Abstract
In the past decade, genetic epidemiological analyses in infectious diseases have increased drastically since the publication of human genome and all the subsequent projects analyzing human diversity at molecular level. The great majority of studies use classical epidemiological designs applied to genetic data, and more than 80% of published studies use population-based case-control designs with widely spread genetic markers in human genome, like short tandem repeats (STR) or single nucleotide polymorphisms (SNP), in genes chosen by their physiological association with the disease (candidate genes). Even though genetic data is less prone to several bias issues inherent to case-control studies, some care has to be taken when designing, performing, analyzing and interpreting results from such studies. Here we discuss some basic concepts of genetics and epidemiology as a departure to evaluate and review every step that should be followed to design, conduct, analyze, interpret and present data from those studies, using particularities of infectious diseases, especially leprosy and tuberculosis as models.
Highlights
The explosion of studies using genetic epidemiology, i.e. case controls studies with candidate genes, of infections have risen after common belief that genes greatly influence susceptibility to infectious diseases
Among all of the genes that participate in immune response against infectious disease it is likely that cytokines and other genes associated with inflammatory and immune response play a crucial role
Single nucleotide polymorphisms are mostly biallelic point mutations, present with a frequency higher than 1% in the population, and are observed with variable densities depending on the region of the human genome studied
Summary
The explosion of studies using genetic epidemiology, i.e. case controls studies with candidate genes, of infections have risen after common belief that genes greatly influence susceptibility to infectious diseases. More general problems should be understood and addressed, as proper sample size calculations to get enough power to detect a clinically or biologically significant difference between cases and controls, but at the same time being careful not to waste precious resources with overpowered studies, it is far more common to detect underpowered studies in the literature – for an example in tuberculosis, see Pacheco and colleagues [11]. We review and evaluate some premises of genetic epidemiological studies based on case-control designs using infectious diseases, with leprosy and tuberculosis (TB) as a model
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