Transcriptome and genome sequencing uncovers functional variation in humans
Open accessAbstract
Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.
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It is probable that the models, without any supervision, have learned to distinguish relevant regulatory genomic features associated with gene expression and methylation variation.
“it is probable that the models, without any supervision, have learned to distinguish relevant regulatory genomic features associated with gene expression and methylation variation.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [41]
The Properties of regulatory variants section reports that expression QTLs are enriched in regulatory genomic features, including transcription-factor peaks, promoters, and enhancers. It does not show that unsupervised models learned to distinguish those features or connect them to methylation variation; the Methods instead describe QTL mapping by linear regression. No page numbers are available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMany studies attempt to bypass the requirement for individualized alignment by aligning reads to a common reference and discarding reads that do not align to a unique location.
“Many studies attempt to bypass this requirement by aligning reads to a common reference and discarding reads that do not align to a unique location (Lappalainen et al. 2013; Battle et al. 2014).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Lappalainen et al. 2013
The Methods (page unavailable) state that this study mapped mRNA and small-RNA reads with GEM and miraligner. Our copy does not say that it discarded reads lacking a unique alignment, or describe that practice as a way many studies bypass individualized alignment. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCrucial regulatory elements governing gene expression, such as enhancers and promoters, were consistently detected by all models across multiple heads and layers.
“Crucial regulatory elements governing gene expression, such as enhancers and promoters41–43, were consistently detected by all models across multiple heads and layers.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [41]
Large eQTL studies have shown that genetic variation affects the expression of most genes.
“Large eQTL studies have shown that genetic variation affects the expression of most genes [125–128].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [127]
Previous studies have evaluated the association between genetic variation and transcript expression in humans.
“Previous studies have evaluated the association between variation and transcript expression in humans (1-3).”
From Impact of regulatory variation from RNA to protein (2015) · cites this paper as [2]
Results from SEQC, GEUVADIS, and Polyester (simulated with bias) are expected to better represent typical real-world performance.
“However, we expect results on the SEQC [12], GEUVADIS [11], and Polyester [13] (simulated with bias) data sets to be more representative of typical real-world performance.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [11]
Some eQTL analyses of bulk RNAseq data advocate using >0 reads.
“In our examples we used a threshold of >10 and >3 reads, however, some eQTL analyses of bulk RNAseq data advocate using >0 reads70.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [70]
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- Authors
- The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P. G., Barann, M., Wieland, T., Greger, L., van Iterson, M., Almlöf, J., Ribeca, P., Pulyakhina, I., Esser, D., Giger, T., Tikhonov, A., Sultan, M., Bertier, G., MacArthur, D. G., Lek, M., Lizano, E., Buermans, H. P. J., Padioleau, I., Schwarzmayr, T., Karlberg, O., Ongen, H., Kilpinen, H., Beltran, S., Gut, M., Kahlem, K., Amstislavskiy, V., Stegle, O., Pirinen, M., Montgomery, S. B., Donnelly, P., McCarthy, M. I., Flicek, P., Strom, T. M., Lehrach, H., Schreiber, S., Sudbrak, R., Carracedo, Á., Antonarakis, S. E., Häsler, R., Syvänen, A.-C., van Ommen, G.-J., Brazma, A., Meitinger, T., Rosenstiel, P., Guigó, R., Gut, I. G., Estivill, X., & Dermitzakis, E. T.
- Affiliations
- University of Geneva (CH)
- Universitat Pompeu Fabra (ES)
- Centre for Human Genetics (GB)
- European Bioinformatics Institute (GB)
- Centro Nacional de Análisis Genómico (ES)
- Christian-Albrechts-Universität zu Kiel (DE)
- Helmholtz Munich (DE)
- Uppsala University (SE)
- Leiden University Medical Center (NL)
- Max Planck Institute for Molecular Genetics (DE)
- Broad Institute (US)
- Universidade de Santiago de Compostela (ES)
- Corresponding author
- Lappalainen, Tuuli
- Dermitzakis, Emmanouil T.
- ORCID
- Lappalainen, Tuuli
- Sammeth, Michael
- Friedländer, Marc R.
- Monlong, Jean
- Rivas, Manuel A.
- Gonzàlez-Porta, Mar
- Kurbatova, Natalja
- Griebel, Thasso
- Ferreira, Pedro G.
- Barann, Matthias
- Wieland, Thomas
- Greger, Liliana
- Almlöf, Jonas
- Ribeca, Paolo
- Sultan, Marc
- Bertier, Gabrielle
- MacArthur, Daniel G.
- Lek, Monkol
- Lizano, Esther
- Ongen, Halit
- Kilpinen, Helena
- Amstislavskiy, Vyacheslav
- Stegle, Oliver
- Pirinen, Matti
- Montgomery, Stephen B.
- Donnelly, Peter
- McCarthy, Mark I.
- Flicek, Paul
- Schreiber, Stefan
- Sudbrak, Ralf
- Carracedo, Ángel
- Antonarakis, Stylianos E.
- Häsler, Robert
- Meitinger, Thomas
- Rosenstiel, Philip
- Guigó, Roderic
- Gut, Ivo G.
- Estivill, Xavier
- Dermitzakis, Emmanouil T.
- Venue
- Nature
- Published
- 2013-09
- Volume
- 501
- Issue
- 7468
- Pages
- 506-511
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3918453
- OpenAlex
- W2025943989
- Cited by
- 1,888
- References
- 34
- Citations by year
- 2026: 57 · 2025: 87 · 2024: 103 · 2023: 108 · 2022: 152 · 2021: 166 · 2020: 176 · 2019: 176 · 2018: 182 · 2017: 194 · 2016: 167 · 2015: 172 · 2014: 133 · 2013: 11
- FWCI
- 106.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- RUNA. Repositorio Institucional del Sergas (Servicio Gallego de Salud (SERGAS)) (submittedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (085475/Z/08/Z, 095552/Z/11/Z, 085532, 090367, 098381, 083270, 081917, 075491, WT085532, 095552, 090532, 085475/B/08/Z, 090532/Z/09/Z, 076113)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (130326, 127375, 249968, 144082, 130342)
- National Institute of Mental Health (MH-090941, R01MH090941)
- National Institute of General Medical Sciences (R01GM104371)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- transcriptome variation, gene expression variation, microRNA, lymphoblastoid cell lines, 1000 Genomes Project, causal variants, disease-associated loci, loss-of-function mutations, human genetic variation, variant annotation
- MeSH terms
- Alleles, Cell Line, Transformed, Exons, Humans, RNA, Messenger, Genetic Variation, Genome, Human, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Gene Expression Profiling, Quantitative Trait Loci, High-Throughput Nucleotide Sequencing, Transcriptome
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