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Let’s Know about SNP Genotyping and Analysis

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Lucy Kart
Let’s Know about SNP Genotyping and Analysis

Single nucleotide polymorphisms or SNPs are the most common type of genetic variations among individuals. Advances in biotechnology has enabled scientists to genotype and analyze millions of SNPs across populations and individuals. SNP genotyping provides clues about human evolution, migration patterns, disease risk factors and personalized medicine.

SNP Genotyping Techniques

There are several high throughput techniques available now to genotype thousands to millions of SNPs simultaneously in a cost effective manner. Some of the commonly used techniques include:

Microarray Based Genotyping: Microarrays have been a workhorse platform for large scale SNP genotyping for over a decade. It uses DNA probes immobilized on a solid surface to assay for SNPs through hybridization. Several microarray chips like Affymetrix and Illumina arrays can genotype up to million SNPs in one go.

Sequencing Based Genotyping: Recent advances in next generation sequencing technologies have enabled direct low cost sequencing of whole genomes and exomes for SNP discovery and genotype calling. Platforms like Illumina NovaSeq and BGISEQ provide accurate SNP calling from sequencing data at very high throughput.

Luminex Based Genotyping: The Luminex xTAG system uses unique dye labeled microspheres to multiplex SNP Genotyping and Analysis. It provides medium throughput genotyping of hundred to thousands of SNPs simultaneously.

Other Techniques: Methods like SNaPshot, MassARRAY, pyrosequencing are also commonly used for targeted lower throughput genotyping of few hundred SNPs.

SNP Data Analysis

Several bioinformatics tools and statistical methods are applied to genotype data to gain meaningful biological insights:

Quality Control: Rigorous quality control is performed to filter out poor quality samples and SNPs based on criteria like call rate, heterozygosity and Hardy-Weinberg equilibrium.

Population Genetics Analysis: Tools like PLINK, EIGENSTRAT help analyze population structure, ancestry, linkage disequilibrium patterns, ancient migrations etc. in large reference populations.

Heritability & Genome Wide Association: GCTA, GEMMA, PLINK/SEQ are used to estimate trait heritability and identify genome wide significant trait associated SNPs through case control studies.

Functional Annotation: Plugins in ANNOVAR, VEP help annotating SNPs to their location, effect on genes, evolutionary conservation, regulatory regions etc. to prioritize potential functional SNPs.

Pharmacogenomics Analysis: Important pharmacogenes affecting drug response are interrogated for variants tied to drug efficacy, safety, dosage requirements in diverse populations.

Key Insights From SNP Studies

SNP studies in large reference cohorts like 1000 Genomes, UK Biobank and population specific biobanks have provided deep insights into human genetic variation, evolution, ancestry and disease associations:

- Population Structure: helped cluster human genetic diversity into continental populations, trace ancient human migrations like out of Africa.

- Disease & Trait Mapping: Over 15000 trait/disease associated variants identified influencing diseases like cancer, diabetes, mental illnesses through GWAS meta-analyses.

- Precision Medicine: Direct to consumer genetic tests, drug response biomarkers explored that can guide personalized prevention and treatment strategies.

- Ancestry & Forensics: Forensic databanks, direct-to-consumer genealogy tests developed based on ancestry informative marker panels.

- Evolution & Conservation: helped locate signatures of natural selection across human populations, understand genetic constraints shaping human evolution.


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