Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum.

Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, that affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (p < 5×10-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (MYBPC3, BAG3), as well as regulators of lipoprotein (LPL) and glucose metabolism (GIPR, GLP1R), and are enriched in cardiac, muscle, nerve, and vascular tissues, as well as myocyte and adipocyte cell types. Gene burden studies across three biobanks (PMBB, UKB, AOU) including 27,208 individuals with HF and 349,126 without uncover exome-wide significant (p < 3.15×10-6) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN, MYBPC3, FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, and is lower than heritability attributable to common variants (4.3%, 95% CI 3.9-4.7%) which is more diffusely spread throughout the genome. Finally, we demonstrate that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. These findings suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.

DETECT: Feature extraction method for disease trajectory modeling in electronic health records.

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithm DiseasE Trajectory fEature extraCTion (DETECT) for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. Temporal range can be specified to investigate predictors occurring during a specific period of time prior to onset of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.

Gene Interactions in Human Disease Studies-Evidence Is Mounting.

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population.

Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] ß, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Characterization of a recombinant tomato leaf curl New Delhi Virus (ToLCNDV) in a perennial medicinal climber host (Ipomoea cairica (L.) Sweet).

During the year 2020-2021, a disease syndrome very commonly observed in railway creepers (Ipomoea cairica (L.) Sweet) was taken into consideration from Gorakhpur Province (UP East region). Whitefly, a common vector for plant-related viral diseases was observed for wide transmission of disease. DNA from 17 infected leaf samples was isolated and analyzed through PCR using specific primers designed for the detection of Begomoviruses. Further amplification of isolated DNA fragments supporting a begomovirus infection with an estimated size of (2.7 kb). RCA of the isolated DNA sample was carried out using ϕ29 DNA polymerase by digesting it through a set of restriction endonucleases (such as BamHI, Kpn1, HindIII, EcoRI) obtaining the best result through KpnI. The amplified segment was cloned into pUC 18vectors. The obtained sequences were matched using the NCBI BLAST database showing the highest sequence similarity of 98.7% with ToLCNDV of snake gourd (Accession no. KY780199), isolates of CP genes sequence in Varanasi. ToLCNDV, a begomovirus of bipartite nature was first reported to be from Tomato (Solanaceae), later affecting certain members of the Cucurbitaceae family in India and adjacent countries. The obtained DNA sequence was submitted at NCBI with the name ToLCNDV-IP (GenBank Accession no. OM777194). The phylogenetic analysis clubbed the current isolate ToLCNDV-IP with Asian isolates. All European isolates were clubbed in separate clades indicating two distinct groups of ToLCNDV isolates. ToLCNDV-IP isolate was found to be an intralineage recombinant between two Asian isolates viz ToLCNDV isolate from Papaya (India) and ToLCNDV isolate from Tomato (Pakistan). This study shows the association of recombinant ToLCNDV infection in a new host Ipomoea cairica for the first time in India.

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.

MOTIVATION: Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs. RESULTS: We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity. AVAILABILITY AND IMPLEMENTATION: The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations. This session focuses on the overarching theme of enabling early diagnosis, and treatment and preventive measures related to complex diseases and comorbidities.

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure.

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.

Complete Genomic RNA Sequence of Tuberose Mild Mosaic Virus and Tuberose Mild Mottle Virus Acquired by High-Throughput Sequencing.

Tuberose (Polianthes tuberosa) is an ornamental flowering crop of the Amaryllidaceae family. Tuberose mild mosaic virus (TuMMV) and tuberose mild mottle virus (TuMMoV), members of the genus Potyvirus, are ubiquitously distributed in most tuberose growing countries worldwide with low biological incidence. Here, we report the first coding-complete genomic RNA of TuMMV and TuMMoV obtained through high-throughput sequencing (HTS) and further, the presence of both the viruses were confirmed using virus-specific primers in RT-PCR assays. Excluding the poly (A) tail, the coding-complete genomic RNA of TuMMV and TuMMoV was 9485 and 9462 nucleotides (nts) in length, respectively, and contained a single large open reading frame (ORF). Polyprotein encoded by both the viral genomes contained nine putative cleavage sites. BLASTn analysis of TuMMV and TuMMoV genomes showed 72.40-76.80% and 67.95-77% nucleotide sequence similarities, respectively, with the existing potyviral sequences. Phylogenetic analysis based on genome sequences showed that TuMMV and TuMMoV clustered in a distinct clade to other potyviruses. Further studies are required to understand the mechanism of symptom development, distribution, genetic variability, and their possible threat to tuberose production in India.

High-throughput RNA sequencing and genetic structure studies of turnip mosaic virus infecting black and yellow mustard revealing emergence of world-B3 pathotype in India.

The RNA viruses are marked by high genetic diversity, which allows them to quickly adapt to new and resistant hosts. The pathogenic turnip mosaic virus (TuMV) infects Brassicaceae plant species all over the world. AIM: To study the evolution and host expansion of a TuMV for the first time in India using molecular population genetic framework. MATERIALS AND RESULTS: Here, we decipher the complete genome sequences of two TuMV world-B3 strains infecting yellow and black mustard in India through high-throughput RNA sequencing subjecting ribosomal RNA depleted mRNA isolated from leaves exhibiting puckering and mosaic symptoms with 100% incidence and high severity in the experimental field. The viral genomes of the two isolates were 9817 and 9829 nucleotides long. They featured two open reading frames (ORFs), one of which encoded a polyprotein comprised of 3164 amino acids and the other of which encoded a PIPO protein of 62 amino acids. CONCLUSIONS: The two TuMV strains from New Delhi region shared identity with the world-B pathotype and subpathotype world B3 showcasing its emergence first time in South Asia. In contrast, other isolates reported previously from South Asia were all Asian-BR pathotypes. SIGNIFICANCE AND IMPACT OF THE STUDY: According to our knowledge, this is the first instance of TuMV association with black mustard naturally. Their geographical prevalence justifies a lower degree of genetic differentiation and higher rate of gene flow calculated between the world-B and Asian-BR pathotypes. This study provides insights on population structuring, expansions and evolution, level of genetic heterogeneity and variability of worldwide prevalent isolates of TuMV which will further aid in understanding virus epidemiology and help prevent losses.

Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population.

OBJECTIVE: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS: This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS: Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS: The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Retinoic acid receptor regulation of epimorphic and homeostatic regeneration in the axolotl.

Salamanders are capable of regenerating amputated limbs by generating a mass of lineage-restricted cells called a blastema. Blastemas only generate structures distal to their origin unless treated with retinoic acid (RA), which results in proximodistal (PD) limb duplications. Little is known about the transcriptional network that regulates PD duplication. In this study, we target specific retinoic acid receptors (RARs) to either PD duplicate (RA treatment or RARγ agonist) or truncate (RARß antagonist) regenerating limbs. RARE-EGFP reporter axolotls showed divergent reporter activity in limbs undergoing PD duplication versus truncation, suggesting differences in patterning and skeletal regeneration. Transcriptomics identified expression patterns that explain PD duplication, including upregulation of proximal homeobox gene expression and silencing of distal-associated genes, whereas limb truncation was associated with disrupted skeletal differentiation. RARß antagonism in uninjured limbs induced a loss of skeletal integrity leading to long bone regression and loss of skeletal turnover. Overall, mechanisms were identified that regulate the multifaceted roles of RARs in the salamander limb including regulation of skeletal patterning during epimorphic regeneration, skeletal tissue differentiation during regeneration, and homeostatic regeneration of intact limbs.

Characterization of Staphylococcus aureus Cas9: a smaller Cas9 for all-in-one adeno-associated virus delivery and paired nickase applications.

BACKGROUND: CRISPR-Cas systems have been broadly embraced as effective tools for genome engineering applications, with most studies to date utilizing the Streptococcus pyogenes Cas9. Here we characterize and manipulate the smaller, 1053 amino acid nuclease Staphylococcus aureus Cas9. RESULTS: We find that the S. aureus Cas9 recognizes an NNGRRT protospacer adjacent motif (PAM) and cleaves target DNA at high efficiency with a variety of guide RNA (gRNA) spacer lengths. When directed against genomic targets with mutually permissive NGGRRT PAMs, the S. pyogenes Cas9 and S. aureus Cas9 yield indels at comparable rates. We additionally show D10A and N580A paired nickase activity with S. aureus Cas9, and we further package it with two gRNAs in a single functional adeno-associated virus (AAV) vector. Finally, we assess comparative S. pyogenes and S. aureus Cas9 specificity using GUIDE-seq. CONCLUSION: Our results reveal an S. aureus Cas9 that is effective for a variety of genome engineering purposes, including paired nickase approaches and all-in-one delivery of Cas9 and multiple gRNA expression cassettes with AAV vectors.