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Preprint describes embryonic marker-based screen for detecting meiotic drive alleles

A bioRxiv preprint introduces a method using early embryonic markers to screen for meiotic drivers — selfish genetic elements that bias their own transmission through gametogenesis — aiming to make detection more tractable in under-studied organisms.

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Researchers have posted a bioRxiv preprint presenting a new screening methodology for identifying meiotic drive alleles using embryonic markers as a readout. Meiotic drivers are selfish genetic elements that subvert the normally fair segregation of chromosomes during meiosis to increase their own transmission to offspring, even at a cost to the organism's overall fitness. Despite their importance for understanding rapid karyotype evolution, the emergence of reproductive isolation, and evolutionary arms races between selfish elements and host suppressor loci, relatively few meiotic drivers have been well characterised at a molecular level.

The preprint, posted on 26 May 2026 via bioRxiv (Cold Spring Harbor Laboratory), describes a strategy that uses phenotypic markers visible in early embryos as a proxy readout for biased segregation. The approach is designed to be broadly applicable across organisms where conventional population-genetic or cytological screens are difficult to implement at scale.

The authors argue that the scarcity of characterised meiotic drivers reflects methodological limitations as much as biological rarity, and that improved screening tools may uncover a more widespread incidence of drive phenomena across taxa. The preprint has not yet been peer-reviewed; full methodological and experimental details are available in the deposited manuscript.

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  1. Primary sourcePreprint bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-26
    A method to screen for meiotic drive using embryonic markers

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meiotic-drive selfish-genetic-elements gametogenesis karyotype-evolution reproductive-isolation functional-genomics preprint
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Genetic Current is the news section of Evagene, an academic, research, and educational pedigree-modelling platform. Stories are AI-drafted summaries of items from trusted public sources, written for researchers, clinicians, educators, students, genealogists, and patients with an interest in genetics. Summaries are for educational and research purposes only and are not medical advice.

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