Frontiers in Genetics

An Ischemic stroke is one of the main causes of death and long-term disability around the world. There are very few treatment options available, especially for the cases where the short time period to give clot dissolving medicine has passed (Powers et al., 2019). While single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cell-to-cell differences within at issue (cellular heterogeneity) (Tang et al., 2009). However translating these high dimensional datasets into clinically useful biomarkers remains challenging (Rajko-mar et al., 2019). A major problem with current biomarker studies is their reliance on a single machine learning model, where results may reflect model bias rather than true biological signals (Lopez-Rincon et al., 2020). Methods: We developed a multi-model consensus framework integrating eight different machine learning methods from three groups: traditional models (logistic regression, random forest, XGBoost), a hybrid deep learning model (CNN-XGBoost), and transformer-inspired models (scGPT, scBERT, sc-Former, Geneformer). The proposed framework was applied to scRNA-seq data from a mouse middle cerebral artery occlusion (MCAO) model (GSE174574; n=6 mice, 54,599 cells). To prevent data leakage, we implemented rigorous subject-wise train/test splitting, ensuring all cells from individual animals remained either in the training sets or in the testsets. Results: All eight models achieved excellent performance on the independent test data, with AUC scores between 0.9778 and 0.9975. From six models that allowed gene level importance extraction, we identified 63 consensus genes (threshold: [≥] 4/6 models), with 17 genes selected by all 6 models. Pathway enrichment analysis showed significant enrichment of interferon signaling, with a novel dual-interferon pattern. Twelve of the consensus genes respond to interferon, and five of them (Ifitm6, Ifitm1, Gbp2, Igtp, Tgtp1) respond to both Type I (IFN-/{beta}) and Type II (IFN-{gamma}) interferons. Notably the consensus included Il1rn (interleukin-1 receptor antagonist), the target of Anakinra -- an FDA-approved drug that has completed Phase II stroke trials with promising safety profiles (Smith et al., 2018; Emsley et al., 2005). Conclusions: The Multi-model consensus framework, proposed in this paper, provides a robust and reliable approach to biomarker discovery that goes beyond the limitations of any single method and reduces individual algorithm bias. The convergence of classical, ensemble, and deep learning methods on interferon and IL-1 pathway genes supports their biological validity as stroke biomarkers. Our findings provide a prioritized gene list for further clinical validation and highlight interferon signaling as a possible treatment strategy warranting further investigation. Keywords: ischemic stroke; machine learning; single-cell RNA sequencing; biomarker discovery; consensus analysis; interferon signaling; neuroinflammation

Ancestry-related genomic variation may contribute to phenotypic diversity. However, its cellular consequences remain difficult to evaluate. To address this, we developed a human induced pluripotent stem cell (hiPSC)-based framework for investigating such phenotypes. We used the Japanese population as a model of Jomon-related and continental East Asian ancestry admixture. Jomon ancestry, estimated using the Jomon allele score (JAS), was associated with clinical measures, including renal-related traits and serum triglycerides in 34,152 Tohoku Medical Megabank participants. Peripheral blood mononuclear cell transcriptomics of donors from the upper and lower extremes of the JAS distribution (n = 10) suggested group-level differences in immune-related gene expression. To test whether the serum triglyceride association coincided with hepatic cellular phenotypes, we differentiated hiPSCs from these donors into hepatocyte-like cells. Neither intracellular triglycerides nor lipid-related gene expression differed between groups. These findings highlight the value of a genetically characterized hiPSC-based framework for investigating ancestry-associated phenotypes across cellular contexts.

The phenotype of a focal individual can be affected not only by the individuals own genes (direct genetic effects, DGE) but also by genes of other individuals it interacts with (indirect genetic effects, IGE). Better understanding IGE can help better understand the impact and mechanisms of social effects. Here, we investigated, in laboratory mice, the modulation of IGE by genetic background of the focal individual (social epistasis) and by sex. We focused on IGE arising from Epha4, a gene that has been associated with the individuals own stress-related and social behaviours (i.e. DGE) and the stress-coping behaviour and wound healing of the other mice in the cage (i.e. IGE). Among four different genetic backgrounds for focal mice, significant IGE from Epha4 were detected in CBA females only. A formal model selection analysis was consistent with the modulation of IGE by the similarity of the genetic backgrounds of the focal mouse and its Epha4 cage mate, as well as by sex. Observations of fighting in cages of males with dissimilar genetic backgrounds suggested aggression might underpin these modulatory effects, which prompted to us to expand the conceptual framework describing the mechanisms of social epistasis. Thus, our results strengthen the emerging view that IGE are not fixed properties of social genotypes but can depend on the genetic and sex context of interacting individuals, revealing an underexplored layer of their genetic architecture.

Non-coding variants altering mRNA splicing are increasingly recognized as important cause of Mendelian disorders. Deep intronic variants (DIV) that activate cryptic exons (CEs) cause ~20% inherited retinal diseases (IRD) cases, yet it remains challenging to predict intronic variant pathogenicity accurately, thus we used a high throughput splicing assay (HTSA) to measure the effects of rare deep intronic variants in recessive IRDs with one confirmed pathogenic variant. 640 very rare deep intronic variants were chosen in trans-position with known mutation from 76 patients. We tested them with HTSA, which consists of a split-GFP minigene, separated by an SMN1 gene intron into which the 270bp sequences flanking the DIVs were cloned. The plasmid library was transfected into 293HEK cells, and 48 hours later total RNA was extracted from the transfected cells. RNA transcripts produced by HTSA minigene were amplified by RT-PCR and sequenced. The intron sequences spliced between the two GFP exons were identified and quantified. 98 variants activated CE >100 times more in altered oligo compared to reference oligo and were classified as pathogenic in this experimental setting, validated in longer context, 26 variants were classified as VUS. Interestingly, only 6 from 90 variants (6,6%) were predicted to be pathogenic by in silico algorithms (Splice AI) which leaves room for significant improvement of prediction algorithms. The results of this experiment confirmed diagnosis for 50 of 78 patients (64%). So, 19,4% of deep intronic variants were shown to cause CE activation. This can lead to conclusion that significant portion of undiagnosed patients with recessive IRDs carry pathogenic intronic mutation which causes the disease.

Individuals carrying a G4C2 repeat expansion in C9orf72 can develop amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD), at various and unpredictable ages. The contribution of the expansion to this extreme clinical variability remained a matter of debate. In this work, we validated the use of optical genome mapping to efficiently size this expansion from blood and applied this method to a cohort of 54 repeat expansion carriers including patients with FTD (n = 23), ALS (n = 22) and presymptomatic individuals (n = 9). Despite the high variability in expansion profiles, distinct groups can be inferred. Although consensus repeat profiles were similar between FTD and ALS patients, relationships between repeat number, indexes of repeat instability, and the age at sampling differed between clinical conditions. An age at sampling-dependent increase of repeat instability in blood was observed in presymptomatic carriers and FTD patients (p = 0.01 and p = 0.04 respectively), in contrast to patients with ALS (p = 0.9). The proportion of shorter repeats ([500-1500 repeats]) increased with the age at sampling in ALS patients but decreased in presymptomatic and FTD cohorts, while the proportion of larger repeats (>2000 G4C2), in particular those exceeding 4000 repeats, increased along with the age at sampling in presymptomatic carriers and FTD cases (p <1.10-e4 and p = 0.006 respectively) conversely to patients with ALS (p = 0.98). In conclusion, this work strongly supports different dynamics of repeat instability of the C9orf72 pathogenic expansion in blood linked to the clinical evolution. These findings provide new insights into the relationship between expansion profiles and clinical variability.

The sheep research community benefits from a genome assembly that has been refined over the last 15 years. However, the rich diversity of sheep breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, twelve breeds were selected to represent (mostly French) sheep genetic diversity: Basco-Bearnaise, Black Belly, Blanche du Massif Central, Corse, Ile de France, Lacaune, Manech Tete Noire, Noire du Velay, Ouessant, Rambouillet, Vendeen, Soay. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. The reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Lacaune breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 11 standard sheep assemblies that pave the way for pangenome construction and structural variant functional studies.

Abstract Polycystic ovarian syndrome and Type 2 diabetes mellitus are complex multivariate diseases that share well-established clinical pathophysiology, yet the molecular basis of their overlap is unknown. Prior bioinformatics studies have identified common differentially expressed genes between these two conditions. However, they have not accounted for the directionality of the gene expression changes observed, potentially obscuring any biological distinctions they may have. This study applied a direction-aware approach to classify molecular signatures between PCOS and T2DM as concordant or discordant. Transcriptomic analysis of GEO datasets GSE138518 (ovarian granulosa tissue, PCOS) and GSE25724 (pancreatic islet tissue, T2DM) identified 225 and 1,302 DEGs, respectively. Venn diagram analysis showed that only three genes (SLC6A8, RGS4, and SORL1) were shared between PCOS and T2DM from the total of 1,527 genes, and all 3 genes were regulated in opposing directions. Disease gene retrieval from the Comparative Toxicogenomics Database, Online Mendelian Inheritance in Man database, and GeneCards showed 214 shared disease-associated genes, with 311 genes unique to PCOS and 496 genes unique to T2DM. Protein-protein interaction construction using STRING (version 12.0) identified 70 interacting nodes. CytoHubba analysis across 6 scoring methods identified 18 high-confidence hub genes, including INS, BCL2, MTOR, LEP, MFN2, and PIK3CD. Functional enrichment analysis identified biological processes including immune activation, apoptosis, and insulin signaling, which were confirmed as the main pathways in KEGG pathway analysis. These findings show that while PCOS and T2DM share limited DEGs, they share common pathogenic pathways.

In the recent growing interest of AI research toward biology, genetic association studies of AI- derived phenotypes from high-content modalities such as images emerges as a powerful means for biological discovery. However, such AI-phenotyping methods still lacks a good optimization target and an efficient evaluation framework. The number of discovered loci was the major criterion for evaluating the quality of AI-derived phenotypes. However, the time and computational resources required for running the GWAS and subsequent loci-clumping are substantial, limiting the rapid development and iteration of deep learning representation algorithms. Here we present a 1000x faster and lightweight framework, sum-h2, than traditional GWAS framework for evaluating genetic discovery through total heritability. We revisit tr(P^(-1) G), previously proposed in the context of evolutionary studies, as a measure of multi-phenotype heritability. We showed that sum-h2, the sum of heritability over phenotypic Principal Components (PCs), is equivalent to the linear transformation-invariant tr(P^(-1) G), through both theoretical proof and simulation studies. Moreover, sum-h2 can be estimated rapidly with minimal information loss over a relatedness-enriched sample, while preserving the relative ranking of endophenotypes by GWAS loci counts. Based on selected UKB data and sum-h2, we set up a Genetic Discovery Arena, enabling rapid and fair comparisons for the development and optimization of deep learning-derived phenotyping methods.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder with contributions from both maternal and fetal genetic factors, but how both genomes regulate placental transcription remains incompletely understood. Results: We analyzed placental 5'-end RNA sequencing data from 59 pregnancies in the FINNPEC cohort and performed separate maternal and fetal cis-eQTL analyses using gene-level expression and promoter-level expression defined as Transcript Far 5' Ends (TFE). Genotype-by-PE interaction models were used to identify genetic effects that differ according to PE status. No gene-level interaction eQTLs remained significant after multiple-testing correction, although suggestive signals included LILRB2 in the maternal and LPCAT3 in the fetal analyses. In contrast, we identified two significant syntenic promoter-level interaction eQTLs: rs12504642-TFE61820 at the FHDC1 locus in the maternal analysis and rs1557150-TFE9032 at the PPIAP31 locus in the fetal analysis. Conclusion: These findings suggest that PE-dependent genetic interaction effects may be detectable at specific transcript promoter regions even when they are not captured at annotated gene levels.

ABSTRACT Objective: Skeletal muscle growth rate differs substantially among diverse pig breeds. In the present study, RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. CCND2 showed obvious differential expression among pig breeds. Although CCND2 is essential for cell cycle control, its biological function in skeletal muscle differentiation has rarely been reported. This work aims to identify DEGs in pig breeds with different growth rates and explore CCND2 role in porcine skeletal muscle development. Methods: RNA-seq was performed to identify differentially expressed genes (DEGs) across pig breeds with distinct growth rates. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules potentially associated with skeletal muscle growth and development. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was performed to validate the role of CCND2 in skeletal muscle differentiation. Results We identified 5,416, 4,846, and 2,777 DEGs in the Duroc pig (DU) vs. Jiangquan black pig (JQ), DU vs. Laiwu pigs (LW), and JQ vs. LW comparison groups, respectively. WGCNA identified two gene modules (lightgreen and darkmagenta) potentially associated with skeletal muscle growth and development. CCND2 exhibited differential expression in comparisons between DU vs JQ and DU vs LW, with higher expression observed in Duroc pigs. qRT-PCR analysis revealed that overexpression of CCND2 at day 6 (D6) of cell differentiation significantly increased the expression levels of CCND2 gene and markers of myogenic differentiation (p < 0.05). Conversely, knockdown of CCND2 significantly decreased the expression levels of CCND2 gene and myogenic differentiation markers (p < 0.05). Conclusions This study identified breed-specific DEGs and key gene modules in porcine skeletal muscle and verified that CCND2 promotes myogenic differentiation. These findings provide a genetic theoretical basis for further investigations into skeletal muscle growth and development. Keywords pig, skeletal muscle, transcriptome, WGCNA, CCND2

During biogeographical ancestry (BGA) analysis, an unknown individual for whom ancestry informative markers (AIMs) were analyzed is often assigned to one of a few reference populations. For some individuals, such an assignment is not meaningful: the two parents of the individual may come from different populations, or the individual may come from a population not included in the reference database. In this study, we examined classification methods in combination with 104 AIMs from the VISAGE Enhanced Tool to provide one of the following three possible outcomes: (i) in-reference (IR), with an assignment to one of the reference populations; (ii) admixed (AD), with parents from two in-reference populations; and (iii) out-of-reference (OR), where the individual's ancestry is not sufficiently represented by the reference populations. We therefore compared seven classifiers in combination with class-specific acceptance thresholds for the OR decision. The best-performing method according to our selection criterion was a naive Bayes classifier over pairs of populations, which we call the recent naive Bayes (RNB) classifier. Populations held out of the reference panel were correctly rejected in $84.9\%$ of cases. We further tested the method on $800$ individuals from seven additional groups not included in the reference panel. Only $29.4\%$ of individuals from four non-admixed populations were rejected, resembling joint ancestry with reference populations. In addition, we investigated the prediction accuracy in a newly collected dataset of 28 individuals with self-reported ancestry by analyzing AIMs using massively parallel sequencing.

Several tools are available to detect differential gene expression. The predominant approach of different statistical methods is to consider changes in gene expression independent from their genomic context. Previously, DELocal was introduced that takes into consideration gene expression dynamics in the genomic neighbourhood. Whereas DELocal was shown to identify developmentally regulated genes in developing mouse tooth, its performance in other biological conditions remains undetermined. To benchmark DElocal together with other methods, we develop another tool 'broadSeq'. It provides easy to use interface for methods like DELocal , deseq2, limma, edger, EBSeq and many more. Here we report benchmarking results on different data sets that comprise wild type developing mouse organs from different embryonic time points, and also human cancer data. The examined datasets are from the heart, kidney, liver, brain, forebrain, and hindbrain. We found that most methods produce relatively comparable lists of differentially expressed genes. Nevertheless, DELocal identifies partially distinct sets of genes compared to the other methods. Evaluating the identified genes using gene and disease ontology terms show that the uniquely detected genes by DELocal are biologically relevant and also highly expressed compared to the genes identified by the other methods. Because consistently highly expressed genes show relatively subtle fold changes, the neighbourhood concept of DELocal appears to correct for the difficulty in detecting these differential expression of highly expressed genes. Finally, we provide the tools as combined bioconductor packages, allowing researchers to explore and decide the best methods for their data and questions.

Recent deep learning (DL) models predict average gene expression levels from DNA sequences with high overall correlation to measured values. We examine these DL models through the lens of disease research. The seemingly high overall performance of DL models is largely due to capturing whether genes are "On" or "Off", and to a lesser extent, disease-relevant expression level changes for "On" genes. Indeed, the more bimodal the gene expression distribution, the better the reported performance. We track the extent of this issue across tissues, molecular systems, and cancers. Compounding the model evaluation issue, we find inconsistencies between the published code and reported performance, highlighting the importance of versioning and publishing performance evaluation code. These findings indicate that the high reported performance of popular DL models falls unexpectedly short in disease applications, and that the problem of personalized genomic prediction remains far from solved in a disease context.

INTRODUCTION: Throughout history, viruses have been infecting humans, leaving genomic traces called endogenous viral elements (EVEs). This study explores the infectious hypothesis for Alzheimer's disease (AD) that suggests that viral infections may contribute to AD by analyzing EVEs in genomes of AD patients and cognitively healthy centenarians (CHCs) measured by long-read sequencing. METHODS: We implemented an EVE detection pipeline that screened the genomes of 248 AD patients and 240 CHCs. EVEs were measured based on EVE count, length, and chromosome location. RESULTS: The genomes of AD patients contain significantly more EVEs than those of CHCs (p=3.2x10-4). Moreover, certain chromosomal regions in AD patients show a higher EVE count, particularly of the Orthoherpesviridae family, near genes differentially expressed in AD brains. DISCUSSION: These findings suggest that viral infections may increase susceptibility to AD, highlighting that preventive measures against infections potentially reduce AD risk.

Candida auris is an emerging multidrug-resistant fungal pathogen. However, little is known about the role of long noncoding RNAs (lncRNAs) in the human immune response to this fungus. We reanalyzed RNA-sequencing data from peripheral blood mononuclear cells obtained from three healthy donors. The cells were exposed to live C. auris, purified mannan, purified {beta} glucan, or RPMI control for 4 h and 24 h. We identified 607 putative novel polyadenylated lncRNA loci that were absent from GENCODE Release 48. Of the 449 novel loci tested for differential expression, 102 responded to at least one stimulus. Novel-lncRNA responses were limited at 4 h but increased markedly at 24 h. At 24 h, 78 novel lncRNAs responded to live C. auris, 34 to mannan, and 11 to {beta}-glucan. Many of these responses were specific to a particular stimulus. Among the 102 responsive lncRNAs, 94 were significantly co-expressed with protein coding genes. The coding-gene partners of the most highly connected lncRNAs were associated with innate immunity, defense responses, lysosomal functions, and signaling pathways. Temporal analysis identified 19 novel lncRNAs whose responses changed between 4 and 24 h. Fourteen were also differentially expressed and were selected for further analysis. Combining temporal response, co-expression, and genomic proximity identified 4 candidate cis-associated pairs: MSTRG.32011-TLR2, MSTRG.5763-DUSP5, MSTRG.25627-HCK, and MSTRG.18159 ITGB3.These findings reveal a predominantly late and stimulus-specific novel-lncRNA response to C. auris and provide prioritized candidates for experimental validation.

Aims/hypothesis The human pancreas is a central organ for metabolic regulation that is comprised of diverse cell types that uniquely contribute to its function. Previous studies have performed expression quantitative trail loci (eQTL) discovery in either whole pancreas or in pancreatic islets, but due to differences between pancreatic cell types, this approach does not reveal cell type-specific effects. In this study, we sought to either implicate the cell type of action for known eQTLs or identify new eQTLs that may have been masked in bulk studies by performing eQTL discovery in individual pancreatic cell types. Methods We clustered 153,018 single-cell RNA sequencing (scRNA-seq) data from 71 pancreatic islet donors from the Human Pancreas Analysis Program (HPAP). We performed eQTL discovery in six pancreatic cell types using this resource directly. We further utilized this single cell resource as a reference to deconvolute bulk pancreatic RNA sequencing data from 305 Genotype Tissue Expression (GTEx) project donors and performed eQTL discovery in four pancreatic cell types. Finally, we performed fine-mapping and co-localization of pancreatic cell type eQTLs with metabolic GWAS to connect our findings to metabolic disease risk. Results From analyzing 71 individuals with single cell profiles, we identified 112 unique eGenes across six pancreatic cell types, 99 of which had been identified previously and 13 unique to this study. From the deconvoluted eQTLs, we identified 3,134 unique eGenes across four pancreatic cell types, 116 of which were unique to our study. Fine-mapping and co-localization of eQTLs with metabolic GWAS yielded key leads that warrant further investigation, such as the association of rs2168101 with LMO1 expression in alpha cells. Conclusions/interpretation We identified new signals that were previously not found in bulk pancreatic eQTL studies and potential cell type of action for several signals that were identified previously. Although there are limitations to the power, and therefore, discoverability of this study, it provides insights into how individual pancreatic cells differently contribute to metabolic disease.

Myotonic dystrophy type 1 (DM1) is caused by a CTG expansion in the 3' untranslated region of the DMPK gene that results in the expression of expanded CUG RNA which trap splicing factors resulting in widespread alternative splicing dysregulation. A panel of these splicing events, or splicing index (SI), is currently used to measure splicing dysregulation in DM1 patients. Despite significant interest in splicing, little is known about its progression over time in DM1. The objective of this study was to evaluate the changes in SI over 3 years in a well-phenotyped cohort of DM1 participants and identify factors contributing to these changes. RNA was extracted from vastus lateralis (VL) biopsies of 20 DM1 participants taken 3 years apart and the SI score was measured using targeted RNA sequencing. Baseline SI scores correlated with percent predicted muscle strength of the ankle dorsiflexors, knee extensors and hand grip. On average, SI scores worsened over 3 years, but the progression varied markedly among participants. Adult/juvenile phenotypes and lower baseline SI scores were among the factors associated with higher progression of SI scores over time. These findings emphasize the importance of integrating clinical phenotype and baseline molecular severity when interpreting longitudinal SI change, particularly in the context of clinical trials.

Introduction: Killer-cell immunoglobulin-like receptor (KIR) gene content shapes natural killer-cell responses and varies substantially among populations. We examined whether worldwide KIR diversity reflects regional structure, isolation by distance, serial founder effects, or latitudinal gradients. Data and methods: Carrier frequencies for 11 variable KIR genes were obtained from the Allele Frequency Net Database. The primary analysis included 189 locally sampled, non-diaspora, non-mixed populations from 70 countries, comprising 25,469 individuals; 161 populations had complete 11-gene profiles. Diversity indices, ordination, clustering, permutation-based variance decomposition, Mantel tests, and regression models were used to characterize geographic structure. Serial founder effects were evaluated using four distance measures from East Africa, both globally and within the Old World. Models of latitude incorporated spatial autocorrelation. Prespecified sensitivity analyses evaluated alternative population sets, gene panels, geographic classifications, weighting schemes, and multivariate outlier exclusion. Results: Geographic region explained 53.5% of variation in KIR profiles, substantially exceeding the permutation-null expectation of 6.9%, although multivariate dispersion differed among regions. Twenty-five of 28 pairwise regional comparisons remained significant after false-discovery-rate correction. Isolation by distance was modest (Mantel r=0.3003) and weakened after controlling for region (r=0.1557). KIR diversity did not decline with distance from East Africa globally but decreased strongly within the Old World (expected heterozygosity {beta}=-0.0066 and standardized gene content {beta}=-0.0789). B-content decreased nominally with absolute latitude, but latitude was collinear with region and the association survived neither spatial modelling nor sensitivity analyses. Regional differentiation remained robust across analytical specifications, whereas the Old World decline was carried by gene content once standard errors were clustered by country. Conclusion: Worldwide KIR gene-content variation shows pronounced regional differentiation, modest isolation by distance, and no independent latitudinal gradient. Diversity declines with distance from East Africa across the Old World, supporting serial founder effects, but not once the Americas and Oceania are included.

Skeletal dysplasias can occur in animals, including humans, and are attributed to both environmental and genetic causes. As our understanding of the distinguishing phenotypes of different skeletal dysplasias improves, alongside elucidation of the mechanisms and genetic networks that contribute to skeletal development, identifying and controlling genetic mutations in livestock populations has become increasingly possible, including approaches beyond selective breeding alone. Within Scotland an 'achondroplasia' syndrome that was first described in the South Country Cheviot breed of sheep (OMIA 000004-9940), has continued to occur periodically within the same geographical locations, despite culling of affected lambs over many decades. Affected lambs exhibit rounded heads, short legs, a loss of hooves, abnormal stance and short twisted tails. As the condition has been observed in flocks and as progeny from specific ram and ewe matings, and cases have been co-twinned to normal siblings, this suggests a syndrome caused by a recessive genetic condition rather than environmental factors. Through homozygosity mapping and whole genome sequence analyses, we identified a 10 bp deletion in the open reading frame of the PRICKLE1 gene that encodes prickle planar cell polarity protein 1 and is essential for normal vertebrate embryonic development. We identified additional phenotypes present in affected lambs, including a loss of phalanges, neural tube defects, vertebral and articular chondrocyte defects, hair orientation differences and polycystic kidneys, consistent with a loss of PRICKLE1 function based on comparisons to mouse mutant lines. These findings enabled the development of a simple DNA test for the identified PRICKLE1 mutation that was made commercially available to facilitate the elimination of this genetic disease from sheep flocks.

Background: More than five years after the COVID-19 outbreak, the infection by the SARS-CoV-2 virus should still be considered as a global threat with highly heterogeneous clinical outcomes from asymptomatic to fatal depending on existing genetic and phenotypic inter-individual and inter-population variability. Our objective is to identify risk loci associated with COVID-19 in an admixed Latin American population. Methods: We performed a genome-wide association study in the framework of the Chilean C19-GenoNet team, analyzing a cohort of 3,417 individuals recruited from the northernmost to the southernmost regions of the country, who presented variations in the severity of their COVID-19 clinical manifestations. Results: Our investigation replicated the extensively reported association of several variants at the 3p21.31 locus with COVID-19 severity and found a suggestive association for a variant within the FOXP4 gene at 6p21.1 with COVID-19 hospitalization. Both associations appear to be shared with other populations. Importantly, our study identified three previously unreported loci associated with COVID-19 severity or hospitalization at 7q21.11, 16p13.12 and 6p25.3, and one additional locus at 4p13 associated with SARS-CoV-2 positivity, a proxy for infection susceptibility. Conclusions: these new risk loci may represent population-specific effects in Chilean and, potentially, other Latin American admixed groups. Our study highlights the importance of expanding efforts to study underrepresented and genetically different populations, which may provide new clues on the genetic basis of human diseases and may help to anticipate global responses to emerging viral infections and future pandemics.