Public health genomics is the use of genomics information to benefit public health. This is visualized as more effective preventive care and disease treatments with better specificity, tailored to the genetic makeup of each patient. According to the Centers for Disease Control and Prevention (U.S.), Public Health genomics is an emerging field of study that assesses the impact of genes and their interaction with behavior, diet and the environment on the population's health.
This field of public health genomics is less than a decade old. A number of think tanks, universities, and governments (including the U.S., UK, and Australia) have started public health genomics projects. Research on the human genome is generating new knowledge that is changing public health programs and policies. Advances in genomic sciences are increasingly being used to improve health, prevent disease, educate and train the public health workforce, other healthcare providers, and citizens.
Public policy
Public policy has protected people against genetic discrimination, defined in Taber's Cyclopedic Medical Dictionary (2001) as unequal treatment of persons with either known genetic abnormalities or the inherited propensity for disease; genetic discrimination may have a negative effect on employability, insurability and other socio-economic variables. Public policy in the U.S. that protect individuals and groups of people against genetic discrimination include the Americans with Disabilities Act of 1990, (2000) that prohibits genetic discrimination in the workplace for federal employees, and the Genetic Information Nondiscrimination Act of 2008.
Main public concerns regarding genomic information are that of confidentiality, misuse of information by health plans, employers, and medical practitioners, and the right of access to genetic information. Concerns also exist about the equitable deployment of public health genomics, and attention is needed to ensure that the implementation of genomic medicine does not further entrench social‐equity concerns.
Ethical concerns One of the many facets involved in public health genomics is that of bioethics. This has been highlighted in a study in 2005 by Cogent Research, that found when American citizens were asked what they thought the strongest drawback was in using genetic information, they listed "misuse of information/invasion of privacy" as the single most important problem. In 2003, the Nuffield Council on Bioethics published a report, Pharmacogenetics: Ethical Issues. Authors of the document explore four broad categories of ethical and policy issues related to pharmacogenetics: information, resource, equity and control. In the introduction to the report, the authors clearly state that the development and application of pharmacogenetics depend on scientific research, but that policy and administration must provide incentives and restraints to ensure the most productive and just use of this technology. Involving the public in ethical oversight and other ways can improve public trust in public health genomics as well as acceptability of initiatives and ensuring that access to the benefits of genomics research is equitable.
Genetic susceptibility to disease Single nucleotide polymorphisms (SNPs) are single bases within a gene sequence that differ from that gene's consensus sequence, and are present in a subset of the population. SNPs may have no effect on gene expression, or they can change the function of a gene completely. Resulting gene expression changes can, in some cases, result in disease, or in susceptibility to disease (e.g., viral or bacterial infection).
Some current tests for genetic diseases include: cystic fibrosis, Tay–Sachs disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, high cholesterol, some rare cancers and an inherited susceptibility to cancer. A select few are explored below.
Herpesvirus and bacterial infections Since the field of genomics takes into account the entire genome of an organism, and not simply its individual genes, the study of latent viral infection falls into this realm. For example, the DNA of a latent herpesvirus integrates into the host's chromosome and propagates through cell replication, although it is not part of the organism's genome, and was not present at the birth of the individual.
An example of this is found in a study published in Nature, which showed that mice with a latent infection of a herpesvirus were less susceptible to bacterial infections. Murine mice were infected with murine gammaherpesvirus 68 and then challenged with the Listeria monocytogenes bacterium. Mice that had a latent infection of the virus had an increased resistance to the bacteria, but those with a non-latent strain of virus had no change in susceptibility to the bacteria. The study went on to test mice with murine cytomegalovirus, a member of the betaherpesvirinae subfamily, which provided similar results. However, infection with human herpes simplex virus type-1 (HSV-1), a member of the alphaherpesvirinae subfamily, did not provide increased resistance to bacterial infection. They also used Yersinia pestis (the causative agent of the Black Death) to challenge mice with a latent infection of gammaherpesvirus 68, and they found the mice did have an increased resistance to the bacteria. The suspected reason for this is that peritoneal macrophages in the mouse are activated after latent infection of the herpesvirus, and since macrophages play an important role in immunity, this provides the mouse with a stronger, active immune system at the time of bacterial exposure. It was found that the latent herpesvirus caused an increase in interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), cytokines which both lead to activation of macrophages and resistance to bacterial infection.
Influenza and Mycobacterium tuberculosis Variations within the human genome can be studied to determine susceptibility to infectious diseases. The study of variations within microbial genomes will also need to be evaluated to use genomics of infectious disease within public health. The ability to determine if a person has greater susceptibility to an infectious disease will be valuable to determine how to treat the disease if it is present or prevent the person from getting the disease. Several infectious diseases have shown a link between genetics and susceptibility in that families tend to have heritability traits of a disease.
During the course of the past influenza pandemics and the current influenza epizootic there has been evidence of family clusters of disease. Kandun, et al. found that family clusters in Indonesia in 2005 resulted in mild, severe and fatal cases among family members. The findings from this study raise questions about genetic or other predispositions and how they affect a person's susceptibility to and severity of disease. Continued research will be needed to determine the epidemiology of H5N1 infection and whether genetic, behavioral, immunologic, and environmental factors contribute to case clustering.
Host genetic factors play a major role in determining differential susceptibility to major infectious diseases of humans. Infectious diseases in humans appear highly polygenic with many loci implicated but only a minority of these convincingly replicated. Over the course of time, humans have been exposed to organisms like Mycobacterium tuberculosis. It is possible that the human genome has evolved in part from our exposure to M. tuberculosis. Animal model studies and whole genome screens can be used to identify potential regions on a gene that suggest evidence of tuberculosis susceptibility. In the case of M. tuberculosis, animal model studies were used to suggest evidence of a locus which was correlated with susceptibility, further studies were done to prove the link between the suggested locus and susceptibility. The genetic loci that have been identified as associated with susceptibility to tuberculosis are HLA-DR, INF-γ, SLC11A1, VDR, MAL/TIRAP, and CCL2. Further studies will be needed to determine genetic susceptibility to other infectious diseases and ways public health officials can prevent and test for these infections to enhance the concept of personalized medicine.
Type 1 Diabetes, immunomics, and public health The term genomics, referring to the organism's whole genome, is also used to refer to gene informatics, or the collection and storage of genetic data, including the functional information associated with the genes, and the analysis of the data as combinations, patterns and networks by computer algorithms. Systems biology and genomics are natural partners, since the development of genomic information and systems naturally facilitates analysis of systems biology questions involving relationships between genes, their variants (SNPs) and biological function. Such questions include the investigation of signaling pathways, evolutionary trees, or biological networks, such as immune networks and pathways. For this reason, genomics and these approaches are particularly suited to studies in immunology. The study of immunology using genomics, as well as proteomics and transcriptomics (including gene profiles, either genomic or expressed gene mRNA profiles), has been termed immunomics.
Accurate and sensitive prediction of disease, or detection during early stages of disease, could allow the prevention or arrest of disease development as immunotherapy treatments become available. Type-1 diabetes markers associated with disease susceptibility have been identified, for example HLA class II gene variants, however possession of one or more of these genomic markers does not necessarily lead to disease. Lack of progression to disease is likely due to the absence of environmental triggers, absence of other susceptibility genes, presence of protective genes, or differences in the temporal expression or presence of these factors. Combinations of markers have also been associated with susceptibility to type-1 diabetes however again, their presence may not always predict disease development, and conversely, disease may be present without the marker group. Potential variant genes (SNPs) or markers that are linked to the disease include genes for cytokines, membrane-bound ligands, insulin and immune regulatory genes.
Meta-analyses have been able to identify additional associated genes, by pooling a number of large gene datasets. This successful study illustrates the importance of compiling and sharing large genome databases. The inclusion of phenotypic data in these databases will enhance discovery of candidate genes, while the addition of environmental and temporal data should be able to advance the disease progression pathways knowledge. HUGENet, which was initiated by the Centers for Disease Control and Prevention (U.S.), is accomplishing the integration of this type of information with the genome data, in a form available for analysis. This project could be thought of as an example of 'metagenomics', the analysis of a community's genome, but for a human rather than a microbial community. This project is intended to promote international data sharing and collaboration, in addition to creating a standard and framework for the collection of this data.
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