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Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

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  • Xiaoquan Wen
  • Roger Pique-Regi
  • Francesca Luca

Abstract

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.Author summary: Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants that impact complex diseases. However, the roles of such studies in disease etiology remain poorly understood, primarily because a large proportion of the GWAS findings are located in the non-coding region of the genome. Recent advancements in high-throughput sequencing technology enable the systematic investigation of molecular quantitative trait loci (QTLs), which are genetic variants that directly affect molecular phenotypes (e.g., gene expression, transcription factor binding and DNA methylation). Linking molecular QTLs to GWAS findings intuitively represents an important step for interpreting the biological and clinical relevance of the GWAS results. In this paper, we describe a rigorous and efficient computational approach that assesses the enrichment and overlap between the GWAS findings and molecular QTLs. Importantly, we illustrate that the accurate quantification of overlapping between molecular QTL and GWAS signals requires reliable enrichment estimation. Our proposed approach fully accounts for the intrinsic uncertainty embedded in the association analyses of GWAS and molecular QTL mapping, and it outperforms the existing state-of-the-art approaches. Applying the proposed approach to the GWAS data of blood lipid traits and the whole blood expression QTLs (eQTLs) yields some novel biological insights and also illustrates the potential limitations of the currently available molecular QTL data.

Suggested Citation

  • Xiaoquan Wen & Roger Pique-Regi & Francesca Luca, 2017. "Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization," PLOS Genetics, Public Library of Science, vol. 13(3), pages 1-25, March.
  • Handle: RePEc:plo:pgen00:1006646
    DOI: 10.1371/journal.pgen.1006646
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    1. Jacob F. Degner & Athma A. Pai & Roger Pique-Regi & Jean-Baptiste Veyrieras & Daniel J. Gaffney & Joseph K. Pickrell & Sherryl De Leon & Katelyn Michelini & Noah Lewellen & Gregory E. Crawford & Matth, 2012. "DNase I sensitivity QTLs are a major determinant of human expression variation," Nature, Nature, vol. 482(7385), pages 390-394, February.
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    2. Xinyuan Dong & Yu-Ru Su & Richard Barfield & Stephanie A Bien & Qianchuan He & Tabitha A Harrison & Jeroen R Huyghe & Temitope O Keku & Noralane M Lindor & Clemens Schafmayer & Andrew T Chan & Stephen, 2020. "A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-21, August.
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    4. Andrew R. Hamel & Wenjun Yan & John M. Rouhana & Aboozar Monovarfeshani & Xinyi Jiang & Puja A. Mehta & Jayshree Advani & Yuyang Luo & Qingnan Liang & Skanda Rajasundaram & Arushi Shrivastava & Kather, 2024. "Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma," Nature Communications, Nature, vol. 15(1), pages 1-25, December.
    5. Gianluca Ursini & Pasquale Di Carlo & Sreya Mukherjee & Qiang Chen & Shizhong Han & Jiyoung Kim & Maya Deyssenroth & Carmen J. Marsit & Jia Chen & Ke Hao & Giovanna Punzi & Daniel R. Weinberger, 2023. "Prioritization of potential causative genes for schizophrenia in placenta," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    6. Seong Kyu Han & Michelle T. McNulty & Christopher J. Benway & Pei Wen & Anya Greenberg & Ana C. Onuchic-Whitford & Dongkeun Jang & Jason Flannick & Noël P. Burtt & Parker C. Wilson & Benjamin D. Humph, 2023. "Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Kynon J. M. Benjamin & Ria Arora & Arthur S. Feltrin & Geo Pertea & Hunter H. Giles & Joshua M. Stolz & Laura D’Ignazio & Leonardo Collado-Torres & Joo Heon Shin & William S. Ulrich & Thomas M. Hyde &, 2024. "Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    8. Yangqing Deng & Wei Pan, 2020. "A powerful and versatile colocalization test," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-18, April.
    9. Anqi Zhu & Nana Matoba & Emma P Wilson & Amanda L Tapia & Yun Li & Joseph G Ibrahim & Jason L Stein & Michael I Love, 2021. "MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity," PLOS Genetics, Public Library of Science, vol. 17(4), pages 1-24, April.
    10. Xianyong Yin & Lap Sum Chan & Debraj Bose & Anne U. Jackson & Peter VandeHaar & Adam E. Locke & Christian Fuchsberger & Heather M. Stringham & Ryan Welch & Ketian Yu & Lilian Fernandes Silva & Susan K, 2022. "Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    11. Naim Panjwani & Fan Wang & Scott Mastromatteo & Allen Bao & Cheng Wang & Gengming He & Jiafen Gong & Johanna M Rommens & Lei Sun & Lisa J Strug, 2020. "LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-8, October.
    12. Jayshree Advani & Puja A. Mehta & Andrew R. Hamel & Sudeep Mehrotra & Christina Kiel & Tobias Strunz & Ximena Corso-Díaz & Madeline Kwicklis & Freekje Asten & Rinki Ratnapriya & Emily Y. Chew & Dena G, 2024. "QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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