Author

Petri Kemppainen

University of HelsinkiORCID 0000-0002-7228-8133Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Frédéric GuillaumeiD· 2 shared Juha MeriläiD· 1 shared Xueling YiiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 1 mutual co-author link

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2 shared papersEvolutionary Biology

Frédéric GuillaumeORCID

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3 papers

Recent·Most cited

Evolutionary BiologybioRxivPreprintOct 28, 2026

LDscnR: LD-aware complexity reduction for genome scans

Petri Kemppainen, Frédéric Guillaume

LDscnR reduces genome scan tests by grouping correlated markers using LD decay, improving precision while maintaining recall, and showing structure can explain outlier regions.

DOI 10.64898/2026.10.27.762116

whole-genome sequencingstatistical model

𝕏 PostBluesky

Evolutionary BiologybioRxivPreprintJan 21, 2026

Linkage disequilibrium scaling improves robustness and power to detect genomic regions under selection.

Petri Kemppainen, Frédéric Guillaume

This paper introduces an LD-scaled association statistic to improve detection of genomic regions under selection by incorporating linkage disequilibrium and reducing background noise.

DOI 10.64898/2026.01.19.700334

statistical modelgene regulatory network inference

𝕏 PostBluesky

GenomicsMolecular Ecology ResourcesPeer-reviewedJun 8, 2024

SLRfinder: a method to detect candidate sex-linked regions with linkage disequilibrium clustering

Xueling Yi, Petri Kemppainen, Juha Merilä

SLRfinder identifies candidate sex-linked regions using linkage disequilibrium clustering, outperforming SATC in labile sex chromosomes.

2 citations

GWAS summary statisticsstatistical model

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