From genotypes and molecular profiling phenotypes to gene networks associated with disease

Transcription

1 From genotypes and molecular profiling phenotypes to gene networks associated with disease Eric Schadt, Ph.D. Research Genetics Rosetta Inpharmatics/Merck Research Labs 10 May 2005

2 An Integrative Genomics Approach to Discovery Strain 1 X Strain 2 F1 X F1 F2 Comprehensive set of human and mouse arrays White adipose Muscle Liver Brain White adipose Muscle 25k Disease-specific gene networks: 1) identify pathways for disease, 2) key targets, 3) prioritize targets Lots and lots of analysis G49 G50 G1 G2 G52 G3 G53 G8 G4 G48 G51 G5 G47 G7 G31 G71 G6 G54 G56 G72 G9 G46 G45 G70 G13 G10 G57 G69 G55 G11 G32 G12 G30 G44 G67 G68 G14 G33 G43 ATHERO G17 OBESITY G42 G66 G15 G16 G34 G58 G65 G41 G40 G64 G63 G18 G20 G27 G19 G35 G29 G26 G38 DIABETES G62 G21 G36 G24 G22 G28 G37 G59 G61 G25 G23 G39 G60

3 Our Goal: Elucidate networks associated with disease G1 G2 G3 G5 G4 G8 G7 G6 G9 G13 G10 G11 G12 G30 G14 G17 G15 G16 G18 G19 G20 G21 G24 G22 G23 G49 G50 G52 G53 G48 G51 G47 G31 G54 G56 G46 G45 G57 G55 G32 G44 OBESITY G27 G33 G43 ATHERO G42 G34 G58 G41 G40 G35 G29 G26 G38 G36 DIABETES G28 G37 G59 G25 G39 G60 Causal for Obesity Reactive to Obesity Indep. of Obesity G71 G72 G70 G69 G67 G68 G66 G65 G64 G63 G62 G61

4 Embracing Complexity Solving the puzzle of complex diseases, from obesity to cancer, will require holistic understanding of the interplay between factors such as genetics, diet, infectious agents, environment, behavior, and social structures., Elias Zerhouni, The NIH Roadmap, Science 2003, 302: Slide compliments of Gary Churchill

5 Gene expression coupled with genetics to reconstruct directed networks F0 F1 Obesity resistant X Obesity susceptible Gene expression correlations eqtl Phenotype cqtl Genotype F2 Each F2 mouse is genotyped Each F2 mouse is phenotyped (fat pad mass, insulin, lesion, etc) Tissues such as adipose, liver, muscle and brain are harvested and gene expression profiled

6 More sophisticated mouse pedigree: UNL M16 X ICR Cross 24 outbred founders (12 families) M16 mice generated from selective breeding for rapid weight gain Males develop diabetes-like complications

7 K-Means Cluster Highlights Multiple Patterns of Expression Enriched for Metabolic Pathways of Interest Electron transport, steroid metabolism, fatty acid metabolism Serine peptidase activity Defense Response Microsome, vesicular fraction Liver expression profiles for 530 mice over 1500 genes

8 Causal Boundary (Genetic Effects) Reactive Boundary L1 L2 L3 L4 L5 CR1 CR2 CR3 CR4 OBESITY RR1 RR2 RR3 RR4 HEART DISEASE Environmental Effects E1 E2 Associated causal pattern of expression Associated reactive pattern of expression

9 Clusters of genes enriched for eqtl at the chr 11 obesity linkage region and enriched for metabolic pathways Lod Score eqtl Cluster Chromosome 11 (cm) Cluster enriched for electron transport, steroid metabolism, fatty acid metoblism (P-values for enrichment < 1.0E-6

10 Co-Localization of Clinical and Expression QTL Enriched for Metabolic Pathways Clinical Trait QTL Cluster of eqtl co-localized with cqtl Region significantly enriched for genes that are 1) correlated with fat mass and glucose, and 2) LOD > genes on array fall in this region No genes with strong cis eqtl and expression correlated with clinical traits identified Several interesting genes linked to this region Lod Score Gene Expression QTL Chromosome 11 (cm) Riken cdna Clone Cyp2c39 (electron transport) PPARGC1 (regulation of fatty acid ox.) Hsd3b1 (steroid metabolism) Cyp39a1 (electron transport) All eqtl here are trans activing; 750 genes supported in this linkage region; no genes in this region with proximal ( cis ) eqtl are significantly correlated with fat mass and glucose traits

11 Refining the definition of disease is critical (SUBTYPES!) No objective, unbiased method systematically used to identify subtypes of disease Supervised methods are unrealistic for many disease areas such as obesity Different subtypes not known, so starting in a completely blinded fashion The power to identify key drivers of disease can be very significantly enhanced by the identification of subtypes Different pathways underlie different subtypes of disease Different pathways associated with disease give rise to different patterns of expression Perturbations specific to different pathways will define different subtypes of disease Implications for treatment of disease and running clinical trials

12 QTL mapping of genes for obesity related traits in a standard F2 intercross DBA/2J X C57BL6/J FI Hybrids X F2 Intercross Mice All female, put on high-fat, atherogenic diet at 12 months of age for 4 months

13 Can the integration of molecular profiling and DNA variation data provide an efficient way to stratify disease populations into subtypes? Integrating genotypic and molecular profiling data Lod Score Full F2 Set High FPM Group 1 + Low FPM Group High FPM Group 2 + Low FPM Group Chromosome 2 (cm) Obesity subtype 1 Obesity subtype 2 Obesity subtype 3 Extreme lean Extreme obese

14 Expression Profiling Enables Definition of Disease Subtypes in BXD F2 Cross 6 Group 1 4 Lod Score Most lean Most obese 2 F2 Frequency DBA C57BL/6J F2 intercross Fat Mass Full set Group 2 0 RNA: Chromosome 19 (cm) 2 4 Full set Group 1 0 Mouse: Low Fat Mass Group Group 2 Lod Score High Fat Mass Group 2 Full F2 Set High FPM Group 1 + Low FPM Group High FPM Group 2 + Low FPM Group 6 High Fat Mass Group 1 Nature 422: Chromosome 2 (cm)

15 Inferring Causal Associations between Molecular and Clinical Phentoypes and Reconstructing Disease Networks Is Doable QTL Source of systematic, multifactorial perturbations in a complex setting where diseases manifest themselves Segregating populations afford the perfect setting in which to assess impact of these perturbations Ordering of traits in this setting is possible Systematic reordering of traits allows for reconstruction of networks

16 Ingredients for inferring causality Perturbations with a causal anchor KOs/transgenics present a known perturbation (causal anchor) where response can be studied Natural variation in a segregating population provides the same type of causal anchor (ability to identify DNA variations associated with response): DNA Supporting Gene X Central Dogma of Biology AACAGTT AACGGTT Variation in DNA leads to variation in mrna High expression, alt splicing, codon change, etc. Low expression, no alt. splicing, no codon change, etc. Variation in mrna leads to variation in protein, which in turn can lead to disease

17 Does the cis perturbation bring to light genes we know are regulated by the associated gene? Lod Score PTTG1 Mylc2pl (Chr 5) Gene with BTB/POZ Domain (Chr X) EPAS1 (Chr 17) Wnt8b (Chr 10) Pou3f2 (Chr 4) BC (Chr 11) Physical location of PTTG Chromosome 11 (cm)

18 Expression Perturbation in the Chromosome 11 PTTG1 Gene B6 Allele Expression level of PTTG1 (Mlratio) x Genotypic separation is significant Lod Score = 217 C3H Allele -1.0 Genotype at the PTTG1 Locus x2.1

19 Chromosome 11 PTTG1 locus driving expression of a number of genes not physically linked to chromosome Name Chr Location MB loc Lod Score Pttg BC Mylc2pl C13Rik X Epas M22Rik V3R Adamdec Wnt8b Pou3f Gbif KIAA The relationships between PTTG1 and these genes were only detected in the brain, even though PTTG1 is expressed in other profiled tissues (adipose and liver); this indicates the importance of observing the interactions in the right context (under the right environmental conditions) PTTG1 regulate cell proliferation C13 Transcription regulation Pou3f2 Transcription factor activity Epas1 Angiogenesis Wnt8b Signal transducer activity Adamdec1 Zinc ion binding

20 A) Fragment of Chr 1 Genetic Map D1Mit16 88 cm PPOX eqtl Peak (95cM) IFI203 eqtl Peak (98 cm) D1Mit cm Fragment of Chr 1 Physical Map 151 MB 171.4MB (PPOX) 174.0MB (IFI203) 191 MB B) C) Mlratio for NM_ (IFI203) Absolute Value of Correlation Coefficient Mlratio for NM_ (PPOX) cm Distance Between cis eqtl

21 The distribution of gene-gene correlations varies significantly as we consider genes with strong cis-acting eqtl A) B) C) D) Scenario A): Randomly permuted data Scenario B): Upper 10 th percentile of correlations computed between genes with strong cis-acting eqtl and all other genes Scenario C): Upper 10 th percentile of correlations computed between a randomly chosen set of genes and all other genes Scenario D): Upper 10 th percentile of correlations computed between a randomly chosen set of transcriptionally active genes and all other genes

22 An extreme example of why the correlation distributions vary (Simpson s Paradox) mlratios for JTP gene R = 0.53 R = 0.57 R = 0.44 R = B6 Homozygote Heterozygote DBA Homozygote mlratios for Pik4cb gene

23 R = Residuals for Pik4cb gene Residuals for JTP gene

24 LD issues are also prevalent in the population of inbred strains (used to carry out in silico mapping studies) VI V IV Markers ordered along chr 8 and 14 I II III Chromosome 8 Chromosome 14 Markers ordered along chr 8 and 14 Genes near associated markers are highly enriched for fertility pathways

25 Can human studies help with this issue? Cis-eQTL for MFN2 SNPs from HapMap with MAF > 2% 3,50 3,00 2,50 LOD 2,00 1,50 1,00 0,50 0,00 MFN2 (25 cm) 55 MFN2 expression : association analysis 50 -log P Mb 11.9Mb Genotyped: 4 microsatellites and 19 SNPs MFN2 locus Hundreds of SNP markers over the genome

26 Causal Boundary (Genetic Effects) Reactive Boundary L1 L2 L3 L4 L5 CR1 CR2 CR3 CR4 OBESITY RR1 RR2 RR3 RR4 HEART DISEASE Environmental Effects E1 E2 Associated causal pattern of expression Associated reactive pattern of expression

27 Distinguishing Causal from Reactive Genes Causative Model Reactive Model Independent Model ob/ob leptin obesity db/db obesity leptin L T2 T1 L T2 T1 L (,, ) = ( ) ( ) P( L, T1, T2) = P( T2 L) P( T1 T2) P LT T P T L P T T A vy L: L DNA Locus controlling RNA levels and/or clinical traits T1 R: Quantitative trait 1 C: Quantitative trait 2 T eumelanin RNAs T1 T2 obesity (,, ) = ( ) ( ) P L T T P T L P T L

28 Overlaps and phenotypic correlation of novel obesity gene and obesity traits liver, MMT , Tumor growth factor beta receptor Corr_all = 0.56 Corr_lodth = 0.72 Lodth = xfat/weight MMT LOD Score liver, MMT , Tumor growth factor beta receptor 2 Corr_all = 0.81 Corr_lodth = 0.70 Lodth = 1.00 Chromosome 5 (cm) Overlaps and phenotypic correlation of novel obesity gene and weight weight(g) MMT LOD Score Chromosome 1 (cm) Total of 3 overlaps exist for each trait and the gene-expression QTLs

29 Tgfbr2 wildtype vs. KO pvalue =

30 LF1 HF1 LF2 HF2 LF3 HF3 Mixed LF4 Patterns of expression associated with disease G1 G2 G3 G4 G5 G6 G7 G8 Apply novel algorithms to reconstruct gene networks Variations in DNA cause disease and lead to changes in expression traits (QTL Perturbation) Lod Score All Female Mice Subtype HF1 (LF1) Female Mice All non-subtype HF1 Female Mice Cholesterol Chromosome 1 Linkages Expression network associated with disease

31 Mapping disease specific events onto networks to separate upstream and downstream events Disease specific change Datasets Genotypes Gene Expression Subtype classifiers Effector Disease specific signaling Biomarkers Targets? Disease specific effects

32 Bayesian Network of Gene Interactions from Expression and Genetics Data Centered on the Novel Obesity Gene Target for niacin Diabetes association Malic enzyme Obesity Gene protein tyrosine phosphatase, non-receptor type 8 6-phosphofructo-2-kinase/ fructose-2,6-biphosphatase 1 solute carrier family 2 (facilitated glucose transporter), member 4 Phosphoenolpyruvate carboxykinase 1

33 Zoom in on target and use network to predict responder genes Identify druggable target of interest in the network Overlay single gene perturbation data (e.g., sirna) onto the network Genes in the single gene perturbation expression signature Significant overlap between the predicted response in the network and single gene perturbation signature validates the network structure and places perturbation signature in an appropriate context.

34 Segregating Mouse Populations X Disease Tissue Disease-specific Network Genetics allows for increased power to reconstruct networks Normal Tissue Network for Normal Tissue Recall Given at 80% Precision With Genetics No Genetics Recall 300%+ Improvement Cross Size Genetics has potential to dramatically improve the accuracy of the reconstructed network (greater than 300% improvement in recall with genetics vs. without)

35 Connectivity for different subtypes of obesity highlight shifts in the network architecture that underlie the differences Highly interconnected gene module in the transcriptional networkx Hub genes that serve to interconnect highly interconnected modules in the transcriptional, scale free network Obesity subtype 1 Obesity subtype 2

36 Obesity Subtype 2 Modules of highly connected genes correspond to interconnected subnetworks in the network Hub nodes are highly connected nodes that may link two or more modules together, transmitting information between them

37 Key linkers in the network can then be used to define disease/normal in the population, identify key targets, validate targets, and prioritize targets Activity of key genes in the network can help refine complex trait subtypes Responders Non-Responders

38 Research Genetics Acknowledgements for This Present Work Research Genetics John Lamb, Steve Edwards, Debraj Guhathukurta, Jun Zhu, Pek Lum, Amy Leonardson, Haoyuan Zhu UCLA Jake Lusis, Tom Drake, Rich Davis, Margarete Mehrabian, Susanna Wang, Sudheer Doss, Hooman Allayee, Atila van Nas, Rebecca Mar, Jason Aten, Steve Horvath UNL Daniel Pomp MRL Molecular Profiling Alan Sachs, Stephen Friend MRL Obesity/Diabetes Marc Reitman, Charles Rosenblum, Su Chen, David Moller MRL Atherosclerosis Gerry Waters, Carl Sparrow, Rolf Thieringer, Marty Springer, and Sam Wright MRL Banyu MitsuAraki