Single nucleotide polymorphisms or SNPs are single base pairs variations in DNA sequences that occur naturally in human genomes. They are the most common type of genetic variations and small differences in DNA sequences that can have significant medical, biological and clinical importance. SNP genotyping and analysis helps in understanding these DNA variations and their impact on human traits and diseases.

Methods of SNP Genotyping

There are various molecular methods that are commonly employed for SNP genotyping:

Array based methods

Microarrays have become the standard method for large-scale SNP genotyping. High density SNP Genotyping and Analysis arrays contain hundreds of thousands of probes designed to hybridize genomic DNA samples at specific SNP loci. When hybridized to arrays, SNPs are detected based on differences in fluorescence intensity indicating which allele is present. Chips from Illumina and Affymetrix are widely used.

NGS Based Methods

Next generation DNA sequencing technologies like Illumina HiSeq, MiSeq and Ion Torrent have enabled rapid and cost-effective whole genome and targeted resequencing approaches for genome-wide SNP detection. Deep sequencing depth allows SNPs to be identified from comparisons to a reference genome.

TaqMan SNP Genotyping Assays

Real-time PCR based TaqMan SNP genotyping assays employ fluorescent probes specific to each allele. During PCR amplification of the targeted SNP site, probes are cleaved and a signal is emitted depending on which allele is present. Post-PCR fluorescent end point analysis determines genotype calls.

High Resolution Melting Analysis

This technique utilizes the saturating DNA dye SYBR Green I which fluoresces only when bound to double stranded DNA. Changes in the melting curves of amplicons correspond to sequence variations like SNPs. Software analysis enables genotype discrimination.

Applications of SNP Genotyping

SNP genotyping data has been utilized extensively in biomedical research and diagnostics:

Disease Association Studies

Case-control based genome-wide association studies using SNP arrays have linked thousands of genetic variants to common diseases and drug responses. Meta-analyses of large cohorts have provided insights into disease pathogenesis.

Pharmacogenomics

Correlating genetic variations with drug responses and toxicity profiles helps in developing personalized therapies. SNPs associated with drug metabolism and targets influence treatment selection.

Ancestry and Population History

SNPs that show differences in allele frequencies between populations have proven useful in reconstructing human evolutionary histories, tracing ancient migrations and admixed ancestries of current populations.

Complex Trait Analysis

SNPs contribute to variations seen in complex quantitative traits like height, BMI, disease risks etc. Genome-wide profiling is being used to understand genetics of such multifactorial traits and genes involved.

Cancer Genomics

Somatic mutation profiling from tumor biopsies assists in characterizing molecular subtypes of cancers, detecting early signs, disease monitoring and targeted therapy selection based on mutational landscapes.

SNP Analysis and Interpretation

Proper analysis and interpretation of the massive data generated from genome-wide SNP genotyping is crucial:

Quality Control

Samples and SNPs with low call rates or deviations from HWE are excluded to avoid false positives. Population stratification is corrected.

Linkage Disequilibrium Analysis

Patterns of LD between SNPs help in identifying tags that capture most common haplotypes, reducing redundancy and improving imputation accuracy.

Genotype Imputation

Statistical methods impute untyped SNPs based on LD in reference panels to improve marker coverage for increased power in association testing.

Association Testing

Powerful tools like PLINK, EIGENSTRAT, SNPTEST, GEMMA etc. employ techniques like chi-square tests, logistic/linear regression, controlling for covariates to identify disease-associated SNPs.

Functional Annotation

Correlating disease variants or expression quantitative trait loci with functional genomic data assists interpretation on putative functional effects and modes of pathogenicity.

Pathway Analysis

Statistical over-representation tests maps sets of significant SNPs onto pathways/networks to derive biological insights into disease mechanisms.

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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it