Evidence map›Paper›PMID 41509348›Full record

ArticlebioRxiv : the preprint server for biology2025

Improved Bayesian inference of hybrids using genome sequences.

Sneha Chakraborty, Bruce Rannala

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Sneha ChakrabortyDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles.ORCID 0000-0003-3801-1605
Bruce RannalaDepartment of Evolution and Ecology, University of California, Davis.ORCID 0000-0002-8355-9955

Funding

Statistical Methods and Algorithms for Population Genomic InferenceR01GM123306 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RANNALA, BRUCE · 2020 to 2023
$1.6M
NIGMS NIH HHS R01 GM123306
6 · The paper itself

Abstract

A Bayesian hybrid inference method is developed which infers hybrids and backcrosses across two generations using sampled genomes from two populations. The method improves on that of Chakraborty and Rannala (2023) by accounting for uncertainty of population haplotype frequencies and correctly marginalizing over haplotypes while still modeling linkage and recombination across the genome. In analyses of simulated data the new method produced posterior probabilities nearly identical to the method of Chakraborty and Rannala (2023) when sample sizes were large. For small sample sizes, posterior probabilities produced by the new method tended to be lower as expected since it accounts for additional uncertainties of population haplotype frequencies. Statistical performance of the new method as measured by the ROC (Receiver Operation Characteristic) curve, appears equivalent to that of Chakraborty and Rannala (2023). The new method is applied to three recently published datasets for populations of kiwifruit (genus

Indexed as

Bayesian inferencehybrid inferenceposterior predictive distributionrecombinationwhole genome sequence data

Identifiers

PMID41509348
PMCPMC12776265

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Registered trials

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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 OpenQuestion graph.