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

1. Common variants and systems biology

2. Less common variants and direct therapeutic targeting

3. Functional biomarkers and Mendelian randomization

Concepts

1. Common variants and systems biology

2. Less common variants and direct therapeutic targeting

3. Functional biomarkers and Mendelian randomization

Population differences: principal components

East Asian

African

European ancestry

Inga PeterItsik Pe’er Plos Genetics 2012

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. NJHypothesis: polygenic adaptation—positive selection at many loci simultaneously in AJs

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

Improved models of chronic mycobacterial infection of macrophages

MΦ Differentiation IFN-γ Priming BCG Infection Plating Count CFUs1 2 3 4

Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 33

5

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 IFNg--premature introduction of IFNg impairs macrophage survival

0

10

20

30

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AJEA

Pece

ntag

e of

BCG

Kill

ed

CD controls

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

Monica Bowen

Bayesian network analysis: drivers and hubs

cis eQTL

HCK

IL10NOD2

Eric Schadt

In vitro differentiation of M1/M2 macrophages recreates gene cluster observed in omental adipose tissue

Macrophages, 0 hoursMacrophages, 6 hours IL4Macrophages, 24 hours IL4Macrophages, 6 hours IFNgMacrophages, 24 hours IFNg

NOD2HCK

IL10LGALS9RIPK2SLC11A1CARD9

Higher expressionLower expression

M2 M1

Omental adipose tissue co-expression subnetwork

Peripheral blood derived monocytes M1 (IFNg)/M2(IL4)

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

Concepts

1. Common variants and systems biology

2. Less common variants and direct therapeutic targeting

3. Functional biomarkers and Mendelian randomization

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 HuiDermot McGovernInga Peter

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

Concepts

1. Common variants and systems biology

2. Less common variants and direct therapeutic targeting

3. Functional biomarkers and Mendelian randomization

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 2005Voight, Lancet 2012

Mendelian randomization & treating (intermediate) biomarkers

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

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