by SNP Discovery and Genotyping
Single nucleotide polymorphisms, commonly known as SNPs, are variations that occur at a single nucleotide level in the genomic DNA sequence between individuals in a species. SNPs were first discovered in the late 1970s through early sequencing of DNA. However, it was not until the completion of the Human Genome Project in early 2000s that scientists were able to systematically identify SNPs across the entire human genome. Today, thanks to advances in high-throughput DNA sequencing technologies, millions of SNPs have been discovered and catalogued in databases for human and other species.
One of the main approaches used for SNP discovery involves resequencing genomic DNA from multiple individuals and comparing sequences to identify positions where an alternative nucleotide is present in some individuals. Commonly adopted methods for SNP genotyping and analysis include restriction fragment length polymorphism analysis, allele-specific hybridization, primer extension assays, DNA chips etc. These techniques allow scientists to simultaneously genotype hundreds to millions of SNPs in multiple DNA samples. Genotyping arrays containing known SNP loci have become an important tool for large-scale genome-wide association and population genomics studies.
Understanding Linkage Disequilibrium and Haplotypes
Due to genetic recombination occurring over multiple generations, SNP Genotyping and Analysis that are physically close to each other on a chromosome tend to be inherited together in blocks, a phenomenon known as linkage disequilibrium (LD). Haplotypes refer to specific combinations of alleles or variant forms that are linked and tend to be inherited together. Haplotype blocks delimited by recombination hotspots can range from a few kilobases to over 100 kilobases in size. By studying patterns of LD and haplotypes in populations, researchers can gain insights into historical recombination events and local ancestry. Haplotype blocks containing disease-associated SNPs also help narrow down the search for causal variants.
Population Genetics Analysis Using SNP Data
Genetic differences between human populations have been analyzed on the basis of thousands of genotyped SNPs. By correlating SNP allele frequencies with historical records of human migration patterns, it is possible to reconstruct aspects of human demographic history such as ancestral population splits, migrations and admixture events. Advanced statistical tools such as principal component analysis and admixture modeling programs have shown that continental populations cluster into distinct subgroups broadly correlated with their geographic origins. At finer scales, SNP data has revealed subtle population substructures even within geographic regions. These analyses are crucial for matching sample and reference populations in medical association studies.
Applications in Agriculture, Conservation Biology and Forensics
After humans, livestock animals and crop plants have been most extensively studied using SNPs. Marker-trait association studies involving DNA collection from geographically diverse breeds have identified causal polymorphisms influencing economically important agricultural traits. This knowledge accelerates genetic improvement through marker-assisted selection. Conservation genetic studies use SNPs to estimate effective population sizes, degree of inbreeding, and identify distinct genetic clusters within threatened species. In forensics, SNP-based kinship analysis can provide valuable leads in missing persons investigations. Multi-allelic STR markers are being supplemented or replaced by SNPs for individual identification in criminal cases.
Challenges and Future Directions of SNP Genotyping and Analysis
While millions of SNPs have been discovered, a substantial fraction of genetic variation still remains uncharacterized, especially for non-model organisms. Reference genome assemblies are often incomplete. Gene-centric assays are required to assess functional impacts of variants in non-coding regulatory regions. Developing cost-effective methods for whole-genome sequencing in large cohorts is important for comprehensive surveys of sequence variation across global populations. Advancing imputation methods that infer ungenotyped variants will help leverage existing reference panels. With continuing reductions in sequencing costs, the applications of genetic variation data in biomedicine, anthropology and other fields will significantly grow in the coming years.
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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
