Scholarlink

<p>The prevalence of synthetic associations in GWAS, where non-causal variants become significant by tagging multiple undetected causal variants and not necessarily in strong linkage disequilibrium with any single one, remains unexplored. We introduce a novel machine-learning approach using only genotype data to infer such associations in human GWAS. Our analysis reveals that 3–5% of GWAS Catalog peaks may represent potential synthetic associations, often arising from epistatic interactions between common variants rather than multiple rare variants acting independently. Our findings highlight the need for multi-locus models and emphasize careful GWAS interpretation and follow-up analyses like fine-mapping and trait prediction.</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

Potential synthetic associations created by epistasis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

查看全文
  • Hai-Jun Liu,
  • Jingxian Fu,
  • Shuhua Xu,
  • Magnus Nordborg

摘要

The prevalence of synthetic associations in GWAS, where non-causal variants become significant by tagging multiple undetected causal variants and not necessarily in strong linkage disequilibrium with any single one, remains unexplored. We introduce a novel machine-learning approach using only genotype data to infer such associations in human GWAS. Our analysis reveals that 3–5% of GWAS Catalog peaks may represent potential synthetic associations, often arising from epistatic interactions between common variants rather than multiple rare variants acting independently. Our findings highlight the need for multi-locus models and emphasize careful GWAS interpretation and follow-up analyses like fine-mapping and trait prediction.