Misplaced Pages

Genome-wide association study

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.

This is an old revision of this page, as edited by Fnielsen (talk | contribs) at 14:23, 19 June 2008 (→What is an example of a successful GWA Study?: More information from study). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Revision as of 14:23, 19 June 2008 by Fnielsen (talk | contribs) (→What is an example of a successful GWA Study?: More information from study)(diff) ← Previous revision | Latest revision (diff) | Newer revision → (diff)

In genetic epidemiology, a genome-wide association study (GWA study) - also known as whole genome association study (WGA study) - is an examination of genetic variation across the human genome, designed to identify genetic associations with observable traits, such as blood pressure or weight, or why some people get a disease or condition.

The completion of the Human Genome Project in 2003 made it possible to find the genetic contributions to common diseases and analyze whole-genome samples for genetic variations that contribute to their onset.

These studies require two groups of participants: people with the disease and similar people without. After obtaining samples from each participant, the set of markers such as SNPs are scanned into computers. The computers survey each participant's genome for markers of genetic variation.

If genetic variations are more frequent in people with the disease, the variations are said to be "associated" with the disease. The associated genetic variations are then considered pointers to the region of the human genome where the disease-causing problem resides. Since the entire genome is analysed for the genetic associations of a particular disease, this technique allows the genetics of a disease to be investigated in a non-hypothesis-driven manner.

Why are these a good idea?

Humans differ in genetic makeup by only 0.1%, but that small part of the genome contains the key differences that can determine a person’s susceptibility to disease. GWA Studies allow researchers to identify factors in many areas, including asthma, cancer, diabetes, heart disease and mental illness research and clinical care.

What are the challenges?

As people have migrated and married over generations, it has become more difficult to limit studies to biological data; for example, people with tuberculosis moving to Colorado might lead to conclusions that Colorado people are biologically inclined to Tuberculosis if correction for population stratification is not properly factored in.

What is an example of a successful GWA Study?

In 2005 it was learned through a small scale GWA Studies that age-related macular degeneration is associated with variation in the gene for complement factor H, which produces a protein that regulates inflammation.

The first major GWA was published in Nature in February 2007 by Robert Sladek et al.: A study searching for type II diabetes variants. The work was mainly carried out the Genome Quebec centre in McGill University although it included collaboration with scientists at Imperial College London and other research institutions. The group tested 392'935 single-nucleotide polymorphisms and identified several associations, among others in the genes called TCF7L2 and SLC30A8.

In 2007 the Wellcome Trust Case-Control Consortium (WTCCC) carried out genome-wide association studies for the diseases coronary heart disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, Crohn's disease, bipolar disorder and hypertension. This study was successful in uncovering many new disease genes underlying these diseases.

Criticism

Some critics of GWA studies regard them as extremely expensive "factory science". Alternatives such as linkage analysis have advantages over GWAs such as robustness to allelic heterogeneity.

Robert Elston is a prominent proponent of linkage, although he does accept association may occasionally be useful.

According to Pearson and Manolio's assessment of the technique, "the GWA approach can also be problematic because the massive number of statistical tests performed presents an unprecedented potential for false-positive results".

External links

References

  1. ^ Pearson, Thomas A. (2008-03-19). "How to Interpret a Genome-wide Association Study". JAMA. 299 (11): 1335–1344. doi:10.1001/jama.299.11.1335. Retrieved 2008-06-16. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  2. Robert Sladek, Ghislain Rocheleau, Johan Rung, Christian Dina, Lishuang Shen, David Serre, Philippe Boutin, Daniel Vincent, Alexandre Belisle, Samy Hadjadj, Beverley Balkau, Barbara Heude, Guillaume Charpentier, Thomas J. Hudson, Alexandre Montpetit, Alexey V. Pshezhetsky, Marc Prentki, Barry I. Posner, David J. Balding, David Meyre, Constantin Polychronakos and Philippe Froguel (2007). "A genome-wide association study identifies novel risk loci for type 2 diabetes". Nature. 445: 881–885. doi:10.1038/nature05616. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
Categories: