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Analyzing the Role of Genetics and Genomics in Cardiovascular Disease

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Analyzing the Role of Genetics and Genomics in Cardiovascular Disease IT'S COMPLICATED: ANALYZING THE ROLE OF GENETICS AND GENOMICS IN CARDIOVASCULAR DISEASE by JEFFREY HSU Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Adviser: Dr. Jonathan D Smith Department of Molecular Medicine CASE WESTERN RESERVE UNIVERSITY January 2014 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of: Jeffrey Hsu candidate for the Doctor of Philosophy degree*. Thomas LaFramboise Thesis Committee Chair Mina Chung David Van Wagoner David Serre Jonathan D Smith Date: 5/24/2013 We also certify that written approval has been obtained for any proprietary material contained therein. i Contents Abstract 1 Acknowledgments 3 1 Introduction 4 1.1 Heritability of cardiovascular diseases . .4 1.2 Genome wide era and complex diseases . .7 1.3 Functional genomics of disease associated loci . .8 1.4 Animal models for functional studies of CAD . 13 2 Genetic-Genomic Replication to Identify Candidate Mouse Atheroscle- rosis Modifier Genes 14 2.1 Introduction . 14 2.2 Methods . 15 2.2.1 Mouse and cell studies . 15 2.3 Results . 17 2.3.1 Atherosclerosis QTL replication in a new cross . 17 2.3.2 Protein coding differences between AKR and DBA mice resid- ing in ATH QTLs . 22 2.3.3 eQTL in bone marrow derived macrophages and endothelial cells 30 2.3.4 Macrophage eQTL replication between different crosses and dif- ferent platforms . 44 3 Transcriptome Analysis of Genes Regulated by Cholesterol Loading in Two Strains of Mouse Macrophages Associates Lysosome Pathway and ER Stress Response with Atherosclerosis Susceptibility 51 3.1 Introduction . 52 3.2 Methods . 53 3.2.1 Mice . 53 3.2.2 Total, free, and esterified cholesterol quantification . 53 3.2.3 Loading of macrophages with acetylated LDL for transcrip- tome profiling . 54 3.2.4 Isolation of total RNA from BMM cell pellets . 54 3.2.5 Hybridization and detection of gene transcripts . 55 3.2.6 Microarray data analysis . 55 ii 3.2.7 Real-Time quantitative PCR (qPCR) . 56 3.2.8 Western blot . 56 3.3 Results and Discussion . 57 3.3.1 AKR and DBA/2 macrophages respond differently to choles- terol loading . 57 3.3.2 Hierarchical clustering . 58 3.3.3 Strain differences on BMM transcriptome . 58 3.3.4 Cholesterol loading effect on BMM transcriptome . 61 3.3.5 Cholesterol loading{strain interaction effect on BMM transcrip- tome . 72 3.3.6 Validation of data by quantitative Real-Time PCR (qPCR) . 78 3.3.7 Western Blot Analysis . 79 4 Whole Genome Expression Differences in Human Left and Right Atria Ascertained by RNA-Sequencing 82 4.1 Introduction and Background . 82 4.2 Methods . 84 4.2.1 RNA-sequencing for left-right pairs . 84 4.2.2 Paried-end read analysis . 84 4.2.3 RT-PCR . 85 4.3 Results . 86 4.3.1 RNA-seq of left-right atrial appendages . 86 4.3.2 miRNA gene expression differences between the left and the right atria . 87 4.3.3 mRNA gene expression differences between the left and the right atria . 91 4.3.4 Left-right expression differences in poorly annotated transcripts 100 4.4 Discussion . 101 5 Conclusion: How to unravel complicated traits 105 5.1 Roadmap to Identification of Mouse Atherosclerosis Modifier Genes . 105 5.2 Roadmap to Identification of causal variants for AF, and their mecha- nism of action . 107 5.3 The utility of functional genomics . 108 iii List of Tables 2.1 Table 1: Aortic root lesion (log 10) QTLs in DBA/2 x AKR F2 cohort 18 2.2 Corresponding GWAS hits in human orthologous regions to mouse AthQTL . 20 2.2 Corresponding GWAS hits in human orthologous regions to mouse AthQTL . 21 2.3 Polyphen2 scores within Ath intervals. 23 2.3 Polyphen2 scores within Ath intervals. 24 2.3 Polyphen2 scores within Ath intervals. 25 2.3 Polyphen2 scores within Ath intervals. 26 2.3 Polyphen2 scores within Ath intervals. 27 2.3 Polyphen2 scores within Ath intervals. 28 2.3 Polyphen2 scores within Ath intervals. 29 2.4 Top 25 cis-eQTLs by LOD score in BMMs . 31 2.4 Top 25 cis-eQTLs by LOD score in BMMs . 32 2.5 Top 25 trans-eQTLs by LOD score in BMMs . 34 2.5 Top 25 trans-eQTLs by LOD score in BMMs . 35 2.6 Top 25 cis-eQTLs by LOD score in endothelial cells . 37 2.7 Top 25 trans-eQTLs by LOD score in endothelial cells. 38 2.7 Top 25 trans-eQTLs by LOD score in endothelial cells. 39 2.8 cis-eQTLs that are found in both ECCs and BMMs at < 5% FDR that also reside within the AthQTLs ..................... 41 2.9 trans-eQTLs that are found in both endothelial cells (EC) and bone marrow macrophages (BMM) at < 30% FDR . 42 2.9 trans-eQTLs that are found in both endothelial cells (EC) and bone marrow macrophages (BMM) at < 30% FDR . 43 2.10 Summary statistics and replication of bone marrow macrophage cis and trans-eQTLs for the prior and new crosses using the restricted set of common probe. 44 2.11 Replicated trans-eQTL between crosses at the %5 FDR level . 47 2.12 Replicated cis-eQTL within replicated Ath QTL intervals that have replicated direction of expression-lesion correlation . 49 2.12 Replicated cis-eQTL within replicated Ath QTL intervals that have replicated direction of expression-lesion correlation . 50 3.1 Top 10 differentially expressed transcripts between AKR and DBA/2 unloaded . 60 iv 3.2 Top 10 differentially expressed transcripts between AKR and DBA/2 unloaded . 63 3.3 Significantly regulated transcripts upon cholesterol loading involved in lysosome pathway, ranked by fold change (loaded/unloaded). 67 3.3 Significantly regulated transcripts upon cholesterol loading involved in lysosome pathway, ranked by fold change (loaded/unloaded). 68 3.4 Differentially expressed transcripts conserved in both experiments that reside within Ath28, Ath22 and Ath26 QTLs. 81 4.1 Expression differences of miRNAs between the left and right atria at FDR <0.08 ranked by p-value. 89 4.1 Expression differences of miRNAs between the left and right atria at FDR <0.08 ranked by p-value. 90 4.1 Expression differences of miRNAs between the left and right atria at FDR <0.08 ranked by p-value. 91 4.2 The top 20 left-right differentially expressed atrial genes ranked by p-value. 93 4.2 The top 20 left-right differentially expressed atrial genes ranked by p-value. 94 4.3 Top left-right atria differentially regulated genesets by presence of tran- scription factor binding motifs ± 2 kb from the start site of transcrip- tion of atrial expressed genes. 95 4.3 Top left-right atria differentially regulated genesets by presence of tran- scription factor binding motifs ± 2 kb from the start site of transcrip- tion of atrial expressed genes. 96 4.3 Top left-right atria differentially regulated genesets by presence of tran- scription factor binding motifs ± 2 kb from the start site of transcrip- tion of atrial expressed genes. 97 4.4 Top left-right atria differentially regulated genesets by presence of con- served miRNA motif in the 3' UTR of atrial expressed genes. 99 4.5 Novel non-Ensembl annotated transcripts that were differentially ex- pressed between the left and right atria ranked by p-value . 101 v List of Figures 1.1 cis-action on gene expression. .9 1.2 Example of a cis-eQTL . 10 1.3 GWAS SNPs linked to putative functional variant . 11 1.4 An schematic of how aellic imbalance arises. 12 2.1 Combined AthQTL ............................ 19 2.2 SNPs within probes may lead to false eQTLs . 33 2.3 Replicated cis-eQTL . 40 2.4 Combined cis-overlap . 45 2.5 Replicated trans-eQTL . 46 3.1 Hierarchical clustering analysis of 32 samples included in the study . 59 3.2 Conservation of cholesterol induced changes in macrophage gene ex- pression in two independent experiments . 64 3.3 Gene expression and validation of microarray data by quantitative real- time PCR in experiment 1 and 2 macrophages . 72 3.4 Examples of transcripts with strain-loading interaction effect in exper- iment 1 samples . 73 3.5 Examples of transcripts with strain-loading interaction effect in exper- iment 2 samples . 75 3.6 Total, esterified, and free cholesterol mass in unloaded and loaded macrophages . 77 3.7 Validation of microarray expression data . 79 4.1 Size-distribution of small-RNA reads . 87 4.2 Multidimension scaling (MDS) of gene expression of Left-Right atrial pairs . 89 4.3 HAMP and PITX2 display inverse expression pattern . 93 vi It's complicated: Analyzing the role of Genetics and Genomics in Cardiovascular Disease Abstract by JEFFREY HSU Coronary artery disease (CAD) and atrial fibrillation (AF) are two common complex diseases of the heart that both have strong genetic components. Although genome wide association studies in humans and genetic mapping studies in mouse models have identified genomic loci associated with these diseases, the functional genetic variants and the mechanisms by which they alter disease susceptibility are largely unknown. In this thesis we use mouse models and human atrial tissues to perform functional genomic studies to gain insight into these disease associated loci, with the central hypothesis that many disease associated variants function by regulating the expression of nearby genes. The major methods used in these studies are transcrip- tome profiling by expression microarrays and RNA sequencing followed by rigorous statistical analysis. Transcript expression can be considered as an intermediate phe- notype to disease susceptibility; and, the identification of genetic variants that alter transcript expression may lead to the mechanism of disease association.
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