Human genetic variation and its contribution to complex traits

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel

BACKGROUND

Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.

RESULTS

We developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. Applied to simulated datasets and UK Biobank cohorts for Crohn's disease, schizophrenia, and Alzheimer's disease, Ge-SAND achieved up to a 20% improvement in AUC-ROC compared to mainstream methods. Beyond its predictive accuracy, through self-attention-based interaction networks, Ge-SAND provided insights into large-scale genotype relationships and revealed genetic mechanisms underlying these complex diseases. For instance, Ge-SAND identified potential genetic interaction pairs, including novel relationships such as ISOC1 and HOMER2, potentially implicating the brain-gut axis in Crohn's and Alzheimer's diseases.

CONCLUSION

Ge-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.

A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.

Role of the rs6184 growth hormone receptor gene polymorphism in mandibular morphogenesis

BACKGROUND

Polymorphisms in the growth hormone receptor (GHR) gene influence the craniofacial system by affecting the growth of the mandibular body length and ramal height, with significant variations observed across different ethnic populations. This study investigated the relationship between a single nucleotide polymorphism (rs6184) in the GHR gene and its effects on mandibular morphogenesis in a Dravidian population.

METHODS

A total of 250 subjects were divided into three groups. Group 1 included 100 subjects with skeletal Class I malocclusion and average ramal height (control group). Group 2 included 150 patients with skeletal Class II malocclusions and reduced mandibular body length. On the basis of ramal height, group 2 was divided into two groups with 75 subjects each (short ramus: group 2a) and (long ramus: group 2b). DNA extracted from the salivary samples of individuals was analyzed to identify the genotype of the rs6184 using the polymerase chain reaction-restriction fragment length polymorphism method and compare it in the three groups.

RESULTS

The genotype and allele frequencies showed statistically significant differences between the control (group 1) and groups 2a and 2b (P < 0.0001), with the polymorphic homozygous TT genotype found only in subjects with Class II malocclusions and reduced mandibular body length. However, no statistically significant difference was found between individuals with small and long ramal heights (group 2a and 2b, P = 0.7789).

CONCLUSIONS

The rs6184 polymorphic variant of the GHR gene can be considered a candidate gene for mandibular morphogenesis in the Dravidian population.

Evaluating the Impact of Omega-3 Fatty Acids and Genetic Susceptibility on Atopic Dermatitis in Adults

Fatty acids (FAs) involvement in atopic dermatitis (AD) prevention is inconclusive, and the modification effect by genetic risk profiles remains unknown. The aim of this study is to explore the association between circulating FAs, genetic risk factors, and AD in adults. Utilizing the UK Biobank dataset, we evaluated the impacts of FAs on the risk of elderly-onset AD and explored the combined effects of FA levels and genetic susceptibility. Plasma omega-3 levels exhibited an inverse correlation with AD risk (hazard ratio [HR]: 0.93; 95% confidence interval [CI]: 0.89-0.98), regardless of genetic predisposition. Individuals with low genetic risk and high omega-3 levels had the lowest AD risk, a 38% reduction compared to the reference category. Additionally, individuals with GA/AA on rs1692120 exhibited a significantly elevated AD risk, whereas those with more A alleles for rs174448 demonstrated a significantly diminished AD risk (both p trends <0.05). These findings suggest that increasing omega-3 intake could be a preventive strategy against AD, and tailoring prevention strategies based on genetic predispositions may enhance intervention efficacy.

A New Blood-Based Epigenetic Diagnostic Biomarker Test (EpiSwitch®® NST) with High Sensitivity and Positive Predictive Value for Colorectal Cancer and Precancerous Polyps

BACKGROUND/OBJECTIVES

Colorectal cancer (CRC) arises from the epithelial lining of the colon or rectum, often following a progression from benign adenomatous polyps to malignant carcinoma. Screening modalities such as colonoscopy, faecal immunochemical tests (FIT), and FIT-DNA are critical for early detection and prevention, but non-invasive methods lack sensitivity to polyps and early CRC. Chromosome conformations (CCs) are potent epigenetic regulators of gene expression. We have previously developed an epigenetic assay, EpiSwitch®®, that employs an algorithmic-based CCs analysis. Using EpiSwitch®® technology, we have shown the presence of cancer-specific CCs in peripheral blood mononuclear cells (PBMCs) and primary tumours of patients with melanoma and prostate cancer. EpiSwitch®®-based commercial tests are now available to diagnose prostate cancer with 94% accuracy (PSE test) and response to immune checkpoint inhibitors across 14 cancers with 85% accuracy (CiRT test). Methods/Results/Conclusions: Using blood samples collected from n = 171 patients with CRC, n = 44 patients with colorectal polyps and n = 110 patients with a 'clear' colonoscopy we performed whole Genome DNA screening for CCs correlating to CRC diagnosis. Our findings suggest the presence of two eight-marker CC signatures (EpiSwitch®® NST) in whole blood that allow diagnosis of CRC and precancerous polyps, respectively. Independent validation cohort testing demonstrated high accuracy in identifying colorectal polyps and early versus late stages of CRC with an exceptionally high sensitivity of 79-90% and a high positive prediction value of 60-84%. Linking the top diagnostic CCs to nearby genes, we have built pathways maps that likely underline processes contributing to the pathology of polyp and CRC progression, including TGFβ, cMYC, Rho GTPase, ROS, TNFa/NFκB, and APC.

Powerful Rare-Variant Association Analysis of Secondary Phenotypes

Most genome-wide association studies are based on case-control designs, which provide abundant resources for secondary phenotype analyses. However, such studies suffer from biased sampling of primary phenotypes, and the traditional statistical methods can lead to seriously distorted analysis results when they are applied to secondary phenotypes without accounting for the biased sampling mechanism. To our knowledge, there are no statistical methods specifically tailored for rare variant association analysis with secondary phenotypes. In this article, we proposed two novel joint test statistics for identifying secondary-phenotype-associated rare variants based on prospective likelihood and retrospective likelihood, respectively. We also exploit the assumption of gene-environment independence in retrospective likelihood to improve the statistical power and adopt a two-step strategy to balance statistical power and robustness. Simulations and a real-data application are conducted to demonstrate the superior performance of our proposed methods.

Ex vivo functional whole organ in biomedical research: a review

Model systems are critical in biomedical and preclinical research. Animal and in vitro models serve an important role in our current understanding of human physiology, disease pathophysiology, and therapy development. However, if the system is between cell culture and animal models, it may be able to overcome the knowledge gap that exists in the current system. Studies employing ex vivo organs as models have not been thoroughly investigated. Though the integration of other organs and systems has an impact on many biological mechanisms and disorders, it can add a new dimension to modeling and aid in the identification of new possible therapeutic targets. Here, we have discussed why the ex vivo organ model is desirable and the importance of the inclusion of organs from diverse species, described its historical aspects, studied organs as models in scientific research, and its ex vivo stability. We also discussed, how an ex vivo organ model might help researchers better understand organ physiology, as well as organ-specific diseases and therapeutic targets. We emphasized how this ex vivo organ dynamics will be more competent than existing models, as well as what tissues or organs would have potentially viable longevity for ex vivo modeling including human tissues, organs, and/or at least biopsies and its possible advantage in clinical medicine including organ transplantation procedure and precision medicine.

A Catalogue of Structural Variation across Ancestrally Diverse Asian Genomes

Structural variants (SVs) are significant contributors to inter-individual genetic variation associated with traits and diseases. Current SV studies using whole-genome sequencing (WGS) have a largely Eurocentric composition, with little known about SV diversity in other ancestries, particularly from Asia. Here, we present a WGS catalogue of 73,035 SVs from 8392 Singaporeans of East Asian, Southeast Asian and South Asian ancestries, of which ~65% (47,770 SVs) are novel. We show that Asian populations can be stratified by their global SV patterns and identified 42,239 novel SVs that are specific to Asian populations. 52% of these novel SVs are restricted to one of the three major ancestry groups studied (Indian, Chinese or Malay). We uncovered SVs affecting major clinically actionable loci. Lastly, by identifying SVs in linkage disequilibrium with single-nucleotide variants, we demonstrate the utility of our SV catalogue in the fine-mapping of Asian GWAS variants and identification of potential causative variants. These results augment our knowledge of structural variation across human populations, thereby reducing current ancestry biases in global references of genetic variation afflicting equity, diversity and inclusion in genetic research.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Confronting the Legacy of Eugenics and Ableism: Towards Anti-Ableist Bioscience Education

Society and education are inherently ableist. Disabled people are routinely excluded from education, or have poorer outcomes within educational systems. Improving educational experiences and outcomes for people of color has required educators to design antiracist curricula that explicitly address racial inequality. Here, we explore parallel antiableist approaches to bioscience education in an essay coauthored by a disabled bioscience student and able-bodied faculty member in bioscience. Our work is underpinned by Critical Disability Theory and draws on disability and pedagogical scholarship as well as our own experiences. The biosciences has a unique need to confront its history in the discredited pseudoscience of eugenics, which has led to discrimination and human rights abuses against disabled people. We provide a brief history of the relationship between biological sciences research and eugenics and explore how this legacy impacts bioscience education today. We then present a recommended structure for antiableist biology education. Our approach goes beyond providing disability access, to a model that educates all students about disability issues and empowers them to challenge ableist narratives and practices.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Natural variation in starvation sensitivity maps to a point mutation in phospholipase IPLA2-VIA in Drosophila melanogaster

Resistance to starvation is a classic complex trait, where genetic and environmental variables can greatly modify an animal's ability to survive without nutrients. In this study, we investigate the genetic basis of starvation resistance using complementary quantitative and classical genetic mapping in Drosophila melanogaster. Using the Drosophila Genetics Reference Panel (DGRP) as a starting point, we queried the genetic basis of starvation sensitivity in one of the most sensitive DGRP lines. We localize a major effect locus modifying starvation resistance to the phospholipase iPLA2-VIA. This finding is consistent with the work of others which demonstrate the importance of lipid regulation in starvation stress. Furthermore, we demonstrate that iPLA2-VIA plays a role in the maintenance of sugar reserves post-starvation, which highlights a key dynamic between lipid remodeling, sugar metabolism and resistance to starvation stress.

Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes

Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

CAVaLRi: An Algorithm for Rapid Identification of Diagnostic Germline Variation

Clinical exome and genome sequencing (ES/GS) have become indispensable diagnostic tools for rare genetic diseases (RGD). However, the interpretation of ES/GS presents a substantial operational challenge in clinical settings. Test interpretation requires the review of hundreds of genetic variants, a task that has become increasingly challenging given the rising use of ES/GS. In response, we present Clinical Assessment of Variants by Likelihood Ratios (CAVaLRi), which employ a modified likelihood ratio (LR) framework to assign diagnostic probabilities to candidate germline disease genes. CAVaLRi models aspects of the clinical variant assessment process, taking into consideration the predicted impact of the variant, the proband and parental genotypes, and the proband's clinical characteristics. It also factors in computational phenotype noise and weighs the relative significance of genotype, phenotype, and variant segregation information. We trained and tested CAVaLRi on variant and phenotype data from an internal cohort of 655 clinical ES cases. For validation, CAVaLRi's performance was benchmarked against four leading gene prioritization algorithms (Exomiser's hiPHIVE and PhenIX prioritizers, LIRICAL, and XRare) using a distinct cohort of 12,832 ES cases. Our findings reveal that CAVaLRi significantly outperforms its counterparts when clinician-curated phenotype sets are used, as evidenced by its superior precision-recall curve (PR AUC: 0.701) and average diagnostic gene rank (1.59). Notably, even when substituting highly focused clinician-curated phenotype sets with large and potentially nonspecific computationally derived phenotypes, CAVaLRi retains its precision (PR AUC: 0.658; diagnostic gene average rank: 1.68) and markedly outperforms other tools. In a large, heterogeneous validation cohort, CAVaLRi stood out as the most precise prioritization algorithm (PR AUC: 0.335; average diagnostic rank: 1.91). In conclusion, CAVaLRi presents a robust solution for prioritizing diagnostic genes, surpassing current methods. It demonstrates resilience to noisy, computationally-derived phenotypes, providing a scalable strategy to help labs focus on the most diagnostically relevant variants, thus addressing the growing demand for ES/GS interpretation.

Enhancer-promoter activation by the Kaposi sarcoma-associated herpesvirus episome maintenance protein LANA

Higher-order genome structure influences the transcriptional regulation of cellular genes through the juxtaposition of regulatory elements, such as enhancers, close to promoters of target genes. While enhancer activation has emerged as an important facet of Kaposi sarcoma-associated herpesvirus (KSHV) biology, the mechanisms controlling enhancer-target gene expression remain obscure. Here, we discover that the KSHV genome tethering protein latency-associated nuclear antigen (LANA) potentiates enhancer-target gene expression in primary effusion lymphoma (PEL), a highly aggressive B cell lymphoma causally associated with KSHV. Genome-wide analyses demonstrate increased levels of enhancer RNA transcription as well as activating chromatin marks at LANA-bound enhancers. 3D genome conformation analyses identified genes critical for latency and tumorigenesis as targets of LANA-occupied enhancers, and LANA depletion results in their downregulation. These findings reveal a mechanism in enhancer-gene coordination and describe a role through which the main KSHV tethering protein regulates essential gene expression in PEL.

Comparing rare variants versus common in the pathogenesis of nonalcoholic fatty liver disease: a whole exome sequencing approach

BACKGROUND/PURPOSE

Genome-wide association studies have reported the association of common variants with nonalcoholic fatty liver disease in genes, namely, PNPLA3/TM6SF2/MBOAT7/HSD17B13, across ethnicities. However, the approach does not identify rarer variants with a higher effect size. We therefore sequenced the complete exonic regions of patients with nonalcoholic steatohepatitis and controls to compare rare and common variants with a role in the pathogenesis.

METHODS

This is a prospective study that recruited 54 individuals with/without fatty infiltration. Patients with biopsy-proven nonalcoholic steatohepatitis and persistently elevated liver enzymes were included. Controls were with normal CT/MR fat fraction. DNA was isolated from whole blood, amplified (SureSelectXT Human All Exon V5 + UTR kit) and sequenced (Illumina). Data were filtered for quality, aligned (hg19), and annotated (OpenCRAVAT). Pathogenic (Polyphen-2/SIFT/ClinVar) variants and variants reported to be associated with NAFLD based on published literature were extracted from our data and compared between patients and controls.

RESULTS

The mean age of controls (N = 17) and patients (N = 37) was 46.88 ± 6.94 and 37.46 ± 13.34 years, respectively. A total of 251 missense variants out of 89 286 were classified as pathogenic. Of these, 106 (42.23%) were unique to the patients and remaining (n = 145; 57.77%) were found in both patients and controls. Majority (25/37; 67.57%) patients had a minimum of one or more rare pathogenic variant(s) related to liver pathology that was not seen in the controls.

CONCLUSION

Elucidating the contribution of rare pathogenic variants would enhance our understanding of the pathogenesis. Including the rarer genes in the polygenic risk scores would enhance prediction power.

Role of the Growth Hormone Receptor (GHR) Gene in Skeletal Class II Malocclusion and Its Significant Influence on the Skeletal Facial Profile in Both the Sagittal and Vertical Dimensions: A Systematic Review

This systematic review aims to determine the role of the growth hormone receptor (GHR) gene in skeletal malocclusion and its significant influence on the growth of the maxilla and the mandible in both sagittal and vertical dimensions. A search of the electronic databases of PubMed, Google Scholar, and Cochrane up to and including the year 2023 was made. In addition to this, a hand search of orthodontic and dentofacial orthopaedic journals was carried out. This search included randomized control trials. The Mesh terms used were "skeletal class II malocclusion", "mandibular retrognathism", "sagittal malocclusion", "genetic expression", "genetic factors", "genetic study", "genetic polymorphism", and "single nucleotide polymorphism". The inclusion criteria included studies such as clinical trials and orthopaedic appliances in the presurgical phase. The exclusion criteria for the study were studies not in the English language, case reports, case series, and studies with irrelevant data. It has been cited in various literature that polymorphic variations of the GHR gene could cause variations in mandibular morphogenesis affecting both the mandibular body length and ramal height. However, its effects are quite variable and are based on different population groups. Polymorphism of the GHR gene can be considered a reliable indicator predicting variations in affecting the growth of the mandible with greater significance in affecting the vertical ramal height compared to the body length of the mandible. Its effects on the maxillary skeletal base are rather limited comparatively.

Efficient detection of single nucleotide variants in targeted genomic loci

Single nucleotide variants (SNVs), including single nucleotide polymorphisms, are often associated with morphological and/or physiological abnormalities in various organisms. Targeted genomic DNA can be amplified and directly sequenced to detect these mutations, but this method is relatively time consuming and expensive. We recently established the heteroduplex mobility assay to detect genetic mutations as an easy, low-cost method in genome editing, but detecting such small genetic differences remains difficult. Here, we developed a new, simple method to detect single nucleotide changes in the zebrafish genome by polymerase chain reaction (PCR) and electrophoresis. We first designed a specific single stranded DNA with four tandem guanine nucleotides inserted beside the mutation site, called guanine-inserted primer (GIP). When reannealing, hybridized complexes of GIP and PCR amplicons with or without 1-bp-mutated alleles form different bulge structures, presumably leading to different mobilities on a polyacrylamide gel. This GIP-interacting mobility assay is easy to use; therefore, it could contribute to the detection of SNVs in any organism.

CRISPR-Based Therapies: Revolutionizing Drug Development and Precision Medicine

With the discovery of CRISPR-Cas9, drug development and precision medicine have undergone a major change. This review article looks at the new ways that CRISPR-based therapies are being used and how they are changing the way medicine is done. CRISPR technology's ability to precisely and flexibly edit genes has opened up new ways to find, validate, and develop drug targets. Also, it has made way for personalized gene therapies, precise gene editing, and advanced screening techniques, all of which hold great promise for treating a wide range of diseases. In this article, we look at the latest research and clinical trials that show how CRISPR could be used to treat genetic diseases, cancer, infectious diseases, and other hard-to-treat conditions. However, ethical issues and problems with regulations are also discussed in relation to CRISPR-based therapies, which shows how important it is to use them safely and responsibly. As CRISPR continues to change how drugs are made and used, this review shines a light on the amazing things that have been done and what the future might hold in this rapidly changing field.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Genome-wide exploration of genetic interactions for bladder cancer risk

Although GWASs have been conducted to investigate genetic variation of bladder tumorigenesis, little is known about genetic interactions that may influence bladder cancer (BC) risk. By leveraging large-scale participants from UK Biobank, we established a discovery database with 4000 Caucasian participants (2000 cases vs 2000 non-cases), a database with 1648 Caucasian participants (824 cases vs 824 non-cases) and 856 non-Caucasian participants (428 cases vs 428 non-cases) as validation. We then performed a genome-wide SNP-SNP interaction investigation related to BC risk based a machine learning approach (ie, GenEpi). Moreover, we used the selected interactions to build a BC screening model with an integrated interaction-empowered polygenic risk score (iPRS) based on Cox proportional hazard model. With Bonferroni correction, we identified 10 statistically significant pairs of SNPs, which located in 17 chromosomes. Of these, four SNP-SNP interactions were found to be positively associated with BC risk among Caucasian participants (ORs 1.57-2.03), while six SNP-SNP interactions showed negatively associated with BC risk (ORs 0.54-0.65). Only four of the SNP-SNP interactions were consistently identified in non-Caucasian participants located in ST7L-ADSS2, FHIT-CHDH, LARP4B-LHPP and RBFOX3-MPRIP. In addition, the iPRS showed a HR of 1.81 (95% CI: 1.46-2.09) compared the highest tertile to the lowest tertile, with an enhanced AUC (0.91; 95% CI:0.85-0.97) than PRS (AUC: 0.86; 95% CI:0.76-0.95; P-DeLong test  = 2.2 × 10-4 ). In summary, this study identified several important SNP-SNP interactions for BC risk, and developed an iPRS model for BC screening, which may help to identify the people at high-risk state of BC before early manifestation.

A genetic variant in gene NDUFAF4 confers the risk of non-small cell lung cancer by perturbing hsa-miR-215 binding

Hsa-microRNA-215 (hsa-miR-215) plays multiple roles in carcinogenesis through regulating its target genes. Genetic variants in hsa-miR-215 target sites thus may affect hsa-miR-215-mRNA interactions, result in altered expression of target genes and even influence cancer susceptibility. This study aimed to investigate the associations of genetic variants which located in the binding sites of hsa-miR-215 with non-small cell lung cancer (NSCLC) susceptibility in the Chinese population and reveal the potential regulatory mechanism of functional variants in NSCLC development. The candidate genetic variants were predicted and screened through bioinformatics analysis based on the degree of complementarity of hsa-miR-215 sequences. The potential effects of genetic variants on the binding ability of hsa-miR-215 and target genes were also predicted. A case-control study with 932 NSCLC patients and 1036 healthy controls was conducted to evaluate the association of candidate genetic variants with NSCLC susceptibility, and an independent case-control study with 552 NSCLC cases and 571 controls were used to further validate the promising associations. Dual luciferase reporter gene assay was applied to explore the regulation of the genetic variants on transcription activity of target gene. Cell phenotyping experiments in vitro and RNA sequencing (RNA-seq) were then carried out to preliminarily explore the potential regulatory mechanisms of the target genes in NSCLC. A total of five candidate genetic variants located in the binding sites of hsa-miR-215 were screened. The two-stage case-control study showed that a variant rs1854268 A > T, which located in the 3' untranslated (3'UTR) region of NDUFAF4 gene, was associated with decreased risk of NSCLC (additive model, odds ratio [OR] = 0.83, 95% confidence interval [CI]: 0.75-0.92, p < 0.001). Functional annotation displayed that rs1854268 A > T might downregulate the expression of NDUFAF4 by enhancing the binding affinity of hsa-miR-215-5p to NDUFAF4 mRNA. Additionally, transient knockdown of the NDUFAF4 could inhibit lung cancer cell migration and promote lung cancer cell apoptosis. Further RNA-seq analysis revealed that the knockdown of NDUFAF4 may affect NSCLC development by downregulating the nuclear factor kappa B (NF-κB) and phosphoinositide 3 kinase-AKT (PI3K-AKT) signaling pathways. Moreover, the overexpression of CCND1 could partially attenuate the effects of NDUFAF4 knock down on lung cancer cell migration and apoptosis, indicating that CCND1 may be involved in the tumor-promoting effects of NDUFAF4 as a downstream molecule of NDUFAF4 gene. In conclusion, the genetic variant rs1854268 (A > T) on NDUFAF4 confers NSCLC susceptibility by altering the binding affinity of hsa-miR-215-5p, thus regulating the expression of NDUFAF4 and subsequently influencing downstream tumor molecules and pathways such as CCND1, NF kappa B, and PI3K-AKT signaling pathways.

Germline Genetic Associations for Hepatobiliary Cancers

Hepatobiliary cancers (HBCs) include hepatocellular carcinoma, cholangiocarcinoma, and gallbladder carcinoma, which originate from the liver, bile ducts, and gallbladder, respectively. They are responsible for a substantial burden of cancer-related deaths worldwide. Despite knowledge of risk factors and advancements in therapeutics and surgical interventions, the prognosis for most patients with HBC remains bleak. There is evidence from familial aggregation and case-control studies to suggest a familial risk component in HBC susceptibility. Recent progress in genomics research has led to the identification of germline variants including single nucleotide polymorphisms (SNPs) and pathogenic or likely pathogenic (P/LP) variants in cancer-associated genes associated with HBC risk. These findings emerged from genome-wide association studies and next-generation sequencing techniques such as whole-exome sequencing. Patients with other cancer types, including breast, colon, ovarian, prostate, and pancreatic cancer, are recommended by guidelines to undergo germline genetic testing, but similar recommendations are lagging in HBC. This prompts the question of whether multi-gene panel testing should be integrated into clinical guidelines for HBC management. Here, we review the hereditary genetics of HBC, explore studies investigating SNPs and P/LP variants in HBC patients, discuss the clinical implications and potential for personalized treatments and impact on patient's family members, and conclude that additional studies are needed to examine how genetic testing can be applied clinically.

Performance analysis of conventional and AI-based variant callers using short and long reads

BACKGROUND

The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use.

RESULTS

In this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons.

CONCLUSION

This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Evaluation of loci to predict ear morphology using two SNaPshot assays

Human ear morphology prediction with SNP-based genotypes is growing in forensic DNA phenotyping and is scarcely explored in Pakistan as a part of EVCs (externally visible characteristics). The ear morphology prediction assays with 21 SNPs were assessed for their potential utility in forensic identification of population. The SNaPshot™ multiplex chemistries, capillary electrophoresis methods and GeneMapper™ software were used for obtaining genotypic data. A total of 33 ear phenotypes were categorized with digital photographs of 300 volunteers. SHEsis software was applied to make LD plot. Ordinal and multinomial logistic regression was implemented for association testing. Multinomial logistic regression was executed to construct the prediction model in 90% training and 10% testing subjects. Several influential SNPs for ear phenotypic variation were found in association testing. The model based on genetic markers predicted ear phenotypes with moderate to good predictive accuracies demonstrated with the area under curve (AUC), sensitivity and specificity of predicted phenotypes. As an additional EVC, the estimated ear phenotypic profiles have the possibility of determining the human ear morphology differences in unknown biological samples found in crimes that do not result in a criminal database hit. Furthermore, this can help in facial reconstruction and act as an investigational lead.

Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants

BACKGROUND

Short structural variants (SSVs), including insertions/deletions (indels), are common in the human genome and impact disease risk. The role of SSVs in late-onset Alzheimer's disease (LOAD) has been understudied. In this study, we developed a bioinformatics pipeline of SSVs within LOAD-genome-wide association study (GWAS) regions to prioritize regulatory SSVs based on the strength of their predicted effect on transcription factor (TF) binding sites.

METHODS

The pipeline utilized publicly available functional genomics data sources including candidate cis-regulatory elements (cCREs) from ENCODE and single-nucleus (sn)RNA-seq data from LOAD patient samples.

RESULTS

We catalogued 1581 SSVs in candidate cCREs in LOAD GWAS regions that disrupted 737 TF sites. That included SSVs that disrupted the binding of RUNX3, SPI1, and SMAD3, within the APOE-TOMM40, SPI1, and MS4A6A LOAD regions.

CONCLUSIONS

The pipeline developed here prioritized non-coding SSVs in cCREs and characterized their putative effects on TF binding. The approach integrates multiomics datasets for validation experiments using disease models.

vamos: variable-number tandem repeats annotation using efficient motif sets

Roughly 3% of the human genome is composed of variable-number tandem repeats (VNTRs): arrays of motifs at least six bases. These loci are highly polymorphic, yet current approaches that define and merge variants based on alignment breakpoints do not capture their full diversity. Here we present a method vamos: VNTR Annotation using efficient Motif Sets that instead annotates VNTR using repeat composition under different levels of motif diversity. Using vamos we estimate 7.4-16.7 alleles per locus when applied to 74 haplotype-resolved human assemblies, compared to breakpoint-based approaches that estimate 4.0-5.5 alleles per locus.

TRcaller: a novel tool for precise and ultrafast tandem repeat variant genotyping in massively parallel sequencing reads

Calling tandem repeat (TR) variants from DNA sequences is of both theoretical and practical significance. Some bioinformatics tools have been developed for detecting or genotyping TRs. However, little study has been done to genotyping TR alleles from long-read sequencing data, and the accuracy of genotyping TR alleles from next-generation sequencing data still needs to be improved. Herein, a novel algorithm is described to retrieve TR regions from sequence alignment, and a software program TRcaller has been developed and integrated into a web portal to call TR alleles from both short- and long-read sequences, both whole genome and targeted sequences generated from multiple sequencing platforms. All TR alleles are genotyped as haplotypes and the robust alleles will be reported, even multiple alleles in a DNA mixture. TRcaller could provide substantially higher accuracy (>99% in 289 human individuals) in detecting TR alleles with magnitudes faster (e.g., ∼2 s for 300x human sequence data) than the mainstream software tools. The web portal preselected 119 TR loci from forensics, genealogy, and disease related TR loci. TRcaller is validated to be scalable in various applications, such as DNA forensics and disease diagnosis, which can be expanded into other fields like breeding programs. Availability: TRcaller is available at https://www.trcaller.com/SignIn.aspx.

Naturally segregating genetic variants contribute to thermal tolerance in a D. melanogaster model system

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives

BACKGROUND

Genome-wide association studies (GWAS) are an effective way to explore genotype-phenotype associations in humans, animals, and plants. Various GWAS methods have been developed based on different genetic or statistical assumptions. However, no single method is optimal for all traits and, for many traits, the putative single nucleotide polymorphisms (SNPs) that are detected by the different methods do not entirely overlap due to the diversity of the genetic architecture of complex traits. Therefore, multi-tool-based GWAS strategies that combine different methods have been increasingly employed. To take this one step further, we propose an ensemble-like GWAS strategy (E-GWAS) that statistically integrates GWAS results from different single GWAS methods.

RESULTS

E-GWAS was compared with various single GWAS methods using simulated phenotype traits with different genetic architectures. E-GWAS performed stably across traits with different genetic architectures and effectively controlled the number of false positive genetic variants detected without decreasing the number of true positive variants. In addition, its performance could be further improved by using a bin-merged strategy and the addition of more distinct single GWAS methods. Our results show that the numbers of true and false positive SNPs detected by the E-GWAS strategy slightly increased and decreased, respectively, with increasing bin size and when the number and the diversity of individual GWAS methods that were integrated in E-GWAS increased, the latter being more effective than the bin-merged strategy. The E-GWAS strategy was also applied to a real dataset to study backfat thickness in a pig population, and 10 candidate genes related to this trait and expressed in adipose-associated tissues were identified.

CONCLUSIONS

Using both simulated and real datasets, we show that E-GWAS is a reliable and robust strategy that effectively integrates the GWAS results of different methods and reduces the number of false positive SNPs without decreasing that of true positive SNPs.

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalization capability may be limited by the amount of available data. To address this, we propose EvoAug, a suite of evolution-inspired augmentations that enhance the training of genomic DNNs by increasing genetic variation. Random transformation of DNA sequences can potentially alter their function in unknown ways, so we employ a fine-tuning procedure using the original non-transformed data to preserve functional integrity. Our results demonstrate that EvoAug substantially improves the generalization and interpretability of established DNNs across prominent regulatory genomics prediction tasks, offering a robust solution for genomic DNNs.

Gene-environment interaction explains a part of missing heritability in human body mass index

Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.

The Genetic Basis of Childhood Obesity: A Systematic Review

Overweight and obesity in childhood and adolescence represents one of the most challenging public health problems of our century owing to its epidemic proportions and the associated significant morbidity, mortality, and increase in public health costs. The pathogenesis of polygenic obesity is multifactorial and is due to the interaction among genetic, epigenetic, and environmental factors. More than 1100 independent genetic loci associated with obesity traits have been currently identified, and there is great interest in the decoding of their biological functions and the gene-environment interaction. The present study aimed to systematically review the scientific evidence and to explore the relation of single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs) with changes in body mass index (BMI) and other measures of body composition in children and adolescents with obesity, as well as their response to lifestyle interventions. Twenty-seven studies were included in the qualitative synthesis, which consisted of 7928 overweight/obese children and adolescents at different stages of pubertal development who underwent multidisciplinary management. The effect of polymorphisms in 92 different genes was assessed and revealed SNPs in 24 genetic loci significantly associated with BMI and/or body composition change, which contribute to the complex metabolic imbalance of obesity, including the regulation of appetite and energy balance, the homeostasis of glucose, lipid, and adipose tissue, as well as their interactions. The decoding of the genetic and molecular/cellular pathophysiology of obesity and the gene-environment interactions, alongside with the individual genotype, will enable us to design targeted and personalized preventive and management interventions for obesity early in life.

Weighted kernels improve multi-environment genomic prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

[Digitalization in healthcare: today and in the future]

Although Germany continues to struggle with the digital transformation of healthcare, there is reason for optimism. The political will to improve healthcare with digital technologies has been underpinned by numerous legal initiatives since 2018. In addition, there is growing acceptance among healthcare providers and the population. The latter has clearly been driven by the corona pandemic, which underscored the need for more digitized care.Digitalization in healthcare has three key drivers: the rapid technological development in data processing, the ever-improving understanding of the biological basis of human life, and growing patient sovereignty coupled with a growing desire for transparency. Prerequisites for digital medicine are data interoperability and the establishment of a networking (telematics) infrastructure (TI). The status of the most important digital TI applications affecting German healthcare are described: the electronic patient record (ePA) as its core as well as electronic prescriptions, medication plans, and communication tools such as Communication in Medicine (KIM) and TI Messenger (TIM). In addition, various telemedical offerings are discussed as well as the introduction of digital health applications (DiGA) into the statutory healthcare system, which Germany has pioneered. Furthermore, the use of medical data as the basis for artificial intelligence (AI) algorithms is discussed. While helpful and capable of improving diagnostics as well as medical therapy, such AI tools will not replace doctors and nurses.

The SFT2D2 gene is associated with the autoimmune pathology of schizophrenia in a Chinese population

Background

The relative risk of GWAS-confirmed loci strongly associated with schizophrenia may be underestimated due to the decay of linkage disequilibrium between index SNPs and causal variants. This study is aimed to investigate schizophrenia-associated signals detected in the 1q24-25 region in order to identify a causal variant in LD with GWAS index SNPs, and the potential biological functions of the risk gene.

Methods

Re-genotyping analysis was performed in the 1q24-25 region that harbors three GWAS index SNPs associated with schizophrenia (rs10489202, rs11586522, and rs6670165) in total of 9801 case-control subjects of Chinese Han origin. Circulating autoantibody levels were assessed using an in-house ELISA against a protein derived fragment encoded by SFT2D2 in total of 682 plasma samples.

Results

A rare variant (rs532193193) in the SFT2D2 locus was identified to be strongly associated with schizophrenia. Compared with control subjects, patients with schizophrenia showed increased anti-SFT2D2 IgG levels. Receiver operating characteristic (ROC) analysis revealed an area under the ROC curve (AUC) of 0.803 with sensitivity of 28.57% against specificity of 95% for the anti-SFT2D2 IgG assay.

Discussion

Our findings indicate that SFT2D2 is a novel gene for risk of schizophrenia, while endogenous anti-SFT2D2 IgG may underlie the pathophysiology of the immunological aspects of schizophrenia.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

USAT: a bioinformatic toolkit to facilitate interpretation and comparative visualization of tandem repeat sequences

Background

Tandem repeats (TR), highly variable genomic variants, are widely used in individual identification, disease diagnostics, and evolutionary studies. The recent advances in sequencing technologies and bioinformatic tools facilitate calling TR haplotypes genome widely. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision in characterizing TR haplotypes. The need to identify the differences at the single nucleotide level between or among TR haplotypes with an easy-use bioinformatic tool is essential.

Results

In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which takes TR haplotype output from existing tools to perform allele size conversion, sequence comparison of haplotypes, figure plotting, comparison for allele distribution, and interactive visualization. An exemplary application of USAT for analysis of the CODIS core STR loci for DNA forensics with benchmarking human individuals demonstrated the capabilities of USAT. USAT has user-friendly graphic interfaces and runs fast in major computing operating systems with parallel computing enabled.

Conclusion

USAT is a user-friendly bioinformatics software for interpretation, visualization, and comparisons of TRs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-05021-1.

The Genetic Architecture of Non-Syndromic Rhegmatogenous Retinal Detachment

Rhegmatogenous retinal detachment (RRD) is the most common form of retinal detachment (RD), affecting 1 in 10,000 patients per year. The condition has significant ocular morbidity, with a sizeable proportion of patients obtaining poor visual outcomes. Despite this, the genetics underpinning Idiopathic Retinal Detachment (IRD) remain poorly understood; this is likely due to small sample sizes in relevant studies. The majority of research pertains to the well-characterised Mende lian syndromes, such as Sticklers and Wagners, associated with RRD. Nevertheless, in recent years, there has been an increasing body of literature identifying the common genetic mutations and mechanisms associated with IRD. Several recent Genomic Wide Association Studies (GWAS) studies have identified a number of genetic loci related to the development of IRD. Our review aims to provide an up-to-date summary of the significant genetic mechanisms and associations of Idiopathic RRD.

Identifying celiac disease-related chemicals by transcriptome-wide association study and chemical-gene interaction analyses

Celiac disease (CeD) is one of the most common intestinal inflammatory diseases, and its incidence and prevalence have increased over time. CeD affects multiple organs and systems in the body, and environmental factors play a key role in its complex pathogenesis. Although gluten exposure is known to be the causative agent, many unknown environmental factors can trigger or exacerbate CeD. In this study, we investigated the influence of genetic and environmental factors on CeD. Data from a CeD genome-wide association study that included 12,041 CeD cases and 12,228 controls were used to conduct a transcriptome-wide association study (TWAS) using FUSION software. Gene expression reference data were obtained for the small intestine, whole blood, peripheral blood, and lymphocytes. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the significant genes identified by the TWAS and conducted a protein–protein interaction network analysis based on the STRING database to detect the function of TWAS-identified genes for CeD. We also performed a chemical-related gene set enrichment analysis (CGSEA) using the TWAS-identified genes to test the relationships between chemicals and CeD. The TWAS identified 8,692 genes, including 101 significant genes (p_adjusted < 0.05). The CGSEA identified 2,559 chemicals, including 178 chemicals that were significantly correlated with CeD. This study performed a TWAS (for genetic factors) and CGSEA (for environmental factors) and identified several CeD-associated genes and chemicals. The findings expand our understanding of the genetic and environmental factors related to immune-mediated diseases.

Gene-environment correlations across geographic regions affect genome-wide association studies

Gene-environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene-environment correlations, and that migration extends these gene-environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene-environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene-environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene-environment correlations that affect GWAS results.

On the causal interpretation of heritability from a structural causal modeling perspective

Heritability estimated using the analysis of variance (ANOVA) for ascribing causal responsibility to genes for a phenotype has been criticized widely. First, there are problems associated with articulating the exact causal meaning of heritability in the standard model. Second, in conditions of gene-environment interaction or covariation that violate the assumptions made by the standard model, a causal interpretation of heritability is thought to be unwarranted. This paper aims to rethink these ideas and associated disputes from a structural causal modeling (SCM) perspective. Using SCM, we show that, in the standard model, heritability reflects the causal effect of eliminating genotypic differences on the change of phenotypic variance of a population. In the presence of interaction or covariation, heritability is estimated incorrectly using ANOVA. However, SCM can provide the causal effect of genotypes on the phenotypic variance regarding particular interventions. We also show that SCM can identify different types of causal effect and answer individual-level causal questions. We conclude that SCM has the resources to provide a systematic causal interpretation that can supplement traditional heritability estimates via ANOVA and offer a more substantial causal analysis of genetic causation.

Annotation Query (AnnoQ): an integrated and interactive platform for large-scale genetic variant annotation

The Annotation Query (AnnoQ) (http://annoq.org/) is designed to provide comprehensive and up-to-date functional annotations for human genetic variants. The system is supported by an annotation database with ∼39 million human variants from the Haplotype Reference Consortium (HRC) pre-annotated with sequence feature annotations by WGSA and functional annotations to Gene Ontology (GO) and pathways in PANTHER. The database operates on an optimized Elasticsearch framework to support real-time complex searches. This implementation enables users to annotate data with the most up-to-date functional annotations via simple queries instead of setting up individual tools. A web interface allows users to interactively browse the annotations, annotate variants and search variant data. Its easy-to-use interface and search capabilities are well-suited for scientists with fewer bioinformatics skills such as bench scientists and statisticians. AnnoQ also has an API for users to access and annotate the data programmatically. Packages for programming languages, such as the R package, are available for users to embed the annotation queries in their scripts. AnnoQ serves researchers with a wide range of backgrounds and research interests as an integrated annotation platform.

Promoter haplotype structure of solute carrier 22 member 2 (SLC22A2) in the Xhosa population of South Africa and their differential effect on gene expression

SLC22A2 is abundantly expressed in the kidney and facilitates the transport of endogenous and exogenous cationic compounds. It plays a pivotal role in the transport of pharmacologically important compounds such as metformin, cisplatin, lamivudine and cimetidine. Polymorphisms within SLC22A2 could potentially contribute to the inter-individual variable response to drugs. The SLC22A2 gene is known to show polymorphism variability amongst populations of different ethnicities. The present study was undertaken to characterize the promoter haplotype structure of the SLC22A2 gene in the Xhosa population of South Africa. In addition to this, we also investigate the effects of the observed promoter haplotypes on gene expression levels in vitro. We identified six known single nucleotide polymorphisms in the promoter region, namely rs60249401 (G424A), rs113150889 (G289A), rs55920607 (C246T), rs59695691 (A195G), rs572296424 (G156A), rs150063153 (A95C/G) and one novel SNP at location 6:160258967 (A209T). While these polymorphisms appeared in other African and non-African populations, their minor allele frequencies differed considerably from the non-African populations and could be considered to be African specific. A total of nine promoter haplotypes were characterized and the functional significance of each haplotype on promoter activity was determined using a luciferase reporter assay system. Amongst the nine observed haplotypes, three haplotypes (i.e. haplotypes 7, 8 and 9) displayed a significant decrease in expression level when compared to the wild-type with p -values of: 0.0317, <0.0001 and 0.0013 respectively. The data presented here shows African specific promoter haplotypes to cause a decrease in SLC22A2 gene expression levels, which in turn may have an impact on the pharmacokinetic profiles of cationic drugs.

The Thousand Polish Genomes-A Database of Polish Variant Allele Frequencies

Although Slavic populations account for over 4.5% of world inhabitants, no centralised, open-source reference database of genetic variation of any Slavic population exists to date. Such data are crucial for clinical genetics, biomedical research, as well as archeological and historical studies. The Polish population, which is homogenous and sedentary in its nature but influenced by many migrations of the past, is unique and could serve as a genetic reference for the Slavic nations. In this study, we analysed whole genomes of 1222 Poles to identify and genotype a wide spectrum of genomic variation, such as small and structural variants, runs of homozygosity, mitochondrial haplogroups, and de novo variants. Common variant analyses showed that the Polish cohort is highly homogenous and shares ancestry with other European populations. In rare variant analyses, we identified 32 autosomal-recessive genes with significantly different frequencies of pathogenic alleles in the Polish population as compared to the non-Finish Europeans, including C2, TGM5, NUP93, C19orf12, and PROP1. The allele frequencies for small and structural variants, calculated for 1076 unrelated individuals, are released publicly as The Thousand Polish Genomes database, and will contribute to the worldwide genomic resources available to researchers and clinicians.

Identification, analysis of deleterious SNPs of the human GSR gene and their effects on the structure and functions of associated proteins and other diseases

Hereditary glutathione reductase deficiency, caused by mutations of the GSR gene, is an autosomal recessive disorder characterized by decreased glutathione disulfide (GSSG) reduction activity and increased thermal instability. This study implemented computational analysis to screen the most likely mutation that might be associated with hereditary glutathione reductase deficiency and other diseases. Using ten online computational tools, the study revealed four nsSNPs among the 17 nsSNPs identified as most deleterious and disease associated. Structural analyses and evolutionary confirmation study of native and mutant GSR proteins using the HOPE project and ConSruf. HOPE revealed more flexibility in the native GSR structure than in the mutant structure. The mutation in GSR might be responsible for changes in the structural conformation and function of the GSR protein and might also play a significant role in inducing hereditary glutathione reductase deficiency. LD and haplotype studies of the gene revealed that the identified variations rs2978663 and rs8190955 may be responsible for obstructive heart defects (OHDs) and hereditary anemia, respectively. These interethnic differences in the frequencies of SNPs and haplotypes might help explain the unpredictability that has been reported in association studies and can contribute to predicting the pharmacokinetics and pharmacodynamics of drugs that make use of GSR.