A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data DOI Creative Commons

Xinyi Yu,

Xianghong Hu,

Xiaomeng Wan

и другие.

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

Опубликована: Май 27, 2024

Abstract Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for in non-coding regions. Expression quantitative trait loci (eQTLs) linked these variations to gene expression, aiding identifying genes involved disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due unaddressed cellular heterogeneity. Recently, single-cell (scRNA-seq) has provided higher resolution enabling cell-type-specific (ct-eQTL) analyses. However, are limited by their smaller sample sizes technical constraints. In this paper, we present a novel statistical framework, IBSEP, which integrates RNA-seq scRNA-seq data enhanced ct-eQTLs prioritization. Our method employs Bayesian hierarchical model combine summary statistics both types, overcoming limitations while leveraging advantages each technique. Through extensive simulations real-data analyses, including peripheral blood mononuclear cells brain cortex datasets, IBSEP demonstrated superior performance compared existing methods. approach unveils new transcriptional regulatory mechanisms specific cell offering deeper into basis of diseases at resolution.

Язык: Английский

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals DOI Creative Commons
Wenmin Zhang, Tianyuan Lu, Robert Sladek

и другие.

Bioinformatics, Год журнала: 2024, Номер 40(5)

Опубликована: Апрель 29, 2024

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and important for prioritizing targets functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants one genomic locus.

Язык: Английский

Процитировано

7
XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias DOI Creative Commons
Mingxuan Cai, Zhiwei Wang,

Jiashun Xiao

и другие.

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Окт. 28, 2023

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among can limit statistical power and resolution of fine-mapping. Second, it is computationally expensive simultaneously search multiple causal variants. Third, confounding bias hidden in GWAS summary statistics produce spurious signals. To address these challenges, we develop method cross-population (XMAP) leveraging genetic diversity accounting bias. By using from global biobanks genomic consortia, show that XMAP achieve greater power, better control false positive rate, substantially higher computational efficiency identifying signals, compared Importantly, output be integrated with single-cell datasets, which greatly improves interpretation putative their cellular context at resolution.

Язык: Английский

Процитировано

14
A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis DOI Creative Commons
Xiang Li, Pak C. Sham, Yan Zhang

и другие.

The American Journal of Human Genetics, Год журнала: 2024, Номер 111(2), С. 213 - 226

Опубликована: Янв. 2, 2024

Язык: Английский

Процитировано

4
Improved multi-ancestry fine-mapping identifiescis-regulatory variants underlying molecular traits and disease risk DOI Creative Commons
Zeyun Lu, Xinran Wang, Matthew Carr

и другие.

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

Опубликована: Апрель 16, 2024

Abstract Multi-ancestry statistical fine-mapping of cis -molecular quantitative trait loci ( -molQTL) aims to improve the precision distinguishing causal -molQTLs from tagging variants. However, existing approaches fail reflect shared genetic architectures. To solve this limitation, we present Sum Shared Single Effects (SuShiE) model, which leverages LD heterogeneity precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE mRNA measured in PBMCs (n=956) LCLs (n=814) together with plasma protein levels (n=854) individuals diverse ancestries TOPMed MESA GENOA studies. find fine-maps for 16 % more genes compared baselines while prioritizing fewer variants greater functional enrichment. infers highly consistent -molQTL architectures across on average; however, also evidence at predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences sizes ancestries. Lastly, leverage estimated effect-sizes perform individual-level TWAS PWAS six white blood cell-related traits AOU Biobank (n=86k), identify 44 baselines, further highlighting its benefits identifying relevant complex disease risk. Overall, provides new insights into -genetic architecture molecular traits.

Язык: Английский

Процитировано

4
High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges DOI Creative Commons
Tao Cheng, Dongyan Zhang, Gan Zhang

и другие.

Artificial Intelligence in Agriculture, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data DOI

Xinyi Yu,

Xianghong Hu,

Xiaomeng Wan

и другие.

The American Journal of Human Genetics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Using omics data and genome editing methods to decipher GWAS loci associated with coronary artery disease DOI Creative Commons

Arnaud Chignon,

Guillaume Lettre

Atherosclerosis, Год журнала: 2025, Номер 401, С. 118621 - 118621

Опубликована: Фев. 1, 2025

Coronary artery disease (CAD) is due to atherosclerosis, a pathophysiological process that involves several cell-types and results in the accumulation of lipid-rich plaque disrupt normal blood flow through coronary arteries heart. Genome-wide association studies have identified 1000s genetic variants robustly associated with CAD or its traditional risk factors (e.g. pressure, lipids, type 2 diabetes, smoking). However, gaining biological insights from these discoveries remain challenging because linkage disequilibrium difficulty interpret functions non-coding regulatory elements human genome. In this review, we present different statistical methods Mendelian randomization) molecular datasets expression protein quantitative trait loci) helped connect CAD-associated genes, pathways, tissues. We emphasize various strategies make predictions, which need be validated orthologous systems. discuss specific examples where integration omics data GWAS has prioritized causal genes. Finally, review how targeted genome-wide genome editing experiments using CRISPR/Cas9 toolbox been used characterize new genes cells. Researchers now bioinformatic methods, datasets, experimental tools dissect comprehensively loci contribute humans.

Язык: Английский

Процитировано

0
Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies DOI
Zikun Yang, Chen Wang, Yuridia S. Posadas‐García

и другие.

The American Journal of Human Genetics, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain DOI Creative Commons
Xuewei Cao, Haochen Sun, Ru Feng

и другие.

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

Опубликована: Апрель 20, 2025

Abstract Multi-trait QTL (xQTL) colocalization has shown great promises in identifying causal variants with shared genetic etiology across multiple molecular modalities, contexts, and complex diseases. However, the lack of scalable efficient methods to integrate large-scale multi-omics data limits deeper insights into xQTL regulation. Here, we propose ColocBoost , a multi-task learning method that can scale hundreds traits, while accounting for within genomic region interest. employs specialized gradient boosting framework adaptively couple colocalized traits performing variant selection, thereby enhancing detection weaker signals compared existing pairwise multi-trait methods. We applied genome-wide 17 gene-level single-nucleus bulk from aging brain cortex ROSMAP individuals (average N = 595), encompassing 6 cell types, 3 regions modalities (expression, splicing, protein abundance). Across xQTLs, identified 16,503 distinct events, exhibiting 10.7(±0.74)-fold enrichment heritability 57 diseases/traits showing strong concordance element-gene pairs validated by CRISPR screening assays. When against Alzheimer’s disease (AD) GWAS, up 2.5-fold more loci, explaining twice AD fine-mapping without integration. This improvement is largely attributable ’s enhanced sensitivity detecting gene-distal colocalizations, as supported known enhancer-gene links, highlighting its ability identify biologically plausible susceptibility loci underlying regulatory mechanisms. Notably, several genes including BLNK CTSH showed sub-threshold associations but were through colocalizations which provide new functional support their involvement pathogenesis.

Язык: Английский

Процитировано

0
The goldmine of GWAS summary statistics: a systematic review of methods and tools DOI Creative Commons
Panagiota I. Kontou, Pantelis G. Bagos

BioData Mining, Год журнала: 2024, Номер 17(1)

Опубликована: Сен. 5, 2024

Язык: Английский

Процитировано

2