Genome Wide Association Studies - Revision history

WikiSysop: /* Introduction */

2012-08-21T10:29:35Z

‎Introduction

WikiSysop at 18:11, 23 February 2012

2012-02-23T18:11:28Z

Sven at 12:36, 15 September 2009

2009-09-15T12:36:41Z

Sven at 12:36, 15 September 2009

2009-09-15T12:36:18Z

Sven at 12:31, 15 September 2009

2009-09-15T12:31:34Z

Toby: /* Statistical Methodology */

2009-02-12T19:11:51Z

‎Statistical Methodology

Toby: /* Software */

2009-02-06T09:26:39Z

‎Software

Toby at 14:29, 5 February 2009

2009-02-05T14:29:56Z

Toby at 14:21, 5 February 2009

2009-02-05T14:21:38Z

Toby at 14:19, 5 February 2009

2009-02-05T14:19:35Z

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 Genome Wide Association Studies (GWAS) search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.
-Our current focus is on the Cohorte Lausanne (CoLaus), a population-based sample of more than 6'000 individuals from the Lausanne area. The CoLaus phenotypic dataset includes a large range of measurements, including extensive blood chemistry, anatomic and physiological measures, as well as parameters related to life style and history. Genotypes have been measured for ~500`000 SNPs using Affymetrix 500k SNP arrays. Regressing the various phenotypes onto these SNPs has already revealed a number of highly significant associations (see http://serverdgm.unil.ch/bergmann/CBG_publications.html for our publications).
+Our current focus is on the Cohorte Lausanne (CoLaus), a population-based sample of more than 6'000 individuals from the Lausanne area. The CoLaus phenotypic dataset includes a large range of measurements, including extensive blood chemistry, anatomic and physiological measures, as well as parameters related to life style and history. Genotypes have been measured for ~500`000 SNPs using Affymetrix 500k SNP arrays. Regressing the various phenotypes onto these SNPs has already revealed a number of highly significant associations (see our [[publications]]).
 Current GWAS usually include the following steps:

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	== Introduction ==		== Introduction ==

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−	== Introduction ~~reading~~ ==	+	== Introduction ==

	Genome Wide Association Studies (GWAS) search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.		Genome Wide Association Studies (GWAS) search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.

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 Genome Wide Association Studies (GWAS) search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.
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 From the many GWAS that were performed in the last years it became apparent that even well-powered (meta-)studies with many thousands (and even ten-thousands) of samples could at best identify a few (dozen) candidate loci with highly significant associations. While many of these associations have been replicated in independent studies, each locus explains but a tiny (<1%) fraction of the genetic variance of the phenotype (as predicted from twin-studies). Remarkably, models that pool all significant loci into a single predictive scheme still miss out by at least one order of magnitude in explained variance. Thus, while GWAS already today provide new candidates for disease-associated genes and potential drug targets, very few of the currently identified (sets of) genotypic markers are of any practical use for accessing risk for predisposition to any of the complex diseases that have been studied.
-Various solutions to this apparent enigma have been proposed: First, it is important to realize that the expected heritabilities usually have been estimated from twin-studies, often several decades ago. It has been argued that these estimates entail problems of its own, such as: independently raised twins shared a common prenatal environment; they may have undergone intrauterine competition; the mother may be more physically stressed (less nutrients); and twins reared apart are difficult to find, and may reflect certain types of environments. Indeed it is important to remember that heritability estimates are always relative to the genetic and environmental factors in the population, and are not absolute measurements of the contribution of genetic and environmental factors to a phenotype. Heritability estimates reflect the amount of variation in genotypic effects compared to variation in environmental effects.
+Various solutions to this apparent enigma have been proposed: First, it is important to realize that the expected heritabilities usually have been estimated from twin-studies, often several decades ago. It has been argued that these estimates entail problems of its own (independently raised twins shared a common prenatal environment and may have undergone intrauterine competition, etc.).
-Second, the genotypic information is still incomplete. Most analyses used microarrays probing only around half a million of SNPs, which is almost one order of magnitude less than the current estimates of about 4 million common variants from the Hapmap CEU panel (ref). While many of these SNPs can be imputed accurately using information on linkage disequilibrium, there still remains a significant fraction of SNPs which are poorly tagged by the measured SNPs. Furthermore, rare variants with a Minor Allele Frequency (MAF) of less than 1% are not accessed at all with SNP-chips, but may nevertheless be the causal agents for many phenotypes [ref]. Finally, other genetic variants like Copy Number Variations (CNVs) (or even epigenetics) may also play an important role.
+Second, the genotypic information is still incomplete. Most analyses used microarrays probing only around half a million of SNPs, which is almost one order of magnitude less than the current estimates of about 4 million common variants from the Hapmap CEU panel. While many of these SNPs can be imputed accurately using information on linkage disequilibrium, there still remains a significant fraction of SNPs which are poorly tagged by the measured SNPs. Furthermore, rare variants with a Minor Allele Frequency (MAF) of less than 1% are not accessed at all with SNP-chips, but may nevertheless be the causal agents for many phenotypes. Finally, other genetic variants like Copy Number Variations (CNVs) (or even epigenetics) may also play an important role.
 Third, it is important to realize that current analyses usually only employ additive models considering one SNP at a time with few, if any, co-variables, like sex, age and principle components reflecting population substructures. This obviously only covers a small set of all possible interactions between genetic variants and the environment. Even more challenging is taking into account purely genetic interactions, since already the number of all possible pair-wise interactions scales like the number of genetic markers squared.

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-Some people in our group work on genome wide association studies (GWAS).
+Genome Wide Association Studies (GWAS) search for correlations between genetic markers (usually Single Nucleotide Polymorphisms, short SNPs) and any measurable trait in a population of individuals. The motivation is that such associations could provide new candidates for causal variants in genes (or their regulatory elements) that play a role for the phenotype of interest. In the clinical context this may eventually lead to a better understanding of the genetic components of diseases and their risk factors.
-== Introductory reading ==
+Our current focus is on the Cohorte Lausanne (CoLaus), a population-based sample of more than 6'000 individuals from the Lausanne area. The CoLaus phenotypic dataset includes a large range of measurements, including extensive blood chemistry, anatomic and physiological measures, as well as parameters related to life style and history. Genotypes have been measured for ~500`000 SNPs using Affymetrix 500k SNP arrays. Regressing the various phenotypes onto these SNPs has already revealed a number of highly significant associations (see http://serverdgm.unil.ch/bergmann/CBG_publications.html for our publications).
 For an introduction to GWAS, with an emphasis on human studies, you could start with a nice tutorial article <cite>BaldingTutorial</cite>, and a review of more recent issues <cite>McCarthyReview</cite>.  There is also a nice review about approaches for rodent studies <cite>FlintReview</cite>.

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 For an introduction to GWAS, with an emphasis on human studies, you could start with a nice tutorial article <cite>BaldingTutorial</cite>, and a review of more recent issues <cite>McCarthyReview</cite>.  There is also a nice review about approaches for rodent studies <cite>FlintReview</cite>.
-== Statistical Methodology ==
+== More Advanced Statistical Methodology ==
 An important and widely used approach to dealing with cryptic population structure <cite>PricePC</cite>, and key references on genotype imputation <cite>ServinImputation</cite><cite>MarchiniImputation</cite>.

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 == Software ==
-Many statistical methods are implemented in
+[http://pngu.mgh.harvard.edu/~purcell/plink PLINK] is an excellent data handling tool, and
-[http://pngu.mgh.harvard.edu/~purcell/plink PLINK].
+implements many useful statistical methods.  It's the Swiss Army Knife for GWAS.
 [http://www.stats.ox.ac.uk/%7Emarchini/software/gwas/gwas.html IMPUTE and SNPTEST],
 or
 [http://www.sph.umich.edu/csg/abecasis/mach MACH] and
-[http://mga.bionet.nsc.ru/%7Eyurii/ABEL ProbABEL], or [http://stephenslab.uchicago.edu/software.html BimBam].
+[http://mga.bionet.nsc.ru/%7Eyurii/ABEL ProbABEL], or [http://stephenslab.uchicago.edu/software.html BimBam], and all be used to perform more sophisticated model based genotype imputation and association testing.
-Our own software for association testing using uncertain genotypes is
+[http://toby.freeshell.org/software/quicktest.shtml QUICKTEST] is
-[http://toby.freeshell.org/software/quicktest.shtml QUICKTEST].
+our own software for association testing using uncertain genotypes.  For quantitative trait analysis, we think it is faster and better than SNPTEST.
 == References ==

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 # ServinImputation pmid=17676998
 # MarchiniImputation pmid=17572673
-# KangEmma pmid=18385116
+# KangEMMA pmid=18385116
 </biblio>