Specificity, length, and luck: How genes are prioritized by rare and common variant association studies DOI Creative Commons
Jeffrey P. Spence, Hakhamanesh Mostafavi, Mineto Ota

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods conceptually similar, we show by analyzing of 209 quantitative traits in the UK Biobank that they systematically prioritize different This raises question how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance - much a gene quantitatively affects trait; 2) specificity gene's under study relative to its across all traits. find GWAS near trait-specific variants , while . Because non-coding can context specific, highly pleiotropic genes, generally cannot. Both designs also affected distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate reveal aspects biology suggest ways improve interpretation usage.

Язык: Английский

Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis DOI Creative Commons
Yongli Zhao, Yizhe Hao,

Yi‐Jun Ge

и другие.

Alzheimer s Research & Therapy, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 7, 2025

Evidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing function. Our study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from UK Biobank who completed four assessments at baseline. 364 were finally extracted for analysis through a rigorous screening process. performed univariate analyses identify significantly associated with each in two equal-sized split discovery and replication datasets. Subsequently, identified further assessed multivariable model. Additionally, model, we explored associations longitudinal decline. Moreover, one- two- sample Mendelian randomization (MR) conducted confirm genetic associations. Finally, quality of pooled evidence between was evaluated. 252 (69%) exhibited significant least one dataset. Of these, 231 (92%) successfully replicated. our 41 function, spanning categories such as education, socioeconomic status, lifestyle factors, body measurements, mental health, medical conditions, early life household characteristics. Among these variables, 12 more than domain, all subgroup analyses. And LASSO, rigde, principal component indicated robustness primary results. among Furthermore, 22 supported one-sample MR analysis, 5 confirmed two-sample analysis. 10 rated high. Based on adopting favorable 38% 34% decreased risks dementia Alzheimer's disease (AD). Overall, constructed an database which could contribute prevention impairment dementia.

Язык: Английский

Процитировано

1

Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq DOI Creative Commons
Mineto Ota, Jeffrey P. Spence, Tony Zeng

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Genetic association studies provide a unique tool for identifying causal links from genes to human traits and diseases. However, it is challenging determine the biological mechanisms underlying most associations, we lack genome-scale approaches inferring mechanistic pathways cellular functions traits. Here propose new bridge this gap by combining quantitative estimates of gene-trait relationships loss-of-function burden tests with gene-regulatory connections inferred Perturb-seq experiments in relevant cell types. By these two forms data, aim build graphs which directional associations trait can be explained their regulatory effects on programs or direct trait. As proof-of-concept, constructed graph gene hierarchy that jointly controls three partially co-regulated blood We perturbation trait-relevant types, coupled gene-level effect sizes traits, between genetics biology.

Язык: Английский

Процитировано

0

Transforming cancer treatment: integrating patient-derived organoids and CRISPR screening for precision medicine DOI Creative Commons

Ziyi Zhu,

John Paul Shen,

Paul Chi-Lui Ho

и другие.

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Март 25, 2025

The persistently high mortality rates associated with cancer underscore the imperative need for innovative, efficacious, and safer therapeutic agents, as well a more nuanced understanding of tumor biology. Patient-derived organoids (PDOs) have emerged innovative preclinical models significant translational potential, capable accurately recapitulating structural, functional, heterogeneous characteristics primary tumors. When integrated cutting-edge genomic tools such CRISPR, PDOs provide powerful platform identifying driver genes novel targets. This comprehensive review delves into recent advancements in CRISPR-mediated functional screens leveraging across diverse types, highlighting their pivotal role high-throughput genomics microenvironment (TME) modeling. Furthermore, this highlights synergistic potential integrating CRISPR immunotherapy, focusing on uncovering immune evasion mechanisms improving efficacy immunotherapeutic approaches. Together, these technologies offer promise advancing precision oncology.

Язык: Английский

Процитировано

0

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies DOI Creative Commons
Jeffrey P. Spence, Hakhamanesh Mostafavi, Mineto Ota

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods conceptually similar, we show by analyzing of 209 quantitative traits in the UK Biobank that they systematically prioritize different This raises question how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance - much a gene quantitatively affects trait; 2) specificity gene's under study relative to its across all traits. find GWAS near trait-specific variants , while . Because non-coding can context specific, highly pleiotropic genes, generally cannot. Both designs also affected distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate reveal aspects biology suggest ways improve interpretation usage.

Язык: Английский

Процитировано

0