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The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study

Author

Listed:
  • 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
  • Jordana Tzenova Bell
  • Gabriela Surdulescu
  • Antigone S Dimas
  • Catherine Ingle
  • Frank O Nestle
  • Paola di Meglio
  • Josine L Min
  • Alicja Wilk
  • Christopher J Hammond
  • Neelam Hassanali
  • Tsun-Po Yang
  • Stephen B Montgomery
  • Steve O'Rahilly
  • Cecilia M Lindgren
  • Krina T Zondervan
  • Nicole Soranzo
  • Inês Barroso
  • Richard Durbin
  • Kourosh Ahmadi
  • Panos Deloukas
  • Mark I McCarthy
  • Emmanouil T Dermitzakis
  • Timothy D Spector
  • The MuTHER Consortium

Abstract

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits. Author Summary: Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pgen00:1002003
    DOI: 10.1371/journal.pgen.1002003
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    References listed on IDEAS

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    1. Zari Dastani & Marie-France Hivert & Nicholas Timpson & John R B Perry & Xin Yuan & Robert A Scott & Peter Henneman & Iris M Heid & Jorge R Kizer & Leo-Pekka Lyytikäinen & Christian Fuchsberger & Tosh, 2012. "Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals," PLOS Genetics, Public Library of Science, vol. 8(3), pages 1-23, March.
    2. Josine L Min & Jennifer M Taylor & J Brent Richards & Tim Watts & Fredrik H Pettersson & John Broxholme & Kourosh R Ahmadi & Gabriela L Surdulescu & Ernesto Lowy & Christian Gieger & Chris Newton-Cheh, 2011. "The Use of Genome-Wide eQTL Associations in Lymphoblastoid Cell Lines to Identify Novel Genetic Pathways Involved in Complex Traits," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-14, July.
    3. Lina-Marcela Diaz-Gallo & Elena Sánchez & Norberto Ortego-Centeno & Jose Mario Sabio & Francisco J García-Hernández & Enrique de Ramón & Miguel A González-Gay & Torsten Witte & Hans-Joachim Anders & M, 2013. "Evidence of New Risk Genetic Factor to Systemic Lupus Erythematosus: The UBASH3A Gene," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-5, April.
    4. 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.
    5. Maggie C Y Ng & Daniel Shriner & Brian H Chen & Jiang Li & Wei-Min Chen & Xiuqing Guo & Jiankang Liu & Suzette J Bielinski & Lisa R Yanek & Michael A Nalls & Mary E Comeau & Laura J Rasmussen-Torvik &, 2014. "Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes," PLOS Genetics, Public Library of Science, vol. 10(8), pages 1-14, August.
    6. Diptavo Dutta & Yuan He & Ashis Saha & Marios Arvanitis & Alexis Battle & Nilanjan Chatterjee, 2022. "Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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