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Understanding mechanisms underlying human gene expression variation with RNA sequencing

Author

Listed:
  • Joseph K. Pickrell

    (Department of Human Genetics,)

  • John C. Marioni

    (Department of Human Genetics,)

  • Athma A. Pai

    (Department of Human Genetics,)

  • Jacob F. Degner

    (Department of Human Genetics,)

  • Barbara E. Engelhardt

    (Department of Computer Science,)

  • Everlyne Nkadori

    (Department of Human Genetics,
    Howard Hughes Medical Institute,)

  • Jean-Baptiste Veyrieras

    (Department of Human Genetics,)

  • Matthew Stephens

    (Department of Human Genetics,
    The University of Chicago, Chicago 60637, USA)

  • Yoav Gilad

    (Department of Human Genetics,)

  • Jonathan K. Pritchard

    (Department of Human Genetics,
    Howard Hughes Medical Institute,)

Abstract

RNA sequencing unlocks key to gene expression There is currently much interest in the understanding of genetic mechanisms that underlie variation at the gene expression level. Two groups reporting in this issue of Nature use RNA sequencing to study global gene expression in two contrasting populations. Pickrell et al. sequenced RNA from 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals who have been extensively genotyped as part of the HapMap Project. By pooling data from all the individuals it was possible to identify many genetic determinants of variation in gene expression. Montgomery et al. characterize the mRNA fraction of RNA isolated from lymphoblastoid cell lines derived from 63 HapMap individuals of Caucasian origin. They obtain a fine-scale view of the transcriptome and identify genetic variants that affect alternative splicing.

Suggested Citation

  • Joseph K. Pickrell & John C. Marioni & Athma A. Pai & Jacob F. Degner & Barbara E. Engelhardt & Everlyne Nkadori & Jean-Baptiste Veyrieras & Matthew Stephens & Yoav Gilad & Jonathan K. Pritchard, 2010. "Understanding mechanisms underlying human gene expression variation with RNA sequencing," Nature, Nature, vol. 464(7289), pages 768-772, April.
    • Handle: RePEc:nat:nature:v:464:y:2010:i:7289:d:10.1038_nature08872
    DOI: 10.1038/nature08872
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    Cited by:

    1. Urmo Võsa & Tõnu Esko & Silva Kasela & Tarmo Annilo, 2015. "Altered Gene Expression Associated with microRNA Binding Site Polymorphisms," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-24, October.
    2. Asta Laiho & Laura L Elo, 2014. "A Note on an Exon-Based Strategy to Identify Differentially Expressed Genes in RNA-Seq Experiments," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-12, December.
    3. Jean Francois Lefebvre & Emilio Vello & Bing Ge & Stephen B Montgomery & Emmanouil T Dermitzakis & Tomi Pastinen & Damian Labuda, 2012. "Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-15, June.
    4. Pingting Ying & Can Chen & Zequn Lu & Shuoni Chen & Ming Zhang & Yimin Cai & Fuwei Zhang & Jinyu Huang & Linyun Fan & Caibo Ning & Yanmin Li & Wenzhuo Wang & Hui Geng & Yizhuo Liu & Wen Tian & Zhiyong, 2023. "Genome-wide enhancer-gene regulatory maps link causal variants to target genes underlying human cancer risk," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    5. Lulu Shang & Wei Zhao & Yi Zhe Wang & Zheng Li & Jerome J. Choi & Minjung Kho & Thomas H. Mosley & Sharon L. R. Kardia & Jennifer A. Smith & Xiang Zhou, 2023. "meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Tang Clara S. & Ferreira Manuel A. R., 2012. "GENOVA: Gene Overlap Analysis of GWAS Results," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-15, February.
    7. Nicoló Fusi & Oliver Stegle & Neil D Lawrence, 2012. "Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-9, January.
    8. Claudia Giambartolomei & Damjan Vukcevic & Eric E Schadt & Lude Franke & Aroon D Hingorani & Chris Wallace & Vincent Plagnol, 2014. "Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics," PLOS Genetics, Public Library of Science, vol. 10(5), pages 1-15, May.
    9. Hui Jiang & Tianyu Zhan, 2017. "Unit-Free and Robust Detection of Differential Expression from RNA-Seq Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 178-199, June.
    10. Sora Yoon & Seon-Young Kim & Dougu Nam, 2016. "Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    11. Bin Wang, 2020. "A Zipf-plot based normalization method for high-throughput RNA-seq data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
    12. Xiaodong Cai & Juan Andrés Bazerque & Georgios B Giannakis, 2013. "Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-13, May.
    13. Chuan Gao & Ian C McDowell & Shiwen Zhao & Christopher D Brown & Barbara E Engelhardt, 2016. "Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-39, July.
    14. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.
    15. Jin Hyun Ju & Sushila A Shenoy & Ronald G Crystal & Jason G Mezey, 2017. "An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-26, May.
    16. Kensuke Yamaguchi & Kazuyoshi Ishigaki & Akari Suzuki & Yumi Tsuchida & Haruka Tsuchiya & Shuji Sumitomo & Yasuo Nagafuchi & Fuyuki Miya & Tatsuhiko Tsunoda & Hirofumi Shoda & Keishi Fujio & Kazuhiko , 2022. "Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    17. Alexandra C Nica & Leopold Parts & Daniel Glass & James Nisbet & Amy Barrett & Magdalena Sekowska & Mary Travers & Simon Potter & Elin Grundberg & Kerrin Small & Åsa K Hedman & Veronique Bataille & Jo, 2011. "The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-9, February.
    18. David Lamparter & Rajat Bhatnagar & Katja Hebestreit & T Grant Belgard & Alice Zhang & Victor Hanson-Smith, 2020. "A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    19. Thanh Nguyen & Asim Bhatti & Samuel Yang & Saeid Nahavandi, 2016. "RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.
    20. Daria V Zhernakova & Eleonora de Klerk & Harm-Jan Westra & Anastasios Mastrokolias & Shoaib Amini & Yavuz Ariyurek & Rick Jansen & Brenda W Penninx & Jouke J Hottenga & Gonneke Willemsen & Eco J de Ge, 2013. "DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts," PLOS Genetics, Public Library of Science, vol. 9(6), pages 1-15, June.

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