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| == Association Analysis for Rare Variants == * '''Statistical analysis strategies for association studies involving rare variants.'''<<BR>> Bansal V, Libiger O, Torkamani A, Schork NJ.<<BR>> Nat Rev Genet. 2010 Nov;11(11):773-85. Epub 2010 Oct 13. Review.<<BR>> 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.'''<<BR>> Morris AP, Zeggini E.<<BR>> Genet Epidemiol. 2010 Feb;34(2):188-93.<<BR>> PMID: 19810025 == Gene-set Association Tests for Rare Variants == |
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| * 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. |
== Similar Gene-set Analysis Strategies for Genetics Data == === Cancer Mutations === * '''Patient-oriented gene set analysis for cancer mutation data'''<<BR>> Boca SM, Kinzler K, Velculescu VE, Vogelstein B, Parmigiani G.<<BR>> Genome Biol. 2010 Nov 23;11(11):R112.<<BR>> PMID: 21092299 |
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| * 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). |
* 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). == Other paper to look at == * Interesting large data-set<<BR>> Nature. 2010 Apr 1;464(7289):713-20.<<BR>> Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.<<BR>> Wellcome Trust Case Control Consortium,<<BR>> http://www.ncbi.nlm.nih.gov/pubmed/20360734 * Bioinformatics challenges for genome-wide association studies.<<BR>> Moore JH, Asselbergs FW, Williams SM.<<BR>> Bioinformatics. 2010 Feb 15;26(4):445-55. Epub 2010 Jan 6.<<BR>> http://bioinformatics.oxfordjournals.org/content/26/4/445.abstract * Functional genomics complements quantitative genetics in identifying disease-gene associations.<<BR>> Guan Y, Ackert-Bicknell CL, Kell B, Troyanskaya OG, Hibbs MA.<<BR>> PLoS Comput Biol. 2010 Nov 11;6(11):e1000991<<BR>> http://www.ncbi.nlm.nih.gov/pubmed/21085640 * Pathway-based analysis using reduced gene subsets in genome-wide association studies <<BR>> http://www.biomedcentral.com/1471-2105/12/17/abstract |
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
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).
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/20360734Bioinformatics 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.abstractFunctional 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/21085640Pathway-based analysis using reduced gene subsets in genome-wide association studies
http://www.biomedcentral.com/1471-2105/12/17/abstract