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PubMed2005

Genestrace: phenomic knowledge discovery via structured terminology.

The era of applied genomic medicine is quickly approaching accompanied by the increasing availability of detailed genetic information. Understanding the genetic etiology behind complex, multi-gene diseases remains an important challenge. In order to uncover the putative genetic etiology of complex diseases, we designed a method that explores the relationships between two major terminological and ontological resources: the Unified Medical Language System (UMLS) and the Gene Ontology (GO). The UMLS has a mainly clinical emphasis; Gene Ontology has become the standard for biological annotations of genes and gene products. Using statistical and semantic relationships within and between the two resources, we are able to infer relationships between disease concepts in the UMLS and gene products annotated using GO and its associated databases. We validated our inferences by comparing them to the known gene-disease relationships, as defined in the Online Mendelian Inheritance in Man's morbidmap (OMIM). The proof-of-concept methods presented here are unique in that they bypass the ambiguity of the direct extraction of gene or disease term from MEDLINE. Additionally, our methods provide direct links to clinically significant diseases through established terminologies or ontologies. The preliminary results presented here indicate the potential utility of exploiting the existing, manually curated relationships in biomedical resources as a tool for the discovery of potentially valuable new gene-disease relationships.

Computational Biology↗

PubMed2006

From genome to phenome--RNAi library screening and hit characterization using signaling pathway analysis.

Comprehensive, high-throughput analysis of gene function using RNA interference (RNAi)-based screens is emerging as a significant step forward for preliminary drug-target identification. Until quite recently drug target identification depended heavily on the analysis of changes in gene expression, which in turn needed to be correlated with gene function. The promise of obtaining preliminary 'gene to phenotype' information using a single high-throughput platform is propelling major investment in this area by biotechnology and pharmaceutical companies. This review discusses recent developments in the use of RNAi library screens, and the various assay formats and techniques currently in use for data analysis downstream of such screens.

Animals↗

PubMed1996

[Taxonomic study of species of the Mycobacterium genus isolated in Cuba].

40 strains of the Mycobacterium genus corresponding to 12 species, which were subjected to 62 microbiological and biochemical tests, were studied. Each one was considered as a character. As a result of the similitude coefficient and their grouping, 9 phenomes represented by: Phenome I (Mycobacterium fortuitum), Phenome II (MAI Complex), Phenome III (Mycobacterium phlei), Phenome IV (Mycobacterium triviale), Phenome V (Mycobacterium smegmatis), Phenome VI (Mycobacterium gordonae), Phenome VII (Mycobacterium szulgai), Phenome VIII (MAI Complex), and Phenome IX (Mycobacterium scrofulaceum), were obtained. The strain identification work was consistent with grouping from the phenotypic point of view.

Cuba↗

PubMed2026

Sparse phenotyping for wheat grain yield enabled by multiomics prediction.

Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.

Triticum↗

PubMed2023

The phenotype-genotype reference map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Humans↗

PubMed2025

Characterizing trends in clinical genetic testing: A single-center analysis of EHR data from 1.8 million patients over two decades.

A lack of structural data in electronic health records (EHRs) makes assessing the impact of genetic testing on clinical practice challenging. We extracted clinical genetic tests from the EHRs of more than 1.8 million patients seen at Vanderbilt University Medical Center from 2002 to 2022. With these data, we quantified the use of clinical genetic testing in healthcare and described how testing patterns and results changed over time. We assessed trends in types of genetic tests, tracked usage across medical specialties, and introduced a new measure, the genetically attributable fraction (GAF), to quantify the proportion of observed phenotypes attributable to a genetic diagnosis over time. We identified 104,392 tests and 19,032 molecularly confirmed diagnoses. The proportion of patients with genetic testing in their EHRs increased from 1.0% in 2002 to 6.1% in 2022, and testing became more comprehensive with the growing use of multi-gene panels. The number of unique diseases diagnosed with genetic testing increased from 51 in 2002 to 509 in 2022, and there was a rise in the number of variants of uncertain significance. The phenome-wide GAF for 6,505,620 diagnoses made in 2022 was 0.46%, and the GAF was greater than 5% for 74 phenotypes, including pancreatic insufficiency (67%), chorea (64%), atrial septal defect (24%), microcephaly (17%), paraganglioma (17%), and ovarian cancer (6.8%). Our study provides a comprehensive quantification of the increasing role of genetic testing at a major academic medical institution and demonstrates its growing utility in explaining the observed medical phenome.

Humans↗

PubMed2026

Association between 1400 blood metabolites and the risk of ankylosing spondylitis: A 2-stage, 2-sample Mendelian randomization study.

Human blood metabolites have been closely linked to ankylosing spondylitis (AS) in observational studies, yet direct causal evidence remains limited. This study aims to use Mendelian randomization (MR) to pinpoint causal metabolites associated with AS and to predict potential side effects of metabolite interventions. Genetic instruments for exposure were sourced from a genome-wide association study of 1400 blood metabolites, while genome-wide association study data for AS outcomes were derived from the FinnGen cohort. The primary MR analysis was conducted using the inverse variance weighted method. Supplemental analyses were conducted using weighted median, MR-Egger, simple mode, and weighted mode methods, while sensitivity analyses were performed to evaluate heterogeneity and pleiotropy. A replication analysis using an additional the UK Biobank cohort was also performed to determine metabolites associated with AS. The Steiger test and linkage disequilibrium score regression were used to further strengthen causal inference. Lastly, a phenome-wide Mendelian randomization analysis was performed to investigate the potential on-target side effects of metabolite interventions. After comprehensive analyses, 3 metabolites (the 2'-deoxyuridine levels, the hate to mannose ratio, and the Uridine to 2'-deoxyuridine ratio) were identified as being genetically associated with AS. The phenome-wide Mendelian randomization analysis revealed that the hate to mannose ratio might have deleterious effects on 4 other diseases, while no significant associations were found for the 2'-deoxyuridine levels or the uridine to 2'-deoxyuridine ratio with other diseases. This systematic MR analysis unveiled the potential role of the 2'-deoxyuridine levels, hate to mannose ratio and uridine to 2'-deoxyuridine ratio as the causal mediator in the development of AS. Considering the advantages and disadvantages, 2'-deoxyuridine appears as the most promising prospective therapeutic target for the prevention of AS.

Humans↗

PubMed2006

Expression genetics and the phenotype revolution.

Genetic analysis of variation demands large numbers of individuals and even larger numbers of genotypes. The identification of alleles associated with Mendelian disorders has involved sample sizes of a thousand or more. Pervasive and common diseases that afflict human populations--cancer, heart disease, diabetes, neurodegeneration, addiction--are all polygenic and are even more demanding of large numbers. DeCode Genetics (http://www.decode.com) has harnessed the human resources of Iceland to unravel genetic and molecular causes of complex disease. The UK BioBank project (http://www.ukbiobank.ac.uk/) will incorporate 500,000 adult volunteers. The murine Collaborative Cross is the experimental equivalent of these human populations and will consist of a panel of approximately 1000 recombinant strains, expandable by intercrossing to much larger numbers of isogenic but heterozygous F(1)s. Massive projects of these types require efficient technologies. We have made enormous progress on the genotyping front, and it is now important to focus energy on devising ultrahigh-throughput methods to phenotype. Molecular phenotyping of the transcriptome has matured, and it is now possible to acquire hundreds of thousands of mRNA phenotypes at a cost matching those of SNPs. Proteomic and cell-based assays are also maturing rapidly. The acquisition of a personal genome along with a personal molecular phenome will provide an effective foundation for personalized medicine. Rodent models will be essential to test our ability to predict susceptibility and disease outcome using SNP data, molecular phenomes, and environmental exposures. These models will also be essential to test new treatments in a robust systems context that accounts for genetic variation.

Animals↗

PubMed2004

The future of motor neuron disease: the challenge is in the genes.

Adult-onset motor neuron disease (MND) includes sporadic and familial forms of amyotrophic lateral sclerosis (ALS), lower motor neuron disease including progressive and segmental spinal muscular atrophy (LMND) and primary lateral sclerosis (PLS). ALS/MND can be considered to be a spectrum of neurodegenerative diseases characterised by a preferential degeneration of upper and/or lower motor neurons. ALS and LMND have a complex multifactorial aetiology and a large clinical variability. This combination warrants an increasing genomics approach in future research. Genomics is the structural and functional study of genomes--i. e. the complete set of chromosomes and the genes they contain. Several methods may help to understand gene functions, and every method has led to its own "omics". The study of the complex relationship between on the one hand genomics data, transcriptomics data, proteomics data, and interactomics data and on the other hand the phenotype, is called "phenomics". In phenomics, the extensive and detailed phenotyping by the clinician is a prerequisite for meaningful associations. As a consequence, in ALS/MND clinicians have the task to agree about different clinical subtypes in order to make these associations and hence to gain further insight into the complex pathogenesis and identification of diagnostic markers in ALS/MND. Also, several new approaches in the treatment of ALS/MND are here discussed, including the viral delivery of protective compounds, RNA-interference, and stem cell therapy. Further, we argue that a future challenge is to allow for patients to have early access to multidisciplinary centres with specialist knowledge of ALS/MND. These centres can apply specific models of care for people with ALS/MND, but must be designed in a patient-centred format. Ultimately, these models should be assessed according to their outcomes.

Forecasting↗

PubMed2026

Identifying potential drug targets for physical and cognitive frailty: an integrative analysis of CHARLS cohort, mendelian randomization, and gene colocalization.

With the aging of the population, frailty has become a common syndrome that severely affects the quality of life of older adults. This study aims to analyze the correlation between cognition and frailty, physical activity and frailty, and elucidate the potential pharmacological targets of cognitive frailty and physical frailty.We conducted logistic regression analyses using data from the China Health and Retirement Longitudinal Study (CHARLS) to examine the associations between total cognition and frailty, physical activity and frailty. Furthermore, summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (TSMR) were employed to explore potential pharmacological targets for frailty. Genes associated with physical frailty and cognitive frailty were identified, followed by analysis via colocalization analysis, phenome-wide association studies (PheWAS), and DsigDB drug prediction. Cross-sectional analysis of CHARLs revealed that total cognition(OR 0.93, 95% CI 0.92-0.95) and middle physical activity(OR 0.95, 95% CI 0.92-0.97) were negatively correlated with frailty. SMR identified 41 drug genes associated with frailty, and subsequent TSMR validation and co-localization analysis showed that 11 candidate genes exhibited strong colocalization (PP.H4 > 0.8). GRPEL 1, PABPC 4, and WBP 2NL were ultimately identified as potential drug targets associated with physical frailty, while LANCL1, LRPPRC, FADS1, and WBP2NL were identified as potential drug targets associated with cognitive frailty. Phenome-wide association analysis(PheWAS) did not reveal any significant associations between these genes and other phenotypes at the genome-wide significance threshold. Laudanosine, 25-hydroxycholesterol, and hexadecanal emerged as the top three candidate compounds for therapeutic intervention. We identified potential drug targets for physical frailty and cognitive frailty through comprehensive analysis and elucidated drugs associated with potentially relevant genetic markers, thereby laying the foundation for a deeper understanding of the mechanisms of frailty.

Humans↗

PubMed1983

Language lateralization as measured by dichotic VRT and recognition tasks.

A dichotic color-naming VRT task and a dichotic digits recognition task were administered. The first required the naming of five colors paired with their backward pronunciation: the second of five nonsimilar digits. Forty-eight right-handed male and female subjects were used, controlling for the factors of sex, handedness, and familial sinistrality. A highly significant left-ear advantage was obtained, suggesting that previous results of dichotic studies may only pertain to a limited class of phenomic stimuli. Results implicate different cerebral processes in the analysis of familiar and novel auditory and phenomic stimuli.

Adult↗

PubMed1989

The artificial worlds approach to emergent evolution.

Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map that connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but rather emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.

Artificial Intelligence↗

PubMed2026

Cell-type-specific genetic associations in Lewy body dementia identified using single-cell eQTL-based Mendelian randomization.

BACKGROUND: Lewy body dementia (LBD) is a complex neurodegenerative disorder marked by α-synuclein aggregation and dual impairment of cognitive and motor function.While genome-wide association studies have identified risk loci, the cellular mechanisms linking genetic variation to disease susceptibility remain largely unexplored. METHODS: We performed single-cell transcriptome-wide Mendelian randomization using brain cell-type-specific eQTLs across eight major cell types. Genetic associations were evaluated using inverse-variance weighted models, followed by Bayesian colocalization analysis. Replication was performed in independent stratified LBD cohorts based on APOE ε4 carrier status. Phenome-wide association analysis was included as a supplementary, descriptive assessment of cross-trait associations. RESULTS: Expression of ANKRD65 in excitatory neurons was significantly associated with reduced LBD risk (odds ratio = 0.65, 95 % CI: 0.52-0.81, p = 0.00013). This association passed a false discovery rate of 0.1 and showed strong evidence of colocalization (posterior probability = 0.93). Effect direction was consistent across APOE ε4+ and ε4- LBD subgroups in independent cohorts. No genome-wide significant associations were observed with non-neurological traits in the phenome-wide analysis. CONCLUSIONS: Our findings identify a genetically supported, cell-type-resolved association between ANKRD65 expression in excitatory neurons and LBD risk. This study demonstrates the value of integrating cell-resolved transcriptomic regulation with genetic inference to pinpoint functionally relevant targets in neurodegenerative diseases.

Humans↗

PubMed2026

Trans-omics integration underscores distinct roles of polyunsaturated phospholipids in bidirectional offspring birth weight deviations.

BACKGROUND: Abnormal birth weights are associated with adverse pregnancy outcomes and future metabolic consequences. We aimed to examine cord blood lipidomes from low, normal and high birth weight (LBW, NBW, HBW) infants to identify core lipid signatures associated with non-optimum birth weight, and to derive biological insights through trans-omics data integration with placental proteome, maternal plasma lipidome and clinical phenome. METHODS: We conducted quantitative lipidomics of cord blood samples from two independent cohorts: a retrospective discovery cohort (n = 147) and a prospective validation cohort (n = 73). Integration with placental proteomics, maternal plasma lipidomics and clinical phenomics was conducted to elucidate potential biological implications. FINDINGS: We identified substantial reductions in cord blood polyunsaturated phospholipids (PUFA-PLs) (FDR <0.05) associated with placental vesicle trafficking and formation in LBW, and altered neutrophil degranulation in HBW. Combinatorial analyses of paired maternal plasma and cord blood samples indicated that cord blood PUFA-PL reductions were not attributable to deficient maternal supply, but rather to impeded assimilation (LBW) and increased utilisation (HBW). INTERPRETATION: Our findings provide biological insights that may inform targetable, lipid-oriented nutritional and/or pharmacological strategies to modulate foetal growth and development, with the goal of optimising clinical outcomes for both mother and child. FUNDING: This work was supported by the National Natural Science Foundation of China (82170854, 81870579, 81870545, 82571043, 2357308); National High Level Hospital Clinical Research Funding (2022-PUMCH-C-019); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0530200 and 2024ZD0530204); Beijing Municipal Science & Technology Commission (Z201100005520011); Peking University Clinical Scientist Training Program (No. BMU2023PYJH022); Beijing Municipal Natural Science Foundation (7202163, 7184252).

Humans↗

PubMed2026

Mapping the immune-genetic architecture of Epstein-Barr virus-related phenotypes and multiple sclerosis through a single-cell genetic framework for target prioritization and pharmacologic hypothesis generation.

BACKGROUND: Multiple sclerosis (MS) is a severe neuroinflammatory disease causing substantial long-term disability. Strong epidemiologic evidence links Epstein-Barr virus (EBV) exposure with MS risk, but genetic evidence for immune target prioritization in EBV-related phenotypes remains limited. METHODS: We integrated single-cell cis-eQTL data from 14 immune cell types with GWASs of an EBV-related clinical phenotype and MS using a single-cell Mendelian randomization framework with colocalization analyses. Candidate eGenes were evaluated in independent cohorts. For multi-SNP instruments, we performed heterogeneity, pleiotropy, MR-Egger, weighted median, mode-based, and MR-PRESSO sensitivity analyses. We also conducted phenome-wide association analyses and queried DrugBank to annotate candidate compounds targeting prioritized genes. RESULTS: We prioritized 43 immune-cell-specific candidate eGenes with convergent genetic support, including 6 for the EBV-related phenotype and 37 for MS. SERPINB1 in NK cells was associated with increased risk of the EBV-related phenotype, whereas HLA-G was associated with decreased risk. For MS, APOM and MSH5 showed protective associations, while AHI1 showed cell-type-dependent, bidirectional associations across immune lineages. Colocalization and independent cohort evaluation supported these findings. Among FDR-significant multi-SNP associations, MR-Egger intercept tests did not indicate directional pleiotropy, although a small subset showed heterogeneity or MR-PRESSO signals. Phenome-wide analyses identified no significant adverse phenotypic associations among evaluable genes at the prespecified threshold. DrugBank annotation nominated sodium nitroprusside, fasudil, artenimol, and choline as hypothesis-generating compounds for experimental follow-up. CONCLUSIONS: This study provides a single-cell genetic framework for prioritizing immune-cell-specific candidate targets for EBV-related phenotypes and MS, and nominates genetically supported targets and pharmacologic hypotheses for experimental investigation.

Humans↗

PubMed2026

Chiari I malformation.

Chiari I malformation (CM1), the most common structural hindbrain disorder in humans, is traditionally characterized by the downward displacement of the cerebellar tonsils through the foramen magnum. However, this definition does not reflect the variability in clinical presentation, natural history and treatment response of this disorder. Some individuals with minimal tonsillar descent have severe neurological symptoms and syringomyelia, whereas others with extensive descent remain asymptomatic. Emerging evidence from neuroimaging, developmental biology and human genetics indicates that CM1 is not a single anatomical entity but a spectrum of disorders resulting from disruptions in coordinated growth and homeostasis across the cerebellum, posterior fossa, craniocervical junction, cerebrospinal fluid and neurovascular systems. CM1 may be best understood as a disorder of disrupted developmental scaling, in which the tightly regulated relationships between cerebellar growth and cranial accommodation are altered within a dynamic neurovascular and cerebrospinal fluid environment. In this context, tonsillar herniation is a geometric consequence rather than the primary disease process. This Primer synthesizes current knowledge on the epidemiology, mechanisms, diagnosis and management of CM1 across the lifespan. We highlight advances in neuroimaging, genomics and phenomics that support a shift from anatomy-based definitions towards an integrated genomic-phenomic classification.

Humans↗

PubMed2026

Robust replication of associations across patient-mediated and provider-sourced EHR data in the All of Us research program.

The All of Us Research Program is assembling a nationwide cohort with electronic health record (EHR) resources through two complementary pathways: healthcare provider organization (HPO)-sourced EHRs and patient-mediated EHR (PME) contributed through patient portal linkages. The comparative research utility of these two data sources has not been systematically evaluated. Here, we compared PME and HPO EHRs with respect to disease prevalence, phenotype-phenotype associations, and replication of established genotype-phenotype associations using data from 19,703 PME and 373,887 HPO participants. We benchmarked disease prevalence against national estimates, conducted phenome-wide association studies for 10 commonly studied diseases, and tested replication of more than 5000 established genotype-phenotype associations across multiple ancestral groups. Disease prevalence was consistently lower in PME than in HPO, although prevalence of most diseases in both cohorts exceeded national estimates. Both data sources reproduced known phenotype-phenotype associations and showed moderate-to-strong concordance in effect sizes across the phenome. The overall genotype-phenotype replication rate was 49.1% (5399/10,999) in HPO and 5.9% (381/6482) in PME across ancestral groups, with effect sizes strongly correlated among well-powered associations (R = 0.84, P < 0.001). To disentangle the impact of sample size from data quality, we performed 1:1 propensity score matching. After matching, the replication gap in genotype-phenotype associations narrowed from 8.3-fold to 1.3-fold, with equivalent replication rates among adequately powered associations and strongly concordant effect sizes; comorbidity patterns were also consistent across all 10 diseases tested. These findings demonstrate that both data sources are valuable for clinical and genomic research and can inform other cohorts integrating provider-derived and patient-mediated EHRs.

Computational biology and bioinformatics↗

PubMed2006

OmicBrowse: a browser of multidimensional omics annotations.

UNLABELLED: OmicBrowse is a browser to explore multiple datasets coordinated in the multidimensional omic space integrating omics knowledge ranging from genomes to phenomes and connecting evolutional correspondences among multiple species. OmicBrowse integrates multiple data servers into a single omic space through secure peer-to-peer server communications, so that a user can easily obtain an integrated view of distributed data servers, e.g. an integrated view of numerous whole-genome tiling-array data retrieved from a user's in-house private-data server, along with various genomic annotations from public internet servers. OmicBrowse is especially appropriate for positional-cloning purposes. It displays both genetic maps and genomic annotations within wide chromosomal intervals and assists a user to select candidate genes by filtering their annotations or associated documents against user-specified keywords or ontology terms. We also show that an omic-space chart effectively represents schemes for integrating multiple datasets of multiple species. AVAILABILITY: OmicBrowse is developed by the Genome-Phenome Superbrain Project and is released as free open-source software under the GNU General Public License at http://omicspace.riken.jp.

Chromosome Mapping↗