Trait Genome-wide Association Studies Research Articles

XWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BackgroundIntegrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.MethodsIn this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.ResultsOur findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.ConclusionsThe analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

Interpreting population- and family-based genome-wide association studies in the presence of confounding.

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Current Insight on the Role of Glucokinase and Glucokinase Regulatory Protein in Diabetes.

The glucokinase regulator (GCKR) gene encodes an inhibitor of the glucokinase enzyme (GCK), found only in hepatocytes and responsible for glucose metabolism. A common GCKR coding variation has been linked to various metabolic traits in genome-wide association studies. Rare GCKR polymorphisms influence GKRP activity, expression, and localization. Despite not being the cause, these variations are linked to hypertriglyceridemia. Because of their crystal structures, we now better understand the molecular interactions between GKRP and the GCK. Finally, small molecules that specifically bind to GKRP and decrease blood sugar levels in diabetic models have been identified. GCKR allelic spectrum changes affect lipid and glucose homeostasis. GKRP dysfunction has been linked to a variety of molecular causes, according to functional analysis. Numerous studies have shown that GKRP dysfunction is not the only cause of hypertriglyceridemia, implying that type 2 diabetes could be treated by activating liver-specific GCK via small molecule GKRP inhibition. The review emphasizes current discoveries concerning the characteristic roles of glucokinase and GKRP in hepatic glucose metabolism and diabetes. This information has influenced the growth of directed molecular therapies for diabetes, which has improved our understanding of lipid and glucose physiology.

MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups

Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.

The non-canonical poly(A) polymerase FAM46C promotes erythropoiesis

The post-transcriptional regulation of mRNA is a crucial component of gene expression. The disruption of this process can have detrimental effects on normal development and give rise to various diseases. The search for novel post-transcriptional regulators and the exploration of their roles are essential for understanding development and disease. Through a multimodal analysis of red blood cell trait genome-wide association studies (GWAS) and transcriptomes of erythropoiesis, we identified FAM46C, a non-canonical RNA poly(A) polymerase, as a necessary factor for proper red blood cell development. FAM46C is highly expressed in the late stages of the erythroid lineage, and its developmental upregulation is controlled by an erythroid-specific enhancer. We demonstrate that FAM46C stabilizes mRNA and regulates erythroid differentiation in a polymerase activity-dependent manner. Furthermore, we identified transcripts of lysosome and mitochondria components as highly confident in vivo targets of FAM46C, which aligns with the need of maturing red blood cells for substantial clearance of organelles and maintenance of cellular redox homeostasis. In conclusion, our study unveils a novel role of FAM46C in positively regulating lysosome and mitochondria components, thereby promoting erythropoiesis.

Causal effects of gut microbiota on sepsis and sepsis-related death: insights from genome-wide Mendelian randomization, single-cell RNA, bulk RNA sequencing, and network pharmacology

BackgroundGut microbiota alterations have been implicated in sepsis and related infectious diseases, but the causal relationship and underlying mechanisms remain unclear.MethodsWe evaluated the association between gut microbiota composition and sepsis using two-sample Mendelian randomization (MR) analysis based on published genome-wide association study (GWAS) summary statistics. Sensitivity analyses were conducted to validate the robustness of the results. Reverse MR analysis and integration of GWAS and expression quantitative trait loci (eQTL) data were performed to identify potential genes and therapeutic targets.ResultsOur analysis identified 11 causal bacterial taxa associated with sepsis, with increased abundance of six taxa showing positive causal relationships. Ten taxa had causal effects on the 28-day survival outcome of septic patients, with increased abundance of six taxa showing positive associations. Sensitivity analyses confirmed the robustness of these associations. Reverse MR analysis did not provide evidence of reverse causality. Integration of GWAS and eQTL data revealed 76 genes passing the summary data-based Mendelian randomization (SMR) test. Differential expression of these genes was observed between sepsis patients and healthy individuals. These genes represent potential therapeutic targets for sepsis. Molecular docking analysis predicted potential drug-target interactions, further supporting their therapeutic potential.ConclusionOur study provides insights for the development of personalized treatment strategies for sepsis and offers preliminary candidate targets and drugs for future drug development.

Genome-Wide Association Study Meta-Analysis Elucidates Genetic Structure and Identifies Candidate Genes of Teat Number Traits in Pigs.

The teat number is a pivotal reproductive trait that significantly influences the survival rate of piglets. A meta-analysis is a robust instrument, enhancing the universality of research findings and improving statistical power by increasing the sample size. This study aimed to identify universal candidate genes associated with teat number traits using a genome-wide association study (GWAS) meta-analysis with three breeds. We identified 21 chromosome threshold significant single-nucleotide polymorphisms (SNPs) associated with five teat number traits in single-breed and cross-breed meta-GWAS analyses. Using a co-localization analysis of expression quantitative trait loci and GWAS loci, we detected four unique genes that were co-localized with cross-breed GWAS loci associated with teat number traits. Through a meta-analysis and integrative analysis, we identified more reliable candidate genes associated with multiple-breed teat number traits. Our research provides new information for exploring the genetic mechanism affecting pig teat number for breeding selection and improvement.

ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits

Common genetic variants in glucokinase regulator (GCKR), which encodes GKRP, a regulator of hepatic glucokinase (GCK), influence multiple metabolic traits in genome-wide association studies (GWASs), making GCKR one of the most pleiotropic GWAS loci in the genome. It is unclear why. Prior work has demonstrated that GCKR influences the hepatic cytosolic NADH/NAD+ ratio, also referred to as reductive stress. Here, we demonstrate that reductive stress is sufficient to activate the transcription factor ChREBP and necessary for its activation by the GKRP-GCK interaction, glucose, and ethanol. We show that hepatic reductive stress induces GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerol species, which are also influenced by ChREBP. We define the transcriptional signature of hepatic reductive stress and show its upregulation in fatty liver disease and downregulation after bariatric surgery in humans. These findings highlight how a GCKR-reductive stress-ChREBP axis influences multiple human metabolic traits.

Integrative analysis of genome‐wide association study and transcriptomics to identify potential candidate genes influencing drip loss in Beijing Black pigs

AbstractUnderstanding the genetic factors related to meat drip loss is of great importance for animal breeding and production. In this study, we employed a combination of genome‐wide association study (GWAS) mapping and RNA sequencing (RNA‐seq) data to effectively identify potentially functional single nucleotide polymorphisms (SNPs) as well as candidate genes associated with drip loss (DL) in Beijing Black pigs. Initially, we conducted a single‐ and multi‐trait GWAS on drip loss traits in 441 Beijing Black pigs at 24 (DL24) and 48 (DL48) hours postmortem using the Illumina pig 50K SNP chip. Five SNPs with annotations for four genes (FGGY, LHFPL6, OSBPL1A, and NMNAT3) were consistently identified in single or multiple trait GWAS results, indicating their potential pleiotropic effects on drip loss. Next, a comprehensive comparative transcriptomic analysis was performed on samples of Beijing Black pigs exhibiting extremely high and low drip loss, resulting in the identification of 21 differentially expressed genes (DGEs) as potential candidates. Additionally, protein–protein interaction (PPI) network analysis revealed reciprocal regulatory relationships between FOXO1, OSBPL1A, DOCK1 (identified from GWAS) and the candidate DGEs obtained from RNA‐seq data. Therefore, we propose that these genes may impact drip loss traits through gene interactions. In conclusion, our integrative analysis screened candidate genes that may affect the drip loss traits in Beijing Black pigs, which provides crucial insights into the molecular mechanisms of drip loss and serves as a theoretical reference for improving meat quality in Beijing Black pigs.

Anterior Open Bite Malocclusion: From Clinical Treatment Strategies towards the Dissection of the Genetic Bases of the Disease Using Human and Collaborative Cross Mice Cohorts.

Anterior open bite malocclusion is a complex dental condition characterized by a lack of contact or overlap between the upper and lower front teeth. It can lead to difficulties with speech, chewing, and biting. Its etiology is multifactorial, involving a combination of genetic, environmental, and developmental factors. Genetic studies have identified specific genes and signaling pathways involved in jaw growth, tooth eruption, and dental occlusion that may contribute to open bite development. Understanding the genetic and epigenetic factors contributing to skeletal open bite is crucial for developing effective prevention and treatment strategies. A thorough manual search was undertaken along with searches on PubMed, Scopus, Science Direct, and Web of Science for relevant studies published before June 2022. RCTs (clinical trials) and subsequent observational studies comprised the included studies. Orthodontic treatment is the primary approach for managing open bites, often involving braces, clear aligners, or other orthodontic appliances. In addition to orthodontic interventions, adjuvant therapies such as speech therapy and/or physiotherapy may be necessary. In some cases, surgical interventions may be necessary to correct underlying skeletal issues. Advancements in technology, such as 3D printing and computer-assisted design and manufacturing, have improved treatment precision and efficiency. Genetic research using animal models, such as the Collaborative Cross mouse population, offers insights into the genetic components of open bite and potential therapeutic targets. Identifying the underlying genetic factors and understanding their mechanisms can lead to the development of more precise treatments and preventive strategies for open bite. Here, we propose to perform human research using mouse models to generate debatable results. We anticipate that a genome-wide association study (GWAS) search for significant genes and their modifiers, an epigenetics-wide association study (EWAS), RNA-seq analysis, the integration of GWAS and expression-quantitative trait loci (eQTL), and micro-, small-, and long noncoding RNA analysis in tissues associated with open bite in humans and mice will uncover novel genes and genetic factors influencing this phenotype.

MyD88 in myeloid cells promotes angiotensin II-induced vascular inflammation and is associated with all-cause mortality and incident heart failure in humans with arterial hypertension

Abstract Background Angiotensin II (AngII) causes hypertension and promotes vascular accumulation of inflammatory cells. We aimed to determine how myeloid factor of differentiation 88 (MyD88) contributes to vascular dysfunction and arterial hypertension in mice and humans. Methods Male C57BL/6 as well as MyD88-/-, LysMCre/wtMyD88LSL/LSL, LysMCre/wt, TLR2-/-, TLR4-/-, TLR7-/- and TLR9-/- were investigated in the AngII-model of hypertension (1 mg/kg/d for 7days). Biodata analyses were performed in the Gutenberg-Health Study (GHS), a population based cohort. In addition, we performed computational analyses of known hypertension-related genes from genome-wide association studies (GWAS) to investigate whether they preferentially interact with MyD88 in the human protein network. Results MyD88 deficiency attenuated AngII-induced blood pressure increase and endothelial dysfunction in conductance and resistance vessels. Vascular mRNA expression levels of vcam-1, nos2, nox2 were decreased in AngII-infused MyD88-/- mice compared to C57BL/6 controls. Flow cytometric analyses revealed that AngII-induced aortic accumulation of CD11b+Ly6Chi inflammatory monocytes was significantly dampened in MyD88-/- mice. AngII increased mRNA expression of cd62L, cd68 and ccl2, which was blocked in MyD88-/- mice, indicating a role of MyD88 for myeloid cell activation and differentiation. Additionally, aortic interferon-g+ NK cells and mRNA levels of interleukin-12 and interleukin-1b were reduced in AngII-treated MyD88-/- mice. Bone marrow transfer experiments demonstrated a vasoprotective effect of MyD88 deficiency in bone marrow-derived cells, and selective expression of MyD88 in LysMCre/wtMyD88LSL/LSL mice restored AngII-induced pathology, revealing that myelomonocytic cells drives vascular dysfunction in a MyD88-dependent manner. Furthermore, data from 1,274 individuals in the GHS indicated, that MyD88 mRNA expression was associated with all cause mortality and incident chronic heart failure after a median follow-up of 11.6 years. Computational analysis of the human protein interaction network demonstrated that MyD88 is significantly connected to proteins encoded by genetic loci associated with blood pressure traits in multiple GWAS. Conclusion We provide evidence, that MyD88 expressed by myelomonocytic cells promotes AngII-induced vascular dysfunction and arterial hypertension. MyD88 might be used as an inflammatory diagnostic marker for the risk of death in hypertensive patients.

Comprehensive Deciphering the Complexity of the Deep Bite: Insight from Animal Model to Human Subjects.

Deep bite is a malocclusion phenotype, defined as the misalignment in the vertical dimension of teeth and jaws and characterized by excessive overlap of the upper front teeth over the lower front teeth. Numerous factors, including genetics, environmental factors, and behavioral ones, might contribute to deep bite. In this study, we discuss the current clinical treatment strategies for deep bite, summarize the already published findings of genetic analysis associated with this complex phenotype, and their constraints. Finally, we propose a comprehensive roadmap to facilitate investigations for determining the genetic bases of this complex phenotype development. Initially, human deep bite phenotype, genetics of human deep bite, the prevalence of human deep bite, diagnosis, and treatment of human deep bite were the search terms for published publications. Here, we discuss these findings and their limitations and our view on future strategies for studying the genetic bases of this complex phenotype. New preventative and treatment methods for this widespread dental issue can be developed with the help of an understanding of the genetic and epigenetic variables that influence malocclusion. Additionally, malocclusion treatment may benefit from technological developments like 3D printing and computer-aided design and manufacture (CAD/CAM). These technologies enable the development of personalized surgical and orthodontic guidelines, enhancing the accuracy and effectiveness of treatment. Overall, the most significant results for the patient can only be achieved with a customized treatment plan created by an experienced orthodontic professional. To design a plan that meets the patient's specific requirements and expectations, open communication between the patient and the orthodontist is essential. Here, we propose to conduct a genome-wide association study (GWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with deep bite malocclusion in human, and complement it by the same approaches in the collaborative cross (CC) mouse model which offer a novel platform for identifying genetic factors as a cause of deep bite in mice, and subsequently can then be translated to humans. An additional direct outcome of this study is discovering novel genetic elements to advance our knowledge of how this malocclusion phenotype develops and open the venue for early identification of patients carrying the susceptible genetic factors so that we can offer early prevention and treatment strategies, a step towards applying a personalized medicine approach.

Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods.

In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate type I error rates and poor statistical power. Owing to the lack of analysis methods, GWAS of ordinal traits has been known to be problematic and gaining attention. In this paper, we develop a general framework for identifying ordinal traits associated with genetic variants in pedigree-structured samples by collapsing and kernel methods. We use the local odds ratios GEE technology to account for complicated correlation structures between family members and ordered categorical traits. We use the retrospective idea to treat the genetic markers as random variables for calculating genetic correlations among markers. The proposed genetic association method can accommodate ordinal traits and allow for the covariate adjustment. We conduct simulation studies to compare the proposed tests with the existing models for analyzing the ordered categorical data under various configurations. We illustrate application of the proposed tests by simultaneously analyzing a family study and a cross-sectional study from the Genetic Analysis Workshop 19 (GAW19) data.

Genomic basis of selective breeding from the closest wild relative of large-fruited tomato.

The long and intricate domestication history of the tomato (Solanum lycopersicum) includes selection sweeps that have not been fully explored, and these sweeps show significant evolutionary trajectories of domestication traits. Using three distinct selection strategies, we represented comprehensive selected sweeps from 53 Solanum pimpinellifolium (PIM) and 166 S. lycopersicum (BIG) accessions, which are defined as pseudo-domestication in this study. We identified 390 potential selection sweeps, some of which had a significant impact on fruit-related traits and were crucial to the pseudo-domestication process. During tomato pseudo-domestication, we discovered a minor-effect allele of the SlLEA gene related to fruit weight (FW), as well as the major haplotypes of fw2.2/cell number regulator (CNR), fw3.2/SlKLUH, and fw11.3/cell size regulator (CSR) in cultivars. Furthermore, 18 loci were found to be significantly associated with FW and six fruit-related agronomic traits in genome-wide association studies. By examining population differentiation, we identified the causative variation underlying the divergence of fruit flavonoids across the large-fruited tomatoes and validated BRI1-EMS-SUPPRESSOR 1.2 (SlBES1.2), a gene that may affect flavonoid content by modulating the MYB12 expression profile. Our results provide new research routes for the genetic basis of fruit traits and excellent genomic resources for tomato genomics-assisted breeding.

Topr: an R package for viewing and annotating genetic association results

BackgroundThe successful identification of genetic loci for complex traits in genome-wide association studies (GWAS) has resulted in thousands of GWAS summary statistics becoming publicly available for hundreds of complex traits from multiple cohorts and studies. Visualisation is an important aid for interpreting, comparing, validating, and obtaining an overview of large amounts of data. However, the current software is limited in its ability and flexibility to annotate and simultaneously display multiple GWAS results which is useful when interpreting and comparing association results. Therefore, I created the topr R package to facilitate visualisation, annotation, and comparisons of single or multiple GWAS results. It contains functions tailored for viewing and analysing GWAS results.Resultstopr provides a fast and elegant visual display of association results, along with the annotation of association peaks with their nearest gene. Association results from multiple analyses can be viewed simultaneously over the entire genome or in a more detailed regional view along with gene information. Users can perform the essential steps of visually exploring and annotating association results and generating elegant publication-ready plots.Conclusionstopr is developed as a package for the R statistical computing environment, released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=topr). The source code is available at GitHub (https://github.com/totajuliusd/topr). topr provides several advantages and advances over the current alternatives, particularly in its gene annotation functionality and customisable display of single- or multiple-association results. With topr, I provide a flexible tool with multiple features to aid in the analysis and evaluation of GWAS association results.

Blood cell traits’ GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.

Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens.

Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.

Univariate and multivariate genome-wide association studies for hematological traits in Murciano-Granadina goats.

Hematological traits are important indicators of health status, and they are frequently used as criteria for clinical diagnosis. In humans, the genomic architecture of blood traits has been investigated in depth and thousands of associations with genetic variants have been found. In contrast, the association between marker genotypes and the variation of hematological traits has not been investigated in goats yet. Herewith, we have recorded 12 hematological parameters in 882 Murciano-Granadina goats that were also genotyped with the Goat SNP50 BeadChip (Illumina). Performance of a univariate genome-wide association study (GWAS) made it possible to detect one genomic region on goat chromosome (CHI) 21 (19.2-19.5 Mb) associated, at the genome-wide level of significance, with 4 red blood cell traits. The three markers displaying the highest significances were rs268272996 (CHI21: 19225290 bp), rs268273004 (CHI21: 19565629 bp) and rs268239059 (CHI13: 9615190 bp). Consistently, a multivariate GWAS indicated that the rs268273004 marker on chromosome 21 is associated with seven blood cell traits. Interestingly, this marker maps close to the FA Complementation Group I (FANCI) gene (CHI21: 20021947-20077025 bp), which is functionally related to Fanconi anemia, a syndrome characterized by bone marrow failure, aplastic anemia, and congenital disorders. We have also uncovered additional chromosome-wide significant associations between genetic markers and erythrocyte and leukocyte traits in the univariate GWAS. These findings evidence that the phenotypic variation of hematological traits in goats is regulated, at least to some extent, by polygenic determinants distributed in multiple chromosomes.

Towards Genetic Dissection of Skeletal Class III Malocclusion: A Review of Genetic Variations Underlying the Phenotype in Humans and Future Directions.

Skeletal abnormalities and malocclusions have varied features that impact populations globally, impairing aesthetics and lowering life quality. The prevalence of the Skeletal Class III disease is the lowest among all angle malocclusions, with varied prevalence across nations. Environmental, genetic, and societal factors play a role in its numerous etiologies. In this study, we conducted a thorough search across the published data relating to quantitative trait loci (QTL) and the genes associated with Class III progression in humans, discussed these findings and their limitations, and proposed future directions and strategies for studying this phenotype. An inclusive search of published papers in the PubMed and Google Scholar search engines using the following terms: 1. Human skeletal Class III; 2. Genetics of Human skeletal Class III; 3. QTL mapping and gene associated with human skeletal Class III; 4. enriched skeletal Class-III-malocclusion-associated pathways. Our search has found 53 genes linked with skeletal Class III malocclusion reported in humans, genes associated with epigenetics and phenomena, and the top 20 enriched pathways associated with skeletal Class III malocclusion. The human investigations yielded some contentious conclusions. We conducted a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNA-seq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro- and small-RNA, and long non-coding RNA analysis in tissues connected to skeletal Class III malocclusion phenotype in tissues connected with the skeletal phenotype. Finally, we invite regional, national, and international orthodontists and surgeons to join this effort by contributing human samples with skeletal Class III malocclusion following the accepted Helsinki ethical protocol to challenge these phenomena jointly.

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.