Biology subjects

Pierotti, S.

Publications and source records attributed to Pierotti, S..

4 recordsLinked to original sources

bioRxiv2026

A Scalable, High-Throughput Optomotor Response Pipeline for Quantitative Analysis of Vision Using Infinity-Pools

The optomotor response (OMR) provides a robust readout assay for evaluating visually guided locomotion and has been widely applied to various animal models. We present a scalable, high-throughput OMR platform with tuneable and moving stripe patterns simultaneously projected to the side of 50 petri dishes. The setup is optimized for hatchling-stage medaka (Oryzias latipes and Oryzias sakaizumii) and larval stage zebrafish (Danio rerio) and integrates custom software for automated generation of visual stimuli and specimen detection, enabling precise and automated quantification of optomotor behaviour across thousands of individuals. Using this platform, we systematically assess the visual performance of zebrafish and five medaka strains. By varying stripe width, direction, speed, contrast, and colour, we address spatial resolution, contrast, and colour sensitivity. Albino mutant medaka exhibited the highest sensitivity, responding at lower stripe widths and across a broader range of contrasts and chromatic conditions. Interestingly, some Cab and HO5 hatchlings displayed uni-directional response, revealing strain-specific visuomotor differences. Our platform enables reliable detection of OMR behaviour already at hatching and supports robust analysis of visuomotor function. The modular design, automated detection, analysis pipeline, and capacity for large sample numbers make it a powerful tool for comparative vision research, high-throughput genetic screening, and systematic behavioural profiling.

animal behavior and cognition↗

bioRxiv2025

Discovery and characterisation of gene by environment and epistatic genetic effects in a vertebrate model

Phenotypic variation arises from the interplay between genetic and environmental factors. However, disentangling these interactions for complex traits remains challenging in observational cohorts such as human biobanks. Instead, model organisms where genetic (G) and environmental (E) variation can be controlled offer a valuable complement to human studies in the analysis of higher-order genetic effects such as GxE interactions, dominance, and epistasis. Here, we utilized 76 medaka strains of the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel, to compare heart rate plasticity across temperatures. An F2 segregation analysis identified 16 quantitative trait loci (QTLs), with many exhibiting dominance, GxE, GxG, and GxGxE interactions. We experimentally validated four candidate genes using gene editing, revealing their temperature-sensitive impact on heart function. Finally, we devised simulations to assess how GWAS discovery power is influenced by the choice of statistical models, showing that the apparent additivity in human GWAS is to be expected given study design and sample sizes of current studies. This work demonstrates the value of controlled model organism studies for dissecting the genetics of complex traits and provides guidance on the design of genetic association studies.

genetics↗

bioRxiv2024

Measurement and classification of bold-shy behaviours in medaka fish

MotivationBoldness-shyness is considered a fundamental axis of behavioural variation in humans and other species, with obvious adaptive causes and evolutionary implications. Besides an individuals own genetics, this phenotype is also affected by the genetic makeup of peers in the individuals social environment. To identify genetic determinants of variation along the bold-shy behavioural axis, a reliable experimental and analytical setup able to highlight direct and indirect genetic effects is needed. ResultsWe describe a custom assay designed to detect bold-shy behaviours in medaka fish, combining an open-field and novel-object component. We use this assay to explore direct and social genetic effects on the behaviours of 307 pairs of fish from five inbred medaka strains. Applying a Hidden Markov Model (HMM) to classify behavioural modes, we find that direct genetic effects influence the proportions of time the five strains spent in slow-moving states, explaining up to 29.7% of the variance in time spent in those states. We also found that an individuals behaviour is influenced by the genetics of its tank partner, explaining up to 8.64% of the variance in the time spent in slow-moving states. Our behavioural assay in combination with the HMM analysis is applicable to follow-up genetic linkage studies of genetic variants involved in direct behavioural effects and indirect social genetic effects. A suitable genetic resource for such studies, the Medaka Inbred Kiyosu-Karlsruhe panel (MIKK) has recently been established.

bioinformatics↗

bioRxiv2023

Genotype imputation in F2 crosses of inbred lines

MotivationCrosses among inbred lines are a fundamental tool for the discovery of genetic loci associated with phenotypes of interest. In organisms for which large reference panels or SNP chips are not available, imputation from low-pass whole-genome sequencing is an effective method for obtaining genotype data from a large number of individuals. To date, a structured analysis of the conditions required for optimal genotype imputation has not been performed. ResultsWe report a systematic exploration of the effect of several design variables on imputation performance in F2 crosses of inbred medaka lines using the imputation software STITCH. We determined that, depending on the number of samples, imputation performance reaches a plateau when increasing the per-sample sequencing coverage. We also systematically explored the trade-offs between cost, imputation accuracy, and sample numbers. We developed a computational pipeline to streamline the process, enabling other researchers to perform a similar cost-benefit analysis on their population of interest. Availability and implementationThe source code for the pipeline is available at https://github.com/birneylab/stitchimpute. While our pipeline has been developed and tested for an F2 population, the software can also be used to analyse populations with a different structure.

bioinformatics↗