A brief history of human disease genetics

An integrative approach to identifying NPC1 as a susceptibility gene for gestational diabetes mellitus

OBJECTIVE

The objective of this study was to identify a novel gene and its potential mechanisms associated with susceptibility to gestational diabetes mellitus (GDM) through an integrative approach.

METHODS

We analyzed data from genome-wide association studies (GWAS) of GDM in the FinnGen R11 dataset (16,802 GDM cases and 237,816 controls) and Genotype Tissue Expression v8 expression quantitative trait locus data. We used summary-data-based Mendelian randomization to determine associations between transcript levels and phenotypes, transcriptome-wide association studies to provide insights into gene-trait associations, multi-marker analysis of genomic annotation to perform gene-based analysis, genome-wide complex trait analysis-multivariate set-based association test-combo to determine gene prioritization, and polygenic priority scores to prioritize the causal genes to screen candidate genes. Subsequent Mendelian randomization analysis was performed to infer causality between the candidate genes and GDM and phenome-wide association study (PheWAS) analysis was used to explore the associations between selected genes and other characteristics. Furthermore, to gain a deeper understanding of the functional implications of these susceptibility genes, GeneMANIA analysis was used to determine the fundamental biological functions of the therapeutic targets and protein-protein interaction network analysis was performed to identify intracellular protein interactions.

RESULTS

We identified two novel susceptibility genes associated with GDM: NPC1 and KIAA1191. Magnetic resonance imaging revealed a strong correlation between NPC1 expression levels and a lower incidence of GDM (odds ratio: 0.922, 95% confidence interval: 0.866-0.981, p = 0.011). PheWAS at the gene level indicated that NPC1 was not associated with any other trait. The biological significance of this gene was evidenced by its strong association with sterol metabolism.

CONCLUSION

Our study identified NPC1 as a novel gene whose predicted expression level is linked to a reduced risk of GDM, providing new insights into the genetic framework of this disease.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Assessing the contribution of rare protein-coding germline variants to prostate cancer risk and severity in 37,184 cases

To assess the contribution of rare coding germline genetic variants to prostate cancer risk and severity, we perform here a meta-analysis of 37,184 prostate cancer cases and 331,329 male controls from five cohorts with germline whole exome or genome sequencing data, and one cohort with imputed array data. At the gene level, our case-control collapsing analysis confirms associations between rare damaging variants in four genes and increased prostate cancer risk: SAMHD1, BRCA2 and ATM at the study-wide significance level (P < 1×10-8), and CHEK2 at the suggestive threshold (P < 2.6×10-6). Our case-only analysis, reveals that rare damaging variants in AOX1 are associated with more aggressive disease (OR = 2.60 [1.75-3.83], P = 1.35×10-6), as well as confirming the role of BRCA2 in determining disease severity. At the single-variant level, our study reveals that a rare missense variant in TERT is associated with substantially reduced prostate cancer risk (OR = 0.13 [0.07-0.25], P = 4.67×10-10), and confirms rare non-synonymous variants in a further three genes associated with reduced risk (ANO7, SPDL1, AR) and in three with increased risk (HOXB13, CHEK2, BIK). Altogether, this work provides deeper insights into the genetic architecture and biological basis of prostate cancer risk and severity, with potential implications for clinical risk prediction and therapeutic strategies.

Genetic analysis for diagnosing local recurrence of sigmoid colon cancer mimicking a small intestinal tumor: a case report

BACKGROUND

With recent advances in genetics research, genetic analysis is increasingly being used in clinical practice. We report a case in which genetic analysis aided in diagnosing a local recurrence of sigmoid colon cancer, initially suspected to be a primary neoplasm of the small intestine.

CASE PRESENTATION

A 61-year-old male underwent laparoscopic sigmoidectomy for stage IIIB sigmoid colon cancer, followed by 8 cycles of CAPOX adjuvant chemotherapy, one and a half years prior. A follow-up CT scan performed one and a half years postoperatively revealed a mass in the small intestine near the ileal end, adjacent to the staple line of the previous colonic anastomosis. PET imaging showed high uptake in the small intestine but no significant uptake at the site of the prior anastomotic ring. Based on these findings, a primary small intestine neoplasm was suspected, rather than a local recurrence of the sigmoid cancer, prompting laparoscopic surgery. Intraoperative findings revealed an inflamed mass in the ileum, approximately 30 cm proximal to the cecum, involving staples from the previous anastomotic site. Consequently, an ileocecal resection combined with resection of the prior colonic anastomosis was performed. Macroscopically, the resected specimen revealed a 25-mm Type 2 tumor in the ileum extending into the previous anastomotic site of the large intestine, while the colonic mucosa remained intact. Histopathological examination identified a moderately differentiated tubular adenocarcinoma, consistent with the histology of the primary sigmoid cancer, raising the possibility of local recurrence. To analyze the origin of the ileal tumor, we performed whole-genome sequencing and subsequent PCR direct sequencing. As a result, identical mutations in two key driver genes (KRAS c.35G > A and PIK3CA c.1624G > A), as well as a mutation in a passenger gene (BBS9 c.2218_2222del), were identified in the primary and ileal tumors. These findings confirmed that the ileal tumor was a local recurrence rather than a new primary malignancy.

CONCLUSIONS

The present case highlights the practical application of genetic analysis in clinical practice, particularly when clinical diagnosis and histopathological findings are inconclusive or conflicting.

Functional characterization of eQTLs and asthma risk loci with scATAC-seq across immune cell types and contexts

cis-regulatory elements (CREs) control gene transcription dynamics across cell types and in response to the environment. In asthma, multiple immune cell types play an important role in the inflammatory process. Genetic variants in CREs can also affect gene expression response dynamics and contribute to asthma risk. However, the regulatory mechanisms underlying control of transcriptional dynamics across different environmental contexts and cell types at single-cell resolution remain to be elucidated. To resolve this question, we performed single-cell ATAC-seq (scATAC-seq) in peripheral blood mononuclear cells (PBMCs) from 16 children with asthma. PBMCs were activated with phytohemagglutinin (PHA) or lipopolysaccharide (LPS) and treated with dexamethasone (DEX), an anti-inflammatory glucocorticoid. We analyzed changes in chromatin accessibility, measured transcription factor motif activity, and identified treatment- and cell-type-specific transcription factors that drive changes in both gene expression mean and variability. We observed a strong positive linear dependence between motif response and their target gene expression changes but a negative relationship with changes in target gene expression variability. This result suggests that an increase of transcription factor binding tightens the variability of gene expression around the mean. We then annotated genetic variants in chromatin accessibility peaks and response motifs, followed by computational fine-mapping of expression quantitative trait loci (eQTL) from a pediatric asthma cohort. We found that eQTLs were 5-fold enriched in peaks with response motifs and refined the credible set for 410 asthma risk genes, with 191 having the causal variant in response motifs. In conclusion, scATAC-seq enhances the understanding of molecular mechanisms for asthma risk variants mediated by gene expression.

Saturation genome editing-based clinical classification of BRCA2 variants

Sequencing-based genetic tests have uncovered a vast array of BRCA2 sequence variants1. Owing to limited clinical, familial and epidemiological data, thousands of variants are considered to be variants of uncertain significance2-4 (VUS). Here we have utilized CRISPR-Cas9-based saturation genome editing in a humanized mouse embryonic stem cell line to determine the functional effect of VUS. We have categorized nearly all possible single nucleotide variants (SNVs) in the region that encodes the carboxylate-terminal DNA-binding domain of BRCA2. We have generated function scores for 6,551 SNVs, covering 96.4% of possible SNVs in exons 15-26 spanning BRCA2 residues 2479-3216. These variants include 1,282 SNVs that are categorized as missense VUS in the clinical variant database ClinVar, with 77.2% of these classified as benign and 20.4% classified as pathogenic using our functional score. Our assay provides evidence that 3,384 of the SNVs in the region are benign and 776 are pathogenic. Our classification aligns closely with pathogenicity data from ClinVar, orthogonal functional assays and computational meta predictors. We have integrated our embryonic stem cell-based BRCA2-saturation genome editing dataset with other available evidence and utilized the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines for clinical classification of all possible SNVs. This classification is available as a sequence-function map and serves as a valuable resource for interpreting unidentified variants in the population and for physicians and genetic counsellors to assess BRCA2 VUS in patients.

Genome-wide association studies with prolapsed gland of the third eyelid in dogs

Cherry eye, the common name for the prolapse of the third eyelid gland in dogs, is a widespread ophthalmic disease affecting dogs of various breeds. This condition severely affects the quality of life of affected dogs, and its underlying cause remains unresolved. In this study, 170K SNP microarray data were collected from 653 brachycephalic dogs and 788 brachycephalic and mesocephalic dogs. These two datasets were analyzed separately in genome-wide association studies (GWAS) involving 12 dog breeds affected by cherry eye. The GWAS analysis of 653 short-headed dogs revealed that four SNPs in the CFA3:15627075-15983629 bp region exceeded the genome-level significance threshold. Association analysis of this region also indicated that these four SNPs were strongly associated. Gene annotation showed that the region contained genes such as KIAA0825, FAM172A, and NR2F1, of which NR2F1 was associated with eye development. The results showed that GWAS analysis performed on 788 short- and medium-headed dogs identified five SNPs in the CFA22:15627075-15983629 bp region that exceeded the genome-level significance threshold, and association analysis was performed in this region, which showed that these five SNPs were strongly associated. In addition, 104 annotated genes were identified in both GWAS. To explore the genes involved in cherry eyes, we performed GO functional enrichment analysis. The genes involved in the high pathway were DIO3 and TTC8. In addition, an in-depth analysis revealed 33 genes associated with eye development and diseases. Our study provides new perspectives for further understanding cherry eye in dogs.

A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice

BACKGROUND

Despite the use of Next-Generation Sequencing (NGS) as the gold standard for the diagnosis of rare diseases, its clinical implementation has been challenging, limiting the cost-effectiveness of NGS and the understanding, control and safety essential for decision-making in clinical applications. Here, we describe a personalized NGS-based strategy integrating precision medicine into a public healthcare system and its implementation in the routine diagnosis process during a five-year pilot program.

METHODS

Our approach involved customized probe designs, the generation of virtual panels and the development of a personalized medicine module (PMM) for variant prioritization. This strategy was applied to 6500 individuals including 6267 index patients and 233 NGS-based carrier screenings.

RESULTS

Causative variants were identified in 2061 index patients (average 32.9%, ranging from 12 to 62% by condition). Also, 131 autosomal-recessive cases could be partially genetically diagnosed. These results led to over 5000 additional studies including carrier, prenatal and preimplantational tests or pharmacological and gene therapy treatments.

CONCLUSION

This strategy has shown promising improvements in the diagnostic rate, facilitating timely diagnosis and gradually expanding our services portfolio for rare diseases. The steps taken towards the integration of clinical and genomic data are opening new possibilities for conducting both retrospective and prospective healthcare studies. Overall, this study represents a major milestone in the ongoing efforts to improve our understanding and clinical management of rare diseases, a crucial area of medical research and care.

The Era of Preemptive Medicine: Developing Medical Digital Twins through Omics, IoT, and AI Integration

Preemptive medicine represents a paradigm shift from reactive treatment to proactive disease prevention. The integration of omics technologies, the Internet of Things (IoT), and artificial intelligence (AI) has facilitated the development of personalized, predictive, and preemptive healthcare strategies. Omic technologies, such as genomics, proteomics, and metabolomics, provide comprehensive insights into molecular profile of an individual, revealing potential disease predispositions and health trajectories. IoT devices, such as wearables and smartphones, enable continuous and periodic monitoring of physiological parameters, thus providing a dynamic view of an individual's health status. AI algorithms analyze comprehensive and complex data from omics and IoT technologies to identify patterns and correlations that inform predictive models of disease risk, progression, and response to interventions. Medical digital twins, or virtual replicas of an individual's biological processes, have emerged as the cornerstone of preemptive medicine. The integration of omics, IoT, and AI enables the development of medical digital twins, which in turn allows for precise simulation of human physiological profiles, prediction of future health outcomes, and virtual individual clinical trials, facilitating personalized proactive interventions and preemptive disease control. This review demonstrates the convergence of omics, IoT, and AI in preemptive medicine, highlighting their potential to revolutionize healthcare by enabling early disease detection, personalized treatment strategies, and chronic disease prevention. We show how AI leverages omics and IoT in preemptive medicine through several case studies while also discussing the necessary data for developing medical digital twins and addressing ethical and social aspects that warrant consideration. Medical digital twins signify a fundamental transformation in health management, shifting from treating diseases after their occurrence to controlling them before their occurrence. This approach enhances the effectiveness of medical interventions and improves overall health outcomes, preparing for a healthier future.

Rapid and quantitative functional interrogation of human enhancer variant activity in live mice

Functional analysis of non-coding variants associated with congenital disorders remains challenging due to the lack of efficient in vivo models. Here we introduce dual-enSERT, a robust Cas9-based two-color fluorescent reporter system which enables rapid, quantitative comparison of enhancer allele activities in live mice in less than two weeks. We use this technology to examine and measure the gain- and loss-of-function effects of enhancer variants previously linked to limb polydactyly, autism spectrum disorder, and craniofacial malformation. By combining dual-enSERT with single-cell transcriptomics, we characterise gene expression in cells where the enhancer is normally and ectopically active, revealing candidate pathways that may lead to enhancer misregulation. Finally, we demonstrate the widespread utility of dual-enSERT by testing the effects of fifteen previously uncharacterised rare and common non-coding variants linked to neurodevelopmental disorders. In doing so we identify variants that reproducibly alter the in vivo activity of OTX2 and MIR9-2 brain enhancers, implicating them in autism. Dual-enSERT thus allows researchers to go from identifying candidate enhancer variants to analysis of comparative enhancer activity in live embryos in under two weeks.

The interplay between mitochondrial DNA genotypes, female infertility, ovarian response, and mutagenesis in oocytes

STUDY QUESTION

Is there an association between different mitochondrial DNA (mtDNA) genotypes and female infertility or ovarian response, and is the appearance of variants in the oocytes favored by medically assisted reproduction (MAR) techniques?

SUMMARY ANSWER

Ovarian response was negatively associated with global non-synonymous protein-coding homoplasmic variants but positively associated with haplogroup K; the number of oocytes retrieved in a cycle correlates with the number of heteroplasmic variants in the oocytes, principally with variants located in the hypervariable (HV) region and rRNA loci, as well as non-synonymous protein-coding variants.

WHAT IS KNOWN ALREADY

Several genes have been shown to be positively associated with infertility, and there is growing concern that MAR may facilitate the transmission of these harmful variants to offspring, thereby passing on infertility. The potential role of mtDNA variants in these two perspectives remains poorly understood.

STUDY DESIGN SIZE DURATION

This cohort study included 261 oocytes from 132 women (mean age: 32 ± 4 years) undergoing ovarian stimulation between 2019 and 2020 at an academic center. The oocyte mtDNA genotypes were examined for associations with the women's fertility characteristics.

PARTICIPANTS/MATERIALS SETTING METHODS

The mtDNA of the oocytes underwent deep sequencing, and the mtDNA genotypes were compared between infertile and fertile groups using Fisher's exact test. The impact of the mtDNA genotype on anti-Müllerian hormone (AMH) levels and the number of (mature) oocytes retrieved was assessed using the Mann-Whitney U test for univariate analysis and logistic regression for multivariate analysis. Additionally, we examined the associations of oocyte maturation stage, infertility status, number of ovarian stimulation units, and number of oocytes retrieved with the type and load of heteroplasmic variants using univariate analysis and Poisson or linear regression analysis.

MAIN RESULTS AND THE ROLE OF CHANCE

Neither homoplasmic mtDNA variants nor haplogroups in the oocytes were associated with infertility status or with AMH levels. Conversely, when the relationship between the number of oocytes retrieved and different mtDNA genotypes was examined, a positive association was observed between the number of metaphase (MII) oocytes (P = 0.005) and haplogroup K. Furthermore, the presence of global non-synonymous homoplasmic variants in the protein-coding region was significantly associated with a reduced number of total oocytes and MII oocytes retrieved (P < 0.001 for both). Regarding the type and load of heteroplasmic variants in the different regions, there were no significant associations according to maturation stage of the oocyte or to fertility status; however, the number of oocytes retrieved correlated positively with the total number of heteroplasmic variants, and specifically with non-synonymous protein-coding, HV and rRNA variants (P < 0.001 for all).

LIMITATIONS REASONS FOR CAUTION

The current work is constrained by its retrospective design and single-center approach, potentially limiting the generalizability of our findings. The small sample size for specific types of infertility restricts this aspect of the findings.

WIDER IMPLICATIONS OF THE FINDINGS

This work suggests that mitochondrial genetics may have an impact on ovarian response and corroborates previous findings indicating that the size of the oocyte cohort after stimulation correlates with the presence of potentially deleterious variants in the oocyte. Future epidemiological and functional studies based on the results of the current study will provide valuable insights to address gaps in knowledge to assess any prospective risks for MAR-conceived offspring.

STUDY FUNDING/COMPETING INTERESTS

This work was supported by the Research Foundation Flanders (FWO, Grant numbers 1506617N and 1506717N to C.S.), by the Fonds Wetenschappelijk Fonds, Willy Gepts Research Foundation of Universitair Ziekenhuis Brussel (Grant numbers WFWG14-15, WFWG16-43, and WFWG19-19 to C.S.), and by the Methusalem Grant of the Vrije Universiteit Brussel (to K.S.). M.R. and E.C.d.D. were supported predoctoral fellowships by the FWO, Grant numbers 1133622N and 1S73521N, respectively. The authors declare no conflict of interests.

TRIAL REGISTRATION NUMBER

N/A.

Estimating the Genetic Risk of First-Degree Relatives for Chronic Diseases Using the Short Tandem Repeat Score as Model of Polygenic Inheritance

This study aims to establish a genetic risk assessment model based on a score of short tandem repeats (STRs) of polygenic inheritance. A total of 396 children and their biological parents were collected for STR genotyping. The numbers of tandem repeats of two alleles in one STR locus were assumed to be a quantitative genetic strength for disease incidence. The sums of 19 STR loci were considered a quantitative genetic strength per individual. Various thresholds of the STRs between paternal, maternal, and childhood data were recorded. As an exemplar, for thresholds of 25%, the first quarter = 1. All other samples = 0. The consistency rate for heredity (CH) was calculated from the difference in the morbidity of children between parents with and without disease groups. The ratio of observed CH to expected CH was defined as the heredity index (HI). Actual Pedigree data (finger-crossing test) confirmed the accuracy of the STR score. The genetic risk of first-degree relatives could be estimated using easily acquired data (incidence in an unrelated population). Our findings can provide a polygenic genetic model for estimating the incidence and genetic risk of chronic disease in first-degree relatives.

Association and function analysis of genetic variants and the risk of gestational diabetes mellitus in a southern Chinese population

Background

Gestational diabetes mellitus (GDM) is a complex metabolic disease that has short-term and long-term adverse effects on mothers and infants. However, the specific pathogenic mechanism has not been elucidated.

Objective

The aim of this study was to confirm the associations between candidate genetic variants (rs4134819, rs720918, rs2034410, rs11109509, and rs12524768) and GDM risk and prediction in a southern Chinese population.

Methods

Candidate variants were genotyped in 538 GDM cases and 626 healthy controls. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were calculated to assess the associations between genotypes and GDM risk. Then, the false-positive report probability (FPRP) analysis was adopted to confirm the significant associations, and bioinformatics tools were used to explore the potential biological function of studied variants. Finally, risk factors of genetic variants and clinical indicators identified by logistics regression were used to construct a nomogram model for GDM prediction.

Results

It was shown that the XAB2 gene rs4134819 was significantly associated with GDM susceptibility (CT vs. CC: adjusted OR = 1.38, 95% CI: 1.01-1.87, p = 0.044; CT/TT vs. CC: crude OR = 1.42, 95% CI: 1.08-1.86, p = 0.013). Functional analysis suggested that rs4134819 can alter the specific transcription factors (CPE bind and GATE-1) binding to the promoter of the XAB2 gene, regulating the transcription of XAB2. The nomogram established with factors such as age, FPG, HbA1c, 1hPG, 2hPG, TG, and rs4134819 showed a good discriminated and calibrated ability with an area under the curve (AUC) = 0.931 and a Hosmer-Lemeshow test p-value > 0.05.

Conclusion

The variant rs4134819 can significantly alter the susceptibility of the Chinese population to GDM possibly by regulating the transcription of functional genes. The nomogram prediction model constructed with genetic variants and clinical factors can help distinguish high-risk GDM individuals.

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance - how much a gene quantitatively affects a trait; and 2) trait specificity - a gene's importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

Characterizing the genetic architecture of drug response using gene-context interaction methods

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

An overview of key online resources for human genomics: a powerful and open toolbox for in silico research

Recent advances in high-throughput molecular methods have led to an extraordinary volume of genomics data. Simultaneously, the progress in the computational implementation of novel algorithms has facilitated the creation of hundreds of freely available online tools for their advanced analyses. However, a general overview of the most commonly used tools for the in silico analysis of genomics data is still missing. In the current article, we present an overview of commonly used online resources for genomics research, including over 50 tools. This selection will be helpful for scientists with basic or intermediate skills in the in silico analyses of genomics data, such as researchers and students from wet labs seeking to strengthen their computational competencies. In addition, we discuss current needs and future perspectives within this field.

Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph

Genome-wide association studies (GWASs) have identified tens of thousands of disease associated variants and provided critical insights into developing effective treatments. However, limited sample sizes have hindered the discovery of variants for uncommon and rare diseases. Here, we introduce KGWAS, a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to improve detection power in small-cohort GWASs significantly. KGWAS assesses the strength of a variant's association to disease based on the aggregate GWAS evidence across molecular elements interacting with the variant within the knowledge graph. Comprehensive simulations and replication experiments showed that, for small sample sizes ( N =1-10K), KGWAS identified up to 100% more statistically significant associations than state-of-the-art GWAS methods and achieved the same statistical power with up to 2.67× fewer samples. We applied KGWAS to 554 uncommon UK Biobank diseases ( N case <5K) and identified 183 more associations (46.9% improvement) than the original GWAS, where the gain further increases to 79.8% for 141 rare diseases (N case <300). The KGWAS-only discoveries are supported by abundant functional evidence, such as rs2155219 (on 11q13) associated with ulcerative colitis potentially via regulating LRRC32 expression in CD4+ regulatory T cells, and rs7312765 (on 12q12) associated with the rare disease myasthenia gravis potentially via regulating PPHLN1 expression in neuron-related cell types. Furthermore, KGWAS consistently improves downstream analyses such as identifying disease-specific network links for interpreting GWAS variants, identifying disease-associated genes, and identifying disease-relevant cell populations. Overall, KGWAS is a flexible and powerful AI model that integrates growing functional genomics data to discover novel variants, genes, cells, and networks, especially valuable for small cohort diseases.

Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population

Human craniofacial shape is highly variable yet highly heritable with numerous genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the general population. We compare three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores reveals a polygenic basis for facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples, both human and mouse, shows craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing insights into the genetic intersection of complex traits and Mendelian disorders.

FURIN, IFNL4, and TLR2 gene polymorphisms in relation to COVID-19 severity: a case-control study in Egyptian patients

BACKGROUND AND AIM

A wide range of clinical manifestations and outcomes, including liver injury, have been reported in COVID-19 patients. We investigated the association of three substantial gene polymorphisms (FURIN, IFNL4, and TLR2) with COVID-19 disease susceptibility and severity to help predict prognosis.

METHODS

150 adult COVID-19-assured cases were categorized as follows: 78 patients with a non-severe presentation, 39 patients with severe disease, and 33 critically ill patients. In addition, 74 healthy controls were included. Clinical and laboratory evaluations were carried out, including complete and differential blood counts, D-dimer, lactate dehydrogenase (LDH), C-reactive protein (CRP), procalcitonin, ferritin, interleukin-6 (Il-6), and liver and kidney functions. FURIN (rs6226), IFNL4 (rs12979860), and TLR2 (rs3804099) genotyping allelic discrimination assays were conducted using real-time PCR.

RESULTS

The FURIN, IFNL4, and TLR2 genotypes and their alleles differed significantly between COVID-19 patients and controls, as well as between patients with severe or critical illness and those with a non-severe presentation. According to a multivariable regression analysis, FURIN (C/T + T/T) and TLR2 (T/C + C/C) mutants were associated with COVID-19 susceptibility, with odds ratios of 3.293 and 2.839, respectively. FURIN C/C and IFNL4 T/T mutants were significantly linked to severe and critical illnesses. Multivariate regression analysis showed that FURIN (G/C + C/C) genotypes and IFNL4 T/T homozygosity were independent risk factors associated with increased mortality.

CONCLUSION

FURIN, IFNL4, and TLR2 gene variants are associated with the risk of COVID-19 occurrence as well as increased severity and poor outcomes in Egyptian patients.

Multimodal scanning of genetic variants with base and prime editing

Mutational scanning connects genetic variants to phenotype, enabling the interrogation of protein functions, interactions and variant pathogenicity. However, current methodologies cannot efficiently engineer customizable sets of diverse genetic variants in endogenous loci across cellular contexts in high throughput. Here, we combine cytosine and adenine base editors and a prime editor to assess the pathogenicity of a broad spectrum of variants in the epithelial growth factor receptor gene (EGFR). Using pooled base editing and prime editing guide RNA libraries, we install tens of thousands of variants spanning the full coding sequence of EGFR in multiple cell lines and assess the role of these variants in tumorigenesis and resistance to tyrosine kinase inhibitors. Our EGFR variant scan identifies important hits, supporting the robustness of the approach and revealing underappreciated routes to EGFR activation and drug response. We anticipate that multimodal precision mutational scanning can be applied broadly to characterize genetic variation in any genetic element of interest at high and single-nucleotide resolution.

A 30-nation investigation of lay heritability beliefs

Lay beliefs about human trait heritability are consequential for cooperation and social cohesion, yet there has been no global characterisation of these beliefs. Participants from 30 countries (N = 6128) reported heritability beliefs for intelligence, personality, body weight and criminality, and transnational factors that could influence these beliefs were explored using public nation-level data. Globally, mean lay beliefs differ from published heritability (h2) estimated by twin studies, with a worldwide majority overestimating the heritability of personality and intelligence, and underestimating body weight and criminality. Criminality was seen as substantially less attributable to genes than other traits. People from countries with high infant mortality tended to ascribe greater heritability for most traits, relative to people from low infant mortality countries. This study provides the first systematic foray into worldwide lay heritability beliefs. Future research must incorporate diverse global perspectives to further contextualise and extend upon these findings.

Cryo-EM structure of the human subcortical maternal complex and the associated discovery of infertility-associated variants

The functionally conserved subcortical maternal complex (SCMC) is essential for early embryonic development in mammals. Reproductive disorders caused by pathogenic variants in NLRP5, TLE6 and OOEP, three core components of the SCMC, have attracted much attention over the past several years. Evaluating the pathogenicity of a missense variant in the SCMC is limited by the lack of information on its structure, although we recently solved the structure of the mouse SCMC and proposed that reproductive disorders caused by pathogenic variants are related to the destabilization of the SCMC core complex. Here we report the cryogenic electron microscopy structure of the human SCMC and uncover that the pyrin domain of NLRP5 is essential for the stability of SCMC. By combining prediction of SCMC stability and in vitro reconstitution, we provide a method for identifying deleterious variants, and we successfully identify a new pathogenic variant of TLE6 (p.A396T). Thus, on the basis of the structure of the human SCMC, we offer a strategy for the diagnosis of reproductive disorders and the discovery of new infertility-associated variants.

Essentials of genomics in nursing undergraduate education: A discussion paper

AIM

To map the 2021 American Association of Colleges of Nursing Essentials to the American Nurses Association Essentials of Genomic Nursing for all nurses and provide resources for nursing faculty to support the seamless integration of genomics into existing undergraduate curricula.

BACKGROUND

Since the completion of the Human Genome Project in 2003, rapid advancements in genomic science leading to practical applications of genomics have revolutionized all areas of healthcare. Nursing is built on foundational life sciences, including genomics. As the largest segment of the healthcare workforce, who spend the most time with patients and families, nurses play a critical role in healthcare teams integrating genomic knowledge into patient care to improve health and well-being. Consequently, nurses must be equipped with foundational genomic knowledge and skills during their undergraduate education. However, there is wide variability in whether and how nursing programs have incorporated genomics into their curricula. Additionally, nursing faculty may have limited knowledge of foundational genomic concepts and lack confidence in teaching genomics.

DESIGN

Discussion paper METHODS: We aligned domains from the American Association of Colleges of Nursing Essentials and American Nurses Association Essentials of Genomic Nursing.

RESULTS

A map illustrating alignment in multiple areas, which provide examples of ways to integrate genomics into existing nursing curricula.

CONCLUSION

Although based on domains developed in the United States, the map, curricular resources, example learning outcomes, and clinical vignettes can be used by nursing faculty globally to prepare future nurses who are competent in providing genomics-informed nursing care on entry-to-practice.

Unraveling mechanisms of human brain evolution

Evolutionary changes in human brain structure and function have enabled our specialized cognitive abilities. How these changes have come about genetically and functionally has remained an open question. However, new methods are providing a wealth of information about the genetic, epigenetic, and transcriptomic differences that set the human brain apart. Combined with in vitro models that allow access to developing brain tissue and the cells of our closest living relatives, the puzzle pieces are now coming together to yield a much more complete picture of what is actually unique about the human brain. The challenge now will be linking these observations and making the jump from correlation to causation. However, elegant genetic manipulations are now possible and, when combined with model systems such as organoids, will uncover a mechanistic understanding of how evolutionary changes at the genetic level have led to key differences in development and function that enable human cognition.

Striving for clarity in language about gene expression

What do we mean when we say 'gene expression'? In the decades following Crick's 1958 central dogma of molecular biology, whereby genetic information flows from DNA (genes) to RNA (transcripts) to protein (products), we have learned a great deal about DNA, RNA, proteins, and the ensuing phenotypic changes. With the advent of high-throughput technologies (1990s), molecular biologists and computer scientists forged critical collaborations to understand the vast amount of data being generated, rapidly escalating gene expression research to the 'omics' level: entire sets of genes (genomes), transcribed RNAs (transcriptomes), and synthesized proteins (proteomes). However, some concessions came to be made for molecular biologists and computer scientists to understand each other-one of the most prevalent being the increasingly widespread use of 'gene' to mean 'RNAs originating from a DNA segment'. This loosening of terminology, we will argue, creates ambiguity and confusion. We propose guidelines to increase precision and clarity when communicating about gene expression, most notably to reserve 'gene' for the DNA template and 'transcript' for the RNA transcribed from that gene. Striving to use perspicuous terminology will promote rigorous gene expression science and accelerate discovery in this highly promising area of biology.

Utilizing non-invasive prenatal test sequencing data for human genetic investigation

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

Genome-wide association study of maternal plasma metabolites during pregnancy

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Polygenic risk and rare variant gene clustering enhance cancer risk stratification for breast and prostate cancers

Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing, the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare diseases. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery, which should, in turn, increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks such as Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, and researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Certainty in uncertainty: Determining the rate and reasons for reclassification of variants of uncertain significance in haematological malignancies

Introduction

Variants of uncertain significance (VUS) are commonly reported in cancer with the widespread adoption of diagnostic massive parallel sequencing. The rate of reclassification of VUS in patients with haematological malignancy is not known and we evaluated this retrospectively. We also investigated whether re-evaluating VUS in 12-24 months or greater than 24 months post-initial classification was significant.

Method

A retrospective audit of patients with haematological malignancies referred to the Molecular Medicine Department at the John Hunter Hospital in Newcastle, Australia between September 2018 and December 2021. Data was analysed for VUS, which was then re-analysed in standard software using current somatic variant guidelines. Proportions of VUS at baseline were compared to post-re-analysis.

Results

The most common diagnoses in the patient cohort (n = 944) were acute myelogenous leukaemia (41%), myelodysplastic syndrome (31%), and chronic myelomonocytic leukaemia (7%). A total of 210 VUS were re-analysed. The most common VUS were in the TET2 (20%), RUNX1 (10%) and DNMT3A (9%) genes. A total of 103 were re-analysed at 24-39 months post-initial classification and 107 variants were re-analysed between 12 and 24 months post-initial classification. Of these, 33 (16%) of VUS were re-classified at 24-39 months and 12 (11%) were re-classified at 12-24 months post-initial classification. The most common variants that were re-classified in both groups were CSF3R (32%), TET2 (29%), ASXL1 (11%) and ZRSR2 (11%).

Conclusion

This study on reclassification of VUS in blood cancers demonstrated that one in seven VUS were re-classified 12 months post initial classification. This can inform practice guidelines and potentially impact the prognosis, diagnosis and treatment of haematological malignancies.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Sperm-origin paternal effects on root stem cell niche differentiation

Fertilization introduces parental genetic information into the zygote to guide embryogenesis. Parental contributions to postfertilization development have been discussed for decades, and the data available show that both parents contribute to the zygotic transcriptome, suggesting a paternal role in early embryogenesis1-6. However, because the specific paternal effects on postfertilization development and the molecular pathways underpinning these effects remain poorly understood, paternal contribution to early embryogenesis and plant development has not yet been adequately demonstrated7. Here our research shows that TREE1 and its homologue DAZ3 are expressed exclusively in Arabidopsis sperm. Despite presenting no evident defects in sperm development and fertilization, tree1 daz3 unexpectedly led to aberrant differentiation of the embryo root stem cell niche. This defect persisted in seedlings and disrupted root tip regeneration, comparable to congenital defects in animals. TREE1 and DAZ3 function by suppression of maternal RKD2 transcription, thus mitigating the detrimental maternal effects from RKD2 on root stem cell niche. Therefore, our findings illuminate how genetic deficiencies in sperm can exert enduring paternal effects on specific plant organ differentiation and how parental-of-origin genes interact to ensure normal embryogenesis. This work also provides a new concept of how gamete quality or genetic deficiency can affect specific plant organ formation.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Whole exome sequencing identifies a novel mutation in Annexin A4 that is associated with recurrent spontaneous abortion

Background

Recurrent spontaneous abortion (RSA) is a multifactorial disease, the exact causes of which are still unknown. Environmental, maternal, and genetic factors have been shown to contribute to this condition. The aim of this study was to investigate the presence of mutations in the ANXA4 gene in patients with RSA.

Methods

Genomic DNA was extracted from 325 patients with RSA and 941 control women with a normal reproductive history for whole-exome sequencing (WES). The detected variants were annotated and filtered, and the pathogenicity of the variants was predicted through the SIFT online tool, functional enrichment analyses, Sanger sequencing validation, prediction of changes in protein structure, and evolutionary conservation analysis. Furthermore, plasmid construction, Western blotting, RT-qPCR, and cell migration, invasion and adhesion assays were used to detect the effects of ANXA4 mutations on protein function.

Results

An ANXA4 mutation (p.G8D) in 1 of the 325 samples from patients with RSA (RSA-219) was identified through WES. This mutation was not detected in 941 controls or included in public databases. Evolutionary conservation analysis revealed that the amino acid residue affected by the mutation (p.G8D) was highly conserved among 13 vertebrate species, and the SIFT program and structural modeling analysis predicted that this mutation was harmful. Furthermore, functional assays revealed that this mutation could inhibit cell migration, invasion and adhesion.

Conclusion

Our study suggests that an unreported novel ANXA4 mutation (p.G8D) plays an important role in the pathogenesis of RSA and may contribute to the genetic diagnosis of RSA.

Selective Effect of DNA N6-Methyladenosine Modification on Transcriptional Genetic Variations in East Asian Samples

Genetic variations and DNA modification are two common dominant factors ubiquitous across the entire human genome and induce human disease, especially through static genetic variations in DNA or RNA that cause human genetic diseases. DNA N6-methyladenosine (6mA) methylation, as a new epigenetic modification mark, has been widely studied for regulatory biological processes in humans. However, the effect of DNA modification on dynamic transcriptional genetic variations from DNA to RNA has rarely been reported. Here, we identified DNA, RNA and transcriptional genetic variations from Illumina short-read sequencing data in East Asian samples (HX1 and AK1) and detected global DNA 6mA modification using single-molecule, real-time sequencing (SMRT) data. We decoded the effects of DNA 6mA modification on transcriptional genetic variations in East Asian samples and the results were extensively verified in the HeLa cell line. DNA 6mA modification had a stabilized distribution in the East Asian samples and the methylated genes were less likely to mutate than the non-methylated genes. For methylated genes, the 6mA density was positively correlated with the number of variations. DNA 6mA modification had a selective effect on transcriptional genetic variations from DNA to RNA, in which the dynamic transcriptional variations of heterozygous (0/1 to 0/1) and homozygous (1/1 to 1/1) were significantly affected by 6mA modification. The effect of DNA methylation on transcriptional genetic variations provides new insights into the influencing factors of DNA to RNA transcriptional regulation in the central doctrine of molecular biology.

Neuronal enhancers fine-tune adaptive circuit plasticity

Neuronal activity-regulated gene expression plays a crucial role in sculpting neural circuits that underpin adaptive brain function. Transcriptional enhancers are now recognized as key components of gene regulation that orchestrate spatiotemporally precise patterns of gene transcription. We propose that the dynamics of enhancer activation uniquely position these genomic elements to finely tune activity-dependent cellular plasticity. Enhancer specificity and modularity can be exploited to gain selective genetic access to specific cell states, and the precise modulation of target gene expression within restricted cellular contexts enabled by targeted enhancer manipulation allows for fine-grained evaluation of gene function. Mounting evidence also suggests that enduring stimulus-induced changes in enhancer states can modify target gene activation upon restimulation, thereby contributing to a form of cell-wide metaplasticity. We advocate for focused exploration of activity-dependent enhancer function to gain new insight into the mechanisms underlying brain plasticity and cognitive dysfunction.

dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility

Histone modifications (HMs) are pivotal in various biological processes, including transcription, replication, and DNA repair, significantly impacting chromatin structure. These modifications underpin the molecular mechanisms of cell-type-specific gene expression and complex diseases. However, annotating HMs across different cell types solely using experimental approaches is impractical due to cost and time constraints. Herein, we present dHICA (deep histone imputation using chromatin accessibility), a novel deep learning framework that integrates DNA sequences and chromatin accessibility data to predict multiple HM tracks. Employing the transformer architecture alongside dilated convolutions, dHICA boasts an extensive receptive field and captures more cell-type-specific information. dHICA outperforms state-of-the-art baselines and achieves superior performance in cell-type-specific loci and gene elements, aligning with biological expectations. Furthermore, dHICA's imputations hold significant potential for downstream applications, including chromatin state segmentation and elucidating the functional implications of SNPs (Single Nucleotide Polymorphisms). In conclusion, dHICA serves as a valuable tool for advancing the understanding of chromatin dynamics, offering enhanced predictive capabilities and interpretability.

Human genetics and genomics as a unifying factor for harmony and progress in Africa: a report from the 12th African Society of Human Genetics meeting in Bamako, Mali

Since its inception in 2003, the African Society of Human Genetics (AfSHG) has been central to the promotion of genetics research on the continent, and facilitated the networking of African researchers within Africa and abroad, thereby significantly contributing to the career development of African geneticists. The continuation of these accomplishments was stimulated by the 12th international conference of AfSHG held jointly with the 1st Congress of the Malian Society of Human Genetics (MSHG) in Bamako, Mali from September 18th to 21st 2019. The main theme of the conference was "Human Genetics and Genomics as a Unifying Factor for Harmony and Progress in Africa". The goals of the meeting were to promote the work conducted mainly by African researchers and to contribute to scientific knowledge through genetic research. Despite challenges due to security issues in Mali, this conference attracted many scientists, including key experts in genetics and associated fields, making the conference successful scientifically and geographically. Overall, 172 delegates from 24 countries attended. Sessions on various topics relevant to Africa were held. These included the genetics of infectious diseases, cancer, and rare diseases as well as bioinformatics, pharmacogenomics, population genetics, and ethical, legal, and social issues, particularly with respect to genetic research in African populations. The need for genetic data sharing to improve research and health and the focus of actionable research for African populations was stressed throughout the meeting.

Genetics and Traumatic Brain Injury: Findings from an Exome-Based Study of a 50-Patient Case Series

Traumatic brain injury (TBI) is the leading cause of global mortality and morbidity. Because TBI is accident-related, the role of genetics in predisposing to TBI has been largely unexplored. However, the likelihood of injury may not be entirely random and may be associated with certain physical and mental characteristics. In this study, we analyzed the exomes of 50 patients undergoing rehabilitation after TBI. Patients were divided into three groups according to rehabilitation outcome: improvement, no change, and deterioration/death. We focused on rare, potentially functional missense and high-impact variants in genes intolerant to these variants. The concordant results from the three independent groups of patients allowed for the suggestion of the existence of a genetic predisposition to TBI, associated with rare functional variations in intolerant genes, with a prevalent dominant mode of inheritance and neurological manifestations in the genetic phenotypes according to the OMIM database. Forty-four of the 50 patients had one or more rare, potentially deleterious variants in one or more neurological genes. Comparison of these results with those of a 50-sampled matched non-TBI cohort revealed significant differences: P = 2.6 × 10-3, OR = 4.89 (1.77-13.47). There were no differences in the distribution of the genes of interest between the TBI patient groups. Our exploratory study provides new insights into the impact of genetics on TBI risk and is the first to address potential genetic susceptibility to TBI.

Women's preferences for genetic screening in routine care: A qualitative study

OBJECTIVE

Examine decision-making regarding when women would prefer to receive reproductive carrier and cancer predisposition screening and from what clinician.

METHODS

20 women completed in-depth interviews via Zoom exploring their views on the provision of reproductive carrier and cancer predisposition screening. Our analysis identified themes related to what informs women's preferences for when they would like to receive a genetic screening offer and by which clinician.

RESULTS

Participants' responses to questions about when they would be interested in receiving genetic screening were best understood through the lens of the Extended Parallel Process Model. Specifically, personal utility of the information, a woman's family health history and cost were key factors in decision-making. Women considered their clinician's knowledge and their trust in and relationship with the clinician when deciding from whom they would prefer to receive an offer of genetic screening.

CONCLUSION

OB/GYN clinic patients may accept an offer of genetic screening from a knowledgeable and trusted clinician for carrier and cancer predisposition screening preconceptionally or prenatally.

PRACTICE IMPLICATIONS

Integrating genetic reproductive and cancer predisposition screening into the care provided to reproductive age OB/GYN patients may be acceptable to this population.

Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

Implementation of a dyadic nomenclature for monogenic diseases

A core task when establishing the strength of evidence for a gene's role in a monogenic disorder is determining the appropriate disease entity to curate. Establishing this concept determines which evidence can be applied and quantified toward the final gene-disease validity, variant pathogenicity, or actionability classification. Genes with implications in more than one phenotype can necessitate a process of lumping and splitting, disease reorganization, and updates to disease nomenclature. Reappraisal of the names that are used as labels for disease entities is therefore a necessary and perpetual process. The Clinical Genome Resource (ClinGen), in collaboration with representatives from Monarch Disease Ontology (Mondo) and Online Inheritance in Man (OMIM), formed the Disease Naming Advisory Committee (DNAC) to develop guidance for groups faced with the need to establish the "curated disease entity" for gene-phenotype validity and variant pathogenicity and to update disease names for clinical use when necessary. The objective of this group was to harmonize guidance for disease naming across these nosologic entities and among ClinGen curation groups in collaboration with other disease-related professional groups. Here, we present the initial guidance developed by the DNAC with representative examples provided by the ClinGen expert panels and working groups that warranted nomenclature updates. We also discuss the broader implications of these efforts and their benefits for harmonization of gene-disease validity curation. Overall, this work sheds light on current inconsistencies and/or discrepancies and is designed to engage the broader community on how ClinGen defines monogenic disorders using a consistent approach for disease naming.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans due to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, our research shows a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (~0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Notably, young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes due to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potentials for phenotypic innovation in young genes compared to older genes, a process that is subject to natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

The Role of Patient Organizations in Shaping Research, Health Policies, and Health Services for Rare Genetic Diseases: The Dutch Experience

In 2023, the genetics scientific community celebrated two special anniversaries: the discovery of the double helix structure of DNA was published in 1953 and in 2003 the Human Genome Project was declared completed and made publicly available. To this day, genetics and genomics research is continuing to evolve at high pace and is identifying a steadily increasing number of genes as causal for distinct genetic diseases. The success story of genetics and genomics would not be complete without taking due account of the role of patient advocacy organizations in this process. This paper is based on the personal narrative (oral history) of a father whose daughter was born with a rare genetic disease (RGD) in the 1960s. The first-hand experience of living as a family with an RGD in those days made him a leading pioneer not only in the foundation of patient organizations at national, pan-European, and international levels but also in the development of multi-stakeholder co-operation and networking. Today, patient advocacy organizations play an active role in shaping health and research policies at national, EU, and international levels to ensure that their needs in regard to advancing RGD diagnostics, care, and treatment are addressed.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Impact of putatively beneficial genomic loci on gene expression in little brown bats (Myotis lucifugus, Le Conte, 1831) affected by white-nose syndrome

Genome-wide scans for selection have become a popular tool for investigating evolutionary responses in wildlife to emerging diseases. However, genome scans are susceptible to false positives and do little to demonstrate specific mechanisms by which loci impact survival. Linking putatively resistant genotypes to observable phenotypes increases confidence in genome scan results and provides evidence of survival mechanisms that can guide conservation and management efforts. Here we used an expression quantitative trait loci (eQTL) analysis to uncover relationships between gene expression and alleles associated with the survival of little brown bats (Myotis lucifugus) despite infection with the causative agent of white-nose syndrome. We found that 25 of the 63 single-nucleotide polymorphisms (SNPs) associated with survival were related to gene expression in wing tissue. The differentially expressed genes have functional annotations associated with the innate immune system, metabolism, circadian rhythms, and the cellular response to stress. In addition, we observed differential expression of multiple genes with survival implications related to loci in linkage disequilibrium with focal SNPs. Together, these findings support the selective function of these loci and suggest that part of the mechanism driving survival may be the alteration of immune and other responses in epithelial tissue.

Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts

Penetrance is the probability that an individual with a pathogenic genetic variant develops a specific disease. Knowing the penetrance of variants for monogenic disorders is important for counseling of individuals. Until recently, estimates of penetrance have largely relied on affected individuals and their at-risk family members being clinically referred for genetic testing, a 'phenotype-first' approach. This approach substantially overestimates the penetrance of variants because of ascertainment bias. The recent availability of whole-genome sequencing data in individuals from very-large-scale population-based cohorts now allows 'genotype-first' estimates of penetrance for many conditions. Although this type of population-based study can underestimate penetrance owing to recruitment biases, it provides more accurate estimates of penetrance for secondary or incidental findings. Here, we provide guidance for the conduct of penetrance studies to ensure that robust genotypes and phenotypes are used to accurately estimate penetrance of variants and groups of similarly annotated variants from population-based studies.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Genetic modifiers and ascertainment drive variable expressivity of complex disorders

Variable expressivity of disease-associated variants implies a role for secondary variants that modify clinical features. We assessed the effects of modifier variants towards clinical outcomes of 2,252 individuals with primary variants. Among 132 families with the 16p12.1 deletion, distinct rare and common variant classes conferred risk for specific developmental features, including short tandem repeats for neurological defects and SNVs for microcephaly, while additional disease-associated variants conferred multiple genetic diagnoses. Within disease and population cohorts of 773 individuals with the 16p12.1 deletion, we found opposing effects of secondary variants towards clinical features across ascertainments. Additional analysis of 1,479 probands with other primary variants, such as 16p11.2 deletion and CHD8 variants, and 1,084 without primary variants, showed that phenotypic associations differed by primary variant context and were influenced by synergistic interactions between primary and secondary variants. Our study provides a paradigm to dissect the genomic architecture of complex disorders towards personalized treatment.