Evidence map›Paper›PMID 39025905›Full record

ArticleNature communications2024

A robust cis-Mendelian randomization method with application to drug target discovery.

Zhaotong Lin, Wei Pan

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  11. Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zhaotong LinDivision of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA. zl23k@fsu.edu.ORCID 0000-0001-8723-4392
Wei PanDivision of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, 55455, USA.ORCID 0000-0002-1159-0582

Funding

Causal and integrative deep learning for Alzheimer's disease geneticsU01AG073079 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2021 to 2025
$3.5M
Deep Learning with Neuroimaging Genetic Data for Alzheimer's DiseaseR01AG069895 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2020 to 2024
$3.4M
Association analysis of rare variants with sequencing dataR01HL116720 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, WEI, PENG · 2013 to 2020
$3.3M
Estimation and inference in directed acyclic graphical models for biological networksR01AG074858 · NIA · UNIVERSITY OF MINNESOTA · PI Wei Pan, XIAOTONG Tom SHEN · 2022 to 2026
$3.2M
Discovering causal genes, brain regions and other risk factors for Alzheimer's DiseaseR01AG065636 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2024
$3.1M
Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL dataRF1AG067924 · NIA · UNIVERSITY OF MINNESOTA · PI PAN, WEI · 2020 to 2020
$1.9M
Estimation and Inference of Gene Regulatory NetworksR01GM126002 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2017 to 2020
$1.4M
NHLBI NIH HHS R01 HL116720NIA NIH HHS R01 AG065636NIA NIH HHS R01 AG069895NIA NIH HHS R01 AG074858NIA NIH HHS RF1 AG067924NIA NIH HHS U01 AG073079NIGMS NIH HHS R01 GM126002U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01 AG065636, R01 AG069895, RF1 AG067924, U01 AG073079, R01 AG074858, R01 HL116720 and R01 GM126002
6 · The paper itself

Abstract

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.

Indexed as

Drug DiscoveryLinkage DisequilibriumMendelian Randomization AnalysisPolymorphism, Single NucleotideCoronary Artery DiseaseGenetic PleiotropyGenome-Wide Association StudyHumansLikelihood FunctionsProprotein Convertase 9Quantitative Trait LociPCSK9 protein, humanProprotein Convertase 9

Identifiers

PMID39025905
PMCPMC11258283

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.