Author

Brieuc Lehmann

University College LondonORCID 0000-0002-7302-4391Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Assay:whole-genome sequencing· 2 Approach:Bayesian inference· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Cam‐CAN· 2 shared Linda GeerligsiD· 2 shared Richard N. HensoniD· 2 shared Simon Richard WhiteiD· 2 shared Chris C. HolmesiD· 1 shared Gil McVeaniD· 1 shared Gregor GorjanciD· 1 shared Hanbin LeeiD· 1 shared

Activity by Year

2017-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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2 shared papersNeuroscience

Cam‐CAN

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4 papers

Recent·Most cited

On ARGs, pedigrees, and genetic relatedness matrices

Brieuc Lehmann, Hanbin Lee, Luke Anderson-Trocmé, Jerome Kelleher, Gregor Gorjanc, Peter L. Ralph

This paper introduces branch relatedness and efficient algorithms for computing branch GRMs using tree sequences, enabling large-scale genomic analysis.

Genetics · Oct 8, 2025Peer-reviewedGenomics

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Brieuc Lehmann, Maxine E. Mackintosh, Gil McVean, Chris C. Holmes

This study shows that optimal polygenic score strategies vary by trait and ancestry, with smaller African-ancestry training sets outperforming larger European-ancestry sets for some traits.

Nature Communications · Jul 7, 2023Peer-reviewedGenomics

Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models

Brieuc Lehmann, Richard N. Henson, Linda Geerligs, Cam‐CAN, Simon Richard White

This paper introduces a Bayesian framework using exponential random graph models to characterise group-level brain connectivity patterns across different age groups using resting-state fMRI data.

NeuroImage · Oct 21, 2020Peer-reviewedNeuroscience

Assessing dynamic functional connectivity in heterogeneous samples

Brieuc Lehmann, Simon Richard White, Richard N. Henson, Cam‐CAN, Linda Geerligs

This study investigates how individual differences in non-dynamic features affect dynamic functional connectivity estimates in fMRI data and shows that common methods can detect spurious group differences.

NeuroImage · Jun 1, 2017Peer-reviewedNeuroscience