Transcriptome and genome sequencing uncovers functional variation in humans

By Tuuli Lappalainen, M. Sammeth, Marc R. Friedländer, P. '. ’t Hoen, Jean Monlong, M. Rivas, Mar Gonzàlez-Porta, N. Kurbatova, Thasso Griebel, Pedro G. Ferreira and 47 more. Nature, 2013

2,017 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/328ffc2e64c790a9467503c3ba37cca3f9ab66a7

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Citations in the corpus

Joint analysis of functional genomic data and genome-wide association studies of 18 human traits (2013, background): “, 2013] and among those that influence gene expression [Lappalainen et al., 2013; Nicolae et al., 2010].”
RNA Sequencing and Analysis (2015, positive): “In addition, genetic studies of gene expression using RNA-Seq have observed genetically correlated variability in expression, splicing, and ASE (Montgomery et al. 2010; Pickrell et al. 2010; Battle et al. 2013; Lappalainen et al. 2013).”
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, positive): “The 1kGP resources have been collectively cited over 16,000 times to date and have been utilized for foundational applications such as genotype imputation, eQTL mapping, variant pathogenicity prioritization, population history, and evolutionary genetics studies (Almeida et al., 2014; Hara et al., 20”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, methodology): “A null distribution for transcript abundances was learned from the largest homogeneous population in the GEUVADIS data set: 59 samples of Finnish females 30 .”
The human splicing code reveals new insights into the genetic determinants of disease (2014, methodology): “Our approach contrasts with techniques that use functional annotations of the genome (2, 8, 46), tools that are trained using existing disease annotations and thus suffer from overfitting to known mutations or severe selection bias (7–9, 47, 48); genome-wide association studies (GWAS) (49, 50); and ”
Integrative omics for health and disease (2018, background): “In order to maintain consistency across samples, many large-scale research studies have been performed on readily available samples, such as blood or cell lines, including transformed lympho-blastoid cell lines 90,95 .”
Gene expression inference with deep learning (2015, background): “The relative improvements of D-GEX-10%-9000×3 are 15.33% over LR and 45.38% over KNN-GE.”
svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “I loaded data from the GEUVADIS project [25, 26] that we recently processed using Cufflinks and Ballgown [27].”