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Bloomberg School BIO 624 - disease

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Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24Slide 25Slide 26Slide 27Slide 28Slide 29Slide 30Slide 31Slide 32Slide 33An integrative genomics approach to infer causal associations between gene expression and diseaseSchadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., Guhathakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., Lum, P. Y., Leonardson, A., Thieringer, R., Metzger, J. M., Yang, L., Castle, J., Zhu, H., Kash, S. F., Drake, T. A., Sachs, A., and Lusis, A. J.Nature Genetics (37): 710-717 Speaker: Yen-Yi Ho Advisor: Giovanni ParmigianiDepartment of Biostatistics, Johns Hopkins UniversityOutline•Introduction–Background & Definitions–Scientific Questions•Previous eQTL Studies–Gene Expression Data in Humans–Statistical Analytic Approaches–Results•Schadt et al. 2005: An Integrative Approach–Causality Models–Application: Gene Expression in BXD Mice–Results from Application•Discussion of New ApproachQTL (Quantitative Trait Locus) Genetic locus (QTL; L), Disease (D) •More than 1000 monogenic Mendelian diseases controlling genes have been identified using traditional gene mapping approach.•Multiple genes, environmental factors, and interactions have limited the successes in human complex traits (such as cancer, diabetes, asthma). L DIntroductionmRNADNAGenotype Data(SNP polymorphism)Gene expression DataExpression QTL (eQTL) Goal : Identify genomic locations where genotype significantly affects gene expression.We have more information…Cis-, trans- , master trans- eQTLscis- eQTLtrans- eQTLmastertrans- eQTL1. 1 (B) = cis2. 2 (A) = cis controlled by 1 (B)3. No controls4. 4(D) = cis controlled by 3 (F)5. Not a cis, controlled by 1 2 4 36. Not a cis, controlled by allConstructing regulatory networks (hypothetical example) Genetic locus ExpressionJansen, R.C. & Nap, J.P. (2001)Trends Genet, 2001, 17, 388-391Genetic locus ExpressionScientific Questions•What is the variation and heritability of gene expression?•Are there associations between genetic loci and target gene expression?•What is the proportion of cis-/trans-eQTLs?•How do we verify of cis-?•Are there any master trans-eQTLs?•Annotation and functional categories do cis-, trans- and master trans-eQTLs (KEGG, GO,… ).•Transcript abundance may act as intermediate phenotype between genetic loci and the clinical phenotype. Secondary goal•Incorporate information of genotype, expression, and clinical traits together to construct regulatory networks and to improve understanding of disease etiologies. Scientific questions and goalsData•They all used lymphoblastoid cell line from CEPH families to measure expression. Differences•1. Selected different expression traits•2. Platforms to measure expression / preprocess•3. SNP markers density•4. Different statistical approaches.The dataStatistical methods of human eQTL mapping studyLinkage•Nonparametric linkage analysis1. Sib-pair analysis for quantitative trait (ASP)2. Variance component analysis (VC)Association (Linkage disequilibrium)•Family-based association analysis (QTDT)•Population-based association analysis (GWA)Generally, the resolution of association approach would be greater than linkage.Comparison of resolution between linkage and association analysis Literature ReviewGenes with between / within individual variation > 1Literature reviewHeritabilityNoneLiterature ReviewLiterature Review•Hit rate: The proportion of expression traits significantly linked to eQTLs (range from 0.8-4%)•Proportion of cis-eQTL is about 30 %•2 master trans-eQTLs were identifiedeQTL findings from previous studiesLiterature ReviewMaster trans-eQTLsLiterature Review14q32 20q13Genetic locus ExpressionAn Integrative Approach: Schadt et al., Nature Genetics, 2005•Models for causality–Causal Model –Reactive Model–Independent Model LmRNADiseaseLmRNADiseaseL DiseasemRNAA integrative approachNew approach•Causal Model–Joint Probability–Likelihood 2|2( )1( | ) exp{ }2R LRRrL r Lmsps-= -( , , ) ( ) ( | ) ( | )p L R D p L p R L p D R=311( | 1) ( ) ( | ) ( | )Nj i j i ijiL M p L L r L L d rq===�� L: Genotype R: mRNA level D: DiseaseL mRNADiseaseM1 Likelihood( | , )= ( | )p D R L p D R2|2||( )1( | ) exp{ }2D RD RD RdL d rmsps-= -•Reactive Model –Joint probability–Likelihood( , , ) ( ) ( | ) ( | )P L R D P L P D L P R D=311( | 2) ( ) ( | ) ( | )Nj i j i ijiL M p L L d L L r dq===��2|2||( )1( | ) exp{ }2R DR DR DrL r Dmsp s-= -LmRNADiseaseM2 Likelihood L: Genotype R: mRNA level D: Disease221 ( )( | ) exp{ }2DDDdL d Lmsps-= -( |D, )= ( |D)p R L p R•Independent Model –Joint Probability–Likelihood( , , ) ( ) ( | ) ( | , )P L R D P L P R L P D R L=311( | 3) ( ) ( | ) ( | , )Nj i j i i jjiL M p L L r L L d r Lq===��2|22( )1( | ) exp{ }2R LRRrL r Lmsps-= - L : Genotype R: mRNA level D: DiseaseL DiseasemRNAM3 Likelihood2|2||( )1( | , ) exp{ }2D RLD RD RdL d R Lmsps-= -Model Selection•Likelihood-based Causality Model Selection (LCMS)–Calculating the Likelihood based on the data. –The model best supported by the data : smallest AIC (Akaike Information Criterion) ˆAIC=-2ln ( ) 2L pq +Simulation studyi iT Lb e= � +The model with an AIC significantly smaller than the AIC’s of the competing models was noted. 12,L TR1 22,T TR22,L TRL T1Application to BXD mice dataThe dataBXD mice: F2 offspring from C57BL/6J (B6) and DBA/2J (DBA). •C57BL/6J: ob mutation in the C57BL/6J mouse background (B6-ob/ob) causes obesity, but only mild and transient diabetes (Coleman and Hummel, 1973).•DBA/2J: mice show a low susceptibility to developing atherosclerotic aortic lesions Gene expression•Liver extracted at 16 months of age•23,574 gene expression measured using Agilent arraysGenetic loci•139 autosomal genetic loci (microsatellite markers, 13 cM)Disease•Omental fat pad mass (OFPM) traitNew approachFilteringL mRNADisease•Identify 4 candidate regions for OFPM traitschr1 at 95cM, chr6 at 43 cM, chr9 at 8cM, chr19 at 28cM.•Expression traits significantly correlated with OFPM440 intermediate expression traits were selected (P<0.001)•Expression trait with significant linkage eQTLs at the candidate regions. 113 expression trait and 267 eQTLs are identified•Perform LCM model selections for the 113 expression traits and ranked the expression traits by


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