Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology.
Ontology highlight
ABSTRACT:
SUBMITTER: Pandey AK
PROVIDER: S-EPMC8333144 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium.Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction.The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html
| S-EPMC4765855 | biostudies-literature
Project description:The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.
| S-EPMC10822856 | biostudies-literature
Project description:Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related physiological data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples.
| S-EPMC4832048 | biostudies-literature
Project description:Rheumatic heart disease (RHD) is a heritable inflammatory condition characterized by carditis, arthritis, and systemic disease. Although remaining neglected, the last 3 years has seen some promising advances in RHD research. Whilst it is clear that RHD can be triggered by recurrent group A streptococcal infections, the mechanisms driving clinical progression are still poorly understood. This review summarizes our current understanding of the genetics implicated in this process and the genetic determinants that predispose some people to RHD. The evidence demonstrating the importance of individual cell types and cellular states in delineating causal genetic variants is discussed, highlighting phenotype/genotype correlations where possible. Genetic fine mapping and functional studies in extreme phenotypes, together with large-scale omics studies including genomics, transcriptomics, epigenomics, and metabolomics, are expected to provide new information not only on RHD but also on the mechanisms of other autoimmune diseases and facilitate future clinical translation.
| S-EPMC8024521 | biostudies-literature
Project description:The microtubule-associated protein tau (MAPT) locus has long been associated with sporadic neurodegenerative disease, notably progressive supranuclear palsy and corticobasal degeneration, and more recently with Alzheimer's disease and Parkinson's disease. However, the functional biological mechanisms behind the genetic association have only now started to emerge. The genomic architecture in the region spanning MAPT is highly complex, and includes a approximately 1.8 Mb block of linkage disequilibrium (LD). The region is divided into two major haplotypes, H1 and H2, defined by numerous single nucleotide polymorphisms and a 900 kb inversion which suppresses recombination. Fine mapping of the MAPT region has identified sub-clades of the MAPT H1 haplotype which are specifically associated with neurodegenerative disease. Here we briefly review the role of MAPT in sporadic and familial neurodegenerative disease, and then discuss recent work which, for the first time, proposes functional mechanisms to link MAPT haplotypes with the neuropathology seen in patients.
| S-EPMC2801069 | biostudies-literature
Project description:Correlation of resistance associated mutations and minimum inhibitory concentrations of 900 Mycobacterium tuberculosis complex isolates
| PRJEB48275 | ENA
Project description:Alterations of the N-methyl-d-aspartate receptor (NMDAR) subunit GluN2A, encoded by GRIN2A, have been associated with a spectrum of neurodevelopmental disorders with prominent speech-related features, and epilepsy. We performed a comprehensive assessment of phenotypes with a standardized questionnaire in 92 previously unreported individuals with GRIN2A-related disorders. Applying the criteria of the American College of Medical Genetics and Genomics to all published variants yielded 156 additional cases with pathogenic or likely pathogenic variants in GRIN2A, resulting in a total of 248 individuals. The phenotypic spectrum ranged from normal or near-normal development with mild epilepsy and speech delay/apraxia to severe developmental and epileptic encephalopathy, often within the epilepsy-aphasia spectrum. We found that pathogenic missense variants in transmembrane and linker domains (misTMD+Linker) were associated with severe developmental phenotypes, whereas missense variants within amino terminal or ligand-binding domains (misATD+LBD) and null variants led to less severe developmental phenotypes, which we confirmed in a discovery (P = 10-6) as well as validation cohort (P = 0.0003). Other phenotypes such as MRI abnormalities and epilepsy types were also significantly different between the two groups. Notably, this was paralleled by electrophysiology data, where misTMD+Linker predominantly led to NMDAR gain-of-function, while misATD+LBD exclusively caused NMDAR loss-of-function. With respect to null variants, we show that Grin2a+/- cortical rat neurons also had reduced NMDAR function and there was no evidence of previously postulated compensatory overexpression of GluN2B. We demonstrate that null variants and misATD+LBD of GRIN2A do not only share the same clinical spectrum (i.e. milder phenotypes), but also result in similar electrophysiological consequences (loss-of-function) opposing those of misTMD+Linker (severe phenotypes; predominantly gain-of-function). This new pathomechanistic model may ultimately help in predicting phenotype severity as well as eligibility for potential precision medicine approaches in GRIN2A-related disorders.
| S-EPMC6308310 | biostudies-literature
Project description:The genotype-phenotype map is an essential object to understand organismal complexity and adaptability. However, its experimental characterisation is a daunting task. Thus, simple models have been proposed and investigated. They have revealed that genotypes differ in their robustness to mutations; phenotypes are represented by a broadly varying number of genotypes, and simple point mutations suffice to navigate the space of genotypes while maintaining a phenotype. Nonetheless, most current models focus only on one level of the map (folded molecules, gene regulatory networks, or networks of metabolic reactions), so that many relevant questions cannot be addressed. Here we introduce toyLIFE, a multi-level model for the genotype-phenotype map based on simple genomes and interaction rules from which a complex behaviour at upper levels emerges -remarkably plastic gene regulatory networks and metabolism. toyLIFE is a tool that permits the investigation of how different levels are coupled, in particular how and where mutations affect phenotype or how the presence of certain metabolites determines the dynamics of toyLIFE gene regulatory networks. The model can easily incorporate evolution through more complex mutations, recombination, or gene duplication and deletion, thus opening an avenue to explore extended genotype-phenotype maps.
| S-EPMC4269896 | biostudies-other
Project description:Complex genetic interactions lie at the foundation of many diseases. Understanding the nature of these interactions is critical to developing rational intervention strategies. In mammalian systems hypothesis testing in vivo is expensive, time consuming, and often restricted to a few physiological endpoints. Thus, computational methods that generate causal hypotheses can help to prioritize targets for experimental intervention. We propose a Bayesian statistical method to infer networks of causal relationships among genotypes and phenotypes using expression quantitative trait loci (eQTL) data from genetically randomized populations. Causal relationships between network variables are described with hierarchical regression models. Prior distributions on the network structure enforce graph sparsity and have the potential to encode prior biological knowledge about the network. An efficient Monte Carlo method is used to search across the model space and sample highly probable networks. The result is an ensemble of networks that provide a measure of confidence in the estimated network topology. These networks can be used to make predictions of system-wide response to perturbations. We applied our method to kidney gene expression data from an MRL/MpJ × SM/J intercross population and predicted a previously uncharacterized feedback loop in the local renin-angiotensin system.
| S-EPMC3070524 | biostudies-literature
Project description:Global warming is expected to increase agricultural water scarcity; thus, optimized irrigation schedules are important and timely for sustainable crop production. Deficit irrigation, which balances crop growth and water consumption, has been proposed, but the critical threshold is not easily quantified. Here, we conducted experiments on strawberry plants subjecting progressive drought following various water recovery treatments on the high-throughput physiological phenotyping system "Plantarray". The critical soil water contents (θcri), below which the plant transpiration significantly decreased, were calculated from the inflection point of the transpiration rate (Tr) - volumetric soil water content (VWC) curve fitted by a piecewise function. The physiological traits of water relations were compared between the well-watered plants (CK), plants subjecting the treatment of rewatering at the point of θcri following progressive drought (WR_θcri), and the plants subjecting the treatment of rewatering at severe drought following progressive drought (WR_SD). The results showed that midday Tr, daily transpiration (E), and biomass gain of the plants under WR_θcri treatment were equivalent to CK during the whole course of the experiment, but those under WR_SD treatment were significantly lower than CK during the water stress phase that could not recover even after rehydration. To explore the gene regulatory mechanisms, transcriptome analysis of the samples collected 12 h before, 12 h post and 36 h post water recovery in the three treatments was conducted. GO and KEGG enrichment analyses for the differentially expressed genes indicated that genes involved in mineral absorption and flavonoid biosynthesis were among the most striking transcriptionally reversible genes under the WR_θcri treatment. Functional physiological phenotyping and transcriptome data provide new insight into a potential, quantitative, and balanceable water-saving strategy for strawberry irrigation and other agricultural crops.
| S-EPMC9730707 | biostudies-literature