Genetic studies of gene expression have provided important insight into the architecture of gene regulation and gene regulatory networks as well as new opportunities to identify molecular mechanisms underlying diverse traits. At the moment, this experimental system enables us to identify regulatory variants and model the functional spectrum of human variation. Furthermore, such studies have been fundamental to current insight into the tissue-specific context of genetic effects. The Genotype-Tissue Expression (GTEx) project is now proposing considerable future effort to characterize the tissue-specificity of genetic effects on gene expression; by 2016, GTEx proposes to collect 20,000 tissue transcriptomes from nearly 900 individuals. Essential to maximizing this investment and developing a gold-standard resource, will be application of experimental methods which can validate discoveries particularly expression quantitative trait loci (eQTL). One such method for validating eQTL are tests of allele-specific expression (ASE);Unlike genotypic association with drives eQTL discovery, allelic tests implicate the presence of a cis-linked regulatory variant within an individual and are independent of the population frequency of a causal variant. However, a limitation of ASE data from RNA-Seq is that they can be biased by differences in sampling depths due to changes in expression level across genes; a challenge that is exacerbated in multi-tissue studies. To address this, we have developed and refined an assay which combines established methods of microfluidics-based multiplex PCR and deep sequencing (mmPCR-Seq) to provide deep and high-throughput resolution of allele-specific expression (ASE). This approach provides a high-throughput, efficient and low-cost solution for obtaining ASE data (<9 minutes and <$50 per sample) and we have demonstrated that mmPCR-Seq works effectively on RNA with low quantity (10ng) and low quality (RIN=2.8). In this project, we aim to apply this approach to the validation of 800 tissue- specific and shared eQTL genes across at least 20-30 tissues in 96 individuals. Furthermore, we validate patterns of ASE for rare deleterious and loss-of-function variation across at least 30 tissues in 50 individuals. This data will be quality-controlled and regularly released to the GTEX Statistical Analysis Working Group and larger GTEx Consortia. We expect that this data will aid discovery and validation of eQTL particularly in tissues with low or intermediate expression and for shared eQTL which may exhibit as weaker effects in some tissues. Furthermore, this data will enable sensitive tissue-based interpretation of deleterious and LoF variation with the goal of improving our ability to understand systemic patterns which may underlie variable penetrance and disease expressivity as well as novel approaches to triage presumably pathogenic variants from personal exomes and genomes. As studies of the tissue context of genetic variation have been and are principally driven by genetic studies of gene expression, application of mmPCR-Seq to the validation of eQTL and patterns of ASE is an essential step towards developing high-quality maps of regulatory variation.

Public Health Relevance

Essential to understanding the influence of genetic variation on gene regulation and expression in different cells and tissues are validated maps of expression quantitative trait loci (eQTL). Our project proposes to produce high-throughput and sensitive allele-specific expression estimates to validate eQTL and to further improve our understanding of systemic interactions between regulatory variants and pathogenic protein-coding variants. This activity will improve our understanding of genome function and provide new insight into genetic architecture and interactions which define human traits.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1)
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Volpi, Simona
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Stanford University
Schools of Medicine
United States
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Tan, Meng How; Li, Qin; Shanmugam, Raghuvaran et al. (2017) Dynamic landscape and regulation of RNA editing in mammals. Nature 550:249-254
Babak, Tomas; DeVeale, Brian; Tsang, Emily K et al. (2015) Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse. Nat Genet 47:544-9
Rivas, Manuel A; Pirinen, Matti; Conrad, Donald F et al. (2015) Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348:666-9