Hasil untuk "Genetics"

N. Dracopoli

BJ Staskawicz, F. Ausubel, BJ Baker et al.

L. Csonka, Andrew D. Hanson

H. Orr

S. Folstein, B. Rosen-Sheidley

J. Doyle, Lex E. Flagel, A. Paterson et al.

M. Farrer

Douglas T. Carrell, Douglas T. Carrell, S. J. Conner et al.

M. Rockman, L. Kruglyak

B. Charlesworth, D. Charlesworth

Muhammad Muneeb, David B. Ascher

Human genetics offers a promising route to therapeutic discovery, yet practical frameworks translating genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when matched disease transcriptomics are unavailable. Here we present G2DR, a genotype-first prioritization framework propagating inherited variation through genetically predicted expression, multi-method gene-level testing, pathway enrichment, network context, druggability, and multi-source drug--target evidence integration. In a migraine case study with 733 UK Biobank participants under stratified five-fold cross-validation, we imputed expression across seven transcriptome-weight resources and ranked genes using a reproducibility-aware discovery score from training and validation data, followed by a balanced integrated score for target selection. Discovery-based prioritization generalized to held-out data, achieving gene-level ROC-AUC of 0.775 and PR-AUC of 0.475, while retaining enrichment for curated migraine biology. Mapping prioritized genes to compounds via Open Targets, DGIdb, and ChEMBL yielded drug sets enriched for migraine-linked compounds relative to a global background, though recovery favoured broader mechanism-linked and off-label space over migraine-specific approved therapies. Directionality filtering separated broadly recovered compounds from mechanistically compatible candidates. G2DR is a modular framework for genetics-informed hypothesis generation, not a clinically actionable recommendation system. All outputs require independent experimental, pharmacological, and clinical validation.

John J. Connolly, H. Hakonarson

Autism is a neurodevelopmental disorder of complex etiology and is amongst the most heritable of neuropsychiatric disorders while sharing genetic liability with other neurodevelopmental disorders such as intellectual disability (ID). Autism spectrum disorders (ASDs) are defined more broadly and include autism, Asperger syndrome, childhood disintegrative disorder and pervasive developmental disorder not otherwise specified. Under the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition Revised (DSM-IVTR), these disorders are grouped together with Rett syndrome (“Rett’s disorder”) as pervasive developmental disorders. However, Rett syndrome has a reportedly distinct pathophysiology, clinical course, and diagnostic strategy (Levy & Schultz, 2009) and will likely be removed in the impending publication of DSM-V (APA, 2010). The new diagnostic manual will formally adopt the single diagnostic category “ASDs”, which is used here. Reported prevalence rates for ASDs range from 20 (Newschaffer et al. 2007) to 116 (Baird et al., 2006) per 10,000 children, and vary in accordance with diagnostic, sampling, and screening criteria. The Centers for Disease Control and Prevention (CDC) suggest that in the United States, the prevalence of ASDs is 1 in 110 (1/70 in boys and 1/315 in girls) (ADDM, 2009). The three primary characteristics of ASDs are communication impairments, social impairments, and repetitive/stereotyped behaviors. The DSM-IVTR, ICD-10, and many other diagnostic instruments require impairment in each of these domains for a diagnosis of autistic disorder. Within the last decade, a number of major technological developments have transformed our understanding of the genetic causes of autism, and the field continues to evolve rapidly. In this chapter, we will review three approaches to identifying genetic factors that contribute to the pathogenesis of ASDs: 1) common variants and genome-wide association studies (GWAS); 2) rare variants and copy number variation (CNV) studies, and 3) familial forms of autism and the role of next-generation sequencing (NGS) methods. Data from all three approaches underscores the conclusion that autism is a highly complex and heterogeneous disorder, involving a multifactorial etiology. Moreover, it is becoming increasingly apparent that autism is not a unitary disorder, and that the spectrum may consist of any number of different autisms that share similar symptoms or phenotypes. This conclusion has important implications for evaluation and treatment, which are discussed in the conclusion.

M. Kayser, P. Knijff

J. Ayroles, M. A. Carbone, Eric A. Stone et al.

Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits.

Boyi Hu, Zhongjian Wang, Jack Xin et al.

We develop and analyze a stochastic genetic interacting particle method (SGIP) for reaction-diffusion-advection (RDA) equations. The SGIP method employs operator splitting to approximate the advection-diffusion and reaction processes, treating the former using particle drift-diffusion and the latter via exact or implicit integration of reaction dynamics over bins, where particle density is estimated using a histogram. A key innovation is the incorporation of adaptive resampling to close the loop of particle and density field description of solutions, mimicking the selection mechanism in genetics. Resampling is also crucial for maintaining long-term stability by redistributing particles in accordance with the evolving density field. We provide a comprehensive error analysis and establish convergence bounds under appropriate regularity assumptions. Numerical experiments in one to three space dimensions demonstrate the method's effectiveness across various reaction types (Fisher-Kolmogorov-Petrovsky-Piskunov (FKPP), cubic, Arrhenius) and flow configurations (shear, cellular, cat's eye, Arnold-Beltrami-Childress (ABC) flows), showing excellent agreement with the finite difference method (FDM) while offering computational advantages for complex flow geometries and higher-dimensional problems.

Olivier Labayle, Breeshey Roskams-Hieter, Joshua Slaughter et al.

Population genetics seeks to quantify DNA variant associations with traits or diseases, as well as interactions among variants and with environmental factors. Computing millions of estimates in large cohorts in which small effect sizes are expected, necessitates minimising model-misspecification bias to control false discoveries. We present TarGene, a unified statistical workflow for the semi-parametric efficient and double robust estimation of genetic effects including k-point interactions among categorical variables in the presence of confounding and weak population dependence. k-point interactions, or Average Interaction Effects (AIEs), are a direct generalisation of the usual average treatment effect (ATE). We estimate AIEs with cross-validated and/or weighted versions of Targeted Minimum Loss-based Estimators (TMLE) and One-Step Estimators (OSE). The effect of dependence among data units on variance estimates is corrected by using sieve plateau variance estimators based on genetic relatedness across the units. We present extensive realistic simulations to demonstrate power, coverage, and control of type I error. Our motivating application is the targeted estimation of genetic effects on trait, including two-point and higher-order gene-gene and gene-environment interactions, in large-scale genomic databases such as UK Biobank and All of Us. All cross-validated and/or weighted TMLE and OSE for the AIE k-point interaction, as well as ATEs, conditional ATEs and functions thereof, are implemented in the general purpose Julia package TMLE.jl. For high-throughput applications in population genomics, we provide the open-source Nextflow pipeline and software TarGene which integrates seamlessly with modern high-performance and cloud computing platforms.

Jessica M. Scott, Jessica M. Scott, Zhuyu Qiu et al.

We assessed the safety, tolerability, and effects of exercise therapy in three patients with cancer and hospitalization for SARS-CoV-2 infection in an early-phase prospective trial. All study assessments and exercise sessions were conducted remotely (decentralized) in patient’s homes. Patients received five escalated doses of aerobic exercise therapy (range, 90 to 375 minutes per week) following a tolerability-based adapted schedule over 30 consecutive weeks. Exercise therapy was safe (i.e., no serious adverse events), tolerable (i.e., all exercise therapy doses were completed, with an overall average relative exercise dose intensity of 89%), and associated with improvements in patient physiology (e.g., exercise capacity) and patient-reported outcomes (e.g., quality of life). Correlative proteomic and single-cell immune sequencing of peripheral blood samples revealed marked alterations in protein and immune phenotypes implicated in post COVID-19 condition. (ClinicalTrials.gov number, NCT04824443).

S. Franks, A. Hoffmann

Tommaso Boccato, Matteo Ferrante, Nicola Toschi

There is growing consensus among neuroscientists that neural circuits critical for survival are the result of genomic decompression processes. We introduce SynaptoGen, a novel computational framework--member of the Connectome Models family--bringing synthetic biological intelligence closer, facilitating neural biological agent development through precise genetic control of synaptogenesis. SynaptoGen is the first model of its kind offering mechanistic explanation of synaptic multiplicity based on genetic expression and protein interaction probabilities. The framework connects genetic factors through a differentiable function, working as a neural network where synaptic weights equal average numbers of synapses between neurons, multiplied by conductance, derived from genetic profiles. Differentiability enables gradient-based optimization, allowing generation of genetic expression patterns producing pre-wired biological agents for specific tasks. Validation in simulated synaptogenesis scenarios shows agents successfully solving four reinforcement learning benchmarks, consistently surpassing control baselines. Despite gaps in biological realism requiring mitigation, this framework has potential to accelerate synthetic biological intelligence research.

Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond et al.

We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions. This analysis is made possible by improved algorithms for equality saturation, which we use to improve the Exhaustive Symbolic Regression algorithm; this produces the set of semantically unique expression structures, orders of magnitude smaller than the full symbolic regression search space. We compare the efficiency of random search in the set of unique expressions and genetic programming. For our experiments we use two real-world datasets where symbolic regression has been used to produce well-fitting univariate expressions: the Nikuradse dataset of flow in rough pipes and the Radial Acceleration Relation of galaxy dynamics. The results show that genetic programming in such limited settings explores only a small fraction of all unique expressions, and evaluates expressions repeatedly that are congruent to already visited expressions.