A brief history of human disease genetics

RP1 Dominant p.Ser740* Pathogenic Variant in 20 Knowingly Unrelated Families Affected by Rod-Cone Dystrophy: Potential Founder Effect in Western Sicily

Background and Objectives. Retinitis pigmentosa (RP) is the most common inherited rod-cone dystrophy (RCD), resulting in nyctalopia, progressive visual field, and visual acuity decay in the late stages. The autosomal dominant form (ADRP) accounts for about 20% of RPs. Among the over 30 genes found to date related to ADRP, RP1 pathogenic variants have been identified in 5-10% of cases. In a cohort of RCD patients from the Palermo province on the island of Sicily, we identified a prevalent nonsense variant in RP1, which was associated with ADRP. The objective of our study was to analyse the clinical and molecular data of this patient cohort and to evaluate the potential presence of a founder effect. Materials and Methods. From 2005 to January 2023, 84 probands originating from Western Sicily (Italy) with a diagnosis of RCD or RP and their relatives underwent deep phenotyping, which was performed in various Italian clinical institutions. Molecular characterisation of patients and familial segregation of pathogenic variants were carried out in different laboratories using Sanger and/or next-generation sequencing (NGS). Results. Among 84 probands with RCD/RP, we found 28 heterozygotes for the RP1 variant c.2219C>G, p.Ser740* ((NM_006269.2)*, which was therefore significantly prevalent in this patient cohort. After a careful interview process, we ascertained that some of these patients shared the same pedigree. Therefore, we were ultimately able to define 20 independent family groups with no traceable consanguinity. Lastly, analysis of clinical data showed, in our patients, that the p.Ser740* nonsense variant was often associated with a late-onset and relatively mild phenotype. Conclusions. The high prevalence of the p.Ser740* variant in ADRP patients from Western Sicily suggests the presence of a founder effect, which has useful implications for the molecular diagnosis of RCD in patients coming from this Italian region. This variant can be primarily searched for in RP-affected subjects displaying compatible modes of transmission and phenotypes, with an advantage in terms of the required costs and time for analysis. Moreover, given its high prevalence, the RP1 p.Ser740* variant could represent a potential candidate for the development of therapeutic strategies based on gene editing or translational read-through therapy for suppression of nonsense variants.

Convergence of coronary artery disease genes onto endothelial cell programs

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Improved phenotypic classification of male infertility to promote discovery of genetic causes

An increasing number of genes are being described in the context of non-syndromic male infertility. Linking the underlying genetic causes of non-syndromic male infertility with clinical data from patients is important to establish new genotype-phenotype correlations. This process can be facilitated by using universal nomenclature, but no standardized vocabulary is available in the field of non-syndromic male infertility. The International Male Infertility Genomics Consortium aimed at filling this gap, providing a standardized vocabulary containing nomenclature based on the Human Phenotype Ontology (HPO). The "HPO tree" was substantially revised compared with the previous version and is based on the clinical work-up of infertile men, including physical examination and hormonal assessment. Some causes of male infertility can already be suspected based on the patient's clinical history, whereas in other instances, a testicular biopsy is needed for diagnosis. We assembled 49 HPO terms that are linked in a logical hierarchy and showed examples of morphological features of spermatozoa and testicular histology of infertile men with identified genetic diagnoses to describe the phenotypes. This work will help to record patients' phenotypes systematically and facilitate communication between geneticists and andrologists. Collaboration across institutions will improve the identification of patients with the same phenotypes, which will promote the discovery of novel genetic causes for non-syndromic male infertility.

Inborn Errors of Immunity Contribute to the Burden of Skin Disease and Create Opportunities for Improving the Practice of Dermatology

Opportunities to improve the clinical management of skin disease are being created by advances in genomic medicine. Large-scale sequencing increasingly challenges notions about single-gene disorders. It is now apparent that monogenic etiologies make appreciable contributions to the population burden of disease and that they are underrecognized in clinical practice. A genetic diagnosis informs on molecular pathology and may direct targeted treatments and tailored prevention strategies for patients and family members. It also generates knowledge about disease pathogenesis and management that is relevant to patients without rare pathogenic variants. Inborn errors of immunity are a large class of monogenic etiologies that have been well-studied and contribute to the population burden of inflammatory diseases. To further delineate the contributions of inborn errors of immunity to the pathogenesis of skin disease, we performed a set of analyses that identified 316 inborn errors of immunity associated with skin pathologies, including common skin diseases. These data suggest that clinical sequencing is underutilized in dermatology. We next use these data to derive a network that illuminates the molecular relationships of these disorders and suggests an underlying etiological organization to immune-mediated skin disease. Our results motivate the further development of a molecularly derived and data-driven reorganization of clinical diagnoses of skin disease.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms

PURPOSE

The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework.

METHODS

Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated.

RESULTS

We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources.

CONCLUSION

The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

The Born in Guangzhou Cohort Study enables generational genetic discoveries

Genomic research that targets large-scale, prospective birth cohorts constitutes an essential strategy for understanding the influence of genetics and environment on human health1. Nonetheless, such studies remain scarce, particularly in Asia. Here we present the phase I genome study of the Born in Guangzhou Cohort Study2 (BIGCS), which encompasses the sequencing and analysis of 4,053 Chinese individuals, primarily composed of trios or mother-infant duos residing in South China. Our analysis reveals novel genetic variants, a high-quality reference panel, and fine-scale local genetic structure within BIGCS. Notably, we identify previously unreported East Asian-specific genetic associations with maternal total bile acid, gestational weight gain and infant cord blood traits. Additionally, we observe prevalent age-specific genetic effects on lipid levels in mothers and infants. In an exploratory intergenerational Mendelian randomization analysis, we estimate the maternal putatively causal and fetal genetic effects of seven adult phenotypes on seven fetal growth-related measurements. These findings illuminate the genetic links between maternal and early-life traits in an East Asian population and lay the groundwork for future research into the intricate interplay of genetics, intrauterine exposures and early-life experiences in shaping long-term health.

Letter to Editor on "Impact of smart phone use on adolescence health in India"

This letter to the editor with reference to Mahalakshmi et al. (2023) provides two additional views. In a tech-savvy world study in this field is of importance yet there is a huge gap. Such study should also consider screen time engagement of hospitalized patients given their predisposed physical condition in addition to student survey. Genetic analysis should also be included along with the questionnaire and counselling-based surveys. Thus, considering the known study pipeline and focusing on the two afore-mentioned aspects such research should be considered as a "High Priority" area.

TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants

Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Optimal population-specific HLA imputation with dimension reduction

Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0.66 for HLA-B. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population.

Parkinson's Disease is Predominantly a Genetic Disease

The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson's disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. In this Debate article, we highlight multiple avenues of research that suggest an important - and in some cases even predominant - role for genetics in PD aetiology, including familial clustering, high rates of monogenic PD in selected populations, and complete penetrance with certain forms. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.

Open Science Practices in Psychiatric Genetics: A Primer

Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.

Multiple molecular diagnoses in the field of intellectual disability and congenital anomalies: 3.5% of all positive cases

PURPOSE

Wide access to clinical exome/genome sequencing (ES/GS) enables the identification of multiple molecular diagnoses (MMDs), being a long-standing but underestimated concept, defined by two or more causal loci implicated in the phenotype of an individual with a rare disease. Only few series report MMDs rates (1.8% to 7.1%). This study highlights the increasing role of MMDs in a large cohort of individuals addressed for congenital anomalies/intellectual disability (CA/ID).

METHODS

From 2014 to 2021, our diagnostic laboratory rendered 880/2658 positive ES diagnoses for CA/ID aetiology. Exhaustive search on MMDs from ES data was performed prospectively (January 2019 to December 2021) and retrospectively (March 2014 to December 2018).

RESULTS

MMDs were identified in 31/880 individuals (3.5%), responsible for distinct (9/31) or overlapping (22/31) phenotypes, and potential MMDs in 39/880 additional individuals (4.4%).

CONCLUSION

MMDs are frequent in CA/ID and remain a strong challenge. Reanalysis of positive ES data appears essential when phenotypes are partially explained by the initial diagnosis or atypically enriched overtime. Up-to-date clinical data, clinical expertise from the referring physician, strong interactions between clinicians and biologists, and increasing gene discoveries and improved ES bioinformatics tools appear all the more fundamental to enhance chances of identifying MMDs. It is essential to provide appropriate patient care and genetic counselling.

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible

Background

Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits.

Methods

Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes.

Results

The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions.

Conclusions

According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.

Polygenic risk alters the penetrance of monogenic kidney disease

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Rapid and Quantitative Functional Interrogation of Human Enhancer Variant Activity in Live Mice

Functional analysis of non-coding variants associated with human 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 of any genetic background. We use this new technology to examine and measure the gain- and loss-of-function effects of enhancer variants linked to limb polydactyly, autism, and craniofacial malformation. By combining dual-enSERT with single-cell transcriptomics, we characterize variant enhancer alleles at cellular resolution, thereby implicating candidate molecular pathways in pathogenic enhancer misregulation. We further show that independent, polydactyly-linked enhancer variants lead to ectopic expression in the same cell populations, indicating shared genetic mechanisms underlying non-coding variant pathogenesis. Finally, we streamline dual-enSERT for analysis in F0 animals by placing both reporters on the same transgene separated by a synthetic insulator. Dual-enSERT allows researchers to go from identifying candidate enhancer variants to analysis of comparative enhancer activity in live embryos in under two weeks.

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 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 normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed 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 new insights into the genetic intersection of complex traits and Mendelian disorders.

Applications of genomic research in pediatric endocrine diseases

Recent advances in molecular genetics have advanced our understanding of the molecular mechanisms involved in pediatric endocrine disorders and now play a major role in mainstream medical practice. The spectrum of endocrine genetic disorders has 2 extremes: Mendelian and polygenic. Mendelian or monogenic diseases are caused by rare variants of a single gene, each of which exerts a strong effect on disease risk. Polygenic diseases or common traits are caused by the combined effects of multiple genetic variants in conjunction with environmental and lifestyle factors. Testing for a single gene is preferable if the disease is phenotypically and/or geneically homogeneous. Next-generation sequencing (NGS) can be applied to phenotypically and genetically heterogeneous conditions. Genome-wide association studies (GWASs) have examined genetic variants across the entire genome in a large number of individuals who have been matched for population ancestry and assessed for a disease or trait of interest. Common endocrine diseases or traits, such as type 2 diabetes mellitus, obesity, height, and pubertal timing, result from the combined effects of multiple variants in various genes that are frequently found in the general population, each of which contributes a small individual effect. Isolated founder mutations can result from a true founder effect or an extreme reduction in population size. Studies of founder mutations offer powerful advantages for efficiently localizing the genes that underlie Mendelian disorders. The Korean population has settled in the Korean peninsula for thousands of years, and several recurrent mutations have been identified as founder mutations. The application of molecular technology has increased our understanding of endocrine diseases, which have impacted on the practice of pediatric endocrinology related to diagnosis and genetic counseling. This review focuses on the application of genomic research to pediatric endocrine diseases using GWASs and NGS technology for diagnosis and treatment.

Understanding molecular signaling cascades in neural disease using multi-resolution models

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

A micro-nano interface integrated SERS-based microfluidic sensor for miRNA detection using DNAzyme walker amplification

BACKGROUND

Precise and specific miRNA detection plays a vital role in exploring development mechanisms of cancer disease, thereby it can significantly improve relevant prevention and treatment strategies.

RESULTS

In this work, a surface-enhanced Raman spectroscopy (SERS)-based microfluidic chip has been devised with a microcone array SERS substrate (MCASS) for the miR-141 detection. This substrate excels in unique SERS activity and large surface area for DNA oligonucleotide modification. As the presence of miR-141, the DNAzyme walker induced cleavage reaction took place on the finely designed and prepared dual DNA conjugated SERS nanoprobes. The SERS nanoprobes can anchor on MCASS by the DNA hybridization that achieved an impressive detection limit in the femtomolar level.

SIGNIFICANCE

With this integrated SERS-based microfluidic chip, we provided a miRNA detection strategy using DNAzyme walker amplification technology. It is believed that this strategy could be a powerful tool for miRNA detection and related cancer screening test.

A Non-Coding Variant in SLC15A4 Modulates Enhancer Activity and Lysosomal Deacidification Linked to Lupus Susceptibility

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a strong genetic basis. Despite the identification of several single nucleotide polymorphisms (SNPs) near the SLC15A4 gene that are significantly associated with SLE across multiple populations, specific causal SNP(s) and molecular mechanisms responsible for disease susceptibility are unknown. To address this gap, we employed bioinformatics, expression quantitative trait loci (eQTLs), and 3D chromatin interaction analysis to nominate a likely functional variant, rs35907548, in an active intronic enhancer of SLC15A4. Through luciferase reporter assays followed by chromatin immunoprecipitation (ChIP)-qPCR, we observed significant allele-specific enhancer effects of rs35907548 in diverse cell lines. The rs35907548 risk allele T is associated with increased regulatory activity and target gene expression, as shown by eQTLs and chromosome conformation capture (3C)-qPCR. The latter revealed long-range chromatin interactions between the rs35907548 enhancer and the promoters of SLC15A4, GLTLD1, and an uncharacterized lncRNA. The enhancer-promoter interactions and expression effects were validated by CRISPR/Cas9 knock-out (KO) of the locus in HL60 promyeloblast cells. KO cells also displayed dramatically dysregulated endolysosomal pH regulation. Together, our data show that the rs35907548 risk allele affects multiple aspects of cellular physiology and may directly contribute to SLE.

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 structural novelties pivotal in mammalian evolution. Their phenotypic impact on humans, however, remains elusive due to the technical and ethical complexities in functional studies. Through combining gene age dating with Mendelian disease phenotyping, our research reveals that new genes associated with disease phenotypes steadily integrate into the human genome at a rate of ~ 0.07% every million years over macroevolutionary timescales. Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures between young and old genes. Notably, young genes show significant enrichment in the male reproductive system, indicating strong sexual selection. Young genes also exhibit functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, bipedal locomotion, and color vision. Our findings further reveal increasing levels of pleiotropy over evolutionary time, which accompanies stronger selective constraints. We propose a "pleiotropy-barrier" model that delineates different potentials for phenotypic innovation between young and older genes subject to natural selection. Our study demonstrates that evolutionary 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.

Leveraging information between multiple population groups and traits improves fine-mapping resolution

Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data

MOTIVATION

Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories.

RESULTS

To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification.

AVAILABILITY AND IMPLEMENTATION

https://github.com/bixuehua/SSLpheno.

Direct-to-Consumer Genetic Testing: A Comprehensive Review

Emerged in the early 2000s, direct-to-consumer (DTC) genetic testing has helped consumers access and understand their genetic information without the involvement of a healthcare provider. Unlike traditional clinical-based testing, in which a healthcare provider is responsible for ordering, testing, interpreting, and communicating the results, DTC testing provides valuable insights directly to individuals about their genetic information. It empowers consumers and their families to be proactive about their health and lifestyle. The online testing format has become increasingly popular due to its accessibility and affordability. However, it raises concerns about the accuracy and reliability of the results, data security and how to ensure privacy for consumers and regulators. A hybrid model combining elements from both DTC and clinical-based genetic testing has surfaced in the market recently. In the US, current health-related DTC genetic tests are not recognized for diagnostic purposes; instead, these tests are intended to provide genetic information that is associated with certain conditions, which may encourage consumers to take the opportunity to discuss the results and their implications with a healthcare provider. This DTC genetic testing review focuses on the fundamental concepts, applications, benefits, limitations, risks, and consumer concerns, as well as the state of the DTC framework compared with the clinical-based and hybrid models. Additionally, the regulatory oversight, data protection, and healthcare professional perspective on DTC genetic testing in the US will be discussed, including current policies and regulations.

ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction

MOTIVATION

Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a framework combining genetic data into pathway structure and using an ensemble of convolutional neural networks (CNNs) along with a Canonical Correlation Regularizer layer for comprehensive prediction of disease risk. The novelty of our approach lies in our two-step framework: (i) utilizing the CNN's effectiveness to extract the complex gene associations within individual genetic pathways and (ii) fusing features from ensemble of CNNs through Canonical Correlation Regularization layer to incorporate the interactions between pathways which share common genes. During prediction, we also address the important issues of interpretability of neural network models, and identifying the pathways and genes playing an important role in prediction.

RESULTS

Implementation of our methodology into three real cancer genetic datasets for different prediction tasks validates our model's generalizability and robustness. Comparing with conventional models, our methodology provides consistently better performance with AUC improvement of 11% on predicting early/late-stage kidney cancer, 10% on predicting kidney versus liver cancer type and 7% on predicting survival status in ovarian cancer as compared to the next best conventional machine learning model. The robust performance of our deep learning algorithm indicates that disease prediction using neural networks in multiple functionally related genes across different pathways improves genetic data-based prediction and understanding molecular mechanisms of diseases.

AVAILABILITY AND IMPLEMENTATION

https://github.com/divya031090/ReGeNNe.

Systematic differences in discovery of genetic effects on gene expression and complex traits

Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

The Role of Genetics in the Management of Heart Failure Patients

Over the last decades, the relevance of genetics in cardiovascular diseases has expanded, especially in the context of cardiomyopathies. Its relevance extends to the management of patients diagnosed with heart failure (HF), given its capacity to provide invaluable insights into the etiology of cardiomyopathies and identify individuals at a heightened risk of poor outcomes. Notably, the identification of an etiological genetic variant necessitates a comprehensive evaluation of the family lineage of the affected patients. In the future, these genetic variants hold potential as therapeutic targets with the capability to modify gene expression. In this complex setting, collaboration among cardiologists, specifically those specializing in cardiomyopathies and HF, and geneticists becomes paramount to improving individual and family health outcomes, as well as therapeutic clinical results. This review is intended to offer geneticists and cardiologists an updated perspective on the value of genetic research in HF and its implications in clinical practice.

A comparative atlas of single-cell chromatin accessibility in the human brain

Recent advances in single-cell transcriptomics have illuminated the diverse neuronal and glial cell types within the human brain. However, the regulatory programs governing cell identity and function remain unclear. Using a single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq), we explored open chromatin landscapes across 1.1 million cells in 42 brain regions from three adults. Integrating this data unveiled 107 distinct cell types and their specific utilization of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly a third of the cCREs demonstrated conservation and chromatin accessibility in the mouse brain cells. We reveal strong links between specific brain cell types and neuropsychiatric disorders including schizophrenia, bipolar disorder, Alzheimer's disease (AD), and major depression, and have developed deep learning models to predict the regulatory roles of noncoding risk variants in these disorders.

Variants in the DDX6-CXCR5 autoimmune disease risk locus influence the regulatory network in immune cells and salivary gland

Fine mapping and bioinformatic analysis of the DDX6-CXCR5 genetic risk association in Sjögren's Disease (SjD) and Systemic Lupus Erythematosus (SLE) identified five common SNPs with functional evidence in immune cell types: rs4938573, rs57494551, rs4938572, rs4936443, rs7117261. Functional interrogation of nuclear protein binding affinity, enhancer/promoter regulatory activity, and chromatin-chromatin interactions in immune, salivary gland epithelial, and kidney epithelial cells revealed cell type-specific allelic effects for all five SNPs that expanded regulation beyond effects on DDX6 and CXCR5 expression. Mapping the local chromatin regulatory network revealed several additional genes of interest, including lnc-PHLDB1-1. Collectively, functional characterization implicated the risk alleles of these SNPs as modulators of promoter and/or enhancer activities that regulate cell type-specific expression of DDX6, CXCR5, and lnc-PHLDB1-1, among others. Further, these findings emphasize the importance of exploring the functional significance of SNPs in the context of complex chromatin architecture in disease-relevant cell types and tissues.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE-gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP-ARE-gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Nature and nurture: understanding phenotypic variation in inborn errors of immunity

The overall disease burden of pediatric infection is high, with widely varying clinical outcomes including death. Among the most vulnerable children, those with inborn errors of immunity, reduced penetrance and variable expressivity are common but poorly understood. There are several genetic mechanisms that influence phenotypic variation in inborn errors of immunity, as well as a body of knowledge on environmental influences and specific pathogen triggers. Critically, recent advances are illuminating novel nuances for fundamental concepts on disease penetrance, as well as raising new areas of inquiry. The last few decades have seen the identification of almost 500 causes of inborn errors of immunity, as well as major advancements in our ability to characterize somatic events, the microbiome, and genotypes across large populations. The progress has not been linear, and yet, these developments have accumulated into an enhanced ability to diagnose and treat inborn errors of immunity, in some cases with precision therapy. Nonetheless, many questions remain regarding the genetic and environmental contributions to phenotypic variation both within and among families. The purpose of this review is to provide an updated summary of key concepts in genetic and environmental contributions to phenotypic variation within inborn errors of immunity, conceptualized as including dynamic, reciprocal interplay among factors unfolding across the key dimension of time. The associated findings, potential gaps, and implications for research are discussed in turn for each major influencing factor. The substantial challenge ahead will be to organize and integrate information in such a way that accommodates the heterogeneity within inborn errors of immunity to arrive at a more comprehensive and accurate understanding of how the immune system operates in health and disease. And, crucially, to translate this understanding into improved patient care for the millions at risk for serious infection and other immune-related morbidity.

Identifying genetic regulatory variants that affect transcription factor activity

Genetic variants affecting gene expression levels in humans have been mapped in the Genotype-Tissue Expression (GTEx) project. Trans-acting variants impacting many genes simultaneously through a shared transcription factor (TF) are of particular interest. Here, we developed a generalized linear model (GLM) to estimate protein-level TF activity levels in an individual-specific manner from GTEx RNA sequencing (RNA-seq) profiles. It uses observed differential gene expression after TF perturbation as a predictor and, by analyzing differential expression within pairs of neighboring genes, controls for the confounding effect of variation in chromatin state along the genome. We inferred genotype-specific activities for 55 TFs across 49 tissues. Subsequently performing genome-wide association analysis on this virtual trait revealed TF activity quantitative trait loci (aQTLs) that, as a set, are enriched for functional features. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omics approach. The transparent peer review record is available.

A Non-Coding Variant in SLC15A4 Modulates Enhancer Activity and Lysosomal Deacidification Linked to Lupus Susceptibility

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a strong genetic basis. Despite the identification of several single nucleotide polymorphisms (SNPs) near the SLC15A4 gene that are significantly associated with SLE across multiple populations, specific causal SNP(s) and molecular mechanisms responsible for disease susceptibility are unknown. To address this gap, we employed bioinformatics, expression quantitative trait loci (eQTLs), and 3D chromatin interaction analysis to nominate a likely functional variant, rs35907548, in an active intronic enhancer of SLC15A4 . Through luciferase reporter assays followed by chromatin immunoprecipitation (ChIP)-qPCR, we observed significant allele-specific enhancer effects of rs35907548 in diverse cell lines. The rs35907548 risk allele T is associated with increased regulatory activity and target gene expression, as shown by eQTLs and chromosome conformation capture (3C)-qPCR. The latter revealed long-range chromatin interactions between the rs35907548 enhancer and the promoters of SLC15A4, GLTLD1 , and an uncharacterized lncRNA. The enhancer-promoter interactions and expression effects were validated by CRISPR/Cas9 knock-out (KO) of the locus in HL60 promyeloblast cells. KO cells also displayed dramatically dysregulated endolysosomal pH regulation. Together, our data show that the rs35907548 risk allele affects multiple aspects of cellular physiology and may directly contribute to SLE.

Workforce survey of PAs' genetic-genomic knowledge, attitudes, and application in practice

OBJECTIVE

This study surveyed practicing physician associates/assistants (PAs) about their genetics-genomics knowledge, attitudes, and application in practice.

METHODS

A 25-question electronic survey was emailed to each constituent organization of the American Academy of Physician Associates (AAPA) with a description of the study and a request to forward to their members. Additionally, a posting was displayed in the bulletin board section of the online AAPA Huddle.

RESULTS

Of the 420 PAs who completed the survey, few are knowledgeable (25%) about or confident (13%) in applying a genomic approach to patient care, although most (61%) think genetics-genomics is important to delivering high-quality care. Remarkably, 97% of PAs surveyed are interested in genetics-genomics continuing medical education.

CONCLUSIONS

PAs lack knowledge and confidence in integrating genetics-genomics into patient care; however, they have a positive attitude toward genetics-genomics and want to improve their knowledge and confidence through education.

Application of facial analysis Technology in Clinical Genetics: Considerations for diverse populations

Facial analysis technology in rare diseases has the potential to shorten the diagnostic odyssey by providing physicians with a valuable diagnostic tool. Given that most clinical genetic resources focus on populations of European descent, we compare craniofacial features in genetic syndromes across different populations and review how machine learning algorithms perform on diagnosing genetic syndromes in geographically and ethnically diverse populations. We also discuss the value of populations from ancestrally diverse backgrounds in the training set of machine learning algorithms. Finally, this review demonstrates that across diverse population groups, machine learning models have outstanding accuracy as supported by the area under the curve values greater than 0.9. Artificial intelligence is only in its infancy in the diagnosis of rare disease in diverse populations and will become more accurate as larger and more diverse training sets, including a wider spectrum of ages, particularly infants, are studied.

Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity

Alzheimer's disease is characterized by 2 pathological proteins, amyloid beta 42 and tau. The majority of Alzheimer's disease cases in the population are sporadic and late-onset Alzheimer's disease, which exhibits high levels of heritability. While several genetic risk factors for late-onset Alzheimer's disease have been identified and replicated in independent studies, including the ApoE ε4 allele, the great majority of the heritability of late-onset Alzheimer's disease remains unexplained, likely due to the aggregate effects of a very large number of genes with small effect size, as well as to biases in sample collection and statistical approaches. Here, we present an unbiased forward genetic screen in Drosophila looking for naturally occurring modifiers of amyloid beta 42- and tau-induced ommatidial degeneration. Our results identify 14 significant SNPs, which map to 12 potential genes in 8 unique genomic regions. Our hits that are significant after genome-wide correction identify genes involved in neuronal development, signal transduction, and organismal development. Looking more broadly at suggestive hits (P < 10-5), we see significant enrichment in genes associated with neurogenesis, development, and growth as well as significant enrichment in genes whose orthologs have been identified as significantly or suggestively associated with Alzheimer's disease in human GWAS studies. These latter genes include ones whose orthologs are in close proximity to regions in the human genome that are associated with Alzheimer's disease, but where a causal gene has not been identified. Together, our results illustrate the potential for complementary and convergent evidence provided through multitrait GWAS in Drosophila to supplement and inform human studies, helping to identify the remaining heritability and novel modifiers of complex diseases.

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.

Progress on RNA-based therapeutics for genetic diseases

RNA therapeutics inhibit the expression of specific proteins/RNAs by targeting complementary sequences of corresponding genes or encode proteins for the synthesis desired genes to treat genetic diseases. RNA-based therapeutics are categorized as oligonucleotide drugs (antisense oligonucleotides, small interfering RNA, RNA aptamers), and mRNA drugs. The antisense oligonucleotides and small interfering RNA for treatment of genetic diseases have been approved by the FDA in the United States, while RNA aptamers and mRNA drugs are still in clinical trials. Chemical modifications can be applied to RNA drugs, such as pseudouridine modification of mRNA, to reduce immunogenicity and improve the efficacy. The secure and effective delivery systems such as lipid-based nanoparticles, extracellular vesicles, and virus-like particles are under development to address stability, specificity, and safety issues of RNA drugs. This article provides an overview of the specific molecular mechanisms of eleven RNA drugs currently used for treating genetic diseases, and discusses the research progress of chemical modifications and delivery systems of RNA drugs.

Toward Precision Weight-Loss Dietary Interventions: Findings from the POUNDS Lost Trial

The POUNDS Lost trial is a 2-year clinical trial testing the effects of dietary interventions on weight loss. This study included 811 adults with overweight or obesity who were randomized to one of four diets that contained either 15% or 25% protein and 20% or 40% fat in a 2 × 2 factorial design. By 2 years, participants on average lost from 2.9 to 3.6 kg in body weight in the four intervention arms, while no significant difference was observed across the intervention arms. In POUNDS Lost, we performed a series of ancillary studies to detect intrinsic factors particular to genomic, epigenomic, and metabolomic markers that may modulate changes in weight and other cardiometabolic traits in response to the weight-loss dietary interventions. Genomic variants identified from genome-wide association studies (GWASs) on obesity, type 2 diabetes, glucose and lipid metabolisms, gut microbiome, and dietary intakes have been found to interact with dietary macronutrients (fat, protein, and carbohydrates) in relation to weight loss and changes of body composition and cardiometabolic traits. In addition, we recently investigated epigenomic modifications, particularly blood DNA methylation and circulating microRNAs (miRNAs). We reported DNA methylation levels at NFATC2IP, CPT1A, TXNIP, and LINC00319 were related to weight loss or changes of glucose, lipids, and blood pressure; we also reported thrifty miRNA expression as a significant epigenomic marker related to changes in insulin sensitivity and adiposity. Our studies have also highlighted the importance of temporal changes in novel metabolomic signatures for gut microbiota, bile acids, and amino acids as predictors for achievement of successful weight loss outcomes. Moreover, our studies indicate that biochemical, behavioral, and psychosocial factors such as physical activity, sleep disturbance, and appetite may also modulate metabolic changes during dietary interventions. This review summarized our major findings in the POUNDS Lost trial, which provided preliminary evidence supporting the development of precision diet interventions for obesity management.

Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.

Rare disease variant curation from literature: assessing gaps with creatine transport deficiency in focus

BACKGROUND

Approximately 4-8% of the world suffers from a rare disease. Rare diseases are often difficult to diagnose, and many do not have approved therapies. Genetic sequencing has the potential to shorten the current diagnostic process, increase mechanistic understanding, and facilitate research on therapeutic approaches but is limited by the difficulty of novel variant pathogenicity interpretation and the communication of known causative variants. It is unknown how many published rare disease variants are currently accessible in the public domain.

RESULTS

This study investigated the translation of knowledge of variants reported in published manuscripts to publicly accessible variant databases. Variants, symptoms, biochemical assay results, and protein function from literature on the SLC6A8 gene associated with X-linked Creatine Transporter Deficiency (CTD) were curated and reported as a highly annotated dataset of variants with clinical context and functional details. Variants were harmonized, their availability in existing variant databases was analyzed and pathogenicity assignments were compared with impact algorithm predictions. 24% of the pathogenic variants found in PubMed articles were not captured in any database used in this analysis while only 65% of the published variants received an accurate pathogenicity prediction from at least one impact prediction algorithm.

CONCLUSIONS

Despite being published in the literature, pathogenicity data on patient variants may remain inaccessible for genetic diagnosis, therapeutic target identification, mechanistic understanding, or hypothesis generation. Clinical and functional details presented in the literature are important to make pathogenicity assessments. Impact predictions remain imperfect but are improving, especially for single nucleotide exonic variants, however such predictions are less accurate or unavailable for intronic and multi-nucleotide variants. Developing text mining workflows that use natural language processing for identifying diseases, genes and variants, along with impact prediction algorithms and integrating with details on clinical phenotypes and functional assessments might be a promising approach to scale literature mining of variants and assigning correct pathogenicity. The curated variants list created by this effort includes context details to improve any such efforts on variant curation for rare diseases.

Inborn Errors of Immunity in Hidradenitis Suppurativa Pathogenesis and Disease Burden

Hidradenitis suppurativa (HS), also known as Verneuil's disease and acne inversa, is a prevalent, debilitating, and understudied inflammatory skin disease. It is marked by repeated bouts of pathological inflammation causing pain, hyperplasia, aberrant healing, and fibrosis. HS is difficult to manage and has many unmet medical needs. There is clinical and pharmacological evidence for extensive etiological heterogeneity with HS, suggesting that this clinical diagnosis is capturing a spectrum of disease entities. Human genetic studies provide robust insight into disease pathogenesis. They also can be used to resolve etiological heterogeneity and to identify drug targets. However, HS has not been extensively investigated with well-powered genetic studies. Here, we review what is known about its genetic architecture. We identify overlap in molecular, cellular, and clinical features between HS and inborn errors of immunity (IEI). This evidence indicates that HS may be an underrecognized component of IEI and suggests that undiagnosed IEI are present in HS cohorts. Inborn errors of immunity represent a salient opportunity for rapidly resolving the immunological landscape of HS pathogenesis, for prioritizing drug repurposing studies, and for improving the clinical management of HS.

The rs1421085 variant within FTO promotes brown fat thermogenesis

One lead genetic risk signal of obesity-the rs1421085 T>C variant within the FTO gene-is reported to be functional in vitro but lacks evidence at an organism level. Here we recapitulate the homologous human variant in mice with global and brown adipocyte-specific variant knock-in and reveal that mice carrying the C-allele show increased brown fat thermogenic capacity and resistance to high-fat diet-induced adiposity, whereas the obesity-related phenotypic changes are blunted at thermoneutrality. Both in vivo and in vitro data reveal that the C-allele in brown adipocytes enhances the transcription of the Fto gene, which is associated with stronger chromatin looping linking the enhancer region and Fto promoter. Moreover, FTO knockdown or inhibition effectively eliminates the increased thermogenic ability of brown adipocytes carrying the C-allele. Taken together, these findings identify rs1421085 T>C as a functional variant promoting brown fat thermogenesis.

Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal

To address the challenge of translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight, we have developed the T1D Knowledge Portal (T1DKP), an open-access resource for hypothesis development and target discovery in T1D.