• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种分析相关性状共享遗传背景的新框架。

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

机构信息

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia.

Vavilov Institute of General Genetics, Russian Academy of Sciences, 117971 Moscow, Russia.

出版信息

Genes (Basel). 2022 Sep 21;13(10):1694. doi: 10.3390/genes13101694.

DOI:10.3390/genes13101694
PMID:36292579
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9602050/
Abstract

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

摘要

我们提出了一个新颖的有效框架，用于使用 SNP 水平 GWAS 汇总统计信息分析一组遗传相关性状的共享遗传背景。这个名为 SHAHER 的框架基于通过最大化由共享遗传因素解释的性状遗传方差比例来构建性状的线性组合。SHAHER 仅需要完整的 GWAS 汇总统计信息和性状之间的遗传和表型相关性矩阵作为输入。我们的框架允许有效分析共享和非共享遗传因素。我们使用模拟研究对我们的框架进行了测试，将其与以前的研究进展进行了比较，并使用三个真实数据集评估了其性能：人体测量性状、精神状况和脂质浓度。SHAHER 具有通用性，适用于具有任意样本量和样本重叠的 GWAS 汇总统计信息，允许合并不同的 GWAS 模型（Cox、线性和逻辑），并且计算速度快。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/2bb720003271/genes-13-01694-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/7cae33702a7e/genes-13-01694-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/074f6c704c29/genes-13-01694-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/be5912613ae3/genes-13-01694-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/2bb720003271/genes-13-01694-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/7cae33702a7e/genes-13-01694-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/074f6c704c29/genes-13-01694-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/be5912613ae3/genes-13-01694-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47da/9602050/2bb720003271/genes-13-01694-g004.jpg

相似文献

1
A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.一种分析相关性状共享遗传背景的新框架。
Genes (Basel). 2022 Sep 21;13(10):1694. doi: 10.3390/genes13101694.
2
Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data.利用 GWAS 汇总数据对多种表型进行强大且高效的 SNP 集关联测试。
Bioinformatics. 2019 Apr 15;35(8):1366-1372. doi: 10.1093/bioinformatics/bty811.
3
An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.一种利用全基因组关联研究汇总统计数据对多种表型进行的适应性关联测试。
Genet Epidemiol. 2015 Dec;39(8):651-63. doi: 10.1002/gepi.21931. Epub 2015 Oct 22.
4
Methods for meta-analysis of multiple traits using GWAS summary statistics.使用全基因组关联研究（GWAS）汇总统计量进行多性状荟萃分析的方法。
Genet Epidemiol. 2018 Mar;42(2):134-145. doi: 10.1002/gepi.22105. Epub 2017 Dec 10.
5
Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach.利用 GWAS 汇总数据和自适应检验方法整合多种性状，以检测新的性状-基因关联。
Bioinformatics. 2019 Jul 1;35(13):2251-2257. doi: 10.1093/bioinformatics/bty961.
6
PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.PLEIO：一种使用 GWAS 汇总统计数据进行基因多效性位点映射和解释的方法。
Am J Hum Genet. 2021 Jan 7;108(1):36-48. doi: 10.1016/j.ajhg.2020.11.017. Epub 2020 Dec 21.
7
LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.LD Hub：一个集中式数据库和网络界面，用于执行连锁不平衡（LD）评分回归，最大限度地发挥汇总水平全基因组关联研究（GWAS）数据在单核苷酸多态性（SNP）遗传力和遗传相关性分析方面的潜力。
Bioinformatics. 2017 Jan 15;33(2):272-279. doi: 10.1093/bioinformatics/btw613. Epub 2016 Sep 22.
8
SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.SumVg：基于具有标准误差估计的汇总统计数据的全基因组关联研究中所有变异解释的总遗传力。
Int J Mol Sci. 2024 Jan 22;25(2):1347. doi: 10.3390/ijms25021347.
9
Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data.基于汇总关联数据对比30种复杂性状的遗传结构
Am J Hum Genet. 2016 Jul 7;99(1):139-53. doi: 10.1016/j.ajhg.2016.05.013. Epub 2016 Jun 23.
10
Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics.利用 GWAS 汇总统计数据估计复杂性状遗传相关性的方法比较。
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa442.

引用本文的文献

1
A multi-trait approach identified 7 novel genes for back pain.一种多性状方法鉴定出了7个与背痛相关的新基因。
Pain Rep. 2024 Dec 24;10(1):e1218. doi: 10.1097/PR9.0000000000001218. eCollection 2025 Feb.
2
The goldmine of GWAS summary statistics: a systematic review of methods and tools.全基因组关联研究汇总统计数据的宝库：方法与工具的系统综述
BioData Min. 2024 Sep 5;17(1):31. doi: 10.1186/s13040-024-00385-x.
3
Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes.多特征外显子组关联研究与腰痛相关表型。

本文引用的文献

1
Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging.对基因独立的衰老表型进行孟德尔随机化分析，确定脂蛋白A和血管细胞黏附分子1为人类衰老的生物学靶点。
Nat Aging. 2022 Jan;2(1):19-30. doi: 10.1038/s43587-021-00159-8. Epub 2022 Jan 20.
2
Nontrivial Replication of Loci Detected by Multi-Trait Methods.多性状方法检测到的基因座的重要复制
Front Genet. 2021 Feb 3;12:627989. doi: 10.3389/fgene.2021.627989. eCollection 2021.
3
Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions.
Genes (Basel). 2023 Oct 19;14(10):1962. doi: 10.3390/genes14101962.
4
A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics.基于 GWAS 汇总统计数据的多表型关联研究的聚类线性组合方法。
Sci Rep. 2023 Feb 28;13(1):3389. doi: 10.1038/s41598-023-30415-3.
分析遗传上独立的表型可识别与慢性肌肉骨骼疼痛疾病相关的共享遗传因素。
Commun Biol. 2020 Jun 25;3(1):329. doi: 10.1038/s42003-020-1051-9.
4
Genetic evidence for assortative mating on alcohol consumption in the UK Biobank.遗传证据表明英国生物库中存在与饮酒相关的选择性交配。
Nat Commun. 2019 Nov 19;10(1):5039. doi: 10.1038/s41467-019-12424-x.
5
Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.多变量全基因组关联研究的补充方法鉴定血细胞性状的新易感基因。
Front Genet. 2019 Apr 26;10:334. doi: 10.3389/fgene.2019.00334. eCollection 2019.
6
Genome-wide association study identifies 30 loci associated with bipolar disorder.全基因组关联研究确定了 30 个与双相情感障碍相关的位点。
Nat Genet. 2019 May;51(5):793-803. doi: 10.1038/s41588-019-0397-8. Epub 2019 May 1.
7
Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error.基于交叉验证预测误差的关联研究中多种表型的联合分析
Sci Rep. 2019 Jan 31;9(1):1073. doi: 10.1038/s41598-018-37538-y.
8
Shared heritability and functional enrichment across six solid cancers.六种实体瘤的共享遗传率和功能富集。
Nat Commun. 2019 Jan 25;10(1):431. doi: 10.1038/s41467-018-08054-4.
9
Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry.对 694649 名欧洲血统个体的体脂分布进行全基因组关联研究的荟萃分析。
Hum Mol Genet. 2019 Jan 1;28(1):166-174. doi: 10.1093/hmg/ddy327.
10
Analysis of shared heritability in common disorders of the brain.脑常见疾病的遗传共享分析。
Science. 2018 Jun 22;360(6395). doi: 10.1126/science.aap8757.