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Genome-wide association study
, E3 t! P5 n$ H6 k" BFrom Wikipedia, the free encyclopedia& m) [% D/ C' u% l
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& z, E& p; [: IIn genetic epidemiology, a genome-wide association study (GWA study, or GWAS) - also known as whole genome association study (WGA study) - is an examination of genetic variation across a given genome, designed to identify genetic associations with observable traits. In human studies, this might include traits such as blood pressure or weight, or why some people get a disease or condition.[1]. r. N& k/ X7 V1 r* w. E
- h7 a1 L; P7 V0 m' `8 m/ e+ H2 G7 T% NThe completion of the Human Genome Project in 2003 made it possible to find the genetic contributions to common diseases and analyse whole-genome samples for genetic variations that contribute to their onset.+ D/ x8 G+ U# p
& C8 ^$ N# p* Y7 ]+ n4 w% vThese studies normally require two groups of participants: people with the disease (cases) and similar people without (controls). After genotyping each participant, the set of markers, such as SNPs, are scanned into computers. Then bioinformatics is applied to survey participants' genomes for markers of genetic variation.) F! p4 x5 h3 }7 ~! [/ _9 S1 o
! ~) ~3 Q# y( g/ S+ tIf 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 as pointers to the region of the human genome where the disease-causing problem is likely to reside. 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.[1]6 z: F1 R- z6 q& D# s/ [
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Contents [hide]
8 L! @+ t1 X2 v Z, W" k2 Y1 Background , w5 H9 S; @' w" A4 Y( T+ I' R* k
2 Successes
- Z9 j; J8 f2 K6 n2.1 From a successful GWAS to the promised personal medicine
9 f; [5 c# Y9 S7 g# M0 F6 |) E3 Problems
! }/ K2 Y2 i* f( Q1 I4 References
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5.1 Reviews
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1 @5 {6 O' B+ ]# S: n, x7 ~$ Z5 F[edit] Background
6 |5 L* J+ U% r$ D, [4 ?7 D+ mThe human genome contains many millions of single-nucleotide polymorphisms, and thousands more variations in the number of copies of large and small segments of the genome (copy number variation), which may either directly cause changes in phenotype or which tag nearby mutations containing the key differences that influence individual variation and susceptibility to disease. GWA studies allow researchers to sample 500,000 or more SNPs from each subject in a study capturing variation uniformly across the genome. To date, these studies have identified risk and protective factors for asthma, cancer, diabetes, heart disease, mental illness and other human differences.1 n7 o' O8 n6 {$ h4 i8 L
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Most genetic variations are associated with the geographical and historical populations in which the mutations first arose. This ability of SNPs to tag surrounding blocks of ancient DNA (haplotypes) underlies the rationale for GWAS. However, because of this, studies must take account of the geographical and racial background of participants - controlling for what is called population stratification. As the peoples of the world have migrated and inter-married over many generations, these geographical variations also become broken down and mixed over time[2]
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( C" A& h4 m- c6 [2 W5 G7 U6 gIn 2005, it was learned through a small scale GWAS that age-related macular degeneration is associated with variation in the gene for complement factor H, which produces a protein that regulates inflammation.[3]
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The first major GWAS was published in Nature in February 2007 by Robert Sladek et al. in a study searching for type II diabetes variants.[4] 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.
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In 2007, the Wellcome Trust Case Control Consortium 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.[5][6]
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1 h% k5 a, T. gDespite the initial successes, genome-wide association studies have seldom uncovered loci which confer more than an odds ratio of 2 for common diseases.[7]
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[edit] From a successful GWAS to the promised personal medicine0 X/ v/ H) T+ X* |0 S" d1 \& P) V
One of the challenges for a successful GWAS in the future will be to apply the findings in a way that accelerates drug and diagnostics development, including better integration of genetic studies into the drug-development process and a focus on the role of genetic variation in maintaining health as a blueprint for designing new drugs and diagnostics[8]. One of such successes is related to identifying the genetic variant associating with response to anti-hepatitis C virus treatment. For genotype 1 hepatitis C treated with Pegylated_interferon-alpha-2a or Pegylated_interferon-alpha-2b (brand names Pegasys or PEG-Intron) combined with ribavirin, a GWAS study [9] has shown that genetic polymorphisms near the human IL28B gene, encoding interferon lambda 3, are associated with significant differences in response to the treatment. A later report from Nature [10] demonstrated that the same genetic variants are also associated with the natural clearance of the genotype 1 hepatitis C virus.8 R7 s6 Z/ p' o# h9 g) n
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[edit] Problems+ p9 e9 D/ h! N+ R" |
GWA studies are necessarily hypothesis-free: that is they search the entire genome for associations rather than focusing on small candidate areas. This aspect of GWA has attracted the criticism as expensive "factory science". Robert Elston is a prominent proponent of linkage, although he does accept association may occasionally be useful. Methodologically, the power of association to localize a mutation translates directly into the need for extremely dense searches. This led Pearson and Manolio's to note that "the GWA approach can also be problematic because the massive number of statistical tests performed presents an unprecedented potential for false-positive results".[1] Alternative strategies such as linkage analysis act as systematic studies of variation, without needing variants at each region.
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There are more unresolved problems as viewed from the inferential point of view. Specifically, a number of classification problems are still not solved adequately.[citation needed] |
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发表于 2010-5-13 12:14
发表于 2010-5-13 18:15
