Scholarlink

<p>Genetic sequencing technologies are powerful tools for identifying rare variants and genes associated with Mendelian and complex traits; indeed, whole-exome and whole-genome sequencing are increasingly popular methods for population-scale genetic studies. However, careful quality control steps should be taken to ensure study accuracy and reproducibility, and sequencing data require extensive quality filtering to delineate true variants from technical artifacts. Although processing standards are harmonized across pipelines to call variants from sequencing reads, there currently exists no standardized pipeline for conducting quality filtering on variant-level datasets for the purpose of population-scale association analysis. In this Tutorial, we discuss key quality control parameters, provide guidelines for conducting quality filtering of samples and variants, and compare commonly used software programs for quality control of samples, variants and genotypes from sequencing data. As sequencing data continue to gain popularity in genetic research, establishing standardized quality control practices is crucial to ensure consistent, reliable and reproducible results across studies.</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

Tutorial: guidelines for quality filtering of whole-exome and whole-genome sequencing data for population-scale association analyses

  • 期刊论文

  • 发表日期：

    2025年3月28日

查看全文
  • Julia M. Sealock,
  • Franjo Ivankovic,
  • Calwing Liao,
  • Siwei Chen,
  • Claire Churchhouse,
  • Konrad J. Karczewski,
  • Daniel P. Howrigan,
  • Benjamin M. Neale

摘要

Genetic sequencing technologies are powerful tools for identifying rare variants and genes associated with Mendelian and complex traits; indeed, whole-exome and whole-genome sequencing are increasingly popular methods for population-scale genetic studies. However, careful quality control steps should be taken to ensure study accuracy and reproducibility, and sequencing data require extensive quality filtering to delineate true variants from technical artifacts. Although processing standards are harmonized across pipelines to call variants from sequencing reads, there currently exists no standardized pipeline for conducting quality filtering on variant-level datasets for the purpose of population-scale association analysis. In this Tutorial, we discuss key quality control parameters, provide guidelines for conducting quality filtering of samples and variants, and compare commonly used software programs for quality control of samples, variants and genotypes from sequencing data. As sequencing data continue to gain popularity in genetic research, establishing standardized quality control practices is crucial to ensure consistent, reliable and reproducible results across studies.