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HOST GENETICS AND SYMPTOMATIC DENGUE INFECTION

HOST GENETICS AND SYMPTOMATIC DENGUE INFECTION
宿主遗传学和有症状的登革热感染
批准号:
7420629
负责人:
KATRINA A. GODDARD
金额:
$0.74万
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-08-01 至 2007-07-31

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中文摘要
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英文摘要
This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This proposal will investigate the genetic basis for variation in human responses to dengue, and perhaps other viruses, using a case-control study in the Brazilian city of Salvador. The flavivirus, Dengue virus, has four serotypes and is efficiently transmitted to humans by mosquitoes during a blood meal. Most primary dengue infections are symptomatic and only rarely associated with dengue hemorrhagic fever (DHF) and dengue shock syndrome. Rather, severe dengue complications occur most frequently following a secondary infection with a new dengue serotype, primarily by antibody mediated immune enhancement. This hypothesis, however, does not adequately explain the episodic distribution of severe dengue infection in many humans, nor the occasional DHF following primary infection. Several lines of evidence suggest that host genetics may also contribute to susceptibility, as evidenced by certain African populations who appear highly resistant to severe DHF infections. The plans include collection, isolation, and validation of DNA for cases and controls with differing clinical presentations, selecting common polymorphisms in a region of Chromosome-12, and genotyping that region in cases and controls, then looking for associations with the clinical presentations. Cases and controls will be matched for age, sex and for primary or secondary infection. Analysi\es will be performed using McNemar's test and logistic regression with correction for population structure via a genomic control approach.
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