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What are the next steps in understanding the role of the genome in IBD?

What are the next steps in understanding the role of the genome in IBD?. Judy H. Cho Icahn School of Medicine in New York Dec 2013. Concepts. Common variants and systems biology Less common variants and direct therapeutic targeting Functional biomarkers and Mendelian randomization .

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What are the next steps in understanding the role of the genome in IBD?

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  1. What are the next steps in understanding the role of the genome in IBD? Judy H. Cho Icahn School of Medicine in New York Dec 2013

  2. Concepts • Common variants and systems biology • Less common variants and direct therapeutic targeting • Functional biomarkers and Mendelian randomization

  3. Concepts • Common variants and systems biology • Less common variants and direct therapeutic targeting • Functional biomarkers and Mendelian randomization

  4. Population differences: principal components East Asian African European ancestry Inga Peter Itsik Pe’er Plos Genetics 2012

  5. Ashkenazi Jews with similar association directions, but higher absolute risk allele frequencies • Prediction: Risk alleles should be higher in Ashkenazim vs. non-Jewish risk alleles • 57 CD SNPs tested (Kenny et al., Plos Genetics 2012) • 54/57 loci (95%) : risk allele same in AJ and NJ (same direction of effect) • 36/54 loci (67%): higher risk allele frequency in AJ • Most recently: of 116 CD SNPs tested-- • 94/116 (81%) risk allele same in AJ and NJ (same direction of effect) • 63/94 loci (67%, p=0.0012) higher risk allele frequency in AJ vs. NJ • Hypothesis: polygenic adaptation—positive selection at many loci simultaneously in AJs

  6. Polygenic adaptation • “Polygenic adaptation: occurs by simultaneous selection on variants at many loci (perhaps tens or hundreds or more)…..due to small frequency shifts of many alleles….. • To make real progress on these problems will require much greater integration of selection studies with biological information.” Modeling the relevant selection factors Prichard J.K. et al., Current Biology 2010

  7. Improved models of chronic mycobacterial infection of macrophages • Macrophage differentiation • Growth factor: GMCSF—control lost with MCSF • Reduced oxygen concentation • TNF during 7d differentiation • Priming with IFNg--premature introduction of IFNg impairs macrophage survival 2 3 1 4 5 Count CFUs IFN-γ Priming BCG Infection Plating MΦ Differentiation Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 33 Vogt and Nathan. J. Clin. Invest.121, 3889–3901 (2011).

  8. Trend toward greater fraction of BCG killed in AJ vs. non-Jewish EA cases and controls CD controls Monica Bowen

  9. Bayesian network analysis: drivers and hubs NOD2 IL10 HCK ciseQTL Eric Schadt

  10. In vitro differentiation of M1/M2 macrophages recreates gene cluster observed in omental adipose tissue Omental adipose tissue co-expression subnetwork Peripheral blood derived monocytes M1 (IFNg)/M2(IL4)

  11. Regulation of hub expression by TNF • Hubs (squares) regulate gene expression of many genes around them • Hubs near NOD2: • Regulate cellular morphology & cytoskeleton: WAS, AIF1, NCKAP1L • Down-regulated by TNF • 2° neighbors: 6.0-fold • 3 ° neighbors: 2.7-fold • More distant: 1.6 fold Nature 2012; Supplementary info—cytoscape file

  12. Concepts • Common variants and systems biology • Less common variants and direct therapeutic targeting • Functional biomarkers and Mendelian randomization

  13. AJ-based custom exome chip study • Sequenced 50 AJ CD cases added unique variants to base exome chip • Genotyped: 1,477 AJ cases and 2,614 controls • Goal: to identify rare variants associated to IBD in AJs Ken Hui Dermot McGovern Inga Peter

  14. Value of uncommon, loss-of-function protective alleles in precisely defining therapeutic targets • PCSK9, LDL cholesterol & coronary artery disease • IL23R (Arg381Gln) in IBD/psoriasis/ankylosingspondylitis Using genetics directly to identify new drug targets Cohen JC, N Engl J Med 2006

  15. Concepts • Common variants and systems biology • Less common variants and direct therapeutic targeting • Functional biomarkers and Mendelian randomization

  16. Mendelian randomization & treating (intermediate) biomarkers • Coronary artery disease & lipid profiles • High LDL correlates with CAD risk. • Lowering LDL decreases CAD • Low HDL correlates with CAD risk • Increasing HDL does NOT decrease CAD risk • Genetic markers modulating biomarkers • 13 genetic markers associated with LDL • 14 genetic markers associated with HDL Hingorini, Lancet 2005 Voight, Lancet 2012

  17. Treat LDL, not HDL to lower CAD risk • Relevance to IBD?? • Needs: biomarker of relevance in large numbers; genetic factors that modulate variability. Vitamin D? Anti-GMCSF Ab? fecal calprotectin? Key transcript levels? Voight, Lancet 2012

  18. More broadly… • Better, faster, cheaper • Leveraging existing resources—archived blood specimens, pathology specimens, EMRs • Massive numbers • Central power of genetic approaches: primary drivers of disease pathogenesis

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