MOTIVATION: Boldness-shyness is considered a fundamental axis of behavioural variation in humans and other species, with obvious adaptive causes and evolutionary implications. Besides an individual's own genetics, this phenotype is also affected by the genetic make-up of peers in the individual's social environment. To identify genetic determinants of variation along the bold-shy behavioural axis, a reliable experimental and analytical set-up able to highlight direct and indirect genetic effects is needed. RESULTS: We 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 individual's 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 (MIKK) panel has recently been established. AVAILABILITY AND IMPLEMENTATION: The code associated with this work is available on GitHub (https://github.com/birneylab/medaka_behaviour_pilot) and Software Heritage (swh: 1: dir: c9abec1c5d62d22e43c9e97d995c56261784d9ab). Experimental data have been uploaded to the EBI Bioimage Archive (https://doi.org/10.6019/S-BIAD1421).

SUMMARY: Linear mixed models (LMMs) are a commonly used statistical approach in genome-wide association studies when population structure is present. However, naive permutations of the phenotype to empirically estimate the null distribution of a statistic of interest are not appropriate in the presence of population structure or covariates. This is because the samples are not exchangeable with each other under the null hypothesis, and because permuting the phenotypes breaks the relationship among those and eventual covariates. For this reason, we developed FlexLMM, a Nextflow pipeline that can perform appropriate permutations in LMMs while allowing for flexibility in the definition of the exact statistical model to be used. FlexLMM can set a significance threshold via permutations, thanks to a two-step process where the population structure is first regressed out, and only then are the permutations performed on the uncorrelated residuals. We envision this pipeline will be particularly useful for researchers working on multi-parental crosses among inbred lines of model organisms or farm animals and plants. AVAILABILITY AND IMPLEMENTATION: The source code and documentation for the FlexLMM is available at https://github.com/birneylab/flexlmm.

MOTIVATION: Genome-wide association studies (GWAS) have been remarkably successful in identifying associations between genetic variants and imaging-derived phenotypes. To date, the main focus of these analyses has been on established, clinically-used imaging features. We sought to investigate if deep learning approaches can detect more nuanced patterns of image variability. RESULTS: We used an autoencoder to represent retinal optical coherence tomography (OCT) images from 31 135 UK Biobank participants. For each subject, we obtained a 64-dimensional vector representing features of retinal structure. GWAS of these autoencoder-derived imaging parameters identified 118 statistically significant loci; 41 of these associations were also significant in a replication study. These loci encompassed variants previously linked with retinal thickness measurements, ophthalmic disorders, and/or neurodegenerative conditions. Notably, the generated retinal phenotypes were found to contribute to predictive models for glaucoma and cardiovascular disorders. Overall, we demonstrate that self-supervised phenotyping of OCT images enhances the discoverability of genetic factors influencing retinal morphology and provides epidemiologically informative biomarkers. AVAILABILITY AND IMPLEMENTATION: Code and data links available at https://github.com/tf2/autoencoder-oct.

SUMMARY: High-resolution imaging of model organisms allows the quantification of important physiological measurements. In the case of fish with transparent embryos, these videos can visualize key physiological processes, such as heartbeat. High throughput systems can provide enough measurements for the robust investigation of developmental processes as well as the impact of system perturbations on physiological state. However, few analytical schemes have been designed to handle thousands of high-resolution videos without the need for some level of human intervention. We developed a software package, named FEHAT, to provide a fully automated solution for the analytics of large numbers of heart rate imaging datasets obtained from developing Medaka fish embryos in 96-well plate format imaged on an Acquifer machine. FEHAT uses image segmentation to define regions of the embryo showing changes in pixel intensity over time, followed by the classification of the most likely position of the heart and Fourier Transformations to estimate the heart rate. Here, we describe some important features of the FEHAT software, showcasing its performance across a large set of medaka fish embryos and compare its performance to established, less automated solutions. FEHAT provides reliable heart rate estimates across a range of temperature-based perturbations and can be applied to tens of thousands of embryos without the need for any human intervention. AVAILABILITY AND IMPLEMENTATION: Data used in this manuscript will be made available on request.

The 3D structure of chromatin plays a key role in genome function, including gene expression, DNA replication, chromosome segregation, and DNA repair. Furthermore the location of genomic loci within the nucleus, especially relative to each other and nuclear structures such as the nuclear envelope and nuclear bodies strongly correlates with aspects of function such as gene expression. Therefore, determining the 3D position of the 6 billion DNA base pairs in each of the 23 chromosomes inside the nucleus of a human cell is a central challenge of biology. Recent advances of super-resolution microscopy in principle enable the mapping of specific molecular features with nanometer precision inside cells. Combined with highly specific, sensitive and multiplexed fluorescence labeling of DNA sequences this opens up the possibility of mapping the 3D path of the genome sequence in situ. Here we develop computational methodologies to reconstruct the sequence configuration of all human chromosomes in the nucleus from a super-resolution image of a set of fluorescent in situ probes hybridized to the genome in a cell. To test our approach, we develop a method for the simulation of DNA in an idealized human nucleus. Our reconstruction method, ChromoTrace, uses suffix trees to assign a known linear ordering of in situ probes on the genome to an unknown set of 3D in-situ probe positions in the nucleus from super-resolved images using the known genomic probe spacing as a set of physical distance constraints between probes. We find that ChromoTrace can assign the 3D positions of the majority of loci with high accuracy and reasonable sensitivity to specific genome sequences. By simulating appropriate spatial resolution, label multiplexing and noise scenarios we assess our algorithms performance. Our study shows that it is feasible to achieve genome-wide reconstruction of the 3D DNA path based on super-resolution microscopy images.

MOTIVATION: The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH-usually Cy5 and Cy3-can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries. RESULTS: In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner. CONCLUSIONS: We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).