RNA Sequencing in the Framingham Heart Study Third Generation Cohort Exam 2
Condition: Cardiovascular Disease; Hypertension
MeSH terms: Cardiovascular Diseases
Study type: Observational

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.

      ...
    

Detailed 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.
    

Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Framingham Heart Study participants
      
Keywords: TOPMed; Blood Pressure; Long Noncoding RNA; Gene Expression; MicroRNA; Natural History