Scholarlink

<p>Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with phenotypic traits. However, SNPs form an incomplete set of variation across the genome and since a large percentage of GWAS-significant SNPs lie in non-coding regions, their impact on a given trait is difficult to decipher. Recognizing whether these SNPs are tagging other polymorphisms, like structural variations (SV), is an important step towards understanding the putative causal variation at GWAS loci. Here, we develop GWAS SVatalog (<a href="https://svatalog.research.sickkids.ca/">https://svatalog.research.sickkids.ca/</a>), a novel open-source web tool that computes and visualizes linkage disequilibrium (LD) between SVs and GWAS-associated SNPs throughout the human genome. The tool combines GWAS Catalog’s SNP-trait association data across 14,479 phenotypes with LD statistics calculated between 35,732 SVs and 116,870 SNPs identified in 101 whole-genome long-read sequences. We show that different SV types are more likely to overlap regulatory features, and that SVs less directly tagged by GWAS-associated SNPs more frequently overlap CpG islands and promoters. We use GWAS SVatalog to identify SVs that may explain GWAS loci for iron levels, refractive error, and Alzheimer’s disease, where previously SNPs were unable to provide a causal explanation. GWAS SVatalog advances the fine-mapping of GWAS loci with structural variations, enabling researchers to associate 35,732 common SVs with 14,479 phenotypes, accelerating the understanding of disease etiology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

查看全文
  • Shalvi Chirmade,
  • Zhuozhi Wang,
  • Scott Mastromatteo,
  • Eric Sanders,
  • Bhooma Thiruvahindrapuram,
  • Thomas Nalpathamkalam,
  • Giovanna Pellecchia,
  • Fan Lin,
  • Katherine Keenan,
  • Rohan V. Patel,
  • Wilson WL Sung,
  • Delnaz Roshandel,
  • Joe Whitney,
  • Sana Allana,
  • Julie Avolio,
  • Paul DW Eckford,
  • Felix Ratjen,
  • Lisa J. Strug

摘要

Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with phenotypic traits. However, SNPs form an incomplete set of variation across the genome and since a large percentage of GWAS-significant SNPs lie in non-coding regions, their impact on a given trait is difficult to decipher. Recognizing whether these SNPs are tagging other polymorphisms, like structural variations (SV), is an important step towards understanding the putative causal variation at GWAS loci. Here, we develop GWAS SVatalog (https://svatalog.research.sickkids.ca/), a novel open-source web tool that computes and visualizes linkage disequilibrium (LD) between SVs and GWAS-associated SNPs throughout the human genome. The tool combines GWAS Catalog’s SNP-trait association data across 14,479 phenotypes with LD statistics calculated between 35,732 SVs and 116,870 SNPs identified in 101 whole-genome long-read sequences. We show that different SV types are more likely to overlap regulatory features, and that SVs less directly tagged by GWAS-associated SNPs more frequently overlap CpG islands and promoters. We use GWAS SVatalog to identify SVs that may explain GWAS loci for iron levels, refractive error, and Alzheimer’s disease, where previously SNPs were unable to provide a causal explanation. GWAS SVatalog advances the fine-mapping of GWAS loci with structural variations, enabling researchers to associate 35,732 common SVs with 14,479 phenotypes, accelerating the understanding of disease etiology.