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At least 19 recordsLinked to original sources

PubMed2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPT's power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research.

Bayesian fine mapping↗

PubMed2025

The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: (a) potentially nonlinear confounding effects from phenomic variables and (b) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Brain aging↗

PubMed2026

Polygenic risk factors for comorbid diagnoses in individuals with substance use disorders: A phenome-wide survival analysis.

OBJECTIVE: Persons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome-wide association studies (PheWASs). However, PheWASs are largely cross-sectional, limiting our understanding of whether diagnoses predate the development of an SUD. We characterize whether polygenic scores (PGSs) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. METHODS: Using data from All of Us (N = 393,596), we explored: (1) whether social determinants of health (SDoHs) are associated with lifetime risk of SUD (N cases = 42,568) and (2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, posttraumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. RESULTS: Multiple SDoHs were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g. <$10 K annually vs $100 K-$150 K annually: OR = 4.18; 95% CI = 3.92, 4.45). There were 86 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, posttraumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. CONCLUSIONS: Social determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGSs for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

Humans↗

PubMed2025

Polygenic Risk Factors for Comorbid Diagnoses in Individuals with Substance Use Disorders: A Phenome-Wide Survival Analysis.

OBJECTIVE: Persons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome wide association studies (PheWAS). However, PheWAS are largely cross-sectional, limiting our understanding of whether diagnoses predate development of an SUD. We characterize whether polygenic scores (PGS) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. METHODS: Using data from All of Us (N = 393,596), we explored: 1) whether social determinants of health (SDoH) are associated with lifetime risk of SUD (N cases = 42,568) and 2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, post-traumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. RESULTS: Multiple SDoH were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g., <$10K annually vs $100K-$150K annually: OR = 3.89, 95% CI = 3.66, 4.13). There were 101 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, post-traumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. CONCLUSIONS: Social determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGS for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

Journal Article↗

PubMed2026

Sex-specific aging clocks from a large-scale human phenome reveal distinct aging transitions and circulating signatures.

Aging is a primary risk factor for chronic diseases, yet its progression varies among individuals and between sexes. Here, under the X-Age Project, we profiled the clinical aging phenome of the Multicentric Chinese Aging Study (mCAS) through a cross-sectional analysis of 172 clinical measures from more than 100,000 participants aged 18-98 years across three centers. These profiles enabled sex-specific clinical aging clocks that revealed divergent aging trajectories between women and men during midlife that converged in later life. Phenome-wide analyses revealed age-related accumulation of metabolic factors, including low-density lipoprotein, triglycerides, glucose and uric acid, and tumor markers, such as carcinoembryonic antigen and human epithelial protein 4. These age-accumulating factors induced senescence-related phenotypes in human endothelial cells. Furthermore, a high-fat diet mouse model with dietary reversal supported the modifiability of metabolic burden-induced aging. Together, this work establishes metabolic and tumor marker accumulation as actionable drivers of human aging, paving the way for personalized, sex-stratified geroprotective interventions.

Humans↗

PubMed2023

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

Phenomics↗

PubMed2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs.

MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Genome-Wide Association Study↗

PubMed2026

Aerobic Fitness and Health-Related Phenotypes: A Two-Stage Phenome-Wide Mendelian Randomization Study.

PURPOSE: We investigated potentially causal associations between genetically predicted aerobic fitness and multiple health phenotypes using a two-stage phenome-wide Mendelian randomization (MR) study. METHODS: Genetically determined aerobic fitness, as operationalized by Cai et al., served as the exposure instrument. We screened 712 health-related phenotypes as outcomes using publicly available European-ancestry genome-wide association studies (GWAS) summary statistics from OpenGWAS (Discovery GWAS n > 5000), prioritizing non-UK Biobank/non-FinnGen datasets for Discovery when available and selecting an independent GWAS for validation. Associations were estimated using the MR-Robust Adjusted Profile Score method, controlled for multiple testing (5% false discovery rate) and unaffected by violations of MR assumptions (directional concordance between discovery and validation; no evidence of horizontal pleiotropy across inverse-variance weighted, MR-Egger, weighted-median, and weighted-mode methods; negative control analysis on hair color). RESULTS: We identified 108 discovery associations, of which 34 remained valid and statistically significant after validation. Higher genetically determined aerobic fitness was associated with lower lacunar stroke risk, lower arterial stiffness, higher heart rate variability, lower diastolic blood pressure, more favorable anthropometric measures, lower use of antidiabetic drugs, lower asthma risk, lower C-reactive protein, higher bone mineral density, favorable liver function biomarkers, favorable platelet-related traits, multiple blood count-derived hematological cell indices and counts, as well as higher years of schooling. Adverse associations were confined to atrial fibrillation, valvular heart disease, and systolic blood pressure. CONCLUSIONS: Genetically determined aerobic fitness is linked to a broad pattern of favorable cardiometabolic, inflammatory, musculoskeletal, respiratory, hepatic, and hematological phenotypes, alongside a narrow set of potential cardiovascular hazards.

Humans↗

PubMed2025

Phenomics-Based Discovery of Novel Orthosteric Choline Kinase Inhibitors.

Choline kinase alpha (CHKA) is a central mediator of cell metabolism linked to cancer and immune regulation. Cellular and clinical evaluation of CHKA has been hampered by challenges in the development of drug-like choline kinase inhibitors. Here, we identify CHKA as an unexpected off-target of histone methyltransferase inhibitors using an integrated phenomic approach. We confirm CHKA as a direct protein target of the aminoquinazolines UNC0638 and UNC0737 using a combination of chemoproteomic, biochemical, cellular, and metabolic profiling assays, possibly explaining the previously reported discrepancies observed for different G9a/GLP inhibitor scaffolds in cellular assays. Using primary human cell model systems, we discover that CHKA modulation impairs IgG secretion and B-cell maturation consistent with the notion that choline metabolism plays an important role in immune signalling. Co-crystal structures of UNC0638 and UNC0737 with CHKA unravel an unexpected binding mode and suggest the inhibitors as attractive starting points for the development of selective chemical tools to further explore the biological role of CHKA in cancer and immune metabolism.

Humans↗

PubMed2026

Systemic Comorbidities of Keloid and Hypertrophic Scars: A Phenome-Wide Association Study in a Multiethnic U.S. Pediatric Cohort.

BACKGROUND: Excessive scarring (ES), including keloids and hypertrophic scars, impairs function, appearance, and quality of life in children. Its pediatric comorbidity spectrum is not well defined, limiting anticipatory guidance and multidisciplinary care. This research aims to investigate comorbidities of ES in a diverse pediatric cohort using a phenome-wide association study (PheWAS). METHODS: This population-based study leveraged longitudinal electronic health record (EHR) data from participants enrolled in the Children's Hospital of Philadelphia (CHOP) from 2006. Diagnosis codes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] and Tenth Revision [ICD-10-CM]) were mapped to 3109 phenotype codes (PheCodes). PheWAS analyses were conducted using logistic regression, with Bonferroni correction applied to account for multiple testing. RESULTS: Among 86,092 pediatric participants, 662 (0.77%) were identified with ES; the remaining served as controls. Multivariable PheWAS screening identified 154 significant associations across 16 disease categories, of which 105 were not reported previously to our knowledge. Dermatologic phenotypes (n = 28; 18%) were most enriched, including acne and other follicular disorders, eczema, pigmentary changes, papulosquamous and granulomatous disorders, and cutaneous infections. Respiratory phenotypes (n = 21; 14%) included respiratory failure, pneumonia, asthma, allergic rhinitis, pharyngitis, and tonsillar hypertrophy. Sense organ disorders (n = 19; 12%) comprised conjunctivitis, refractive errors, otitis, and hearing impairment. Infection-related phenotypes (n = 14; 9%) highlighted susceptibility to viral (influenza, human papillomavirus [HPV], molluscum contagiosum), fungal (candidiasis, dermatophytosis), and bacterial infections. CONCLUSIONS: These findings suggest that ES in children indicates not only localized wound-healing impairment, but also systemic immune, developmental, and proliferative dysregulations, emphasizing the need for genetic and mechanistic studies to clarify causal pathways and multidisciplinary surveillance beyond dermatologic care.

Humans↗

PubMed2025

Genetic liability to meniscus degeneration and its comorbidity patterns: a phenome-wide association study in the UK Biobank.

Meniscus degeneration is a common knee pathology causing pain, disability, and osteoarthritis. While mechanical factors are established, the contribution of inherited genetic liability to its systemic disease patterns remains unclear. We applied a polygenic risk score (PRS) for meniscus degeneration, derived from FinnGen genome-wide association study (GWAS) results to 323,999 UK Biobank participants and conducted a phenome-wide association study (PRS-PheWAS) across 962 clinical phenotypes. The PRS-PheWAS revealed significant associations beyond musculoskeletal traits, extending to metabolic, cardiovascular, psychiatric, and gastrointestinal domains, indicating broad shared genetic architecture. To support these findings, linkage disequilibrium score regression confirmed strong genetic correlations with osteoarthritis and related arthropathies, and genotype-tissue expression (GTEx) analysis highlighted tissue-specific expression of lead genes (e.g., GDF5, SOX5, BMP6) in connective and metabolic tissues. The results demonstrate that genetic liability to meniscus degeneration extends beyond the knee, sharing pathways with systemic conditions. This systemic genetic architecture underscores the need for integrative management approaches that combine orthopedic care with metabolic and lifestyle interventions.

Journal Article↗

PubMed2026

High-variance phenome database reveals important roles of WD40 proteins in the plant pathogenic fungus Fusarium graminearum.

WD40 is a highly conserved protein domain in eukaryotes that functions as a versatile platform for protein-protein interactions and participates in diverse biological processes. We performed a genome-wide functional analysis of WD40 domain-containing proteins in Fusarium graminearum, a phytopathogenic fungus that causes severe yield losses and mycotoxin contamination in major cereal crops. Comprehensive phenotypic profiling of 119 WD40 gene deletion mutants across 22 phenotypic traits established a systematic WD40 phenome dataset, revealing the broad functional involvement of WD40 proteins and a strong correlation between sexual reproduction and virulence. Protein interaction analyses of selected WD40 proteins revealed diverse WD40-mediated interaction patterns and provided further insights into WD40-mediated protein interactions and their roles in protein complex formation. This study provides a foundation for further characterization of WD40 proteins in filamentous fungi.

Fusarium graminearum↗

PubMed2025

Study research protocol for Phenome India-CSIR Health Cohort Knowledgebase: A prospective multi-modal follow-up study on a nationwide employee cohort.

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications. Currently, most risk prediction algorithms rely on epidemiological data from the Caucasian population, which often do not translate well to the Indian population due to ethnic diversity, differing dietary and lifestyle habits, and unique risk profiles. In this multi-center prospective longitudinal study conducted across India, we aim to address these challenges by developing clinically relevant risk prediction scores for cardio-metabolic diseases specifically tailored to the Indian population. India, which accounts for nearly 18% of the global population, also has a significant diaspora worldwide. This program targets longitudinal collection and bio-banking of samples from over 10 000 employees both working and retirees of the Council of Scientific and Industrial Research and their spouses, with baseline sample collection already completed. During the baseline collection, we gathered multi-parametric data including clinical questionnaires, lifestyle and dietary habits, anthropometric parameters, lung function assessments, liver elastography by Fibroscan, electrocardiogram readings, biochemical data, and molecular assays, including but not limited to genomics, plasma proteomics, metabolomics, and fecal microbiome analysis. In addition to exploring associations between these parameters and their cardio-metabolic outcomes, we plan to employ artificial intelligence algorithms to develop predictive models for phenotypic conditions. This study could pave the way for precision medicine tailored to the Indian population, particularly for the middle-income strata, and help refine the normative values for health and disease indicators in India.

cardio-metabolic↗

PubMed2025

Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.

Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resistance alleles, and cereal genomic resources are well advanced due to large-scale sequencing and genotyping efforts. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique to initially discover novel disease resistance loci or alleles in these diverse collections. Traditional disease resistance phenotyping methods are reliant on visual estimation of disease symptom severity and have successfully supported genetic mapping studies either via GWAS or QTL mapping in biparental populations facilitating both marker development and gene cloning efforts. Due to foliar pathogens having a high capacity to evolve, there is a need to pyramid disease resistance genes with diverse mechanisms for durable control. Resistance expressed as a quantitative trait, known as quantitative resistance (QR), is hypothesized to be more durable, unlike major R-gene resistance that is race-specific and can be vulnerable to breaking down without gene stewardship. However, assessing QR visually is challenging, particularly when complicated by complex genotype × environment (G × E) effects in the field. High-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS. In this review, we discuss image-based disease phenotyping based on macroscopic (visible symptoms) and microscopic features during the host-pathogen interaction. Quantitative image analysis approaches using conventional and artificial intelligence (AI) algorithms are also discussed.

Disease Resistance↗

PubMed2026

Cross-Ancestry and Phenome-Wide Associations of Cancer-Specific Polygenic Risk Scores.

PURPOSE: Genome-wide association studies have identified many common variants associated at low effect sizes with various cancers. Summing the effects of these variants into polygenic risk scores (PRS) can improve cancer risk prediction. However, cross-cancer and cross-phenotype pleiotropic associations of cancer-specific PRS are limited. METHODS: Using logistic regression models, we tested the association of 13 cancer-specific PRS with curated phenotypes representing the same 13 cancers and 340 cancer and cardiometabolic phecodes in 560,287 individuals (114,255 African ancestry [AFR] and 446,032 European ancestry [EUR]) from the Million Veteran Program. Models were stratified by ancestry and used age, principal components, and cancer-specific PRS per standard deviation as independent variables, and correction was applied for multiple comparisons. RESULTS: All 13 cancer-specific PRS were significantly associated with their respective cancers among EUR individuals with odds ratios per standard deviation of PRS (odds ratio [OR]) 1.05-1.70. Among AFR individuals, the effect sizes of the cancer PRS were lower, with OR 1.01-1.48, and cancer-specific PRS were significantly associated with their respective cancers for five of 13 cancers (bladder, breast in female patients, colorectal, prostate, and thyroid). In cancer-cancer pleiotropy studies, only the renal cancer-specific PRS was significantly associated with skin cancer (OR = 1.04, P = 4.5 × 10-06) among EUR individuals. PheWAS demonstrated five positive pleotropic associations with cardiometabolic conditions (thyroid cancer PRS with thyroid goiter, oral cancer PRS with diabetes phenotypes, and hypothyroidism) and two negative associations (oral and lung cancer PRS separately with coronary artery disease). CONCLUSION: Cancer PRS have stronger associations per cancer among EUR versus AFR individuals. In contrast to PRS of other chronic diseases, the majority of cancer-related PRS are highly specific and pleiotropic associations with other cancers and cardiometabolic traits are uncommon.

Female↗

PubMed2025

Clinical implications of bone marrow adiposity identified by phenome-wide association and Mendelian randomization in the UK Biobank.

Bone marrow adiposity changes in diverse diseases, but the full scope of these, and whether they are directly influenced by marrow adiposity, remains unknown. To address this, we previously measured the bone marrow fat fraction of the femoral head, total hip, femoral diaphysis, and spine of over 48,000 UK Biobank participants. Here, we first use these data for PheWAS to identify diseases associated with marrow adiposity at each site. This reveals associations with 47 incident diseases across 12 disease categories, including osteoporosis, fracture, type 2 diabetes, cardiovascular diseases, cancers, and other conditions that burden public health worldwide. Intriguingly, type 2 diabetes associates positively with spine bone marrow adiposity but negatively with marrow adiposity at femoral sites. We then establish PRSs based on bone-marrow-fat-fraction-associated SNPs and use PRS-PheWAS and Mendelian randomization to explore causal associations between marrow adiposity and disease. PRS-PheWAS reveals that genetic predisposition to increased marrow adiposity is positively associated with osteoporosis and fractures. Mendelian randomization further suggests that increased marrow adiposity at the diaphysis and total hip is causally associated with osteoporosis. Our findings substantially advance understanding of how marrow adiposity impacts human health and highlight its potential as a biomarker and/or therapeutic target for diverse human diseases.

Humans↗

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↗