Genome Wide Association Studies

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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>.
 
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>.
 
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>.

Revision as of 20:11, 12 February 2009

Some people in our group work on genome wide association studies (GWAS).

Contents

Introductory reading

For an introduction to GWAS, with an emphasis on human studies, you could start with a nice tutorial article [1], and a review of more recent issues [2]. There is also a nice review about approaches for rodent studies [3].

More Advanced Statistical Methodology

An important and widely used approach to dealing with cryptic population structure [4], and key references on genotype imputation [5][6].

A powerful approach to deal with strain structure or relatedness between individuals [7].

Software

PLINK is an excellent data handling tool, and implements many useful statistical methods. It's the Swiss Army Knife for GWAS.

EIGENSOFT is widely used for population structure analysis and correction.

IMPUTE and SNPTEST, or MACH and ProbABEL, or BimBam, and all be used to perform more sophisticated model based genotype imputation and association testing.

QUICKTEST is our own software for association testing using uncertain genotypes. For quantitative trait analysis, we think it is faster and better than SNPTEST.

References

  1. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 2006 Oct; 7(10) 781-91. doi:10.1038/nrg1916 pmid:16983374. PubMed HubMed [BaldingTutorial]
  2. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, and Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008 May; 9(5) 356-69. doi:10.1038/nrg2344 pmid:18398418. PubMed HubMed [McCarthyReview]
  3. Flint J, Valdar W, Shifman S, and Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 2005 Apr; 6(4) 271-86. doi:10.1038/nrg1576 pmid:15803197. PubMed HubMed [FlintReview]
  4. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, and Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006 Aug; 38(8) 904-9. doi:10.1038/ng1847 pmid:16862161. PubMed HubMed [PricePC]
  5. Servin B and Stephens M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 2007 Jul; 3(7) e114. doi:10.1371/journal.pgen.0030114 pmid:17676998. PubMed HubMed [ServinImputation]
  6. Marchini J, Howie B, Myers S, McVean G, and Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007 Jul; 39(7) 906-13. doi:10.1038/ng2088 pmid:17572673. PubMed HubMed [MarchiniImputation]
  7. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, and Eskin E. Efficient control of population structure in model organism association mapping. Genetics 2008 Mar; 178(3) 1709-23. doi:10.1534/genetics.107.080101 pmid:18385116. PubMed HubMed [KangEMMA]
All Medline abstracts: PubMed HubMed