---
res:
  bibo_abstract:
  - "Statistical causal learning in genomics relies on the instrumental variable method
    of\r\nMendelian Randomization (MR). Currently, an overwhelming number of MR studies\r\npurport
    to show causal relationships among a wide range of risk factors and outcomes.\r\nHere,
    we show that selecting instrument variables from genome-wide association study\r\nestimates
    leads to high false discovery rates for many MR approaches, which can be\r\ngreatly
    reduced by employing a graphical inference approach which: (i) explicitly tests\r\ninstrumental
    variable assumptions; (ii) distinguishes direct from indirect factors in very\r\nhigh-dimensional
    data; (iii) discriminates pleiotropic from trait-specific markers, controlling
    for LD genome-wide; (iv) accommodates rare variants and binary outcomes in a\r\nprincipled
    way; and (v) identifies potential unobserved latent confounding. For 17 traits\r\nand
    8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that\r\nstandard
    MR analysis gives an abundance of findings that disappear under stringent\r\nassumption
    checks, with many relationships reflecting potential unmeasured confounding. This
    implies that mixtures of temporal precedence and potential for reverse-causality\r\nprohibit
    understanding the underlying nature of phenotypic and genetic correlations in\r\nbiobank
    data. We propose that well-curated longitudinal records are likely needed and\r\nthat
    our approach provides a first-step toward robust principled screening for potential\r\ncausal
    links.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nick N
      foaf_name: Machnik, Nick N
      foaf_surname: Machnik
      foaf_workInfoHomepage: http://www.librecat.org/personId=3591A0AA-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-6617-9742
  - foaf_Person:
      foaf_givenName: Seyed Mahdi
      foaf_name: Mahmoudi, Seyed Mahdi
      foaf_surname: Mahmoudi
      foaf_workInfoHomepage: http://www.librecat.org/personId=b9f6d5ef-7774-11eb-a47f-df2c75c02ee7
  - foaf_Person:
      foaf_givenName: Malgorzata
      foaf_name: Borczyk, Malgorzata
      foaf_surname: Borczyk
  - foaf_Person:
      foaf_givenName: Ilse
      foaf_name: Krätschmer, Ilse
      foaf_surname: Krätschmer
      foaf_workInfoHomepage: http://www.librecat.org/personId=30d4014e-7753-11eb-b44b-db6d61112e73
    orcid: 0000-0002-5636-9259
  - foaf_Person:
      foaf_givenName: Markus J.
      foaf_name: Bauer, Markus J.
      foaf_surname: Bauer
  - foaf_Person:
      foaf_givenName: Matthew Richard
      foaf_name: Robinson, Matthew Richard
      foaf_surname: Robinson
      foaf_workInfoHomepage: http://www.librecat.org/personId=E5D42276-F5DA-11E9-8E24-6303E6697425
    orcid: 0000-0001-8982-8813
  bibo_doi: 10.1101/2023.12.06.570392
  dct_date: 2024^xs_gYear
  dct_language: eng
  dct_title: Causal inference for multiple risk factors and diseases from genomics
    data@
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