Association Analysis for Rare Variants

• Statistical analysis strategies for association studies involving rare variants.
  Bansal V, Libiger O, Torkamani A, Schork NJ.
  Nat Rev Genet. 2010 Nov;11(11):773-85. Epub 2010 Oct 13. Review.
  PMID: 20940738 http://www.ncbi.nlm.nih.gov/sites/entrez/20940738

  • focused on single nucleotide rare variants

  • links to > 10 association tests at the gene or genomic area level, with correction or subpopulation and covariates

  • quite of an effort to understand and follow up on all the methods referenced
  • briefly mentions pathway analysis (ref to Autism Nature paper)

• An evaluation of statistical approaches to rare variant analysis in genetic association studies.
  Morris AP, Zeggini E.
  Genet Epidemiol. 2010 Feb;34(2):188-93.
  PMID: 19810025

Gene-set Association Tests for Rare Variants

Other papers

• Why olfactory receptors as false positives
  PLoS Genet. 2008 Nov;4(11):e1000249. Epub 2008 Nov 7.
  High-resolution copy-number variation map reflects human olfactory receptor diversity and evolution.
  Hasin Y, Olender T, Khen M, Gonzaga-Jauregui C, Kim PM, Urban AE, Snyder M, Gerstein MB, Lancet D, Korbel JO.

Similar Gene-set Analysis Strategies for Genetics Data

Cancer Mutations

• Patient-oriented gene set analysis for cancer mutation data
  Boca SM, Kinzler K, Velculescu VE, Vogelstein B, Parmigiani G.
  Genome Biol. 2010 Nov 23;11(11):R112.
  PMID: 21092299 http://www.ncbi.nlm.nih.gov/sites/entrez/21092299

  • It's basically the same idea applied to cancer instead of autism.
  • However, lacking the control patients (since cancer is high mutation frequency, that would not make sense), they have to define the null hypothesis using randomly placed mutations.
  • What they call "exclusivity principle" is what I usually call the "OR combination logic" (i.e. at least one perturbed gene for the pathway/gene-set to be perturbed).

Network Propagation Algorithms

• cancer mutations

  • Vandin F, Upfal E, Raphael BJ
    Algorithms for Detecting Significantly Mutated Pathways in Cancer
    LECTURE NOTES IN BIOINFORMATICS 2010, Volume: 6044, Pages: 506-521
    PDF

• signaling

  • Stojmirović A, Yu YK.
    Information flow in interaction networks.
    J Comput Biol. 2007 Oct;14(8):1115-43.
    http://www.ncbi.nlm.nih.gov/pubmed/17985991

  • Stojmirović A, Yu YK.
    ITM Probe: analyzing information flow in protein networks.
    Bioinformatics. 2009 Sep 15;25(18):2447-9. Epub 2009 Jun 27.
    http://www.ncbi.nlm.nih.gov/pubmed/19561335

Other paper to look at

• Interesting large data-set
  Nature. 2010 Apr 1;464(7289):713-20.
  Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.
  Wellcome Trust Case Control Consortium,
  http://www.ncbi.nlm.nih.gov/pubmed/20360734

• Bioinformatics challenges for genome-wide association studies.
  Moore JH, Asselbergs FW, Williams SM.
  Bioinformatics. 2010 Feb 15;26(4):445-55. Epub 2010 Jan 6.
  http://bioinformatics.oxfordjournals.org/content/26/4/445.abstract

• Functional genomics complements quantitative genetics in identifying disease-gene associations.
  Guan Y, Ackert-Bicknell CL, Kell B, Troyanskaya OG, Hibbs MA.
  PLoS Comput Biol. 2010 Nov 11;6(11):e1000991
  http://www.ncbi.nlm.nih.gov/pubmed/21085640

• Pathway-based analysis using reduced gene subsets in genome-wide association studies
  http://www.biomedcentral.com/1471-2105/12/17/abstract

Gene-set Tests for GWAS

Not in the Mendeley collection:

• Pathways of Distinction Analysis: a new technique for multi-SNP analysis of GWAS data
  http://arxiv.org/abs/1012.4726

Better publication probably coming soon:

• MAGENTA
  Ayellet V. Segrè, DIAGRAM Consortium, MAGIC investigators, Leif Groop, Vamsi K. Mootha, Mark J. Daly, and David Altshuler (2010).
  Common Inherited Variation in Mitochondrial Genes is not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits.
  PLoS Genetics Aug 12;6(8). pii: e1001058.