Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis

An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms

The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. Due to the lack of protein crystal structure data, about three-quarters of human mutant proteins cannot be predicted or accurately predicted, and the pathogenicity of missense mutations can only be indirectly evaluated by evolutionary conservation. Recently, many computational methods have been developed to predict protein 3D structures with accuracy comparable to experiments. This progress enables the information of structural biology to be further utilized by clinicians. Thus, we developed a user-friendly platform named DPL3D (http://nsbio.tech:3000) which can predict and visualize the 3D structure of mutant proteins. The crystal structure and other information of proteins were downloaded together with the software including AlphaFold 2, RoseTTAFold, RoseTTAFold All-Atom, and trRosettaX-Single. We implemented a query module for 210,180 molecular structures, including 52,248 human proteins. Visualization of protein two-dimensional (2D) and 3D structure prediction can be generated via LiteMol automatically or manually and interactively. This platform will allow users to easily and quickly retrieve large-scale structural information for biological discovery.

A clinical knowledge graph-based framework to prioritize candidate genes for facilitating diagnosis of Mendelian diseases and rare genetic conditions

BACKGROUND

Diagnosing Mendelian and rare genetic conditions requires identifying phenotype-associated genetic findings and prioritizing likely disease-causing genes. This task is labor-intensive for molecular and clinical geneticists, who must review extensive literature and databases to link patient phenotypes with causal genotypes. The challenge is further complicated by the large number of genetic variants detected through next-generation sequencing, which impacts both diagnosis timelines and patient care strategies. To address this, in silico methods that prioritize causal genes based on patient-derived phenotypes offer an effective solution, reducing the time involved in diagnostic case reviews and enhancing the efficiency of clinical diagnosis.

RESULTS

We developed the phenotype prioritization and analysis for rare diseases (PPAR) to rank genes based on human phenotype ontology (HPO) terms, with the specific goal of aiding the interpretation of genetic testing for Mendelian and rare diseases. PPAR leverages embeddings from a knowledge graph and incorporates knowledge from connections between genes, HPO terms, and gene ontology annotations. When applied on a clinical rare disease cohort and the publicly available deciphering developmental disorders (DDD) dataset. PPAR ranked the causal gene in the top 10 for 27% of cases in the clinical cohort and for 85% of cases in the DDD dataset, outperforming other established HPO-based methods.

CONCLUSION

Our findings demonstrate that PPAR, a method developed from the clinical knowledge graph, effectively ranks causal genes based on patient-derived HPO terms in rare and Mendelian disease contexts. PPAR has shown superior performance compared to other well-established HPO-only methods and provides an efficient, accessible solution for clinical geneticists. The Python-based tool is publicly available at https://github.com/dimi-lab/PPAR , offering a user-friendly platform for gene prioritization.

Dominant negative variants in ITPR3 impair T cell Ca2+ dynamics causing combined immunodeficiency

The importance of calcium (Ca2+) as a second messenger in T cell signaling is exemplified by genetic deficiencies of STIM1 and ORAI1, which abolish store-operated Ca2+ entry (SOCE) resulting in combined immunodeficiency (CID). We report five unrelated patients with de novo missense variants in ITPR3, encoding a subunit of the inositol 1,4,5-trisphosphate receptor (IP3R), which forms a Ca2+ channel in the endoplasmic reticulum (ER) membrane responsible for the release of ER Ca2+ required to trigger SOCE, and for Ca2+ transfer to other organelles. The patients presented with CID, abnormal T cell Ca2+ homeostasis, incompletely penetrant ectodermal dysplasia, and multisystem disease. Their predominant T cell immunodeficiency is characterized by significant T cell lymphopenia, defects in late stages of thymic T cell development, and impaired function of peripheral T cells, including inadequate NF-κB- and NFAT-mediated, proliferative, and metabolic responses to activation. Pathogenicity is not due to haploinsufficiency, rather ITPR3 protein variants interfere with IP3R channel function leading to depletion of ER Ca2+ stores and blunted SOCE in T cells.

Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding, exerting small regulatory effects on downstream genes. However, which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. By contrast, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. Here we demonstrate that common and rare genetic diseases can be linked by studying how common disease-associated variants influence gene co-expression in 57 different tissues and cell types. We implemented this method in a framework called Downstreamer and applied it to 88 GWAS traits. We find that predicted downstream "genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. 78% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Based on our findings, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve the identification of these highly connected genes linked to common traits and diseases.

ERICH4 is not involved in the assembly and secretion of intestinal lipoproteins

BACKGROUND AND AIMS

The small intestine plays a central role in lipid metabolism, most notably the uptake of dietary fats that are packaged into chylomicrons and secreted into the circulation for utilisation by peripheral tissues. While microsomal triglyceride transfer protein (MTP) is known to play a key role in this pathway, the intracellular assembly, trafficking, and secretion of chylomicrons is incompletely understood.

METHODS AND RESULTS

Using human transcriptome datasets to find genes co-regulated with MTTP, we identified ERICH4 as a top hit. The gene encodes for glutamate-rich protein 4, a protein of unknown function. REACTOME gene-function prediction tools indicated that ERICH4 is involved in intestinal lipid metabolism. In addition, GWAS data point to a strong relationship between ERICH4 and plasma lipids. To validate ERICH4 as a lipid gene, we generated whole-body Erich4 knockout (Erich4-/-) mice. ERICH4 deficiency, however, did not result in changes in body weight and composition, food intake, circulating plasma lipids, energy absorption and excretion, and tissue weights compared to controls. Additionally, there were no morphological abnormalities seen in the small intestine. Challenging mice with a high-fat diet did not give rise to a phenotype either.

CONCLUSIONS

Despite prediction tools indicating ERICH4 as a strong candidate gene in intestinal lipid metabolism, we here show that ERICH4 does not play a role in intestinal lipid metabolism in mice. It remains to be established whether ERICH4 plays a role in human lipid metabolism.

Identification of Risk Genes for Attention-Deficit/Hyperactivity Disorder During Early Human Brain Development

OBJECTIVE

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with high heritability. A total of 27 genome-wide significant loci for ADHD were previously identified through genome-wide association studies (GWASs), but the identification of risk genes that confer susceptibility to ADHD has remained largely unexplored.

METHOD

As ADHD is a neurodevelopmental disorder, we integrated human brain prenatal gene and transcript expression weight data (n = 120) and ADHD GWAS summary statistics (n = 225,534; 38,691 cases and 186,843 controls) to perform a transcriptome-wide association study (TWAS) by FUSION (an analytic suite).

RESULTS

Our analysis identified 10 genes, including LSM6, HYAL3, METTL15, RPS26, LRRC37A15P, RP11-142I20.1, ABCB9, AP006621.5, AC000068.5, and PDXDC1, that are significantly associated with ADHD, along with 8 transcripts of 7 genes. We also conducted TWAS analysis using CommonMind Consortium (CMC) adult brain gene and gene-splicing expression weights (n = 452), which highlighted several risk genes that showed associations with ADHD in both prenatal and postnatal stages, such as LSM6 and HYAL3.

CONCLUSION

Overall, our TWAS of ADHD, by integrating human prenatal brain transcriptome and ADHD GWAS results, uncovered the cis-effects of gene/transcript regulation that are predicted to be associated with ADHD. By combining colocalization and FOCUS fine-mapping analysis, we further unraveled potential causal candidate risk genes. The risk genes/transcripts that we identified in this study can serve as a valuable resource for further investigation of the disease mechanisms underlying ADHD.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

Induction of oxidative- and endoplasmic-reticulum-stress dependent apoptosis in pancreatic cancer cell lines by DDOST knockdown

The dolichyl-diphosphooligosaccharide-protein glycosyltransferase non-catalytic subunit (DDOST) is a key component of the oligosaccharyltransferase complex catalyzing N-linked glycosylation in the endoplasmic reticulum lumen. DDOST is associated with several cancers and congenital disorders of glycosylation. However, its role in pancreatic cancer remains elusive, despite its enriched pancreatic expression. Using quantitative mass spectrometry, we identify 30 differentially expressed proteins and phosphopeptides (DEPs) after DDOST knockdown in the pancreatic ductal adenocarcinoma (PDAC) cell line PA-TU-8988T. We evaluated DDOST / DEP protein-protein interaction networks using STRING database, correlation of mRNA levels in pancreatic cancer TCGA data, and biological processes annotated to DEPs in Gene Ontology database. The inferred DDOST regulated phenotypes were experimentally verified in two PDAC cell lines, PA-TU-8988T and BXPC-3. We found decreased proliferation and cell viability after DDOST knockdown, whereas ER-stress, ROS-formation and apoptosis were increased. In conclusion, our results support an oncogenic role of DDOST in PDAC by intercepting cell stress events and thereby reducing apoptosis. As such, DDOST might be a potential biomarker and therapeutic target for PDAC.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

SPATS2L is a positive feedback regulator of the type I interferon signaling pathway and plays a vital role in lupus

Through genome-wide association studies (GWAS) and integrated expression quantitative trait locus (eQTL) analyses, numerous susceptibility genes ("eGenes", whose expressions are significantly associated with common variants) associated with systemic lupus erythematosus (SLE) have been identified. Notably, a subset of these eGenes is correlated with disease activity. However, the precise mechanisms through which these genes contribute to the initiation and progression of the disease remain to be fully elucidated. In this investigation, we initially identify SPATS2L as an SLE eGene correlated with disease activity. eSignaling and transcriptomic analyses suggest its involvement in the type I interferon (IFN) pathway. We observe a significant increase in SPATS2L expression following type I IFN stimulation, and the expression levels are dependent on both the concentration and duration of stimulation. Furthermore, through dual-luciferase reporter assays, western blot analysis, and imaging flow cytometry, we confirm that SPATS2L positively modulates the type I IFN pathway, acting as a positive feedback regulator. Notably, siRNA-mediated intervention targeting SPATS2L, an interferon-inducible gene, in peripheral blood mononuclear cells (PBMCs) from patients with SLE reverses the activation of the interferon pathway. In conclusion, our research highlights the pivotal role of SPATS2L as a positive-feedback regulatory molecule within the type I IFN pathway. Our findings suggest that SPATS2L plays a critical role in the onset and progression of SLE and may serve as a promising target for disease activity assessment and intervention strategies.

Role of ERα and Aromatase in Juvenile Gigantomastia

CONTEXT

Approximately 150 patients with juvenile gigantomastia have been reported in the literature but the underlying biologic mechanisms remain unknown.

OBJECTIVE

To conduct extensive clinical, biochemical, immunochemical, and genetic studies in 3 patients with juvenile gigantomastia to determine causative biologic factors.

METHODS

We examined clinical effects of estrogen by blockading estrogen synthesis or its action. Breast tissue aromatase expression and activity were quantitated in 1 patient and 5 controls. Other biochemical markers, including estrogen receptor α (ERα), cyclin D1 and E, p-RB, p-MAPK, p-AKT, BCL-2, EGF-R, IGF-IR β, and p-EGFR were assayed by Western blot. Immunohistochemical analyses for aromatase, ERα and β, PgR, Ki67, sulfotransferase, estrone sulfatase, and 17βHD were performed in all 3 patients. The entire genomes of the mother, father, and patient in the 3 families were sequenced.

RESULTS

Blockade of estrogen synthesis or action in patients resulted in demonstrable clinical effects. Biochemical studies on fresh frozen tissue revealed no differences between patients and controls, presumably due to tissue dilution from the large proportion of stroma. However, immunohistochemical analysis of ductal breast cells in the 3 patients revealed a high percent of ERα (64.1% ± 7.8% vs reference women 9.6%, range 2.3-15%); aromatase score of 4 (76%-100% of cells positive vs 30.4% ± 5.6%); PgR (69.5% ± 15.2% vs 6.0%, range 2.7%-11.9%) and Ki67 (23.7% ± 0.54% vs 4.2%). Genetic studies were inconclusive although some intriguing variants were identified.

CONCLUSION

The data implicate an important biologic role for ERα to increase tissue sensitivity to estrogen and aromatase to enhance local tissue production as biologic factors involved in juvenile gigantomastia.

Exploratory genetic analysis in children with autism spectrum disorder and other developmental disorders using whole exome sequencing

Developmental disorders (DDs), such as autism spectrum disorder (ASD), incorporate various conditions; once identified, further diagnostics are necessary to specify their type and severity. The aim of this exploratory study was to identify genetic variants that can help differentiate ASD early from other DDs. We selected 36 children (mean age 60.1 months) with DDs using Developmental Behavioral Scales (DBS) through "EDUS-Education for All", an organization providing services for children with developmental disorders in Bosnia and Herzegovina. We further rated children's autistic traits with the preschool version of the Childhood Autism Rating Scale, second edition (CARS-II). We defined ASD if scores were >25.5 and other DDs if scores were <25.5. Diagnosis of ASD and DD were independently confirmed by child psychiatrists. Whole exome sequencing (WES) was performed by Veritas Genetics, USA, using Illumina NovaSeq 6000 (Illumina Inc., San Diego, CA, USA) next-generation sequencing (NGS) apparatus. We tested genetic association by applying SKAT-O, which optimally combines the standard Sequence Kernel Association Test (SKAT) and burden tests to identify rare variants associated with complex traits in samples of limited power. The analysis yielded seven genes (DSE, COL10A1, DLK2, CSMD1, FAM47E, PPIA, PYDC2) to potentially differentiate observed phenotypic characteristics between our cohort participants with ASD and other DDs. Our exploratory study in a small sample of participants with ASD and other DDs contributed to gene discovery in differentiating ASD from DDs. A replication study is needed in a larger sample to confirm our results.

STIGMA: Single-cell tissue-specific gene prioritization using machine learning

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin

Previous genome-wide association studies (GWASs) for adiponectin, a complex trait linked to type 2 diabetes and obesity, identified >20 associated loci. However, most loci were identified in populations of European ancestry, and many of the target genes underlying the associations remain unknown. We conducted a cross-ancestry adiponectin GWAS meta-analysis in ≤46,434 individuals from the Metabolic Syndrome in Men (METSIM) cohort and the ADIPOGen and AGEN consortiums. We combined study-specific association summary statistics using a fixed-effects, inverse variance-weighted approach. We identified 22 loci associated with adiponectin (p < 5×10-8), including 15 known and seven previously unreported loci. Among individuals of European ancestry, Genome-wide Complex Traits Analysis joint conditional analysis (GCTA-COJO) identified 14 additional distinct signals at the ADIPOQ, CDH13, HCAR1, and ZNF664 loci. Leveraging the cross-ancestry data, FINEMAP + SuSiE identified 45 causal variants (PP > 0.9), which also exhibited potential pleiotropy for cardiometabolic traits. To prioritize target genes at associated loci, we propose a combinatorial likelihood scoring formalism (Gene Priority Score [GPScore]) based on measures derived from 11 gene prioritization strategies and the physical distance to the transcription start site. With GPScore, we prioritize the 30 most probable target genes underlying the adiponectin-associated variants in the cross-ancestry analysis, including well-known causal genes (e.g., ADIPOQ, CDH13) and additional genes (e.g., CSF1, RGS17). Functional association networks revealed complex interactions of prioritized genes, their functionally connected genes, and their underlying pathways centered around insulin and adiponectin signaling, indicating an essential role in regulating energy balance in the body, inflammation, coagulation, fibrinolysis, insulin resistance, and diabetes. Overall, our analyses identify and characterize adiponectin association signals and inform experimental interrogation of target genes for adiponectin.

Evaluation of input data modality choices on functional gene embeddings

Functional gene embeddings, numerical vectors capturing gene function, provide a promising way to integrate functional gene information into machine learning models. These embeddings are learnt by applying self-supervised machine-learning algorithms on various data types including quantitative omics measurements, protein-protein interaction networks and literature. However, downstream evaluations comparing alternative data modalities used to construct functional gene embeddings have been lacking. Here we benchmarked functional gene embeddings obtained from various data modalities for predicting disease-gene lists, cancer drivers, phenotype-gene associations and scores from genome-wide association studies. Off-the-shelf predictors trained on precomputed embeddings matched or outperformed dedicated state-of-the-art predictors, demonstrating their high utility. Embeddings based on literature and protein-protein interactions inferred from low-throughput experiments outperformed embeddings derived from genome-wide experimental data (transcriptomics, deletion screens and protein sequence) when predicting curated gene lists. In contrast, they did not perform better when predicting genome-wide association signals and were biased towards highly-studied genes. These results indicate that embeddings derived from literature and low-throughput experiments appear favourable in many existing benchmarks because they are biased towards well-studied genes and should therefore be considered with caution. Altogether, our study and precomputed embeddings will facilitate the development of machine-learning models in genetics and related fields.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND

Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.

METHODS

We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.

RESULTS

Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.

CONCLUSIONS

Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. TRANSLATIONAL STATEMENT: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

Unrevealing the shared genetic mechanisms underlying C-reactive protein and schizophrenia

Longitudinal observational studies and Mendelian randomization research have obtained contradictory conclusions regarding the association between C-reactive protein (CRP) level and schizophrenia risk. However, the shared genetic mechanisms underlying CRP and schizophrenia remain poorly understood. Here, we examined the global and local genetic correlations using summary statistics from large-scale genome-wide association studies (GWAS) on CRP level and schizophrenia. Furthermore, we identified their shared genetic variants by applying the conditional false discovery rate approach and performed functional analyses of shared variants to explore the shared genetic mechanisms underlying CRP level and schizophrenia. We found a significant negative genetic correlation at the whole genome level and five significant local genetic correlations between CRP level and schizophrenia. Eight-three shared genetic loci were identified, from which single-nucleotide polymorphism (SNP) presents mixed effects on the increased CRP level and schizophrenia risk. Additionally, we identified 64 and 73 candidate genes that were mapped from SNPs with"concordant effect"(ceSNPs) and"discordant effect"(deSNPs) on the CRP level and schizophrenia risk respectively. Functional analyses revealed that genes mapped from ceSNPs and deSNPs exhibited similar patterns of human brain developmental expression trajectories and biological processes, but differed in expression levels and cell-type-specific enrichment in brain tissues. Our findings demonstrated mixed effects of shared genetic architecture between CRP level and schizophrenia, proving a deeper insight into the shared genetic aetiology underlying the CRP level and schizophrenia.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

The lncRNA expression profile signature of leukemia stem cells is altered upon PI3K/mTOR inhibition: an in vitro and in silico study

Genetic and/or epigenetic alterations in hematopoietic stem cells (HSCs) contribute to leukemia stem cell (LSC) formation. We aimed to identify alterations in the lncRNA expression profile signature of LSCs upon inhibition of PI3K/Akt/mTOR signaling, which provides selective advantages to LSCs. We also aimed to elucidate the potential interaction networks and functions of differentially expressed lncRNAs (DELs). We suppressed PI3K/Akt/mTOR signaling in LSC and HSC cell-lines by specific PI3K/mTOR dual-inhibitor (VS-5584) and confirmed the inhibition by antibody-array. We defined DELs by qRT-PCR. Increased SRA, ZEB2-AS1, antiPeg11, DLX6-AS, SNHG4, and decreased H19, PCGEM1, CAR-Intergenic-10, L1PA16, IGF2AS, and SNHG5 levels (|log2fold-change|>5) were strictly associated with PI3K/Akt/mTOR pathway inhibition in LSC. We performed in silico analyses for DELs. ZEB2-AS1 was found to be specifically expressed in normal bone marrow and predominantly lower in leukemic cell-lines. Three sub-clusters were identified for DELs and they were associated with "abnormality of multiple cell lineages in the bone marrow." DELs were most highly enriched for "glucuronidation" Reactome pathway and "ascorbate and aldarate metabolism" and "inositol phosphate metabolism" KEGG pathways. Transcription factors, MBD4, NANOG, PAX6, RELA, CEBPB, and CEBPA were predicted to be associated with the DEL profile. SRA was predicted to interact with CREB1, RARA, and PPARA. The possible DELs' targets were predicted to form six ontological groups, be highly enriched for phosphoprotein, and be involved in "PPAR signaling pathway" and "ChREBP regulation by carbohydrates and cAMP." These results will help to elucidate the roles of lncRNAs in the mechanisms that provide selective advantages to leukemia stem cells.

Deep structured learning for variant prioritization in Mendelian diseases

Effective computer-aided or automated variant evaluations for monogenic diseases will expedite clinical diagnostic and research efforts of known and novel disease-causing genes. Here we introduce MAVERICK: a Mendelian Approach to Variant Effect pRedICtion built in Keras. MAVERICK is an ensemble of transformer-based neural networks that can classify a wide range of protein-altering single nucleotide variants (SNVs) and indels and assesses whether a variant would be pathogenic in the context of dominant or recessive inheritance. We demonstrate that MAVERICK outperforms all other major programs that assess pathogenicity in a Mendelian context. In a cohort of 644 previously solved patients with Mendelian diseases, MAVERICK ranks the causative pathogenic variant within the top five variants in over 95% of cases. Seventy-six percent of cases were solved by the top-ranked variant. MAVERICK ranks the causative pathogenic variant in hitherto novel disease genes within the first five candidate variants in 70% of cases. MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease. These results represent a significant step towards automated identification of causal variants in patients with Mendelian diseases.

Multi-layered genetic approaches to identify approved drug targets

Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.

Early onset ataxia with comorbid myoclonus and epilepsy: A disease spectrum with shared molecular pathways and cortico-thalamo-cerebellar network involvement

OBJECTIVES

Early onset ataxia (EOA) concerns a heterogeneous disease group, often presenting with other comorbid phenotypes such as myoclonus and epilepsy. Due to genetic and phenotypic heterogeneity, it can be difficult to identify the underlying gene defect from the clinical symptoms. The pathological mechanisms underlying comorbid EOA phenotypes remain largely unknown. The aim of this study is to investigate the key pathological mechanisms in EOA with myoclonus and/or epilepsy.

METHODS

For 154 EOA-genes we investigated (1) the associated phenotype (2) reported anatomical neuroimaging abnormalities, and (3) functionally enriched biological pathways through in silico analysis. We assessed the validity of our in silico results by outcome comparison to a clinical EOA-cohort (80 patients, 31 genes).

RESULTS

EOA associated gene mutations cause a spectrum of disorders, including myoclonic and epileptic phenotypes. Cerebellar imaging abnormalities were observed in 73-86% (cohort and in silico respectively) of EOA-genes independently of phenotypic comorbidity. EOA phenotypes with comorbid myoclonus and myoclonus/epilepsy were specifically associated with abnormalities in the cerebello-thalamo-cortical network. EOA, myoclonus and epilepsy genes shared enriched pathways involved in neurotransmission and neurodevelopment both in the in silico and clinical genes. EOA gene subgroups with myoclonus and epilepsy showed specific enrichment for lysosomal and lipid processes.

CONCLUSIONS

The investigated EOA phenotypes revealed predominantly cerebellar abnormalities, with thalamo-cortical abnormalities in the mixed phenotypes, suggesting anatomical network involvement in EOA pathogenesis. The studied phenotypes exhibit a shared biomolecular pathogenesis, with some specific phenotype-dependent pathways. Mutations in EOA, epilepsy and myoclonus associated genes can all cause heterogeneous ataxia phenotypes, which supports exome sequencing with a movement disorder panel over conventional single gene panel testing in the clinical setting.

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

BACKGROUND

Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals.

RESULTS

We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases.

CONCLUSION

Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Termviewer - A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation

Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.

Five years' experience of the clinical exome sequencing in a Spanish single center

Nowadays, exome sequencing is a robust and cost-efficient genetic diagnostic tool already implemented in many clinical laboratories. Despite it has undoubtedly improved our diagnostic capacity and has allowed the discovery of many new Mendelian-disease genes, it only provides a molecular diagnosis in up to 25-30% of cases. Here, we comprehensively evaluate the results of a large sample set of 4974 clinical exomes performed in our laboratory over a period of 5 years, showing a global diagnostic rate of 24.62% (1391/4974). For the evaluation we establish different groups of diseases and demonstrate how the diagnostic rate is not only dependent on the analyzed group of diseases (43.12% in ophthalmological cases vs 16.61% in neurological cases) but on the specific disorder (47.49% in retinal dystrophies vs 24.02% in optic atrophy; 18.88% in neuropathies/paraparesias vs 11.43% in dementias). We also detail the most frequent mutated genes within each group of disorders and discuss, on our experience, further investigations and directions needed for the benefit of patients.

Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits

Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263-331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. Here, we proposed a new method, TripletGO, to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profile, genetic sequence alignment, protein sequence alignment, and naïve probability. TripletGO was tested on a large set of 5754 genes from 8 species (human, mouse, Arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge (CAFA3). Experimental results show that TripletGO achieves function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet network-based profiling method with the feature space mapping technique, which can accurately recognize function patterns from transcript expression profiles. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanggroup.org/TripletGO/.

OARD: Open annotations for rare diseases and their phenotypes based on real-world data

Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated with additional annotations found in published case reports. Despite their potential, real-world data such as electronic health records (EHRs) have not been fully exploited to derive rare disease annotations. Here, we present open annotation for rare diseases (OARD), a real-world-data-derived resource with annotation for rare-disease-related phenotypes. This resource is derived from the EHRs of two academic health institutions containing more than 10 million individuals spanning wide age ranges and different disease subgroups. By leveraging ontology mapping and advanced natural-language-processing (NLP) methods, OARD automatically and efficiently extracts concepts for both rare diseases and their phenotypic traits from billing codes and lab tests as well as over 100 million clinical narratives. The rare disease prevalence derived by OARD is highly correlated with those annotated in the original rare disease knowledgebase. By performing association analysis, we identified more than 1 million novel disease-phenotype association pairs that were previously missed by human annotation, and >60% were confirmed true associations via manual review of a list of sampled pairs. Compared to the manual curated annotation, OARD is 100% data driven and its pipeline can be shared across different institutions. By supporting privacy-preserving sharing of aggregated summary statistics, such as term frequencies and disease-phenotype associations, it fills an important gap to facilitate data-driven research in the rare disease community.

Identifying platelet-derived factors as amplifiers of B. burgdorferi-induced cytokine production

Previous studies have shown that monocytes can be 'trained' or tolerized by certain stimuli to respond stronger or weaker to a secondary stimulation. Rewiring of glucose metabolism was found to be important in inducing this phenotype. As we previously found that Borrelia burgdorferi (B. burgdorferi), the causative agent of Lyme borreliosis (LB), alters glucose metabolism in monocytes, we hypothesized that this may also induce long-term changes in innate immune responses. We found that exposure to B. burgdorferi decreased cytokine production in response to the TLR4-ligand lipopolysaccharide (LPS). In addition, B. burgdorferi exposure decreased baseline levels of glycolysis, as assessed by lactate production. Using GWAS analysis, we identified a gene, microfibril-associated protein 3-like (MFAP3L) as a factor influencing lactate production after B. burgdorferi exposure. Validation experiments proved that MFAP3L affects lactate- and cytokine production following B. burgdorferi stimulation. This is mediated by functions of MFAP3L, which includes activating ERK2 and through activation of platelet degranulation. Moreover, we showed that platelets and platelet-derived factors play important roles in B. burgdorferi-induced cytokine production. Certain platelet-derived factors, such chemokine C-X-C motif ligand 7 (CXCL7) and (C-C motif) ligand 5 (CCL5), were elevated in the circulation of LB patients in comparison to healthy individuals.

ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19

Despite two years of intense global research activity, host genetic factors that predispose to a poorer prognosis of COVID-19 infection remain poorly understood. Here, we prioritise eight robust (e.g., ELF5) or suggestive but unreported (e.g., RAB2A) candidate protein mediators of COVID-19 outcomes by integrating results from the COVID-19 Host Genetics Initiative with population-based plasma proteomics using statistical colocalisation. The transcription factor ELF5 (ELF5) shows robust and directionally consistent associations across different outcome definitions, including a >4-fold higher risk (odds ratio: 4.88; 95%-CI: 2.47-9.63; p-value < 5.0 × 10-6) for severe COVID-19 per 1 s.d. higher genetically predicted plasma ELF5. We show that ELF5 is specifically expressed in epithelial cells of the respiratory system, such as secretory and alveolar type 2 cells, using single-cell RNA sequencing and immunohistochemistry. These cells are also likely targets of SARS-CoV-2 by colocalisation with key host factors, including ACE2 and TMPRSS2. In summary, large-scale human genetic studies together with gene expression at single-cell resolution highlight ELF5 as a risk gene for severe COVID-19, supporting a role of epithelial cells of the respiratory system in the adverse host response to SARS-CoV-2.

Transcriptomic effects of propranolol and primidone converge on molecular pathways relevant to essential tremor

Essential tremor (ET) is one of the most common movement disorders, affecting nearly 5% of individuals over 65 years old. Despite this, few genetic risk loci for ET have been identified. Recent advances in pharmacogenomics have previously been useful to identify disease related molecular targets. Notably, gene expression has proven to be quite successful for the inference of drug response in cell models. We sought to leverage this approach in the context of ET where many patients are responsive to two drugs: propranolol and primidone. In this study, cerebellar DAOY and neural progenitor cells were treated for 5 days with clinical concentrations of propranolol and primidone, after which RNA-sequencing was used to identify convergent differentially expressed genes across treatments. Propranolol was found to affect the expression of genes previously associated with ET and other movement disorders such as TRAPPC11. Pathway enrichment analysis of these convergent drug-targeted genes identified multiple terms related to calcium signaling, endosomal sorting, axon guidance, and neuronal morphology. Furthermore, genes targeted by ET drugs were enriched within cell types having high expression of ET-related genes in both cortical and cerebellar tissues. Altogether, our results highlight potential cellular and molecular mechanisms associated with tremor reduction and identify relevant genetic biomarkers for drug-responsiveness in ET.

Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

Long Intergenic Noncoding RNA MIAT as a Regulator of Human Th17 Cell Differentiation

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.

Comprehensive genome-wide association study of different forms of hernia identifies more than 80 associated loci

Hernias are characterized by protrusion of an organ or tissue through its surrounding cavity and often require surgical repair. In this study we identify 65,492 cases for five hernia types in the UK Biobank and perform genome-wide association study scans for these five types and two combined groups. Our results show associated variants in all scans. Inguinal hernia has the most associations and we conduct a follow-up study with 23,803 additional cases from four study groups giving 84 independently associated variants. Identified variants from all scans are collapsed into 81 independent loci. Further testing shows that 26 loci are associated with more than one hernia type, suggesting substantial overlap between the underlying genetic mechanisms. Pathway analyses identify several genes with a strong link to collagen and/or elastin (ADAMTS6, ADAMTS16, ADAMTSL3, LOX, ELN) in the vicinity of associated loci for inguinal hernia, which substantiates an essential role of connective tissue morphology.

A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes

BACKGROUND

Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking.

METHODS

We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease.

RESULTS

We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed.

CONCLUSIONS

We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .

Critical changes in hypothalamic gene networks in response to pancreatic cancer as found by single-cell RNA sequencing

OBJECTIVE

Cancer cachexia is a devastating chronic condition characterized by involuntary weight loss, muscle wasting, abnormal fat metabolism, anorexia, and fatigue. However, the molecular mechanisms underlying this syndrome remain poorly understood. In particular, the hypothalamus may play a central role in cachexia, given that it has direct access to peripheral signals because of its anatomical location and attenuated blood-brain barrier. Furthermore, this region has a critical role in regulating appetite and metabolism.

METHODS

To provide a detailed analysis of the hypothalamic response to cachexia, we performed single-cell RNA-seq combined with RNA-seq of the medial basal hypothalamus (MBH) in a mouse model for pancreatic cancer.

RESULTS

We found many cell type-specific changes, such as inflamed endothelial cells, stressed oligodendrocyes and both inflammatory and moderating microglia. Lcn2, a newly discovered hunger suppressing hormone, was the highest induced gene. Interestingly, cerebral treatment with LCN2 not only induced many of the observed molecular changes in cachexia but also affected gene expression in food-intake decreasing POMC neurons. In addition, we found that many of the cachexia-induced molecular changes found in the hypothalamus mimic those at the primary tumor site.

CONCLUSION

Our data reveal that multiple cell types in the MBH are affected by tumor-derived factors or host factors that are induced by tumor growth, leading to a marked change in the microenvironment of neurons critical for behavioral, metabolic, and neuroendocrine outputs dysregulated during cachexia. The mechanistic insights provided in this study explain many of the clinical features of cachexia and will be useful for future therapeutic development.

Predicting genes from phenotypes using human phenotype ontology (HPO) terms

The interpretation of genomic variants following whole exome sequencing (WES) can be aided using human phenotype ontology (HPO) terms to standardize clinical features and predict causative genes. We performed WES on 453 patients diagnosed prior to 18 years of age and identified 114 pathogenic (P) or likely pathogenic (LP) variants in 112 patients. We utilized PhenoDB to extract HPO terms from provider notes and then used Phen2Gene to generate a gene score and gene ranking from each list of HPO terms. We assigned Phen2Gene gene rankings to 6 rank classes, with class 1 covering raw gene rankings of 1 to 10 and class 2 covering rankings from 11 to 50 out of a total of 17,126 possible gene rankings. Phen2Gene ranked causative genes into rank class 1 or 2 in 27.7% of cases and the genes in rank class 1 were all associated with well-characterized phenotypes. We found significant associations between the gene score and the number of years, since the gene was first published, the number of HPO terms with an hierarchical depth greater or equal to 11, and the number of Online Mendelian Inheritance in Man terms associated with the phenotype and gene. We conclude that genes associated with recognizable phenotypes and terms deep in the HPO hierarchy have the best chance of producing a high gene score and ranking in class 1 to 2 using Phen2Gene software with HPO terms. Clinicians and laboratory staff should consider these results when HPO terms are employed to prioritize candidate genes.

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

The role of the WNT signaling pathway in the maxillary sinus squamous cell carcinoma

Paranasal sinus tumors are a rare type of cancer. Most of these tumors are of epithelial origin and 80% of them are maxillary sinus squamous cell carcinoma. The WNT signaling pathway is an essential embryonic regulatory pathway known to play an important role in many cancers, including head and neck cancers. However, the effect of this pathway in maxillary sinus tumors has not been studied before. The aim of the study was to determine the changes in the regulatory genes of the WNT signaling pathway in maxillary sinus tumors. For this purpose, total RNA was isolated from the pathological preparations of 85 patients who had previously been operated on for squamous cell maxillary sinus tumor, and gene expression changes were evaluated by real-time RT-qPCR. The interactions among proteins encoded by genes, whose expression levels were found to be decreased and increased, were determined by protein-protein interaction (PPI) network analysis using string database, and signaling pathways that they are involved in were examined by Reactome database. A significant decrease in the expression of 28 genes compared to the control (fold change < 2.00 and p-value < 0.05) and a significant increase in the expression of 23 genes (fold change < 2.00 and p-value < 0.05) were detected. According to in silico analysis results, Signal Transduction (REACTOME:R-HSA-162582) and Signaling by WNT (REACTOME:R-HSA-195721) pathways were determined as most regulated pathways and FZD4-LRP5 and BCL9-CTNNB1 were determined as the strongest interactions. The current study contributes to illuminating the genetic regulation of maxillary sinus carcinoma in which genetic knowledge is limited. Our findings take attention to the dysregulations of the WNT signaling pathway that may support maxillary sinus carcinogenesis. The results will pave the way for further studies that investigate the therapy target potential of the WNT signaling pathway in this rare cancer.

Genetic Determinants of Serum Calcification Propensity and Cardiovascular Outcomes in the General Population

Background:

Serum calciprotein particle maturation time (T50), a measure of vascular calcification propensity, is associated with cardiovascular morbidity and mortality. We aimed to identify genetic loci associated with serum T50 and study their association with cardiovascular disease and mortality.

Methods:

We performed a genome-wide association study of serum T50 in 2,739 individuals of European descent participating in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, followed by a two-sample Mendelian randomization (MR) study to examine causal effects of T50 on cardiovascular outcomes. Finally, we examined associations between T50 loci and cardiovascular outcomes in 8,566 community-dwelling participants in the Rotterdam study.

Results:

We identified three independent genome-wide significant single nucleotide polymorphism (SNPs) in the AHSG gene encoding fetuin-A: rs4917 (p = 1.72 × 10−101), rs2077119 (p = 3.34 × 10−18), and rs9870756 (p = 3.10 × 10−8), together explaining 18.3% of variation in serum T50. MR did not demonstrate a causal effect of T50 on cardiovascular outcomes in the general population. Patient-level analyses revealed that the minor allele of rs9870756, which explained 9.1% of variation in T50, was associated with a primary composite endpoint of all-cause mortality or cardiovascular disease [odds ratio (95% CI) 1.14 (1.01–1.28)] and all-cause mortality alone [1.14 (1.00–1.31)]. The other variants were not associated with clinical outcomes. In patients with type 2 diabetes or chronic kidney disease, the association between rs9870756 and the primary composite endpoint was stronger [OR 1.40 (1.06–1.84), relative excess risk due to interaction 0.54 (0.01–1.08)].

Conclusions:

We identified three SNPs in the AHSG gene that explained 18.3% of variability in serum T50 levels. Only one SNP was associated with cardiovascular outcomes, particularly in individuals with type 2 diabetes or chronic kidney disease.

Association of Essential Tremor With Novel Risk Loci: A Genome-Wide Association Study and Meta-analysis

Question

Can common genetic variants associated with essential tremor (ET) be identified?

Findings

In this genome-wide association study and meta-analysis including genetic data on 483 054 individuals, 5 genome-wide significant loci were associated with risk of ET and common variants were associated with approximately 18% of ET heritability.

Meaning

Findings of this study may help identify new genes and inform ET biology.

Importance

Essential tremor (ET) is one of the most common movement disorders, affecting 5% of the general population older than 65 years. Common variants are thought to contribute toward susceptibility to ET, but no variants have been robustly identified.

Objective

To identify common genetic factors associated with risk of ET.

Design, Setting, and Participants

Case-control genome-wide association study. Inverse-variance meta-analysis was used to combine cohorts. Multicenter samples collected from European populations were collected from January 2010 to September 2019 as part of an ongoing study. Included patients were clinically diagnosed with or reported having ET. Control individuals were not diagnosed with or reported to have ET. Of 485 250 individuals, data for 483 054 passed data quality control and were used.

Main Outcomes and Measures

Genotypes of common variants associated with risk of ET.

Results

Of the 483 054 individuals included, there were 7177 with ET (3693 [51.46%] female; mean [SD] age, 62.66 [15.12] years), and 475 877 control individuals (253 785 [53.33%] female; mean [SD] age, 56.40 [17.6] years). Five independent genome-wide significant loci and were identified and were associated with approximately 18% of ET heritability. Functional analyses found significant enrichment in the cerebellar hemisphere, cerebellum, and axonogenesis pathways. Genetic correlation (r), which measures the degree of genetic overlap, revealed significant common variant overlap with Parkinson disease (r, 0.28; P = 2.38 × 10⁻⁸) and depression (r, 0.12; P = 9.78 × 10⁻⁴). A separate fine-mapping of transcriptome-wide association hits identified genes such as BACE2, LRRN2, DHRS13, and LINC00323 in disease-relevant brain regions, such as the cerebellum.

Conclusions and Relevance

The results of this genome-wide association study suggest that a portion of ET heritability can be explained by common genetic variation and can help identify new common genetic risk factors for ET.

GWAS of stool frequency provides insights into gastrointestinal motility and irritable bowel syndrome

Gut dysmotility is associated with constipation, diarrhea, and functional gastrointestinal disorders like irritable bowel syndrome (IBS), although its molecular underpinnings are poorly characterized. We studied stool frequency (defined by the number of bowel movements per day, based on questionnaire data) as a proxy for gut motility in a GWAS meta-analysis including 167,875 individuals from UK Biobank and four smaller population-based cohorts. We identify 14 loci associated with stool frequency (p ≤ 5.0 × 10-8). Gene set and pathway analyses detected enrichment for genes involved in neurotransmitter/neuropeptide signaling and preferentially expressed in enteric motor neurons controlling peristalsis. PheWAS identified pleiotropic associations with dysmotility syndromes and the response to their pharmacological treatment. The genetic architecture of stool frequency correlates with that of IBS, and UK Biobank participants from the top 1% of stool frequency polygenic score distribution were associated with 5× higher risk of IBS with diarrhea. These findings pave the way for the identification of actionable pathological mechanisms in IBS and the dysmotility syndromes.

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.

IRF7 and RNH1 are modifying factors of HIV-1 reservoirs: a genome-wide association analysis

BACKGROUND

Combination antiretroviral treatment (cART) cannot eradicate HIV-1 from the body due to the establishment of persisting viral reservoirs which are not affected by therapy and reinitiate new rounds of HIV-1 replication after treatment interruption. These HIV-1 reservoirs mainly comprise long-lived resting memory CD4+ T cells and are established early after infection. There is a high variation in the size of these viral reservoirs among virally suppressed individuals. Identification of host factors that contribute to or can explain this observed variation could open avenues for new HIV-1 treatment strategies.

METHODS

In this study, we conducted a genome-wide quantitative trait locus (QTL) analysis to probe functionally relevant genetic variants linked to levels of cell-associated (CA) HIV-1 DNA, CA HIV-1 RNA, and RNA:DNA ratio in CD4+ T cells isolated from blood from a cohort of 207 (Caucasian) people living with HIV-1 (PLHIV) on long-term suppressive antiretroviral treatment (median = 6.6 years). CA HIV-1 DNA and CA HIV-1 RNA levels were measured with corresponding droplet digital PCR (ddPCR) assays, and genotype information of 522,455 single-nucleotide variants was retrieved via the Infinium Global Screening array platform.

RESULTS

The analysis resulted in one significant association with CA HIV-1 DNA (rs2613996, P < 5 × 10^-8) and two suggestive associations with RNA:DNA ratio (rs7113204 and rs7817589, P < 5 × 10^-7). Then, we prioritized PTDSS2, IRF7, RNH1, and DEAF1 as potential HIV-1 reservoir modifiers and validated that higher expressions of IRF7 and RNH1 were accompanied by rs7113204-G. Moreover, RNA:DNA ratio, indicating relative HIV-1 transcription activity, was lower in PLHIV carrying this variant.

CONCLUSIONS

The presented data suggests that the amount of CA HIV-1 DNA and RNA:DNA ratio can be influenced through PTDSS2, RNH1, and IRF7 that were anchored by our genome-wide association analysis. Further, these observations reveal potential host genetic factors affecting the size and transcriptional activity of HIV-1 reservoirs and could indicate new targets for HIV-1 therapeutic strategies.

Heritability Enrichment of Immunoglobulin G N-Glycosylation in Specific Tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson's disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases.