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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.

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Presentation on theme: "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."— Presentation transcript:

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 1.Common variants and systems biology 2.Less common variants and direct therapeutic targeting 3.Functional biomarkers and Mendelian randomization

3 Concepts 1.Common variants and systems biology 2.Less common variants and direct therapeutic targeting 3.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.” Prichard J.K. et al., Current Biology 2010 Modeling the relevant selection factors

7 Improved models of chronic mycobacterial infection of macrophages MΦ Differentiation IFN-γ PrimingBCG InfectionPlating Count CFUs Day Vogt and Nathan. J. Clin. Invest. 121, 3889–3901 (2011). 1.Macrophage differentiation  Growth factor: GMCSF—control lost with MCSF  Reduced oxygen concentation  TNF during 7d differentiation 2.Priming with IFN  -- premature introduction of IFN  impairs macrophage survival

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

9 Bayesian network analysis: drivers and hubs cis eQTL HCK IL10 NOD2 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 (IFN  )/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 1.Common variants and systems biology 2.Less common variants and direct therapeutic targeting 3.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/ankylosing spondylitis Cohen JC, N Engl J Med 2006 Using genetics directly to identify new drug targets

15 Concepts 1.Common variants and systems biology 2.Less common variants and direct therapeutic targeting 3.Functional biomarkers and Mendelian randomization

16  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 Mendelian randomization & treating (intermediate) biomarkers

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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