PLoS Genetics (Feb 2011)

The architecture of gene regulatory variation across multiple human tissues: the MuTHER study.

  • Alexandra C Nica,
  • Leopold Parts,
  • Daniel Glass,
  • James Nisbet,
  • Amy Barrett,
  • Magdalena Sekowska,
  • Mary Travers,
  • Simon Potter,
  • Elin Grundberg,
  • Kerrin Small,
  • Asa 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,
  • MuTHER Consortium

DOI
https://doi.org/10.1371/journal.pgen.1002003
Journal volume & issue
Vol. 7, no. 2
p. e1002003

Abstract

Read online

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.