Biology subjects

Guhlin, J. G.

Publications and source records attributed to Guhlin, J. G..

9 recordsLinked to original sources

bioRxiv2026

Roost: Avian Influenza Host Genomics Resource

Highly pathogenic avian influenza (HPAI) causes mass mortality in poultry and wildlife, and puts already threatened species at further risk. Understanding differences in host susceptibility requires evidence currently scattered across publications, surveillance reports and genomic resources. We present Roost, a web resource that brings together more than 1,300 publications and reports, surveillance detections, and a catalogue of over 2,000 avian genome assemblies. Large language model-assisted literature analysis and comparative genomics link species, genes and molecular mechanisms to their supporting evidence. Around 51% of species with HPAI detections are not represented in the analysed corpus. Research concentrates on poultry, model organisms, and a small set of immune pathways. Research tracks neither mortality magnitude nor extinction risk; species with the largest recorded die-offs and threatened hosts alike remain largely unstudied. Roost makes this evidence, along with genome analyses, accessible to researchers and conservation practitioners, connecting documented disease with host biology and highlighting priorities for sequencing and functional research. Roost is available at http://josephguhlin.com/Roost

genomics↗

bioRxiv2026

OrthoGLMM: Phylogenetic Association Testing for Gene Content and Trait Evolution

1MotivationComparative genome projects can now assemble and annotate hundreds of species, creating an opportunity to test whether species-level traits are associated with repeated changes in gene content. These tests must account for shared ancestry, sparse orthogroups, rare trait origins, and thousands of simultaneous associations. ResultsWe present OrthoGLMM, a phylogenetically informed framework for the association of traits and orthogroup presence/absence or copy number across species. OrthoGLMM combines deterministic GLMM scans with solver-rerun empirical calibration and calibrated FDR estimation. In three benchmark datasets, OrthoGLMM recovered expected signals for bacterial diazotrophy, plant nodulation, and marine mammals. Availability and ImplementationSource code, documentation, example data, and reproducibility scripts will be available at http://github.com/jguhlin/OrthoGLMM. Contactjoseph.guhlin@gmail.com

bioinformatics↗

bioRxiv2025

D-BLUP: a differentiable genomic BLUP model with learnable variance and marker weights

1.Genomic best linear unbiased prediction (GBLUP) is widely used for genomic selection in livestock and crop breeding. There is growing interest in connecting machine learning with genomic breeding value prediction. Although the BLUP formula is itself mathematically differentiable, existing implementations do not expose a differentiable computational graph and cannot be trained end-to-end. Here, we present [D]-BLUP, a differentiable implementation of the genomic mixed model in JAX that makes the BLUP solve part of the training process rather than an external step. The variance ratio {lambda} and optional block-level kernel weights are treated as trainable parameters, allowing these components to be learned directly from prediction error while preserving the BLUP structure used in animal and plant breeding. This means BLUP can now fit naturally within gradient-based models common in machine learning without losing its interpretability. The method solves the familiar system (Gw + {lambda}I) u = y* with either an unweighted VanRaden genomic relationship matrix G or a block-weighted variant Gw, via automatic differentiation, allowing simultaneous training. On a public dataset, [D]-BLUP with a fixed VanRaden kernel reproduces rrBLUP estimated breeding values and predictive performance, while learning {lambda}, and optionally SNP block weights, from a mean squared error objective. [D]-BLUP preserves the structure of BLUP used in routine breeding programs but makes it differentiable, maintaining its interpretability while making it embeddable in machine learning pipelines.

genomics↗

bioRxiv2025

The genome of Doleromyrma darwiniana and annotations for five additional Dolichoderinae species

Darwins ant, Doleromyrma darwiniana, is a native ant to Australia that has established invasive populations in nearby Aotearoa, New Zealand. Darwins ant belongs to the Dolichoderinae subfamily of the Formicidae family, which contains species with a broad range of invasion histories, including the internationally prolific Argentine ant (Linepithema humile). The genome of Darwins ant, Doleromyrma darwiniana, is presented here, created from a combined sample of 10 female workers. Generated using Oxford Nanopore Technologies PromethION 2 sequencing data, the assembly has a total length of 238.80 Mb and an N50 of 14.23 Mb. The assembly is compared to that of L. humile and four additional species from the same subfamily, the Dolichoderinae. Annotations were generated for all six Dolichoderinae species and sorted into orthogroups to assess the diversity and conservation of the Dolichoderinae genomic repertoire.

genomics↗

bioRxiv2025

Population genomics of yellow-eyed penguins uncovers subspecies divergence and candidate genes linked to respiratory distress syndrome

Yellow-eyed penguins (hoiho/takaraka, Megadyptes antipodes) are among the worlds rarest penguins and are regarded as a taonga (treasured) species in Aotearoa New Zealand. Since 2019, chicks on the New Zealand mainland have been affected by a deadly neonatal disease called respiratory distress syndrome (RDS), contributing to a decline to fewer than 143 breeding pairs. To investigate the putative genetic basis of this disease, we generated high-quality whole-genome data from 249 individuals spanning the species range, including from the New Zealand mainland (Northern range) and subantarctic Enderby and Campbell Islands (Southern). Population genomic analyses unexpectedly revealed three deeply divergent lineages with negligible gene flow, consistent with recognition of three distinct subspecies. Phylogenetic divergence dating suggests that these splits predate human arrival by several millennia, with the Northern lineage diverging from the Southern populations 5-16 ka. Genome scans for local adaptation revealed regions of strong differentiation, and genome-wide association analyses identified candidate immune and respiratory genes linked to RDS. In partnership with Ng[a]i Tahu, who hold indigenous guardianship over yellow-eyed penguins, we recommend recognition of three subspecies, urgent conservation action for the critically small and rapidly declining Northern subspecies, and the need for immediate population size and trend assessments for Auckland and Campbell Island populations.

genomics↗

bioRxiv2024

Rapid and cost-effective production of high-quality insect genome sequences

Insects are the most diverse group of animals on earth, with nearly 1 million species described to date, yet this variation is not reflected within the ranks of genomes currently available. In the current age of genetics, the generation of high-quality reference genomes is invaluable, often necessary and expensive. We prove that insect genomes do not need to come at a large cost. We demonstrate a cost-efficient method for producing high-quality insect genomes, with comparisons to current reference assemblies.

genomics↗

bioRxiv2022

Capturing species-wide diversity of the gut microbiota and its relationship with genomic variation in the critically endangered kakapo

The gut microbiota plays an essential role in host health that has important implications for the conservation management of threatened wildlife. While factors such as diet, medication, and habitat are known to shape the microbiota, our understanding of the entirety of factors, including the complex role of the host genomic background, remains incomplete. Our research on the gut microbiota of the critically endangered k[a]k[a]p[o] (Strigops habroptilus), a flightless parrot species endemic to Aotearoa New Zealand, represents, to our knowledge, the first study to describe the gastrointestinal bacterial diversity for virtually an entire species and to assess the relationship between gut microbiota and host genomic diversity in a highly threatened population. Here we report a 16S rRNA gene-based analysis of k[a]k[a]p[o] faecal samples representing the gut microbiota for 84% of k[a]k[a]p[o] (n = 133). This survey was then leveraged with exceptional metadata to tease apart the impact of host genomic diversity and factors such as sex, diet, antibiotic treatment, disease status, habitat, and time of sampling on the k[a]k[a]p[o] gut microbiota, with sex being the only covariate significantly associated with gut microbiota diversity. We find evidence of a highly polygenic genomic architecture of the gut microbiota and further identify putative associations between gut bacterial diversity and functional biological pathways related to intestinal homeostasis, inflammation, immune response and metabolism. This improved understanding of the k[a]k[a]p[o] gut microbiota - and its relationship with host genomics - can directly benefit k[a]k[a]p[o] management and conservation by providing new insights into the role of the gut microbiome in k[a]k[a]p[o] health and disease mitigation. Overall, we anticipate that an integration of microbiome studies in conservation research and management will improve our understanding of how the concept of One Health with its implications for human, animal and environmental welfare can be achieved.

microbiology↗

bioRxiv2022

Species-wide genomics of kakapo provides transformational tools to accelerate recovery

The k[a]k[a]p[o] is a critically endangered, intensively managed, long-lived nocturnal parrot endemic to Aotearoa New Zealand. We generated and analyzed whole-genome sequence data for nearly all individuals living in early 2018 (169 individuals) to generate a high-quality species-wide genetic variant callset. We leverage extensive long-term metadata to quantify genome-wide diversity of the species over time and present new approaches using probabilistic programming, combined with a phenotype dataset spanning five decades, to disentangle phenotypic variance into environmental and genetic effects while quantifying uncertainty in small populations. We find associations for growth, disease susceptibility, clutch size, and egg fertility within genic regions previously shown to influence these traits in other species. Finally, we generate breeding values to predict phenotype and illustrate that active management over the past 45 years has maintained both genome-wide diversity and diversity in breeding values, and hence, evolutionary potential. We provide new pathways for informing future conservation management decisions for k[a]k[a]p[o], including prioritizing individuals for translocation and monitoring individuals with poor growth or high disease risk. Overall, by explicitly addressing the challenge of small sample size, we provide a template for the inclusion of genomic data that will be transformational for species recovery efforts around the globe.

genomics↗

bioRxiv2022

Germ-stem cells and oocyte production in the Honeybee Queen Ovary

Understanding the reproduction of honeybee queens is crucial to support populations of this economically important insect. Here we examine the structure of the honeybee ovary to determine the nature of the germ-stem cells in the ovary. Using a panel of marker genes that mark somatic or germ-line tissue in other insects we determine which cells in the honeybee ovary are somatic and which germline. We examine patterns of cell division, and demonstrate that, unlike Drosophila, there are no single germ-line stem cells that provide the germ-line in honeybees. Germ-line stem cells are clustered in groups of 8 cells, joined by a polyfusome, and collections of these, in each ovariole, maintain the germ-line during reproduction. We also show that these 8-cell clusters can divide, and that their division occurs such that the numbers of germ-line stem cells are relatively constant over the reproductive life of queen honeybees. This information helps us to understand the diversity of structures in insects reproduction, and provide information to better support honeybee reproduction.

developmental biology↗