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Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits

Author

Listed:
  • Andrew D Bretherick
  • Oriol Canela-Xandri
  • Peter K Joshi
  • David W Clark
  • Konrad Rawlik
  • Thibaud S Boutin
  • Yanni Zeng
  • Carmen Amador
  • Pau Navarro
  • Igor Rudan
  • Alan F Wright
  • Harry Campbell
  • Veronique Vitart
  • Caroline Hayward
  • James F Wilson
  • Albert Tenesa
  • Chris P Ponting
  • J Kenneth Baillie
  • Chris Haley

Abstract

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.Author summary: The targets of most medications prescribed today are proteins. For many common diseases our understanding of the underlying causes is often incomplete, and our ability to predict whether new drugs will be effective is remarkably poor. Attempts to use genetics to identify drug targets have an important limitation: standard study designs link disease risk to DNA but do not explain how the genotype leads to disease. In our study, we made robust statistical links between DNA variants and blood levels of 249 proteins, in two separate groups of Europeans. We then used this information to predict protein levels in large genetic studies. In many cases, this second step gives us evidence that high or low levels of a given protein play a role in causing a given disease. Among dozens of high-confidence links, we found new evidence for a causal role of a protein called SHPS1 in schizophrenia, and of another protein (FABP2) in heart disease. Our method takes advantage of information from large numbers of existing genetic studies to prioritize specific proteins as drug targets.

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  • Andrew D Bretherick & Oriol Canela-Xandri & Peter K Joshi & David W Clark & Konrad Rawlik & Thibaud S Boutin & Yanni Zeng & Carmen Amador & Pau Navarro & Igor Rudan & Alan F Wright & Harry Campbell & , 2020. "Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits," PLOS Genetics, Public Library of Science, vol. 16(7), pages 1-24, July.
  • Handle: RePEc:plo:pgen00:1008785
    DOI: 10.1371/journal.pgen.1008785
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    References listed on IDEAS

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    1. Chen Yao & George Chen & Ci Song & Joshua Keefe & Michael Mendelson & Tianxiao Huan & Benjamin B. Sun & Annika Laser & Joseph C. Maranville & Hongsheng Wu & Jennifer E. Ho & Paul Courchesne & Asya Lya, 2018. "Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    2. Jared O'Connell & Deepti Gurdasani & Olivier Delaneau & Nicola Pirastu & Sheila Ulivi & Massimiliano Cocca & Michela Traglia & Jie Huang & Jennifer E Huffman & Igor Rudan & Ruth McQuillan & Ross M Fra, 2014. "A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness," PLOS Genetics, Public Library of Science, vol. 10(4), pages 1-21, April.
    3. Chen Yao & George Chen & Ci Song & Joshua Keefe & Michael Mendelson & Tianxiao Huan & Benjamin B. Sun & Annika Laser & Joseph C. Maranville & Hongsheng Wu & Jennifer E. Ho & Paul Courchesne & Asya Lya, 2018. "Author Correction: Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
    4. Karsten Suhre & Matthias Arnold & Aditya Mukund Bhagwat & Richard J. Cotton & Rudolf Engelke & Johannes Raffler & Hina Sarwath & Gaurav Thareja & Annika Wahl & Robert Kirk DeLisle & Larry Gold & Marij, 2017. "Connecting genetic risk to disease end points through the human blood plasma proteome," Nature Communications, Nature, vol. 8(1), pages 1-14, April.
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