morbidity in Kenya: evaluation by new ultrasound criteria. J Infect Dis 183 :960–966.

    • Search Google Scholar
    • Export Citation
  • 33

    Muchiri EM, 1991. Association of water contact activities and risk of reinfection for S. haematobium after drug treatment in the Msambweni Area, Kenya. Epidemiology and Biostatistics. Cleveland, OH: Case Western Reserve University, 1–121.

  • 34

    Sturrock RF, Kinyanjui H, Thiongo FW, Tosha S, Ouma JH, King CH, Koech D, Siongok TK, Mahmoud AA, 1990. Chemotherapy-based control of schistosomiasis haematobia. 3. Snail studies monitoring the effect of chemotherapy on transmission in the Msambweni area, Kenya. Trans R Soc Trop Med Hyg 84 :257–261.

    • Search Google Scholar
    • Export Citation
  • 35

    Khoury MJ, Beaty TH, Cohen BH, 1993. Fundamentals of Genetic Epidemiology. New York: Oxford University Press.

 

 

 

 

LOW HERITABLE COMPONENT OF RISK FOR INFECTION INTENSITY AND INFECTION-ASSOCIATED DISEASE IN URINARY SCHISTOSOMIASIS AMONG WADIGO VILLAGE POPULATIONS IN COAST PROVINCE, KENYA

View More View Less
  • 1 Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio; Division of Vector Borne Diseases, Ministry of Health, Nairobi, Kenya; Kenya Medical Research Institute, Nairobi, Kenya; Department of Radiology, Kenyatta National Hospital, Nairobi, Kenya

To estimate their heritable component of risk for Schistosoma haematobium infection intensity and disease, we performed a community-based family study among an endemic population in coastal Kenya. Demography and family linkages were defined by house-to-house interviews, and infection prevalence and disease severity were assessed by standard parasitologic testing and by ultrasound. The total population was 4,408 among 912 households, with 241 identified pedigree-household groups. Although age- and sex-adjusted risk for greater infection intensity was clustered within households (odds ratio = 2.7), analysis of extended pedigree-household groups indicated a relatively low heritability score for this trait (h2 = 0.199), particularly after adjustment for common household exposure effects (adjusted h2 = 0.086). Statistical evidence was slightly stronger (h2 = 0.353) for familial clustering of bladder morbidity, with an adjusted h2 = 0.142 after accounting for household exposure factors. We conclude that among long-established populations of coastal Kenya, heritable variation in host susceptibility is low, and likely plays a minimal role in determining individual risk for infection or disease.

INTRODUCTION

Schistosomiasis remains one of the most prevalent infectious diseases in the world.1 Wherever competent intermediate-host snails are found, trematode parasites of the species Schistosoma haematobium, S. mansoni, or S. japonicum can be transmitted to susceptible humans during contact with fresh water.2 Prevalence is high in less-developed countries, where the local population must seek their water for cooking, drinking, washing, and bathing at schistosomiasis transmission sites.3 It is possible to treat and cure schistosomiasis, yet reinfection is frequent (~15–20% per patient-year) and recurring parasite exposure is common. Typical disease manifestations of longstanding infection include anemia, malnutrition, and, depending on the infecting schistosome species, liver, intestinal, kidney and/or bladder disease.4–6 These disease outcomes are a consequence both of the current intensity of infection (i.e., worm and egg load), and of the aggregate tissue damage resulting from years of persistent infection.

Population-based schistosomiasis control programs are expensive, and substantial efforts have been put into identifying subpopulations who are at particular risk for recurrent heavy infection and for infection-associated disease. Recent segregation and gene mapping studies in Brazil and Senegal have linked human susceptibility to S. mansoni infection (and to higher-intensity infection) with a locus on chromosome 5 (5q31–q33) dubbed Sm1.7–10 Further studies in Sudan have identified a separate locus, Sm2 at chromosome 6q22–q23, that is associated with risk for severe S. mansoni-related liver fibrosis within a selected Sudanese population.11 Identification of these loci, which have each been linked to specific components of anti-parasite immune response,11,12 provides hope of defining individuals who are at particular risk for schistosomiasis-associated disease.

Heritable risk for S. mansoni infection intensity has also been suggested by recent population-based family studies in Brazil, which have indicated that 20–44% of the variance in their infection levels appears related to heritable effects.13,14 In addition, population-based studies of intestinal helminthic infections (Ascaris lumbricoides [roundworm], Trichuris trichiura [whipworm], and hookworm) suggest a moderate but significant heritable component to risk for these other worm infections.13,15–17 With this in mind, we undertook the present study to estimate the influence of hereditary factors in determining risk for infection and urinary tract disease caused by the parasite S. haematobium in a high transmission area of coastal Kenya.

POPULATIONS, MATERIALS, AND METHODS

Location.

This study was performed in the five villages of Marigiza, Milalani, Mabatani, Nganja, and Vindungeni, situated in the Msambweni area of Kwale District in Coast Province, Kenya. This area, which is highly endemic for S. haematobium, is centered 50 km southwest of Mombasa on the coastal plain adjoining the Indian Ocean. The area is rural and mixed agriculture and fishing are the leading occupations. Four of the five villages had previously participated in studies of school-based control of S. haematobium during 1983–1992.18 However, at the time of the present survey (2000–2001), there had been no organized treatment of eight years, and the age profile of infection prevalence and intensity had reverted to precontrol levels. The population of the study villages were predominantly (> 95%) Wadigo, a distinct Bantu ethnic group having their own language. Digo peoples have resided on the coast of Kenya and Tanzania for more than four centuries. By oral tradition, the Wadigo migrated to coastal Kenya from areas north of the Tana River in Kenya and Somalia,19 where urinary schistosomiasis is also endemic.

Study population.

For this study, pedigrees were constructed using family information obtained by household interviews. Parent and grandparent identity were recorded for all individuals, with the total community population census (year 2000) determined to be 4,408. The 4,408 residents included 2,270 females and 2,132 males living in 912 households. These individuals had an age range of 0–100 years, with a mean age of 23.8 years. For the target study population greater than two years of age, infection status and infection intensity were determined by duplicate determination of S. haematobium egg counts per 10 mL of urine in midday voided urines, using a standard Nuclepore (Pleasanton, CA) filtration technique.20 Prevalence of morbidity was determined by urine dipstick for hematuria,21 and by portable ultrasound examination for bladder thickening and inflammation and for renal outflow obstruction.22,23 Complete evaluation was obtained for 3,178 residents, or 76% of the targeted population. Participation of adult women 20–64 years old was 80%, significantly higher than that of adult men (62%), who were frequently absent from the area in pursuit of their employment.

Statistical analysis.

The pedigree database was developed and relationship statistics (phi) were calculated using the PEDSYS program version 2.0 (Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio TX).24 Univariate statistics and preliminary correlation analysis were performed using SAS statistical software version 8.02 (SAS Institute, Cary NC). Because of the skewed distribution of infection intensities (egg counts), individual infection levels were assessed after log-transformation as log ([egg count/10 mL] + 1). Extended analysis of the contribution of genetic versus environmental factors toward the variation in outcomes was performed by variance decomposition modeling analysis using SOLAR software version 1.7.4 (Southwest Foundation for Biomedical Research).25 This program allows partitioning of trait variance into separate components based on 1) inheritance (trait heritability [h2]), 2) shared environmental factors related to household (c2), and 3) random environmental effects, while allowing adjustment for significant covariates (e.g., age and sex).17,25,26 Chi-square testing based on likelihood ratios was used to compare nested models and test for significance of the observed genetic and environmental effects.

Ethical oversight.

This study was performed according to the guidelines of the Declaration of Helsinki under a research protocol approved by the Ethical Review Board of the Kenya Medical Research Institute (KEMRI), and by the Institutional Review Board for Human Investigation of University Hospitals of Cleveland. All adult participants provided individual informed consent, and consent was obtained from parents or legal guardians of each child participating in the study.

RESULTS

Population prevalence of infection and morbidity traits.

The age-prevalence and age-intensity profile of S. haematobium infection among the study population are shown in Figure 1. As is typical for areas that are endemic for urinary schistosomiasis,27 there was an observed increase in infection prevalence and intensity up to the age of ~15 years, above which both prevalence and intensity decreased to lower levels. The observed peak prevalence was 78% with a geometric mean ± SD egg count of 43.6 ± 12.2 in the 10–14-year-old age group. In contrast, prevalence and mean intensity were significantly lower (25% and 2 ± 1, respectively) for groups more than 25 years old. Overall, 46% of the population were passing S. haematobium eggs, with 8.6% having heavy infections (≥ 400 eggs/10 mL of urine). Fifty-one percent had hematuria detected by dipstick examination, while on ultrasound examination, 14% had significant bladder abnormalities (either wall irregularity, thickening, or polyp formation), and 1.2% had moderate-to-severe hydronephrosis by World Health Organization criteria. The age distribution of these morbidity traits is shown in Figure 2. Of note, the prevalence of both heavy infection and bladder abnormalities mirror the age-prevalence profile for infection, whereas the profile of moderate-to-severe hydronephrosis demonstrated distinct early (5–24 years old) and late (≥ 45 years old) peaks of prevalence.

Analysis of variance and logistic regression analysis indicated that across all age groups males had significantly heavier mean infection levels and greater odds of having morbidity (heavy infection, hydronephrosis, or bladder abnormalities) than did females. For this reason, subsequent models of heredity and household effects were all adjusted for age group and for sex to avoid confounding. There was no significant age-sex interaction observed.

Family and household correlations.

As indicated in Table 1, there were 23,412 informative kinship pairings within the study population. Preliminary analyses of the role of kinship versus household effects are shown in Tables 2 and 3. Initial examination of unadjusted infection intensity and prevalence of bladder disease (Table 2) suggested a significantly greater correlation between first-degree relations than between genetically unrelated members of the same household. It also suggested that first-degree relations not sharing a household had a greater correlation of disease status than second-, third-, and fourth-degree relations not sharing living quarters. However, significant variation was possible in the age and sex composition of the different categories of kinship pairs. When disease status was adjusted for the age interval and sex of each partner of the kindred pairings (as shown for infection intensity in Table 3), the observed adjusted household effect appeared to be substantially stronger than the kinship effect. In this analysis, related pairs have a greater correlation in terms of adjusted infection intensity than did unrelated pairs, but there appeared to be little difference between the level of correlation for first-, second-, third-, and fourth-degree relations within the same household. For an effect based on the degree of gene-sharing between pair-mates, a stepwise decrease in correlation would be expected among first-, second-, third-, and fourth-degree pairs,17 and this was not seen.

Variance components analysis.

To better define the relative contributions of household effects versus hereditary factors in the observed population differences in infection and morbidity outcomes, we performed a variance component modeling analysis. General models partitioning the variance in outcomes between estimated polygenic hereditary effects (h2), shared household effects (c2), and residual random or error effects (e2) were compared with nested models containing either hereditary, household, or random environmental effects alone.

The optimal estimates for hereditary, household, and random effects with respect to age/sex-adjusted risk for infection intensity, renal disease, and bladder disease are shown in Table 4. Of note, the general models, which allow for both genetic and shared household effects, estimated the contribution of heritable effects of risk for infection intensity at 9%, for renal disease at 0%, and for bladder disease at 14%. The number of related subject pairs concordant for renal disease was low (n = 6), leading to unstable estimates of heritability for this trait. For infection intensity, the general (saturated) model incorporating genetic, household, and random effects gave significantly better fit to the data than models including only genetic/random or household/random effects alone (P = 0.00015 and 0.0174, respectively). For bladder disease, the general and genetic/random models were not significantly different, indicating the best estimate of heritable effect (h2) for this outcome may be as high as 35%, as estimated in the non-saturated genetic/random model.

DISCUSSION

The results of this population-based pedigree study, performed in a highly endemic region of Kenya, suggest a limited genetic component of risk for S. haematobium infection intensity or for infection-associated kidney or bladder disease in this area. The population-based approach, which allowed for use of extended pedigrees across multiple residence locations, provided estimates of the effects of polygenic hereditary effects as a heritability score (h2) based on a variance component analysis of population data. Here, h2 reflects the component of trait variance attributable to study subjects’ shared degree of kinship. When adjusted for shared household effect, we observed that h2 accounts for less than 15% in the variation in risk for either heavy infection, hydronephrosis, or bladder abnormality in a five-village region where exposure to S. haematobium infection is common, and is nearly universal in childhood.

Non-random variation in intensity of human schistosome infection has been noted in many parts of the world. The typical endemic population harbors a majority of lightly infected individuals, and a small fraction (5–10%) of people with extremely heavy infections. Infection prevalence and intensity is typically reduced in older age groups, and it has been noted that risk for infection-associated disease is generally correlated with duration and intensity of infection. Variation in infection and disease rates has been theorized to be due to age-related changes in exposure, to acquired immunity, and to age-related innate changes in susceptibility to infection over time, and it is likely that a combination of these factors all serve to regulate transmission and disease risk.

Our understanding of the role of genetic predisposition to helminth infection is beginning to evolve, but its relative importance in regulating infection and disease in different populations remains uncertain. Segregation analysis of family pedigrees in a high-risk village in northeastern Brazil have indicated a major co-dominant gene regulating risk for S. mansoni infection intensity.7 Subsequent gene linkage studies have localized this gene (Sm1) in chromosome region 5q31–q33,8 with possible additional effects from genes in the region of chromosomal regions 1p21–q23 and 6p21–q21.28 Studies in a Senegalese population recently exposed to S. mansoni appear to confirm the significance of the Sm1 gene in that population, thus extending the observations made in Brazil.10 Of note, a separate large scale population-based pedigree study in Brazil, similar in design to the present study, has estimated the combined genetic influence on variation in S. mansoni infection intensity to be 20–44%.14

Studies of infectious burden in other helminth diseases, particularly intestinal geohelminths, have varied in their conclusions. Early studies by Chan and others29 in Malaysia found household aggregation of Ascaris and Trichuris infection and of re-infection intensity, but later noted that the correlation of infection status between related individuals was not greater than for unrelated individuals.30 They concluded that individual genetic predisposition if any, was likely to be overwhelmed by environmental and behavioral factors shared in family-based households.30 In contrast, longitudinal studies of Ascaris infection and reinfection in Nepal, using the variance decomposition methods used in the present study, estimated the heritability of Ascaris worm burden to be 44% and of egg count to be 39%.16 Similar studies looking at hookworm infection in Zimbabwe have estimated the heritable component of Necator infection to be 37%,15 while studies examining Trichuris infection in Nepal and China have estimated heritability to be 28% in both populations, with a related shared-household effect < 4%.17

The role of genetic predisposition in the net burden of disease caused by helminth infections has also been an active topic of investigation. Many studies have noted a partial discordance between infection intensity and the associated risk of infection-associated disease, with some lightly infected individuals manifesting severe morbidity. This variation in disease has been found to be based, in part, on variation in individual proinflammatory immune responses.31 Gene linkage studies in a recently exposed population in the Sudan have indicated that polymorphism in the region of chromosome 6q22–q23, called Sm2, is associated with risk for severe hepatic fibrosis due to S. mansoni infection.11 However subsequent population-based studies have failed to identify any familial concordance of fibrosis risk in S. mansoni infection in central Kenya,32 raising doubts about the generalizability of the Sudan findings.

While the large pedigrees spread across households and the large range of relationship types within individual households gave us a better ability to assess the relative impact of heredity and common exposure on study outcomes,17 there are several limitations to the present study. Population size and the extent of the study area prevented us from introducing adjustment for exposure based on water contact, as has been done in some previous studies.7,13,14 Water contact in the present study area takes place in multiple dispersed surface ponds, and both snail infection levels and water use vary extensively from season to season.3,33,34 Previous multivariate analysis of environmental predictors of infection risk in the Msambweni study area have shown the degree and duration of water contact to be much less effective predictors of infection/reinfection than age, gender, and location of residence.33 It is therefore likely that local variations in risk for infection would have been captured by the age-, sex-, and household adjustments included in the present analysis.

Heritability analysis provides a global assessment of the aggregate contribution of multiple genes to the trait under study. The estimates developed in this study are specific to our study population, and will not necessarily apply elsewhere.35 In the Wadigo population, which by tradition has been exposed to S. haematobium for many centuries, there are likely many balanced polymorphisms that limit the range of individual susceptibility to infection and disease. By nature of the variance decomposition analysis, populations that are more genetically homogeneous will produce lower estimates of heritability than heterogeneous populations.35 The trait of kidney hydronephrosis was sufficiently rare that the number of affected concordant relatives was low, which made our estimates of its heritability very uncertain. In addition, the random error term in our analysis is known to include several unspecified genetic components, such as deviations from genetic dominance and genotype-environmental interactions, which are not directly measured or estimated. Also imbedded in this factor is the error variance on parasite egg counts,15 which, if large, might substantially affect phenotype classification.

Overall, our analysis in an extended Coast Province Wadigo population suggests a limited heritable component of risk for infection intensity with S. haematobium in a highly endemic setting. Our estimates are sufficiently low that further pursuit of segregation analysis or specific gene linkage studies appears to be unpromising in this population. Estimates of heritability for bladder disease are higher, 14%, and possibly as high at 35%. This observation is perhaps in accord with recent studies linking risk of S. haematobium-related bladder thickening and deformity to an individual’s innate levels of proinflammatory (tumor necrosis factor-α) and anti-inflammatory (interleukin-10) cytokine responses to both parasite and non-parasite antigens.31 In contrast, estimates of the heritability of S. haematobium-associated hydronephrosis are uncertain. Although predisposition to tissue fibrosis could affect risk for this trait, it is likely that a strong element of chance determines the deposition of eggs in tissues and the subsequent localization of granuloma formation, so that predictability of this phenotype is substantially reduced. Our findings are in contrast to smaller studies in Brazil, Senegal, and Sudan that link aspects of S. mansoni infection and disease to specific genetic loci, and it is apparent that further study will be needed to appreciate the full influence of genetic factors in the risk for infection and disease in schistosomiasis.

Table 1

Number of informative relative pairs in the study population

Village of proband
DegreeTotal pairsNganjaMililaniVindungeniMarigizaMabatani
1st4,3327931,470724428917
2nd4,5767491,5927973521,086
3rd5,4197271,9161,2173431,216
4th4,6347141,4851,249252934
5th3,0355461,00788055547
6th9713801574031120
7th3851961517130
8th583801820
9th220000
Total23,4124,1457,6425,4591,4464,720
Table 2

Correlation in Schistosoma haematobium infection intensity and infection-associated morbidity outcomes: contrast between relative-pairs residing in the same and different households*

Outcomes
Infection intensityHydronephrosisBladder disease
Degree of relatednessHouserPnerPnerPne
* r = correlation coefficient for pairs in each stratum; P = P value of null hypothesis of no correlation; ne = effective number of pairs in stratum.
† Significant at P < 0.05 level.
0Same0.0260.471766−0.0140.616679−0.0090.816678
1Same0.117†<0.0013,4920.0170.4113,3050.129†<0.0013,308
1Different0.126†<0.0018400.0720.1577940.107†0.008794
2Different−0.0140.4443,148−0.0120.3582,9880.0360.0552,990
3Different0.028†0.0464,9470.041†0.0384,7210.039†0.0084,723
4Different0.02700.0704,485−0.0080.4914,2410.0140.3534,241
Table 3

Correlation in age- and sex-adjusted Schistosoma haematobium infection intensity: contrast between relative pairs residing in the same and different households*

Correlation of age/sex-adjusted infection intensity values
Degree of relatednessHouserPne
* r = correlation coefficient for pairs in each stratum; P = P value of null hypothesis of no correlation; ne = effective number of pairs in stratum.
† Significant at P < 0.05 level.
0Same0.0670.0685762
1Same0.126†<0.0013,491
2Same0.106†<0.0011,428
3Same0.104†<0.001472
4Same0.1020.215149
1Different0.0390.255840
2Different0.0090.6013,148
3Different0.0200.1604,947
4Different0.00010.9994,485
Table 4

Maximum likelihood estimates of heritability (h2) and shared environmental effects (c2) for age group- and sex-adjusted outcomes related to Schistosoma haematobium infection*

Modelh2c2P value for comparison of models
* ± values are standard errors.
Infection intensity
    Genetic and household effects (saturated model)0.0855 ± 0.04340.0704 ± 0.0205
    Genetic only0.1993 ± 0.03380.00015
    Household only0.0977 ± 0.01580.0174
Hydronephrosis
    Genetic and household effects0.000.5662 ± 0.0606
    Genetic only1.00.50
    Household only0.566 ± 0.0320.065
Bladder disease
    Genetic and household effects0.1417 ± 0.11190.1295 ± 0.0572
    Genetic only0.3529 ± 0.00130.0781
    Household only0.1852 ± 0.02720.0105
Figure 1.
Figure 1.

Age profile of infection prevalence (shaded bars) and geometric mean intensity (solid line) of Schistosoma haematobium infection in the five villages participating in the study.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 70, 1; 10.4269/ajtmh.2004.70.57

Figure 2.
Figure 2.

Age profile for the prevalence of different traits related to Schistosoma haematobium infection within the study population. A, Heavy S. haematobium infection (≥ 400 eggs per 10 mL of urine); B, moderate-severe hydronephrosis; C, Bladder abnormality (significant thickening irregularity, and/ or polyp formation, as defined by World Health Organization guidelines).22

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 70, 1; 10.4269/ajtmh.2004.70.57

Authors’ addresses: Charles H. King and Ronald E. Blanton, Center for Global Health and Diseases, W137, Case Western Reserve University School of Medicine, 10900 Euclid Avenue, Cleveland, OH 44106-4983, Telephone: 216-368-4818, Fax: 216-368-4825, E-mail: chk@po.cwru.edu. Eric M. Muchiri and H. Curtis Kariuki, Division of Vector Borne Diseases, Ministry of Health, PO Box 20750, Nairobi, Kenya, Telephone: 254-20-725-833, Fax: 254-20-720-030. John H. Ouma, Edmund Ireri, and Davy K. Koech, Kenya Medical Research Institute, Mbagathi Road, Nairobi, Kenya, Telephone: 254-20-722-541, Fax: 254-20-720-030. Peter Mungai, c/o CWRU/DVBD/KEMRI Filariasis-Schistosomiasis Research Unit, PO Box 8, Msambweni, Kenya, Telephone: 254-40-52267. Philip Magak, City X-Ray Services, PO Box 20930, Nairobi, Kenya, Telephone: 254-2-241-105, Fax: 254-2-725624. Hilda Kadzo, Department of Radiology, Kenyatta National Hospital, Nairobi, Kenya, Telephone: 254-2-711-888.

Acknowledgments: We thank the people of Mabatani, Milalani, Marigiza, Nganja, and Vindungeni villages for their ready participation with this project. We also thank Joyce Bongo, Anthony Chome, Moses Machibo, Iddi Masemo, Grace Mathenge, Jackson Muinde, Malick Ndzovu, Saidi Tosha, Mwanaha Chuo, and Massese Naftali for their extensive efforts in the fieldwork that contributed to the success of this project. Our thanks are also extended to Dr. Patrick Muthoka (Ministry of Health for Kwale District) for his support. This work is published with the kind permission of the Director of Medical Services, Ministry of Health, Kenya.

Financial support: This research was supported by the National Institutes of Health (NIH) through grants AI-45473 (National Institute of Allergy and Infectious Diseases) and TW/ES01543 (Fogarty International Center). The development and support of SOLAR software has been supported by NIH grant MH59490.

REFERENCES

  • 1

    WHO, 1993. The Control of Schistosomiasis: Second Report of the WHO Expert Committee. Geneva: World Health Organization.

  • 2

    Sturrock RF, 2001. The schistosomes and their intermediate hosts. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 7–83.

  • 3

    el Kholy H, Arap Siongok TK, Koech D, Sturrock RF, Houser H, King CH, Mahmoud AA, 1989. Effects of borehole wells on water utilization in Schistosoma haematobium endemic communities in Coast Province, Kenya. Am J Trop Med Hyg 41 :212–219.

    • Search Google Scholar
    • Export Citation
  • 4

    Prata A, 2001. Disease in schistosomiasis mansoni in Brazil. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 297–332.

  • 5

    Ouma JH, El-Khoby T, Fenwick A, Blanton RE, 2001. Disease in schistosomiasis mansoni in Africa. Mahmoud AAF, ed. Schistosomiasis. London: Imperial College Press, 333–360.

  • 6

    Smith JH, Christie JD, 1986. The pathobiology of Schistosoma haematobium infection in humans. Hum Pathol 17 :333–345.

  • 7

    Abel L, Demenais F, Prata A, Souza AE, Dessein A, 1991. Evidence for the segregation of a major gene in human susceptibility/resistance to infection by Schistosoma mansoni. Am J Hum Genet 48 :959–970.

    • Search Google Scholar
    • Export Citation
  • 8

    Marquet S, Abel L, Hillaire D, Dessein H, Kalil J, Feingold J, Weissenbach J, Dessein AJ, 1996. Genetic localization of a locus controlling the intensity of infection by Schistosoma mansoni on chromosome 5q31–q33. Nat Genet 14 :181–184.

    • Search Google Scholar
    • Export Citation
  • 9

    Marquet S, Abel L, Hillaire D, Dessein A, 1999. Full results of the genome-wide scan which localises a locus controlling the intensity of infection by Schistosoma mansoni on chromosome 5q31–q33. Eur J Hum Genet 7 :88–97.

    • Search Google Scholar
    • Export Citation
  • 10

    Muller-Myhsok B, Stelma FF, Guisse-Sow F, Muntau B, Thye T, Burchard GD, Gryseels B, Horstmann RD, 1997. Further evidence suggesting the presence of a locus, on human chromosome 5q31–q33, influencing the intensity of infection with Schistosoma mansoni. Am J Hum Genet 61 :452–454.

    • Search Google Scholar
    • Export Citation
  • 11

    Dessein AJ, Hillaire D, Elwali NE, Marquet S, Mohamed-Ali Q, Mirghani A, Henri S, Abdelhameed AA, Saeed OK, Magzoub MM, Abel L, 1999. Severe hepatic fibrosis in Schistosoma mansoni infection is controlled by a major locus that is closely linked to the interferon-gamma receptor gene. Am J Hum Genet 65 :709–721.

    • Search Google Scholar
    • Export Citation
  • 12

    Rodrigues V Jr, Piper K, Couissinier-Paris P, Bacelar O, Dessein H, Dessein AJ, 1999. Genetic control of schistosome infections by the SM1 locus of the 5q31–q33 region is linked to differentiation of type 2 helper T lymphocytes. Infect Immun 67 :4689–4692.

    • Search Google Scholar
    • Export Citation
  • 13

    Bethony J, Gazzinelli A, Lopes A, Pereira W, Alves-Oliveira L, Willams-Blangero S, Blangero J, Loverde P, Correa-Oliveira R, 2001. Genetic epidemiology of fecal egg excretion during Schistosoma mansoni infection in an endemic area in Minas Gerais, Brazil. Mem Inst Oswaldo Cruz 96 (Suppl):49–55.

    • Search Google Scholar
    • Export Citation
  • 14

    Bethony J, Williams JT, Blangero J, Kloos H, Gazzinelli A, Soares-Filho B, Coelho L, Alves-Fraga L, Williams-Blangero S, Loverde PT, Correa-Oliveira R, 2002. Additive host genetic factors influence fecal egg excretion rates during Schistosoma mansoni infection in a rural area in Brazil. Am J Trop Med Hyg 67 :336–343.

    • Search Google Scholar
    • Export Citation
  • 15

    Williams-Blangero S, Blangero J, Bradley M, 1997. Quantitative genetic analysis of susceptibility to hookworm infection in a population from rural Zimbabwe. Hum Biol 69 :201–208.

    • Search Google Scholar
    • Export Citation
  • 16

    Williams-Blangero S, Subedi J, Upadhayay RP, Manral DB, Rai DR, Jha B, Robinson ES, Blangero J, 1999. Genetic analysis of susceptibility to infection with Ascaris lumbricoides. Am J Trop Med Hyg 60 :921–926.

    • Search Google Scholar
    • Export Citation
  • 17

    Williams-Blangero S, McGarvey ST, Subedi J, Wiest PM, Upadhayay RP, Rai DR, Jha B, Olds GR, Guanling W, Blangero J, 2002. Genetic component to susceptibility to Trichuris trichiura: evidence from two Asian populations. Genet Epidemiol 22 :254–264.

    • Search Google Scholar
    • Export Citation
  • 18

    Muchiri EM, Ouma JH, King CH, 1996. Dynamics and control of Schistosoma haematobium transmission in Kenya: an overview of the Msambweni Project. Am J Trop Med Hyg 55 :127–134.

    • Search Google Scholar
    • Export Citation
  • 19

    Gomm R, 1972. Harlots and bachelors: marital instability among the coastal Digo of Kenya. Man 7 :95–113.

  • 20

    Peters PAS, Kazura JW, 1987. Update on diagnostic methods for schistosomiasis. Mahmoud AAF, ed. Bailliere’s Clinical Tropical Medicine and Communicable Diseases, Schistosomiasis. London: Bailliere Tindall, 419–433.

  • 21

    Savioli S, Hatz C, Dixon H, Kisumku UM, Mott KE, 1990. Control of morbidity due to Schistosoma haematobium on Pemba Island: egg excretion and hematuria as indicators of infection. Am J Trop Med Hyg 43 :289–295.

    • Search Google Scholar
    • Export Citation
  • 22

    Richter J, Hatz C, Campagne G, Bergquist NR, Jenkins JM, 2000. Ultrasound in Schistosomiasis: A Practical Guide to the Standardized Use of Ultrasonography for the Assessment of Schistosomiasis-Related Morbidity. Geneva: World Health Organization.

  • 23

    King CH, 2002. Ultrasound monitoring of structural urinary tract disease in S. haematobium infection. Mem Inst Oswaldo Cruz 97 (Suppl 1):149–152.

    • Search Google Scholar
    • Export Citation
  • 24

    Dyke B, 1989. PEDSYS, PGL Tech. Report No. 2. San Antonio, TX: Southwest Foundation for Biomedical Research.

  • 25

    Almasy L, Blangero J, 1998. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62 :1198–1211.

  • 26

    Williams-Blangero S, Vandeberg JL, Blangero J, Teixeira AR, 1997. Genetic epidemiology of seropositivity for Trypanosoma cruzi infection in rural Goias, Brazil. Am J Trop Med Hyg 57 :538–543.

    • Search Google Scholar
    • Export Citation
  • 27

    Woolhouse MEJ, 1998. Patterns in parasite epidemiology: The peak shift. Parasitol Today 14: 428–434.

  • 28

    Zinn-Justin A, Marquet S, Hillaire D, Dessein A, Abel L, 2001. Genome search for additional human loci controlling infection levels by Schistosoma mansoni. Am J Trop Med Hyg 65 :754–758.

    • Search Google Scholar
    • Export Citation
  • 29

    Chan L, Bundy DA, Kan SP, 1994. Aggregation and predisposition to Ascaris lumbricoides and Trichuris trichiura at the familial level. Trans R Soc Trop Med Hyg 88 :46–48.

    • Search Google Scholar
    • Export Citation
  • 30

    Chan L, Bundy DA, Kan SP, 1994. Genetic relatedness as a determinant of predisposition to Ascaris lumbricoides and Trichuris trichiura infection. Parasitology 108 :77–80.

    • Search Google Scholar
    • Export Citation
  • 31

    King CL, Malhotra I, Mungai P, Wamachi A, Kioko J, Muchiri E, Ouma JH, 2001. Schistosoma haematobium-induced urinary tract morbidity correlates with increased tumor necrosis factor-alpha and diminished interleukin-10 production. J Infect Dis 184 :1176–1182.

    • Search Google Scholar
    • Export Citation
  • 32

    Kariuki HC, Mbugua G, Magak P, Bailey JA, Muchiri EM, Thiongo FW, King CH, Butterworth AE, Ouma JH, Blanton RE, 2001. Prevalence and familial aggregation of schistosomal liver morbidity in Kenya: evaluation by new ultrasound criteria. J Infect Dis 183 :960–966.

    • Search Google Scholar
    • Export Citation
  • 33

    Muchiri EM, 1991. Association of water contact activities and risk of reinfection for S. haematobium after drug treatment in the Msambweni Area, Kenya. Epidemiology and Biostatistics. Cleveland, OH: Case Western Reserve University, 1–121.

  • 34

    Sturrock RF, Kinyanjui H, Thiongo FW, Tosha S, Ouma JH, King CH, Koech D, Siongok TK, Mahmoud AA, 1990. Chemotherapy-based control of schistosomiasis haematobia. 3. Snail studies monitoring the effect of chemotherapy on transmission in the Msambweni area, Kenya. Trans R Soc Trop Med Hyg 84 :257–261.

    • Search Google Scholar
    • Export Citation
  • 35

    Khoury MJ, Beaty TH, Cohen BH, 1993. Fundamentals of Genetic Epidemiology. New York: Oxford University Press.

Author Notes

Reprint requests: Charles H. King, Center for Global Health and Diseases, W137, Case Western Reserve University School of Medicine, 10900 Euclid Avenue, Cleveland, OH 44106-4983.
Save