New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.
The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for approximately 27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes.
Petretto
Enrico
E
Medical Research Council Clinical Sciences Centre, Faculty of Medicine, Imperial College London, London, United Kingdom.
Bottolo
Leonardo
L
Langley
Sarah R
SR
Heinig
Matthias
M
McDermott-Roe
Chris
C
Sarwar
Rizwan
R
Pravenec
Michal
M
Hübner
Norbert
N
Aitman
Timothy J
TJ
Cook
Stuart A
SA
Richardson
Sylvia
S
D000273:Adipose Tissue / Q000378:metabolism; D000311:Adrenal Glands / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D053263:Gene Regulatory Networks; D006352:Heart Ventricles / Q000378:metabolism; D006801:Humans; D007668:Kidney / Q000378:metabolism; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
Nature. 2008 Mar 27;452(7186):423-8
18344981
PLoS Genet. 2007 Jul;3(7):e114
17676998
Genetics. 2008 Aug;179(4):2275-89
18689903
Science. 2002 Apr 26;296(5568):752-5
11923494
Plant J. 2008 Oct;56(2):287-296
18643973
Nat Med. 2006 Aug;12(8):917-24
16845389
Nat Genet. 2008 May;40(5):529-37
18443590
Nat Genet. 2007 Oct;39(10):1208-16
17873875
Nat Genet. 2005 Mar;37(3):233-42
15711545
Mamm Genome. 2006 Jun;17(6):643-56
16783644
Genetics. 2005 Apr;169(4):2305-18
15520261
Genet Epidemiol. 2008 Feb;32(2):179-85
18200594
PLoS Biol. 2008 May 6;6(5):e107
18462017
Nat Genet. 2005 Mar;37(3):225-32
15711547
Genetics. 2007 Mar;175(3):1441-50
17179097
Nature. 2004 Aug 12;430(7001):743-7
15269782
Nat Genet. 2005 Mar;37(3):243-53
15711544
Genome Biol. 2007;8(2):R25
17324278
Genome Res. 2006 Aug;16(8):995-1004
16825664
Nat Genet. 2008 May;40(5):546-52
18443592
PLoS Biol. 2005 Aug;3(8):e267
16035920
Nucleic Acids Res. 2003 Feb 15;31(4):e15
12582260
PLoS Genet. 2006 Oct 20;2(10):e172
17054398
Nat Genet. 1999 Jan;21(1):76-83
9916795
Biometrics. 2006 Mar;62(1):19-27
16542225
Am J Hum Genet. 2007 Aug;81(2):304-20
17668380
PLoS One. 2008 Aug 13;3(8):e2962
18698428
PLoS Genet. 2008 Oct;4(10):e1000232
18949031
BMC Bioinformatics. 2008 May 29;9:251
18510743
PLoS Genet. 2009 Aug;5(8):e1000587
19680538
Trends Genet. 2008 Aug;24(8):408-15
18597885
Nat Genet. 2007 Oct;39(10):1202-7
17873877
Circ Res. 2007 Aug 3;101(3):e11-30
17641228
Nat Genet. 2006 Aug;38(8):855-6; author reply 856-9
16874319
Genome Biol. 2009;10(5):R55
19463160