Author

Etienne J. Orliac

University of Lausanne

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetic & Genomic Medicine· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 3 Organism:human· 3 Research Focus:variant effect prediction· 3 Research Focus:genome assembly· 2 Research Focus:GWAS / association mapping· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

Daniel Trejo BañosiD· 3 shared Matthew R. RobinsoniD· 3 shared Reedik MägiiD· 3 shared Sven E. OjaveeiD· 3 shared Athanasios KousathanasiD· 2 shared Kristi LälliD· 2 shared Marion PatxotiD· 2 shared Peter M. VisscheriD· 2 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 44 mutual co-author links

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3 shared papersGenetic & Genomic Medicine

Daniel Trejo BañosORCID

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3 papers

Recent·Most cited

Improving GWAS discovery and genomic prediction accuracy in Biobank data

Etienne J. Orliac, Daniel Trejo Baños, Sven E. Ojavee, Kristi Läll, Reedik Mägi, Peter M. Visscher, Matthew R. Robinson

This paper improves genomic prediction and GWAS discovery in biobank data using a Bayesian grouped mixture of regressions model, achieving higher accuracy and detecting more loci than existing methods.

Proceedings of the National Academy of Sciences · Jul 29, 2022Peer-reviewedGenomics

Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Marion Patxot, Daniel Trejo Baños, Athanasios Kousathanas, Etienne J. Orliac, Sven E. Ojavee, Gerhard Moser, Alexander Holloway, Julia Sidorenko, Zoltán Kutalik, Reedik Mägi, Peter M. Visscher, Lars Rönnegård, Matthew R. Robinson

The paper introduces a Bayesian model, BayesRR-RC, to infer the genetic architecture of complex traits and finds that most genetic variance is attributed to non-proximal regulatory and coding regions.

Nature Communications · Nov 30, 2021Peer-reviewedGenetic & Genomic Medicine

Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis

Sven E. Ojavee, Athanasios Kousathanas, Daniel Trejo Baños, Etienne J. Orliac, Marion Patxot, Kristi Läll, Reedik Mägi, Krista Fischer, Zoltán Kutalik, Matthew R. Robinson

This paper introduces BayesW, a Bayesian method for analyzing time-to-event phenotypes, showing improved statistical power and prediction accuracy for age-at-onset traits in large-scale datasets.

Nature Communications · Apr 20, 2021Peer-reviewedGenetic & Genomic Medicine