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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
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A Bioinformatics Crash Course for Interpreting Genomics Data.

Daniel M Rotroff1

  • 1Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.

Chest
|July 14, 2020
PubMed
Summary

Genome-wide association studies (GWAS) are common for complex diseases. This guide reviews bioinformatics best practices for planning and evaluating GWAS to ensure reliable results.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are increasingly standard for complex disease research due to reduced genotyping costs and enhanced computational power.
  • GWAS involve assessing genetic variants across the genome to identify associations with specific traits.
  • Evolving best practices are crucial for ensuring the scientific rigor and reliability of GWAS conclusions.

Purpose of the Study:

  • To provide a concise summary of key bioinformatics considerations for planning and evaluating genome-wide association studies (GWAS).
  • To serve as a guide for researchers without deep expertise in bioinformatics and GWAS.
  • To equip readers with tools for critically evaluating GWAS methodologies and results.

Main Methods:

Keywords:
bioinformaticsgenomicsstatistics

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  • Review of current best practices in bioinformatics for genome-wide association studies.
  • Identification of potential sources of bias in GWAS.
  • Development of a checklist for evaluating GWAS quality and adherence to best practices.

    • Main Results:

    • The article outlines essential bioinformatics considerations for designing and interpreting GWAS.
    • It highlights common pitfalls and biases that can affect study outcomes.
    • A practical checklist is provided to aid researchers in assessing GWAS quality.

    Conclusions:

    • Adherence to bioinformatics best practices is essential for robust and reliable genome-wide association studies.
    • Critical evaluation of GWAS, guided by established methodologies, is necessary for advancing complex disease research.
    • This review and checklist offer valuable resources for researchers navigating the complexities of GWAS.