RNA Sequencing in the Framingham Heart Study Third Generation Cohort Exam 2

Hypertension Clinical Trial

Official title:

An RNA Sequencing Study in the Framingham Heart Study Third Generation Cohort Exam 2

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03225183
• Other study ID #: 999917133
• Secondary ID: 17-H-N133
• Status: Completed
• Start date: July 14, 2017
• Est. completion date: June 17, 2019

Study information

• Verified date: June 2022
• Source: National Institutes of Health Clinical Center (CC)
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Background:

The Framingham Heart Study (FHS) was initiated by the U.S Public Health Service in 1948 and turned over to the newly established National Heart Institute in 1951. The FHS is now jointly led by the National Heart, Lung, and Blood Institute and Boston University. The FHS currently studies risk factors, and the genetics of heart and blood vessel disease, and other health conditions in three generations of study participants. Scientists want to use the data collected from this study to do more research. They want to use a technique that determines the sequence of ribonucleic acid (RNA) molecules.

Objective:

To study genes related to certain diseases and health conditions. These include heart and blood vessel diseases, lung and blood diseases, stroke, memory loss, and cancer.

Eligibility:

People in the FHS Third Generation cohort who already attended exam 2.

Design:

Researchers will study samples that have already been collected in the FHS. There will be no active examination or burden to participants. During FHS visits, participants gave blood samples. They gave permission for the blood to be used for genetic research. RNA will be generated from the samples. They will be given a new ID separate from any personal data. They will be stored in a secure FHS lab. The samples will be analyzed. Only certified researchers can access them.

No study participants will be contacted in relation to this project.

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Description:

RNA sequencing (RNA-seq) is a powerful tool to evaluate the transcriptome with incredible depth and clarity. As compared to gene expression arrays, RNA-seq allows the identification and quantification of a larger set of known transcripts (including long non-coding RNAs [lncRNAs]), novel transcripts, alternative splicing events, and allele-specific expression (including parent-of-origin allele-specific expression); all with a vastly higher signal-to-noise ratio compared to gene expression profiling via microarrays. The relations of these transcriptomic features to health and disease in very large population studies is underexplored. It is our belief that this proposed project will identify new biomarkers of disease risk and provide insights into disease pathogenesis. The Framingham Heart Study (FHS) is uniquely suited to conduct RNA-seq because of the wealth of existing phenotype resources in conjunction with whole genome sequence (WGS) data from TOPMed and methylomic data, data and other omics data that can be leveraged at extremely low cost to maximize the impact of an investment in RNA-seq.

The advent of high-throughput RNA-seq technology has revolutionized transcriptomic profiling at an unprecedented scale, leading to the discovery of new RNA species and deepening our understanding of transcriptomic dynamics. Compared to microarray-based RNA profiling, RNA-seq is appreciated for its ability to reveal the complexity of the transcriptome, encompassing previously unknown coding and lncRNA species, novel transcribed regions, alternative splicing, allele-specific expression, and fusion genes This project proposes to build upon and extend the work conducted using gene expression arrays in the FHS by examining complex transcriptomic features that cannot be determined using microarray-based expression data.

In this proposal we focus on expression levels of protein-coding RNAs, lncRNAs, alternative splicing, and allele-specific expression. There are ~18,000 mRNA transcripts at the gene-level for protein-coding RNAs. Alternative splicing is a tightly regulated process that produces different mRNA isoforms from genes that contain multiple exons. One major application of RNA-seq is to detect even subtle differences in exon splicing. lncRNAs are non-protein coding transcripts longer than 200 nucleotides and have been implicated in many biological process. For example, some lncRNAs impact the expression of nearby protein-coding genes, some can bind to enzymes regulating transcription patterns, and other lncRNAs are precursors of small RNAs. A number of computational methods have been developed to detect alternative splicing and lncRNAs from RNA-seq data. Identification of alternative splicing and lncRNAs will be standardized across TOPMed studies and we will conduct analyses on centrally called splice data as well as lncRNAs. Allele-specific expression (ASE), which cannot be measured using microarrays, allows the differentiation between transcripts from the two haplotypes of an individual at heterozygous sites. ASE enables a more granular understanding of how a disease-related genotype affects gene expression. ASE has been linked to human disease in small sample sets but has not been examined fully in large populations. Standard

bioinformatics tools have been developed to study ASE. In addition, with TOPMed WGS data on parents from the FHS Offspring cohort, it will be possible to study parent-of-origin ASE, thus furthering our ability to dissect factors that contribute to the transgenerational inheritance of cardiometabolic disease.

In this Application, we propose to extend the investigation of transcriptomics in FHS Third Generation cohort exam 2 participants. The aims of conducting RNA-seq in the FHS Third Generation cohort mirror and extend those of our original microarray-based gene expression profiling. Specifically, we will examine the association of complex transcriptomic variation to: 1) cardiometabolic disease outcomes, 2) genetic sequence variation, and 3) multiple layers of omic data (Aims 1-3). With the proposed RNA-seq data, investigators as well as the general scientific community (via dbGaP access) will have the ability to study transcriptomics from different perspectives always leveraging existing resources to advance the scientific value of this project. To maximize the return on investment, sequencing will be performed by a designated TOPMed RNA-seq laboratory, and the aims of this project will be coordinated with other

TOPMed studies that are conducting RNA-seq.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1700
• Est. completion date: June 17, 2019
• Est. primary completion date: March 15, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 21 Years to 100 Years

Eligibility

• INCLUSION CRITERIA:

To accomplish the Aims of this project we propose to conduct RNA-seq on FHS Third Generation cohort participants with WGS as part of TOPMed. This can only be accomplished in FHS Third Generation cohort participants who attended exam 2 when PaxGene tubes were collected for RNA isolation. Therefore, we propose to conduct RNA-seq on FHS Third Generation cohort exam 2 attendees with PaxGene tubes (total n=3300) and in whom we will have direct or imputed WGS from TOPMed (n=1700).

Related Conditions & MeSH terms

• Cardiovascular Disease
• Cardiovascular Diseases
• Hypertension

Locations

Country	Name	City	State
United States	Framingham Heart Study	Framingham	Massachusetts

Sponsors (1)

Lead Sponsor	Collaborator
National Heart, Lung, and Blood Institute (NHLBI)

Country where clinical trial is conducted

United States, 

References & Publications (3)

Huan T, Esko T, Peters MJ, Pilling LC, Schramm K, Schurmann C, Chen BH, Liu C, Joehanes R, Johnson AD, Yao C, Ying SX, Courchesne P, Milani L, Raghavachari N, Wang R, Liu P, Reinmaa E, Dehghan A, Hofman A, Uitterlinden AG, Hernandez DG, Bandinelli S, Singleton A, Melzer D, Metspalu A, Carstensen M, Grallert H, Herder C, Meitinger T, Peters A, Roden M, Waldenberger M, Dörr M, Felix SB, Zeller T; International Consortium for Blood Pressure GWAS (ICBP), Vasan R, O'Donnell CJ, Munson PJ, Yang X, Prokisch H, Völker U, van Meurs JB, Ferrucci L, Levy D. A meta-analysis of gene expression signatures of blood pressure and hypertension. PLoS Genet. 2015 Mar 18;11(3):e1005035. doi: 10.1371/journal.pgen.1005035. eCollection 2015 Mar. — View Citation

Joehanes R, Johnson AD, Barb JJ, Raghavachari N, Liu P, Woodhouse KA, O'Donnell CJ, Munson PJ, Levy D. Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study. Physiol Genomics. 2012 Jan 18;44(1):59-75. doi: 10.1152/physiolgenomics.00130.2011. Epub 2011 Nov 1. — View Citation

Yao C, Chen BH, Joehanes R, Otlu B, Zhang X, Liu C, Huan T, Tastan O, Cupples LA, Meigs JB, Fox CS, Freedman JE, Courchesne P, O'Donnell CJ, Munson PJ, Keles S, Levy D. Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes. Circulation. 2015 Feb 10;131(6):536-49. doi: 10.1161/CIRCULATIONAHA.114.010696. Epub 2014 Dec 22. Erratum in: Circulation. 2015 May 12;131(19):e474. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	1. To relate transcriptomic variation to CVD and its risk factors (blood pressure, lipids, glycemia, adiposity, smoking, and alcohol), including evaluating RNAs as biomarkers of risk and establishing causation via Mendelian randomization	Will look at CVD events related to RNA sequence. a. Characterize the relation of protein-coding gene expression to CVD and its risk factors; b. Characterize the relations of lncRNAs to CVD and risk factors; c. Characterize the relations of RNA splicing variation to CVD and its risk factors; d. Characterize the relations of allele-specific expression, and parent-oforigin allele specific expression, to CVD and its risk factors	Observational
Primary	2. To determine the association of genetic sequence variation from whole genome sequencing with gene expression via RNA-seq.	Will look at CVD events related to RNA sequence and add gene expression results to analysisa. Identify genetic variants associated with expression of protein coding RNAs (eQTLs); b. Identify genetic variants associated with alternative splicing (sQTLS); c. Identify genetic variants associated with expression of lncRNAs	Observational
Primary	3. To relate complex transcriptomic variation to other blood-based omics	Will look at CVD events related to RNA sequence and add Metabolic profiling data to analysis modela. Determine the association of transcriptomic variation with DNA methylation (methylome); b. Determine the association of transcriptomic variation with circulating protein levels (proteome); c. Determine the association of transcriptomic variation with circulating metabolites (metabolome)	Observational