Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Single nucleotide polymorphisms (SNPs) are abundant genomic variations but individually explain little phenotypic variance.
• Genomic divergence is influenced by factors like ancestry, structural variants, and recombination history.
• SNPs can indirectly reveal these complex genetic differences in large populations.

Purpose of the Study:

• To present a comprehensive collection of bioinformatics tools for analyzing SNP array data.
• To extract diverse genomic information beyond simple SNP genotyping.
• To aid researchers in leveraging publicly available SNP data for deeper genetic insights.

Main Methods:

• Systematic review of bioinformatics tools for SNP array data analysis.
• Categorization of tools into R packages, command-line, and desktop applications.
• Evaluation of tools for analyzing population structure, ancestry, polygenic risk scores, IBD, LD, heritability, and structural variants.

Main Results:

• Identified and described various bioinformatics tools for SNP array data analysis.
• Highlighted tools capable of detecting inversions, copy number variants, mosaicisms, and recombination histories.
• Provided an overview of free and commercial software options.

Conclusions:

• SNP array data can be utilized to study complex genomic structures beyond individual SNPs.
• A wide array of bioinformatics tools are available to extract this information.
• These tools are crucial for understanding the genetic basis of phenotypic variation.