A hidden architecture Researchers use novel methods to uncover gene mutations for common diseases

BOSTON MA Human geneticists have long debated whether the genetic risk of the most common medical conditions derive from many rare mutations each conferring a high degree of risk in different people or common differences throughout the genome that modestly influence risk

A new study by Brigham and Womens Hospital BWH researchers has harnessed data and new analysis tools to address this question in four common diseases rheumatoid arthritis celiac disease coronary artery disease and myocardial infarction heart attack and type 2 diabetes

The study will be electronically published on March 25 2012 in Nature Genetics

The researchers developed a new statistical method built upon quotpolygenic risk score analysisquot to estimate the heritable component of these diseases that is explained by common differences throughout the genome

Their method takes advantage of data from previously published genomewide association studies or GWAS an approach used to scan DNA samples for common genetic markers seen throughout the population called SNPs single nucleotide polymorphisms

According to senior author Robert Plenge MD PhD BWH director of Genetics and Genomics in the Division of Rheumatology Immunology and Allergy quotWe used GWAS data and a Bayesian statistical framework to demonstrate that a substantial amount of risk to these four common diseases is due to hundreds of loci that harbor common causal variants with small effect as well as a smaller number of loci that harbor rare causal variantsquot

Using data on rheumatoid arthritis they estimated that variation in hundreds of locations throughout the genome might explain 20 percent of rheumatoid arthritis risk after excluding all of the known rheumatoid arthritis genetic risk factors

They used computer simulations to demonstrate that the underlying genetic risk in rheumatoid arthritis is largely explained by many common alleles rather than rare mutations

They observed similar results for celiac disease 43 percent myocardial infarction 48 percent and type 2 diabetes 49 percent

quotWhat is remarkable is that our statistical model was broadly applicable to several common diseases not just rheumatoid arthritisquot said Plenge who is also an assistant professor at Harvard Medical School and an associate member of the Broad Institute of MIT and Harvard quotOur study provides a clear strategy for discovering additional risk alleles for these and likely many other common diseasesquot

According to the researchers these methods can be applied to other genomewide datasets eg GWAS or whole genome sequencing to estimate the degree to which there is a genetic component

One exciting possibility is assessing the genetic basis of individual response to drugs

quotOur method may be particularly useful for diseases and related traits that cannot be easily studied in familiesquot said Eli Stahl PhD lead study author BWH research associate and member of the National Institutes of Healthfunded Pharmacogenomic Research Network PGRN quotFor traits such as treatment efficacy or toxicity we often assume there is a genetic basis to the clinical variability observed among patients Now we have the statistical tools to quantify the extent to which this is the case directlyquot

quotOur study reinforces a common thread in the literature that many subtle differences throughout the genome explain much of the differences in risk for individuals for all kinds of diseases this has powerful implications for the genetic architecture of disease for risk prediction and prognosis as well as for basic biology and developing new drug targetsquot said cosenior author Soumya Raychaudhuri MD PhD BWH Division of Immunology Allergy and Rheumatology assistant professor of medicine at Harvard Medical School

Date : 26 Mar, 2012
Reference : http://www.eurekalert.org/pub_releases/2012-03/bawh-aha032212.php

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