Effect of predicted protein-truncating genetic variants on the human transcriptome

Rivas, Manuel A.; Pirinen, Matti; Conrad, Donald F.; Lek, Monkol; Tsang, Emily K.; Karczewski, Konrad J.; Maller, Julian; Kukurba, Kimberly R.; DeLuca, David S.; Fromer, Menachem; Ferreira, Pedro G.; Smith, Kevin S.; Zhang, Rui; Zhao, Fengmei; Banks, Eric; Poplin, Ryan; Ruderfer, Douglas M.; Purcell, Shaun; Tukiainen, Taru; Minikel, Eric Vallabh; Stenson, Peter D.; Cooper, David Neil; Huang, Katharine H.; Sullivan, Timothy J.; Nedzel, Jared L.; Bustamante, Carlos D.; Li, Jin Billy; Daly, Mark J.; Guigó, Roderic; Donnelly, Peter; Ardlie, Kristin; Sammeth, Michael; Dermitzakis, Emmanouil T.; McCarthy, Mark I.; Montgomery, Stephen B.; Lappalainen, Tuuli; MacArthur, Daniel G.

doi:10.1126/science.1261877

Cited by 269 publications

(283 citation statements)

References 54 publications

(43 reference statements)

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“…A large-scale, multi-tissue resource of ASE estimates complements eQTL mapping by providing access to individual-specific effects, which assists in the interpretation of rare variants, somatic mutations and patterns of imprinting 8,13,14 . We measured ASE at more than 135 million sites across tissues and donors, with a median of over 10,000 genes quantified per donor (Supplementary Information 11 and Supplementary Fig.…”

Section: Allele-specific Expression Across Human Tissuesmentioning

confidence: 99%

Genetic effects on gene expression across human tissues

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et al. 2017

Nature

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Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

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Section: Allele-specific Expression Across Human Tissuesmentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

Fund

Nhgri³

et al. 2017

Nature

3,481

2,412

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“…For instance, ASE-based estimation allows for exploring effects of cis-regulatory variation in single individuals, while this is not possible using total expression data (Kukurba et al 2014;Rivas et al 2015;The GTEx Consortium 2015).…”

Section: Cold Spring Harbor Laboratory Press On May 12 2018 -Publishmentioning

confidence: 99%

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

et al. 2017

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Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of cis-eQTLs and a mathematically convenient approach for systematic modeling of cis-regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

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“…Analysis of allelic expression can be a powerful approach for detecting rare genetic effects on gene expression levels (Rivas et al 2015). An essential component in this process is solid understanding of the normal spectrum of variation of the studied cellular trait in the population, which can be obtained from data collected in cellular QTL studies.…”

Section: Wwwgenomeorgmentioning

confidence: 99%

Functional genomics bridges the gap between quantitative genetics and molecular biology

Lappalainen

2015

Genome Res.

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Deep characterization of molecular function of genetic variants in the human genome is becoming increasingly important for understanding genetic associations to disease and for learning to read the regulatory code of the genome. In this paper, I discuss how recent advances in both quantitative genetics and molecular biology have contributed to understanding functional effects of genetic variants, lessons learned from eQTL studies, and future challenges in this field.

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Effect of predicted protein-truncating genetic variants on the human transcriptome

Cited by 269 publications

References 54 publications

Genetic effects on gene expression across human tissues

Genetic effects on gene expression across human tissues

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

Functional genomics bridges the gap between quantitative genetics and molecular biology

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