Research progress on single-cell expression quantitative trait loci.

Q3 Medicine 遗传 Pub Date : 2024-10-01 DOI:10.16288/j.yczz.24-162
Xiao-Peng Xu, Xiao-Ying Fan
{"title":"Research progress on single-cell expression quantitative trait loci.","authors":"Xiao-Peng Xu, Xiao-Ying Fan","doi":"10.16288/j.yczz.24-162","DOIUrl":null,"url":null,"abstract":"<p><p>Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"遗传","FirstCategoryId":"1091","ListUrlMain":"https://doi.org/10.16288/j.yczz.24-162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
单细胞表达定量性状位点的研究进展。
在后全基因组关联研究(GWAS)时代,eQTL分析已成为确定疾病相关遗传变异功能作用的重要方法,并产生了许多重大发现。传统的eQTL分析依赖于全基因组测序和大容量RNA-seq,这掩盖了细胞间基因表达的差异,因此无法识别细胞类型或状态依赖的eQTL。这一局限性使得阐明疾病相关基因变异在特定条件下的作用变得十分困难。近年来,随着单细胞RNA测序(scRNA-seq)技术的发展和广泛应用,基于scRNA-seq的eQTL(sc-eQTL)研究成为焦点。这种方法的优势在于它能够利用单细胞测序的分辨率和粒度,发现依赖于细胞类型、细胞状态和细胞动态的eQTL。这大大提高了我们分析与基因表达相关的遗传变异的能力。因此,这对推动我们了解复杂器官的形成以及疾病的发生、发展、干预和治疗机制具有重要意义。本综述全面探讨了 sc-eQTL 研究的最新进展,重点关注其发展、实验设计策略、建模方法和当前面临的挑战。目的是为研究人员提供新的视角,以确定疾病相关基因座并阐明基因调控机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
遗传
遗传 Medicine-Medicine (all)
CiteScore
2.50
自引率
0.00%
发文量
6699
期刊介绍:
期刊最新文献
Advancements and prospects in reconstructing the genetic genealogies of ancient and modern human populations using ancestral recombination graphs. Advances in high throughput sequencing methods for DNA damage and repair. Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer. Computational dissection of the regulatory mechanisms of aberrant metabolism in remodeling the microenvironment of breast cancer. Gut metagenome-derived image augmentation and deep learning improve prediction accuracy of metabolic disease classification.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1