A compendium of genetic regulatory effects across pig tissues.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
04 Jan 2024
04 Jan 2024
Historique:
received:
23
11
2022
accepted:
13
10
2023
medline:
5
1
2024
pubmed:
5
1
2024
entrez:
4
1
2024
Statut:
aheadofprint
Résumé
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
Identifiants
pubmed: 38177344
doi: 10.1038/s41588-023-01585-7
pii: 10.1038/s41588-023-01585-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : RCUK | Medical Research Council (MRC)
ID : MR/R025851/1
Organisme : RCUK | Medical Research Council (MRC)
ID : MR/P015514/1
Organisme : RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
ID : BBS/E/D/10002070
Organisme : RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
ID : BBS/E/D/30002275
Organisme : United States Department of Agriculture | Agricultural Research Service (USDA Agricultural Research Service)
ID : 2019-67015-29321
Organisme : United States Department of Agriculture | Agricultural Research Service (USDA Agricultural Research Service)
ID : 2021-67015-33409
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 32022078
Informations de copyright
© 2024. The Author(s).
Références
Tibbs Cortes, L., Zhang, Z. & Yu, J. Status and prospects of genome-wide association studies in plants. Plant Genome 14, e20077 (2021).
doi: 10.1002/tpg2.20077
pubmed: 33442955
Hu, Z. L., Park, C. A. & Reecy, J. M. Bringing the animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services. Nucleic Acids Res. 50, D956–D961 (2022).
doi: 10.1093/nar/gkab1116
pubmed: 34850103
Loos, R. J. F. 15 years of genome-wide association studies and no signs of slowing down. Nat. Commun. 11, 5900 (2020).
pmcid: 7677394
doi: 10.1038/s41467-020-19653-5
pubmed: 33214558
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
pubmed: 30305743
Umans, B. D., Battle, A. & Gilad, Y. Where are the disease-associated eQTLs? Trends Genet. 37, 109–124 (2021).
doi: 10.1016/j.tig.2020.08.009
pubmed: 32912663
Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
doi: 10.1038/nrg3891
pubmed: 25707927
Aguet, F. et al. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
doi: 10.1126/science.aaz1776
Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).
pmcid: 8423625
doi: 10.1038/s41588-021-00924-w
pubmed: 34493866
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
pmcid: 7836161
doi: 10.1186/s13059-020-02252-4
pubmed: 33499903
Velez-Irizarry, D. et al. Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs. BMC Genomics 20, 3 (2019).
pmcid: 6319002
doi: 10.1186/s12864-018-5386-2
pubmed: 30606113
Criado-Mesas, L. et al. Identification of eQTLs associated with lipid metabolism in longissimus dorsi muscle of pigs with different genetic backgrounds. Sci. Rep. 10, 9845 (2020).
pmcid: 7300017
doi: 10.1038/s41598-020-67015-4
pubmed: 32555447
Liu, Y. et al. Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits. Genet. Sel. Evol. 52, 59 (2020).
pmcid: 7547458
doi: 10.1186/s12711-020-00579-x
pubmed: 33036552
Clark, E. L. et al. From FAANG to fork: application of highly annotated genomes to improve farmed animal production. Genome Biol. 21, 285 (2020).
pmcid: 7686664
doi: 10.1186/s13059-020-02197-8
pubmed: 33234160
Lunney, J. K. et al. Importance of the pig as a human biomedical model. Sci. Transl. Med. 13, eabd5758 (2021).
doi: 10.1126/scitranslmed.abd5758
pubmed: 34818055
Pan, Z. et al. Pig genome functional annotation enhances the biological interpretation of complex traits and human disease. Nat. Commun. 12, 5848 (2021).
pmcid: 8494738
doi: 10.1038/s41467-021-26153-7
pubmed: 34615879
Gu, X. & Su, Z. Tissue-driven hypothesis of genomic evolution and sequence-expression correlations. Proc. Natl Acad. Sci. USA 104, 2779–2784 (2007).
pmcid: 1815258
doi: 10.1073/pnas.0610797104
pubmed: 17301236
Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).
pmcid: 6823959
doi: 10.1186/s13059-019-1836-7
pubmed: 31675989
Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).
pmcid: 8051643
doi: 10.1126/science.aaz8528
pubmed: 32913075
De Saram, P., Iqbal, A., Murdoch, J. N. & Wilkinson, C. J. BCAP is a centriolar satellite protein and inhibitor of ciliogenesis. J. Cell Sci. 130, 3360–3373 (2017).
pubmed: 28775150
Flynn, E. & Lappalainen, T. Functional characterization of genetic variant effects on expression. Annu. Rev. Biomed. Data Sci. 5, 119–139 (2022).
doi: 10.1146/annurev-biodatasci-122120-010010
pubmed: 35483347
Munro, D. et al. The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats. Nucleic Acids Res. 50, 10882–10895 (2022).
pmcid: 9638908
doi: 10.1093/nar/gkac912
pubmed: 36263809
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).
pmcid: 8432599
doi: 10.1038/s41588-021-00913-z
pubmed: 34475573
Pividori, M. et al. 2020 PhenomeXcan: mapping the genome to the phenome through the transcriptome. Sci. Adv. 6 eaba2083.
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
doi: 10.1038/ng.3538
pubmed: 27019110
Barbeira A. N. et al. 2018 Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9 (1825).
Barbeira, A. N. et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 15, e1007889 (2019).
pmcid: 6358100
doi: 10.1371/journal.pgen.1007889
pubmed: 30668570
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).
doi: 10.1038/s41588-019-0481-0
pubmed: 31427789
Xiang, R. et al. Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations. Nat. Commun. 12, 860 (2021).
pmcid: 7870883
doi: 10.1038/s41467-021-21001-0
pubmed: 33558518
Schmiedel, B. J. et al. Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants. Sci. Immunol. 7, eabm2508 (2022).
pmcid: 9035271
doi: 10.1126/sciimmunol.abm2508
pubmed: 35213211
Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022).
pmcid: 9842455
doi: 10.1038/s41586-022-04713-1
pubmed: 35545678
Wong, E. S. et al. Interplay of cis and trans mechanisms driving transcription factor binding and gene expression evolution. Nat. Commun. 8, 1092 (2017).
pmcid: 5653656
doi: 10.1038/s41467-017-01037-x
pubmed: 29061983
Tewhey, R. et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell 165, 1519–1529 (2016).
pmcid: 4957403
doi: 10.1016/j.cell.2016.04.027
pubmed: 27259153
Freimer J. W. et al. Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks Nat. Genet. 54 1133–1144.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
pubmed: 24695404
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
doi: 10.1093/bioinformatics/bts635
pubmed: 23104886
Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
doi: 10.1093/bioinformatics/btt656
pubmed: 24227677
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).
pmcid: 5032908
doi: 10.1038/nprot.2016.095
pubmed: 27560171
Van der Maaten, L. & Hinton, G. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
pmcid: 5967553
doi: 10.1093/molbev/msy096
pubmed: 29722887
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
pmcid: 4987883
doi: 10.1093/nar/gkw290
pubmed: 27095192
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
pubmed: 20110278
Chen, H. et al. A Pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell 173, 386–399 (2018).
pmcid: 5890960
doi: 10.1016/j.cell.2018.03.027
pubmed: 29625054
Ren, B. Enhancers make non-coding RNA. Nature 465, 173–174 (2010).
doi: 10.1038/465173a
pubmed: 20463730
Zhang, Z. et al. HeRA: an atlas of enhancer RNAs across human tissues. Nucleic Acids Res. 49, D932–D938 (2021).
doi: 10.1093/nar/gkaa940
pubmed: 33119754
Wucher, V. et al. FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome. Nucleic Acids Res. 45, e57 (2017).
pmcid: 5416892
pubmed: 28053114
Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).
doi: 10.1038/s41588-017-0004-9
pubmed: 29229983
Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).
pmcid: 3519454
doi: 10.1093/bioinformatics/bts606
pubmed: 23060615
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
pmcid: 3398141
doi: 10.1038/nprot.2011.457
pubmed: 22343431
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
pmcid: 2864565
doi: 10.1186/gb-2010-11-3-r25
pubmed: 20196867
Aguet, F. et al. Molecular quantitative trait loci. Nat. Rev. Methods Primers 3, 4 (2023).
doi: 10.1038/s43586-022-00188-6
Cui, R. et al. Improving fine-mapping by modeling infinitesimal effects. Preprint at bioRxiv 10.1101/2022.10.21.513123 (2022).
Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019).
doi: 10.1038/s41588-018-0268-8
pubmed: 30478440
Han, B. & Eskin, E. Interpreting meta-analyses of genome-wide association studies. PLoS Genet. 8, e1002555 (2012).
pmcid: 3291559
doi: 10.1371/journal.pgen.1002555
pubmed: 22396665
Vavrek, M. J. Fossil: palaeoecological and palaeogeographical analysis tools. Palaeontol. Electron. 14, 16 (2011).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pmcid: 2922887
doi: 10.1093/bioinformatics/btq340
pubmed: 20616382
Yao, D. W., O’Connor, L. J., Price, A. L. & Gusev, A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52, 626–633 (2020).
pmcid: 7276299
doi: 10.1038/s41588-020-0625-2
pubmed: 32424349
Wen, X., Lee, Y., Luca, F. & Pique-Regi, R. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet. 98, 1114–1129 (2016).
pmcid: 4908152
doi: 10.1016/j.ajhg.2016.03.029
pubmed: 27236919
Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science. 296, 2225–2229 (2002).
doi: 10.1126/science.1069424
pubmed: 12029063
Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).
doi: 10.1214/16-AOAS952
Wen, X., Pique-Regi, R. & Luca, F. Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLoS Genet. 13, 1–25 (2017).
doi: 10.1371/journal.pgen.1006646
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
pmcid: 5834629
doi: 10.1038/s41467-018-03371-0
pubmed: 29500431
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).
pmcid: 4552594
doi: 10.1038/ng.3367
pubmed: 26258848
Bhattacharya, A. et al. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: lessons from the Global Biobank Meta-analysis Initiative. Cell Genomics 2, 100180 (2022).
pmcid: 9631681
doi: 10.1016/j.xgen.2022.100180
pubmed: 36341024
Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pmcid: 4495769
doi: 10.1038/ng.3211
pubmed: 25642630
Teng, J. & FarmGTEx. FarmGTEx/PigGTEx-Pipeline-v0. GitHub. github.com/FarmGTEx/PigGTEx-Pipeline-v0 (2023).