Suppr超能文献

PICS2:通过因果 SNP 的概率识别进行下一代精细映射。

PICS2: next-generation fine mapping via probabilistic identification of causal SNPs.

机构信息

Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California San Francisco, CA, USA.

Department of Microbiology and Immunology, University of California, San Francisco, CA, USA.

出版信息

Bioinformatics. 2021 Sep 29;37(18):3004-3007. doi: 10.1093/bioinformatics/btab122.

Abstract

SUMMARY

The Probabilistic Identification of Causal SNPs (PICS) algorithm and web application was developed as a fine-mapping tool to determine the likelihood that each single nucleotide polymorphism (SNP) in LD with a reported index SNP is a true causal polymorphism. PICS is notable for its ability to identify candidate causal SNPs within a locus using only the index SNP, which are widely available from published GWAS, whereas other methods require full summary statistics or full genotype data. However, the original PICS web application operates on a single SNP at a time, with slow performance, severely limiting its usability. We have developed a next-generation PICS tool, PICS2, which enables performance of PICS analyses of large batches of index SNPs with much faster performance. Additional updates and extensions include use of LD reference data generated from 1000 Genomes phase 3; annotation of variant consequences; annotation of GTEx eQTL genes and downloadable PICS SNPs from GTEx eQTLs; the option of generating PICS probabilities from experimental summary statistics; and generation of PICS SNPs from all SNPs of the GWAS catalog, automatically updated weekly. These free and easy-to-use resources will enable efficient determination of candidate loci for biological studies to investigate the true causal variants underlying disease processes.

AVAILABILITY AND IMPLEMENTATION

PICS2 is available at https://pics2.ucsf.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

摘要

开发了概率识别因果 SNPs(PICS)算法和网络应用程序,作为精细映射工具,以确定与报告的索引 SNP 处于 LD 状态的每个单核苷酸多态性(SNP)成为真正因果多态性的可能性。PICS 的显著特点是它能够仅使用索引 SNP 来识别基因座内的候选因果 SNP,而其他方法需要完整的汇总统计信息或完整的基因型数据。然而,原始的 PICS 网络应用程序一次只能处理单个 SNP,性能缓慢,严重限制了其可用性。我们开发了下一代 PICS 工具 PICS2,它能够大大提高性能,对大量索引 SNP 进行 PICS 分析。其他更新和扩展包括使用 1000 基因组计划第 3 阶段生成的 LD 参考数据;变异后果注释;GTEx eQTL 基因注释和可下载的 GTEx eQTL 的 PICS SNPs;从实验汇总统计数据生成 PICS 概率的选项;以及从 GWAS 目录中的所有 SNPs 自动每周更新生成 PICS SNPs。这些免费且易于使用的资源将能够有效地确定生物研究的候选基因座,以研究疾病过程背后的真正因果变异。

可用性和实施

PICS2 可在 https://pics2.ucsf.edu 上获得。

补充信息

补充数据可在生物信息学在线获得。

相似文献

1
PICS2: next-generation fine mapping via probabilistic identification of causal SNPs.
Bioinformatics. 2021 Sep 29;37(18):3004-3007. doi: 10.1093/bioinformatics/btab122.
2
CandiSNPer: a web tool for the identification of candidate SNPs for causal variants.
Bioinformatics. 2010 Apr 1;26(7):969-70. doi: 10.1093/bioinformatics/btq068. Epub 2010 Feb 19.
4
Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification.
PLoS Genet. 2013;9(8):e1003609. doi: 10.1371/journal.pgen.1003609. Epub 2013 Aug 8.
5
Estimating colocalization probability from limited summary statistics.
BMC Bioinformatics. 2021 May 17;22(1):254. doi: 10.1186/s12859-021-04170-z.
6
Improved methods for multi-trait fine mapping of pleiotropic risk loci.
Bioinformatics. 2017 Jan 15;33(2):248-255. doi: 10.1093/bioinformatics/btw615. Epub 2016 Sep 22.
7
JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.
Bioinformatics. 2015 Apr 15;31(8):1176-82. doi: 10.1093/bioinformatics/btu816. Epub 2014 Dec 12.
8
Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping.
Mol Genet Genomics. 2023 Nov;298(6):1309-1319. doi: 10.1007/s00438-023-02055-9. Epub 2023 Jul 27.
9
HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.
Bioinformatics. 2017 Jan 1;33(1):79-86. doi: 10.1093/bioinformatics/btw565. Epub 2016 Sep 1.
10
Endometrial vezatin and its association with endometriosis risk.
Hum Reprod. 2016 May;31(5):999-1013. doi: 10.1093/humrep/dew047. Epub 2016 Mar 22.

引用本文的文献

1
A haplotype-resolved view of human gene regulation.
bioRxiv. 2025 Jun 2:2024.06.14.599122. doi: 10.1101/2024.06.14.599122.
3
AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.
NAR Genom Bioinform. 2025 May 5;7(2):lqaf038. doi: 10.1093/nargab/lqaf038. eCollection 2025 Jun.
5
The goldmine of GWAS summary statistics: a systematic review of methods and tools.
BioData Min. 2024 Sep 5;17(1):31. doi: 10.1186/s13040-024-00385-x.
6
A genome-wide association study identifies novel loci of vertigo in an Asian population-based cohort.
Commun Biol. 2024 Aug 22;7(1):1034. doi: 10.1038/s42003-024-06603-w.
7
Identification of an novel genetic variant associated with osteoporosis: insights from the Taiwan Biobank Study.
JBMR Plus. 2024 Mar 5;8(5):ziae028. doi: 10.1093/jbmrpl/ziae028. eCollection 2024 May.
9
Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease.
Mol Neurobiol. 2024 Aug;61(8):5337-5352. doi: 10.1007/s12035-023-03903-w. Epub 2024 Jan 8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验