MR-link-2: pleiotropy robust cis Mendelian randomization validated in three independent reference datasets of causality

A Adriaan van der Graaf R Robert Warmerdam C Chiara Auwerx T Toni Boltz D Dorret I. Boomsma A Andrew Brown E Evans Cheruiyot E Emma E. Davenport T Théo Dupuis T Tõnu Esko A Aiman Farzeen L Luigi Ferrucci T Timothy M. Frayling G Greg Gibson C Christian Gieger M Marleen van Greevenbroek B Binisha Hamal Mishra M M. Arfan Ikram M Michael Inouye R Rick Jansen M Mika Kähönen V Viktorija Kukushkina S Sandra Lapinska T Terho Lehtimäki R Reedik Mägi A Angel Martinez-Perez A Allan F. McRae J Joyce van Meurs L Lili Milani G Grant W. Montgomery S Sini Nagpal M Matthias Nauck R Roel Ophoff B Bogdan Pasaniuc D Dirk S. Paul E Elodie Persyn A Annette Peters H Holger Prokisch O Olli T. Raitakari E Emma Raitoharju A Andrew Singleton E Eline Slagboom J José Manuel Soria J Juan Carlos Souto A Alexander Teumer A Alex Tokolyi J Jan Veldink J Joost Verlouw A Ana Viñuela P Peter M. Visscher U Uwe Völker (Department of Functional Genomics, Universitätsmedizin Greifswald, Greifswald, Germany) S Stefan Weiss H Harm-Jan Westra A Andrew R. Wood M Manke Xie U Urmo Võsa M Maria Carolina Borges L Lude Franke Z Zoltán Kutalik

Abstract

Abstract Mendelian randomization (MR) identifies causal relationships from observational data but has increased Type 1 error rates (T1E) when genetic instruments are limited to a single associated region, a typical scenario for molecular exposures. We developed MR-link-2, which leverages summary statistics and linkage disequilibrium (LD) to estimate causal effects and pleiotropy in a single region. We compare MR-link-2 to other cis MR methods: i) In simulations, MR-link-2 has calibrated T1E and high power. ii) We reidentify metabolic reactions from three metabolic pathway references using four independent metabolite quantitative trait locus studies. MR-link-2 often (76%) outperforms other methods in area under the receiver operator characteristic curve (AUC) (up to 0.80). iii) For canonical causal relationships between complex traits, MR-link-2 has lower per-locus T1E (0.096 vs. min. 0.142, at 5% level), identifying all but one of the true causal links, reducing cross-locus causal effect heterogeneity to almost half. iv) Testing causal direction between blood cell compositions and marker gene expression shows MR-link-2 has superior AUC (0.82 vs. 0.68). Finally, analyzing causality between metabolites not directly connected by canonical reactions, only MR-link-2 identifies the causal relationship between pyruvate and citrate ( $$\hat{\alpha }$$ α ̂  = 0.11, P =  7.2⋅10 −7 ), a key citric acid cycle reaction. Overall, MR-link-2 identifies pleiotropy-robust causality from summary statistics in single associated regions, making it well suited for applications to molecular phenotypes.

Article Details

Volume / Issue Vol. 16, Issue 1
Published July 03, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (59)

A

Adriaan van der Graaf

R

Robert Warmerdam

C

Chiara Auwerx

T

Toni Boltz

D

Dorret I. Boomsma

A

Andrew Brown

E

Evans Cheruiyot

E

Emma E. Davenport

T

Théo Dupuis

T

Tõnu Esko

A

Aiman Farzeen

L

Luigi Ferrucci

T

Timothy M. Frayling

G

Greg Gibson

C

Christian Gieger

M

Marleen van Greevenbroek

B

Binisha Hamal Mishra

M

M. Arfan Ikram

M

Michael Inouye

R

Rick Jansen

M

Mika Kähönen

V

Viktorija Kukushkina

S

Sandra Lapinska

T

Terho Lehtimäki

R

Reedik Mägi

A

Angel Martinez-Perez

A

Allan F. McRae

J

Joyce van Meurs

L

Lili Milani

G

Grant W. Montgomery

S

Sini Nagpal

M

Matthias Nauck

R

Roel Ophoff

B

Bogdan Pasaniuc

D

Dirk S. Paul

E

Elodie Persyn

A

Annette Peters

H

Holger Prokisch

O

Olli T. Raitakari

E

Emma Raitoharju

A

Andrew Singleton

E

Eline Slagboom

J

José Manuel Soria

J

Juan Carlos Souto

A

Alexander Teumer

A

Alex Tokolyi

J

Jan Veldink

J

Joost Verlouw

A

Ana Viñuela

P

Peter M. Visscher

U

Uwe Völker

Department of Functional Genomics, Universitätsmedizin Greifswald, Greifswald, Germany

S

Stefan Weiss

H

Harm-Jan Westra

A

Andrew R. Wood

M

Manke Xie

U

Urmo Võsa

M

Maria Carolina Borges

L

Lude Franke

Z

Zoltán Kutalik