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Gene-set analysis

Gene-set analysis. Danielle Posthuma & Christiaan de Leeuw Dept. Complex Trait Genetics, VU University Amsterdam //danielle/2017/PW_dp.ppt. Boulder, TC31, March 8 2017. SNP associations. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. SNP. Gene. Gene.

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Gene-set analysis

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  1. Gene-set analysis Danielle Posthuma & Christiaan de Leeuw Dept. Complex Trait Genetics, VU University Amsterdam //danielle/2017/PW_dp.ppt Boulder, TC31, March 8 2017

  2. SNP associations SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Gene Gene Gene Gene Gene Gene Function Function Function Function Function Function SNP

  3. SNP associations SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Gene Gene Gene Gene Gene Gene Are all associated SNPs randomly distributed or do they cluster in genes?

  4. SNP associations SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Gene Gene Are all associated SNPs randomly distributed or do they cluster in genes?

  5. SNP associations SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Gene Gene Gene Gene Gene Gene Function Function Function Function Function Function Do all implicated genes have different functions or are they functionally related?

  6. SNP associations SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP SNP Gene Gene Gene Gene Gene Gene Function Function Do all implicated genes have different functions or are they functionally related?

  7. Testing for functional clustering of SNP associations Single SNP analysis - GWAS - single (candidate) SNPs SNP-set analysis with gene as unit of analysis - whole genome - candidate gene Gene-based analysis SNP-set analysis with sets of genes as unit of analysis - targeted gene-sets/pathways - all known gene-sets/pathways Gene-set analysis

  8. Testing for functional clustering of SNP associations Single SNP analysis Gene-based analysis Using quantitative characteristics of genes e.g. expression levels or probability of being a member of a gene-set Gene-property analysis Gene-set analysis

  9. Gene based analysis • Instead of testing single SNPs and annotating GWAS-significant ones to genes, we test for the joint association effect of all SNPs in a gene, taking into account LD (correlation between SNPs) • No single SNP needs to reach genome-wide significance, yet if multiple SNPs in the same gene have a lower P-value than expected under the null, the gene-based test can results in low P

  10. Gene Manhattan plot SNP Manhattan plot

  11. Gene based analysis Unit of analysis is the gene • Pro’s: • reduce multiple testing (from 2.5M SNPs to 23k genes) • accounts for heterogeneity in gene • Immediate gene-level interpretation • Cons: • disregards regulatory (often non-genic) information when based on location based annotation • Still a lot of tests

  12. Gene-set analysis Unit of analysis is a set of functionally related genes Pro’s: • Reduce multiple testing by prioritizing genes in biological pathways or in groups of (functionally) related genes • Increases statistical power • Deals with genic heterogeneity • Provides immediate biological insight

  13. Gene-set analysis Cons • Crucial to select reliable sets of genes! • Different levels of information • Different quality of information

  14. Choosing gene-sets Gene-sets can be based on e.g. • protein-protein interaction • co-expression • transcription regulatory network • biological pathway Use public or commercial databases: e.g. KEGG, Gene Ontolog, Ingenuit, Biocart, String database, Human Protein Interaction database Or: Create manually, expert curated lists

  15. Online databases vs. manual Information in online databases tends to be • somewhat biased • not all genes included, disease genes tend to be investigated more often • genes that are investigated more often will have more interactions • not always reliable • interactions often not validated, sometimes only predicted. If experimentally seen, unknown how reliable that experiment was

  16. Statistical issues in gene-set analyses • Self-contained vs. competitive tests • Different statistical algorithms test different alternative hypotheses • Different statistical algorithms have different sensitivity to LD, ngenes, nSNPs, background h2

  17. Self-contained vs. competitive tests Null hypothesis: Self-contained: H0: The gene-sets are not associated with the trait Competitive: H0: The genes in the gene-set are not more strongly associated with the trait than the genes not in the gene-set

  18. Why use competitive tests • Polygenic traits influenced by thousands of SNPs in hundreds of genes • Very likely that many combinations (i.e. gene-sets) of causal genes are significantly related • Competitive tests define which combinations are biologically most interpretable

  19. Polygenicity and number of significant gene-sets in self-contained versus competitive testing De Leeuw, Neale, Heskes, Posthuma. Nat Rev Genet, 2016 For self-contained methods, rates increase with heritability, whereas they are constant for competitive methods. Rates are deflated for the binomial and hypergeometric methods because of their discrete test statistic.

  20. Different statistical algorithms test different alternative hypotheses

  21. Different algorithms: LD & Ngenes De Leeuw, Neale, Heskes, Posthuma. Nat Rev Genet, 2016

  22. Gene-set analysis: Practical

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