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As an important livestock species, sheep exhibit remarkable environmental adaptability. Immunoglobulins, expressed by B cells, are among the most crucial effector molecules in adaptive immunity. However, systematic research on the structure and expression diversity of the sheep immunoglobulins gene loci remains limited. This study annotated the sheep IgH, Igκ, and Igλ loci based on the sheep genome assembly (ARS-UI_Ramb_v3.0). The sheep IgH is located on chromosome 18 and comprises 22 VH, 4 DH, and 6 JH. The Igκ is on chromosome 3, containing 18 Vκ and 4 Jκ. The Igλ is situated on chromosome 17 and consists of 128 Vλ and 3 Jλ. Rearranged IgH, Igκ, and Igλ sequences were obtained from sheep spleen using 5’ RACE PCR. Following PE300 high-throughput sequencing, we analyzed the diversity of V, D, J expression diversity, V(D)J recombination, junctional diversity, and somatic hypermutation in the rearranged sequences. For IgH rearrangement, 4 VH, 4 DH, and 2 JH gene segments were utilized, generating 26 distinct rearrangement types. Igκ rearrangement employed 5 Vκ and 3 Jκ gene segments, resulting in 13 rearrangement types. Igλ rearrangement involved 26 Vλ and 2 Jλ gene segments, producing 28 rearrangement types. Average length of sheep CDR3H is 44 bp (maximum 66 bp), CDR3κ averages 27 bp (maximum 48 bp), and CDR3λ averages 30 bp (maximum 47 bp). N-nucleotide additions contributed more significantly to CDR3 diversity than P-nucleotides in both Igκ and Igλ rearrangements. Simultaneously, 3’ V-deletion and 5’ J-deletion further enriched CDR3 diversity. SHM, especially the hotspot mutation motifs, enriches the diversity caused by the V gene segments. Thus, sheep enrich immunoglobulin diversity through both junctional diversity-driven CDR3 diversification and high-intensity SHM. This study expands our understanding of the sheep immunoglobulin gene loci and their expression diversity, providing theoretical foundation for research on immunoglobulin gene evolution within the Bovidae family.

Background “Omics” approaches may provide useful information for a deeper understanding of speciation events, diversification and function innovation. This can be achieved by investigating the molecular similarities at sequence level between species, allowing the definition of ortholog and paralog genes. However, the spreading of sequenced genome, often endowed with still preliminary annotations, requires suitable bioinformatics to be appropriately exploited in this framework. Results We presented here a multilevel comparative approach to investigate on genome evolutionary relationships and peculiarities of two fleshy fruit species of relevant agronomic interest, Solanum lycopersicum (tomato) and Vitis vinifera (grapevine). We defined 17,823 orthology relationships between tomato and grapevine reference gene annotations. The resulting orthologs are associated with the detected paralogs in each species, permitting the definition of gene networks, useful to investigate the different relationships. The reconciliation of the compared collections in terms of an updating of the functional descriptions was also exploited. All the results were made accessible in ComParaLogs, a dedicated bioinformatics platform available at http://biosrv.cab.unina.it/comparalogs/gene/search . Conclusions The aim of the work was to suggest a reliable approach to detect all similarities of gene loci between two species based on the integration of results from different levels of information, such as the gene, the transcript and the protein sequences, overcoming possible limits due to exclusive protein versus protein comparisons. This to define reliable ortholog and paralog genes, as well as species specific gene loci in the two species, overcoming limits due to the possible draft nature of preliminary gene annotations. Moreover, reconciled functional descriptions, as well as common or peculiar enzymatic classes and protein domains from tomato and grapevine, together with the definition of species-specific gene sets after the pairwise comparisons, contributed a comprehensive set of information useful to comparatively exploit the two species gene annotations and investigate on differences between species with climacteric and non-climacteric fruits. In addition, the definition of networks of ortholog genes and of associated paralogs, and the organization of web-based interfaces for the exploration of the results, defined a friendly computational bench-work in support of comparative analyses between two species.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies. Genome-wide association analyses of 41,917 bipolar disorder cases and 371,549 controls of European ancestry provide new insights into the etiology of this disorder and identify novel therapeutic leads and potential opportunities for drug repurposing.

Schizophrenia has a heritability of 60–80% 1 , much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4 , and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.

Introduction Recent genome-wide association studies (GWAS) of individual sleep traits have identified hundreds of genetic loci, often implicating genes and biological pathways that are shared across sleep phenotypes. Modeling multiple dimensions of sleep may allow identification of common underlying genetic mechanisms and complement analyses of individual traits. Moreover, construction of novel sleep health scores, accounting for correlations between traits, has the potential to enhance specificity and power for genetic analyses. Methods We performed GWAS of composite sleep scores derived from five sleep questions characterizing 413,904 individuals of European ancestry from the UK Biobank. We constructed an additive sleep score (SS-add) as a sum of up to five favorable self-reported sleep behaviors (sleep duration of 7-8 hours, early chronotype, few insomnia symptoms, no snoring, and no excessive daytime sleepiness) as well as five principal component scores (SS-PC1 – SS-PC5) using the underlying sleep traits. SNP-level association studies of the sleep scores were complemented with multiple follow-up analyses, including investigation of pathway, tissue, and cell-type enrichments, as well as comprehensive genetic correlation (381 representative phenotypes) and bi-directional Mendelian randomization (MR) analyses (40 selected phenotypes). Results Sleep scores SS-PC1 (interpretable as longer duration sleep without insomnia symptoms) and SS-PC2 (healthy sleep without snoring or sleepiness) showed higher heritability (respectively 11.7% and 9.3%) compared with their primary underlying traits. SS-PC3 (loading strongly on chronotype) showed highest overall heritability (15.3%). Accounting for the six GWAS, we identified 28 significant novel loci (p< 8.3e-9), 31 additional novel loci (p< 5e-8), and 341 loci previously reported (p< 5e-8) by GWAS of individual sleep traits. Associated loci mapped to genes enriched in expression in brain tissues, and in metabolic and neuronal pathways. Numerous neurological, cardiometabolic and other traits were genetically correlated with sleep health (104 with|rho_g|>0.3, p< 2e-5). MR associations (p< 2e-4) pointed to causes (BMI [SS-PC2/PC4], smoking [SS-add/PC1], lower socio-economic status [SS-add/PC5]) and consequences (coronary heart disease [SS-add/PC1], pain [SS-PC1/5]) of poor sleep. Conclusion Composite sleep health scores revealed novel genetic mechanisms of related sleep behaviors, helped clarify relationships with neurological and cardiometabolic traits, and deserve further investigation. Support (if any) R01HL153814 (to H.W.), NHLBI R35HL135818 (to S.R.), 1R01HL146751 (to R.S.), UKB Application 6818.

Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genome-wide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic regulatory network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied regulatory network in ARABIDOPSIS: Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample ( n  = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets. Genome-wide analyses in >1 million individuals identify new loci and pathways associated with insomnia. The findings implicate key brain areas and cell types in the neurobiology of insomnia and highlight potential targets for developing new treatments.

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene–trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn’s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci. Transcriptome-wide association studies (TWAS) prioritize candidate causal genes at GWAS loci. This Perspective discusses the challenges to TWAS analysis, caveats to interpretation of results and opportunities for improvements to this class of methods.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox. Polygenic Priority Score (PoPS) prioritizes candidate effector genes at complex trait loci by integrating genome-wide association summary statistics with other data types. Combining PoPS with methods that leverage local genetic signals further improves the performance.

Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete 1 , 2 – 3 . Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 20,000 genetic loci that were associated with 232 traits 4 , predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits. The authors present EpiMap, a compendium that comprises 10,000 epigenomic maps across more than 800 biosamples for the annotation of genome-wide association study circuitry.