Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.

Bardet-Biedl Syndrome / genetics Calcium Channels / genetics Cell Line Cerebellar Ataxia / genetics DNA Methylation Gene Expression Genetic Variation Humans Induced Pluripotent Stem Cells / cytology Polymorphism, Single Nucleotide Proteins / genetics Quantitative Trait Loci Rare Diseases / genetics Regulatory Sequences, Nucleic Acid Sequence Analysis, RNA Whole Genome Sequencing

Journal

Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904

Informations de publication

Date de publication:
03 2021
Historique:
received: 04 10 2019
accepted: 25 01 2021
pubmed: 6 3 2021
medline: 10 4 2021
entrez: 5 3 2021
Statut: ppublish

Résumé

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

Identifiants

DOI: 10.1038/s41588-021-00800-7 PMID: 33664507 PMC: PMC7944648
pubmed: 33664507
doi: 10.1038/s41588-021-00800-7
pii: 10.1038/s41588-021-00800-7
pmc: PMC7944648
mid: NIHMS1666585
doi:

Substances chimiques

Bbs2 protein, human 0
CACNA1A protein, human 0
Calcium Channels 0
Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

313-321

Subventions

Organisme : NIDDK NIH HHS
ID : U01 DK105541
Pays : United States
Organisme : NIDDK NIH HHS
ID : DP3 DK112155
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG007708
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK107437
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG010218
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL142015
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG009080
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK116750
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066490
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK116074
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL107388
Pays : United States
Organisme : NLM NIH HHS
ID : T32 LM012409
Pays : United States
Organisme : Wellcome Trust
ID : WT098503
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : U01 HG009431
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG008150
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK106236
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL107442
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK120565
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007033
Pays : United States
Organisme : Medical Research Council
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT090851
Pays : United Kingdom
Organisme : NIH HHS
ID : S10 OD023452
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom

Investigateurs

Marc Jan Bonder (M)
Daniel Seaton (D)
David A Jakubosky (DA)
Christopher D Brown (CD)
YoSon Park (Y)

Commentaires et corrections

Type : CommentIn

Références

Westra, H.-J. et al. Systematic identification of trans-eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
pubmed: 24013639 pmcid: 3991562 doi: 10.1038/ng.2756
Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131–138 (2017).
pubmed: 27918535 doi: 10.1038/ng.3721
Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).
pubmed: 27918533 doi: 10.1038/ng.3737
GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
pmcid: 5776756 doi: 10.1038/nature24277
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
pubmed: 24037378 pmcid: 3918453 doi: 10.1038/nature12531
Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).
pubmed: 29379200 pmcid: 6548559 doi: 10.1038/s41588-018-0046-7
Schwartzentruber, J. et al. Molecular and functional variation in iPSC-derived sensory neurons. Nat. Genet. 50, 54–61 (2018).
pubmed: 29229984 doi: 10.1038/s41588-017-0005-8
Cuomo, A. S. E. et al. Single-cell RNA sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11, 810 (2020).
pubmed: 32041960 pmcid: 7010688 doi: 10.1038/s41467-020-14457-z
Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. https://doi.org/10.1038/s41588-021-00801-6 (2021).
Sun, N. et al. Patient-specific induced pluripotent stem cells as a model for familial dilated cardiomyopathy. Sci. Transl. Med. 4, 130ra47 (2012).
pubmed: 22517884 pmcid: 3657516 doi: 10.1126/scitranslmed.3003552
Lan, F. et al. Abnormal calcium handling properties underlie familial hypertrophic cardiomyopathy pathology in patient-specific induced pluripotent stem cells. Cell Stem Cell 12, 101–113 (2013).
pubmed: 23290139 pmcid: 3638033 doi: 10.1016/j.stem.2012.10.010
Lee, J. et al. Activation of PDGF pathway links LMNA mutation to dilated cardiomyopathy. Nature 572, 335–340 (2019).
pubmed: 31316208 pmcid: 6779479 doi: 10.1038/s41586-019-1406-x
Kodo, K. et al. iPSC-derived cardiomyocytes reveal abnormal TGF-β signalling in left ventricular non-compaction cardiomyopathy. Nat. Cell Biol. 18, 1031–1042 (2016).
pubmed: 27642787 pmcid: 5042877 doi: 10.1038/ncb3411
Wu, H. et al. Modelling diastolic dysfunction in induced pluripotent stem cell-derived cardiomyocytes from hypertrophic cardiomyopathy patients. Eur. Heart J. 40, 3685–3695 (2019).
pubmed: 31219556 doi: 10.1093/eurheartj/ehz326 pmcid: 7963137
Dubois, N. C. et al. SIRPA is a specific cell-surface marker for isolating cardiomyocytes derived from human pluripotent stem cells. Nat. Biotechnol. 29, 1011–1018 (2011).
pubmed: 22020386 pmcid: 4949030 doi: 10.1038/nbt.2005
Sterneckert, J. L., Reinhardt, P. & Schöler, H. R. Investigating human disease using stem cell models. Nat. Rev. Genet. 15, 625–639 (2014).
pubmed: 25069490 doi: 10.1038/nrg3764
Kilpinen, H. et al. Common genetic variation drives molecular heterogeneity in human iPSCs. Nature 546, 370–375 (2017).
pubmed: 28489815 pmcid: 5524171 doi: 10.1038/nature22403
Panopoulos, A. D. et al. iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types. Stem Cell Rep. 8, 1086–1100 (2017).
doi: 10.1016/j.stemcr.2017.03.012
Pashos, E. E. et al. Large, diverse population cohorts of hiPSCs and derived hepatocyte-like cells reveal functional genetic variation at blood lipid-associated loci. Cell Stem Cell 20, 558–570 (2017).
pubmed: 28388432 pmcid: 5476422 doi: 10.1016/j.stem.2017.03.017
Banovich, N. E. et al. Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res. 28, 122–131 (2018).
pubmed: 29208628 pmcid: 5749177 doi: 10.1101/gr.224436.117
Carcamo-Orive, I. et al. Analysis of transcriptional variability in a large human iPSC library reveals genetic and non-genetic determinants of heterogeneity. Cell Stem Cell 20, 518–532.e9 (2017).
pubmed: 28017796 doi: 10.1016/j.stem.2016.11.005
Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat. Med. 25, 911–919 (2019).
pubmed: 31160820 pmcid: 6634302 doi: 10.1038/s41591-019-0457-8
Li, X. et al. The impact of rare variation on gene expression across tissues. Nature 550, 239–243 (2017).
pubmed: 29022581 pmcid: 5877409 doi: 10.1038/nature24267
Choi, J. et al. A comparison of genetically matched cell lines reveals the equivalence of human iPSCs and ESCs. Nat. Biotechnol. 33, 1173–1181 (2015).
pubmed: 26501951 pmcid: 4847940 doi: 10.1038/nbt.3388
Thomas, S. M. et al. Reprogramming LCLs to iPSCs results in recovery of donor-specific gene expression signature. PLoS Genet. 11, e1005216 (2015).
pubmed: 25950834 pmcid: 4423863 doi: 10.1371/journal.pgen.1005216
Donovan, M. K. R., D’Antonio-Chronowska, A., D’Antonio, M. & Frazer, K. A. Cellular deconvolution of GTEx tissues powers eQTL studies to discover thousands of novel disease and cell-type associated regulatory variants. Nat. Commun. 11, 955 (2020).
pubmed: 32075962 pmcid: 7031340 doi: 10.1038/s41467-020-14561-0
Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39, D945–D950 (2010).
pubmed: 20952405 pmcid: 3013785 doi: 10.1093/nar/gkq929
Gerrard, D. T. et al. An integrative transcriptomic atlas of organogenesis in human embryos. eLife 5, e15657 (2016).
pubmed: 27557446 pmcid: 4996651 doi: 10.7554/eLife.15657
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).
pubmed: 30478440 doi: 10.1038/s41588-018-0268-8
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
pubmed: 30445434 doi: 10.1093/nar/gky1120
Jakubosky, D. et al. Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats. Nat. Commun. 11, 2928 (2020).
pubmed: 32522985 pmcid: 7287045 doi: 10.1038/s41467-020-16481-5
Zhao, J. et al. A burden of rare variants associated with extremes of gene expression in human peripheral blood. Am. J. Hum. Genet. 98, 299–309 (2016).
pubmed: 26849112 pmcid: 4746369 doi: 10.1016/j.ajhg.2015.12.023
Li, X. et al. Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. Am. J. Hum. Genet. 95, 245–256 (2014).
pubmed: 25192044 pmcid: 4157143 doi: 10.1016/j.ajhg.2014.08.004
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
pubmed: 22343431 pmcid: 3398141 doi: 10.1038/nprot.2011.457
Ferraro, N. M. et al. Transcriptomic signatures across human tissues identify functional rare genetic variation. Science 369, eaaz5900 (2020).
Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9, eaal5209 (2017).
Kremer, L. S. et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat. Commun. 8, 15824 (2017).
pubmed: 28604674 pmcid: 5499207 doi: 10.1038/ncomms15824
Kernohan, K. D. et al. Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy. Hum. Mutat. 38, 611–614 (2017).
pubmed: 28251733 pmcid: 5889109 doi: 10.1002/humu.23211
McKusick, V. A. Mendelian Inheritance in Man: a Catalog of Human Genes and Genetic Disorders (JHU Press, 1998).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
pubmed: 26773131 pmcid: 4866522 doi: 10.1093/bioinformatics/btw018
Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).
pubmed: 27866706 pmcid: 5142122 doi: 10.1016/j.ajhg.2016.10.003
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics https://doi.org/10.1093/bioinformatics/btz469 (2019).
Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E. & Montgomery, S. B. Abundant associations with gene expression complicate GWAS follow-up. Nat. Genet. 51, 768–769 (2019).
pubmed: 31043754 pmcid: 6904208 doi: 10.1038/s41588-019-0404-0
van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).
pubmed: 29212778 pmcid: 5805277 doi: 10.1161/CIRCRESAHA.117.312086
Liu, J. Z. et al. Dense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis. Nat. Genet. 44, 1137–1141 (2012).
pubmed: 22961000 pmcid: 3459817 doi: 10.1038/ng.2395
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
pubmed: 20686565 pmcid: 3039276 doi: 10.1038/nature09270
Pongor, L. et al. A genome-wide approach to link genotype to clinical outcome by utilizing next-generation sequencing and gene chip data of 6,697 breast cancer patients. Genome Med. 7, 104 (2015).
pubmed: 26474971 pmcid: 4609150 doi: 10.1186/s13073-015-0228-1
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
pubmed: 30124842 pmcid: 6488973 doi: 10.1093/hmg/ddy271
Sanchez, E. et al. POLR1B and neural crest cell anomalies in Treacher Collins syndrome type 4. Genet. Med. 22, 547–556 (2020).
pubmed: 31649276 doi: 10.1038/s41436-019-0669-9
Howson, J. M. M. et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat. Genet. 49, 1113–1119 (2017).
pubmed: 28530674 pmcid: 5555387 doi: 10.1038/ng.3874
Pankratz, N. et al. Meta-analysis of Parkinson’s disease: identification of a novel locus, RIT2. Ann. Neurol. 71, 370–384 (2012).
pubmed: 22451204 pmcid: 3354734 doi: 10.1002/ana.22687
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
pubmed: 24162737 pmcid: 3896259 doi: 10.1038/ng.2802
Scott, R. A. et al. An expanded genome-wide association study of type 2. Diabetes 66, 2888–2902 (2017).
pubmed: 28566273 pmcid: 5652602 doi: 10.2337/db16-1253
Webb, G. J., Siminovitch, K. A. & Hirschfield, G. M. The immunogenetics of primary biliary cirrhosis: a comprehensive review. J. Autoimmun. 64, 42–52 (2015).
pubmed: 26250073 pmcid: 5014907 doi: 10.1016/j.jaut.2015.07.004
1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Streeter, I. et al. The human-induced pluripotent stem cell initiative-data resources for cellular genetics. Nucleic Acids Res. 45, D691–D697 (2017).
pubmed: 27733501 doi: 10.1093/nar/gkw928
D’Antonio, M. et al. Insights into the mutational burden of human induced pluripotent stem cells from an integrative multi-omics approach. Cell Rep. 24, 883–894 (2018).
pubmed: 30044985 pmcid: 6467479 doi: 10.1016/j.celrep.2018.06.091
DeBoever, C. et al. Large-scale profiling reveals the influence of genetic variation on gene expression in human induced pluripotent stem cells. Cell Stem Cell 20, 533–546.e7 (2017).
pubmed: 28388430 pmcid: 5444918 doi: 10.1016/j.stem.2017.03.009
Knowles, J. W., Hao, K., Xie, W., Weedon, M. N. & Zhang, Z. Genetic and functional analyses identify NAT2 as a human insulin sensitivity gene. Circulation 128, A10906 (2013).
Casale, F. P., Rakitsch, B., Lippert, C. & Stegle, O. Efficient set tests for the genetic analysis of correlated traits. Nat. Methods 12, 755–758 (2015).
pubmed: 26076425 doi: 10.1038/nmeth.3439
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901 pmcid: 1950838 doi: 10.1086/519795
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).
pubmed: 26708335 doi: 10.1093/bioinformatics/btv722
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
pubmed: 12883005 doi: 10.1073/pnas.1530509100 pmcid: 170937
Saha, A. & Battle, A. False positives in trans-eQTL and coexpression analyses arising from RNA-sequencing alignment errors. F1000Res. 7, 1860 (2018).
pubmed: 30613398 doi: 10.12688/f1000research.17145.1
Pedersen, B. S., Layer, R. M. & Quinlan, A. R. Vcfanno: fast, flexible annotation of genetic variants. Genome Biol. 17, 118 (2016).
pubmed: 27250555 pmcid: 4888505 doi: 10.1186/s13059-016-0973-5
Hall, C. L. et al. Frequency of genetic variants associated with arrhythmogenic right ventricular cardiomyopathy in the genome aggregation database. Eur. J. Hum. Genet. 26, 1312–1318 (2018).
pubmed: 29802319 pmcid: 6117313 doi: 10.1038/s41431-018-0169-4
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).
doi: 10.1093/nar/gky1016 pubmed: 30371827
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 1000, 2078–2079 (2009).
doi: 10.1093/bioinformatics/btp352
Churchhouse, C & Neale, B. Rapid GWAS of thousands of phenotypes for 337,000 samples in the UK Biobank. https://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank/ (2017).

Auteurs

Marc Jan Bonder (MJ)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. bondermj@gmail.com.
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. bondermj@gmail.com.
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. bondermj@gmail.com.
ORCID: 0000-0002-8431-3180

Craig Smail (C)

Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. csmail@stanford.edu.
Genomic Medicine Center, Children's Mercy Research Institute and Children's Mercy Kansas City, Kansas City, MO, USA. csmail@stanford.edu.
ORCID: 0000-0001-8045-6021

Michael J Gloudemans (MJ)

Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
ORCID: 0000-0002-9924-9943

Laure Frésard (L)

Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.

David Jakubosky (D)

Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.
Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.

Matteo D'Antonio (M)

Department of Pediatrics and Rady Children's Hospital, University of California, San Diego, La Jolla, CA, USA.
ORCID: 0000-0001-5844-6433

Xin Li (X)

CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.

Nicole M Ferraro (NM)

Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.

Ivan Carcamo-Orive (I)

Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
ORCID: 0000-0001-8823-4925

Bogdan Mirauta (B)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.

Daniel D Seaton (DD)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
ORCID: 0000-0002-5222-3893

Na Cai (N)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.
Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.

Dara Vakili (D)

UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Faculty of Medicine, Imperial College London, London, UK.
ORCID: 0000-0001-8251-0589

Danilo Horta (D)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
ORCID: 0000-0003-4832-2124

Chunli Zhao (C)

Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.

Diane B Zastrow (DB)

Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.

Devon E Bonner (DE)

Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.

Matthew T Wheeler (MT)

Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA.
ORCID: 0000-0001-8721-3022

Helena Kilpinen (H)

Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.
UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.

Joshua W Knowles (JW)

Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
ORCID: 0000-0003-1922-7240

Erin N Smith (EN)

Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.

Kelly A Frazer (KA)

Department of Pediatrics and Rady Children's Hospital, University of California, San Diego, La Jolla, CA, USA.
Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
ORCID: 0000-0002-6060-8902

Stephen B Montgomery (SB)

Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA. smontgom@stanford.edu.
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. smontgom@stanford.edu.
ORCID: 0000-0002-5200-3903

Oliver Stegle (O)

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. oliver.stegle@embl.de.
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. oliver.stegle@embl.de.
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. oliver.stegle@embl.de.
Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK. oliver.stegle@embl.de.
ORCID: 0000-0002-8818-7193

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Classifications MeSH

questionsmedicales.fr › Malformations et maladies congénitales, héréditaires et néonatales › Maladies génétiques congénitales › Ciliopathies › Syndrome de Bardet-Biedl › Identification of rare and common regulatory...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Protéines de transport membranaire › Canaux ioniques › Canaux calciques › Identification of rare and common regulatory...
questionsmedicales.fr › Cellules › Cellules cultivées › Lignée cellulaire › Identification of rare and common regulatory...
questionsmedicales.fr › États, signes et symptômes pathologiques › Signes et symptômes › Manifestations neurologiques › Dyskinésies › Ataxie › Ataxie cérébelleuse › Identification of rare and common regulatory...
questionsmedicales.fr › Phénomènes génétiques › Méthylation de l'ADN › Identification of rare and common regulatory...
questionsmedicales.fr › Phénomènes génétiques › Expression des gènes › Identification of rare and common regulatory...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Identification of rare and common regulatory...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Identification of rare and common regulatory...
questionsmedicales.fr › Cellules › Cellules souches › Cellules souches pluripotentes › Cellules souches pluripotentes induites › Identification of rare and common regulatory...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Identification of rare and common regulatory...
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