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.
Bioinformatics. 2021 Sep 29;37(18):3004-3007. doi: 10.1093/bioinformatics/btab122.
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.
PICS2 is available at https://pics2.ucsf.edu.
Supplementary data are available at Bioinformatics online.
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。
PICS2 可在 https://pics2.ucsf.edu 上获得。
补充数据可在生物信息学在线获得。