Abstract
A number of difficulties exist when studying long noncoding RNAs (lncRNAs) from a biological standpoint. As it is uncertain what percentage of human lncRNAs play meaningful roles in biology or consists of transcriptional artifacts, one prominent challenge is to decide which lncRNAs to study out of a potential 70,000 putative lncRNA genes. Integration of GWAS and eQTL signals has led to the identification of functional genes for disease susceptibility (Barbeira et al., Nat Commun 9(1):1825, 2018). In this chapter we describe a protocol for building bioinformatic evidence for lncRNA and trait/disease association.
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References
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Acknowledgments
This study was funded by NHLBI Division of Intramural Research funds to HC (1ZIAHL006103, 1ZIAHL006159).
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Chen, Y., Li, P., Cao, H. (2021). A Bioinformatic Pipeline to Integrate GWAS and eQTL Datasets to Identify Disease Relevant Human Long Noncoding RNAs. In: Cao, H. (eds) Functional Analysis of Long Non-Coding RNAs. Methods in Molecular Biology, vol 2254. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1158-6_7
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DOI: https://doi.org/10.1007/978-1-0716-1158-6_7
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1157-9
Online ISBN: 978-1-0716-1158-6
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