---
OA_place: repository
OA_type: free access
_id: '18648'
abstract:
- lang: eng
text: "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"
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik and members of the Robinson group \r\nat
ISTA for their comments, which improved this manuscript. This work was funded \r\nby
a research collaboration agreement between Boehringer Ingelheim and the research
\r\ngroup of MRR at the Institute of Science and Technology Austria. Additional
funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by \r\ncore funding from the Institute of Science and Technology Austria. We
would like \r\nto acknowledge the participants and investigators of the UK Biobank
study. High- \r\nperformance computing was supported by the Scientific Service Units
(SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). "
article_processing_charge: No
author:
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Seyed Mahdi
full_name: Mahmoudi, Seyed Mahdi
id: b9f6d5ef-7774-11eb-a47f-df2c75c02ee7
last_name: Mahmoudi
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Markus J.
full_name: Bauer, Markus J.
last_name: Bauer
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal
inference for multiple risk factors and diseases from genomics data. bioRxiv.
2024. doi:10.1101/2023.12.06.570392
apa: Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J.,
& Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases
from genomics data. bioRxiv. https://doi.org/10.1101/2023.12.06.570392
chicago: Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer,
Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple
Risk Factors and Diseases from Genomics Data.” BioRxiv, 2024. https://doi.org/10.1101/2023.12.06.570392.
ieee: N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and
M. R. Robinson, “Causal inference for multiple risk factors and diseases from
genomics data,” bioRxiv. 2024.
ista: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2024.
Causal inference for multiple risk factors and diseases from genomics data. bioRxiv,
10.1101/2023.12.06.570392.
mla: Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases
from Genomics Data.” BioRxiv, 2024, doi:10.1101/2023.12.06.570392.
short: N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R.
Robinson, BioRxiv (2024).
corr_author: '1'
date_created: 2024-12-11T10:42:59Z
date_published: 2024-08-10T00:00:00Z
date_updated: 2026-04-28T22:30:26Z
day: '10'
department:
- _id: MaRo
doi: 10.1101/2023.12.06.570392
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2023.12.06.570392
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
- _id: bd936e6f-d553-11ed-ba76-a82299f63e8c
grant_number: '590359'
name: Advanced statistical modelling to facilitate more accurate characterisation
of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis
generation
publication: bioRxiv
publication_status: published
related_material:
record:
- id: '18642'
relation: dissertation_contains
status: public
status: public
title: Causal inference for multiple risk factors and diseases from genomics data
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...