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

Abstract

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Generic Platform for the Multiplexed Targeted Electrochemical Detection of Osteoporosis-Associated Single Nucleotide Polymorphisms Using Recombinase Polymerase Solid-Phase Primer Elongation and Ferrocene-Modified Nucleoside Triphosphates

“…A variety of techniques have been developed for SNP genotyping, including electrophoresis systems such as the cleaved amplified polymorphic sequence (CAPS), derived CAPS (dCAPS), and allele-specific (AS-PCR) . High-throughput SNP-genotyping technologies have been developed and are employed, including the gene chip microarray and the competitive allele-specific PCR-based KAPS platform, and genotyping can also be achieved by sequencing . The first SNP array was developed by the Whiteside Institute together with Affymetrix and was designed to simultaneously detect almost 1500 SNPs, and there are now hundreds of customizable SNP chips available.…”

Cytogenomic epileptology

“…These studies generally focus on disentangling the genomic sources for epilepsy as a symptom [3,9]. Additionally, searching for CNVs associated with idiopathic neurodevelopmental disorders allows the determination of causative variations in epileptic cases [10][11][12]. Therefore, it is not surprising that cytogenomic variations manifesting as individual CNVs or CNV burdens are more profoundly studied as to chromosomal abnormalities in the molecular genetic context.…”

High performance imputation of structural and single nucleotide variants in Atlantic salmon using low-coverage whole genome sequencing

“…The use of whole genome sequencing (WGS) is increasing due to reducing costs and accessibility of sequencing technologies. Nonetheless, WGS, performed at the depths required to accurately detect genetic variants, remains more expensive than strategies that genotype a reduced set of variants, typically single nucleotide variants (SNV); including arrays (Balagué-Dobón et al , 2022) and reduced representation sequencing (Davey et al , 2011). Consequently, WGS remains less commonly used for large-scale genotyping applications like genome-wide association studies (GWAS).…”