Final answer:
The genome-wide polygenic score procedure uses genome-wide association studies to compare the DNA of individuals with and without a disease to identify relevant SNPs. These SNPs are used to calculate an individual's genetic risk for certain diseases, helping in personalized medicine and disease research.
Step-by-step explanation:
The procedure of a genome-wide polygenic score (GPS) involves several steps to predict the risk of developing certain polygenic diseases based on genetic constitution. This process starts by performing a genome-wide association study (GWAS), where two groups of individuals are compared, one with a particular disease and one without. The DNA samples from these groups are analyzed to identify differences in single nucleotide polymorphisms (SNPs). The SNPs associated with the disease are then used to calculate the GPS by summing up the effects of thousands or even millions of SNPs across the entire genome. The score represents the cumulative contribution of genetic variants to the individual's trait or disease risk. These scores can be applied to identify individuals at higher risk for diseases, which is integral to personalized medicine and health management.
Genome mapping is essential in this process, providing the big picture of the location of genes and genetic markers on chromosomes. Genetic mapping, by highlighting distances between genes based on recombination frequencies, and physical mapping, by elucidating precise physical distances in nucleotides, both contribute to the creation of a comprehensive view of the genome. This detailed view is crucial for the accuracy of GWAS, as it allows researchers to pinpoint specific SNPs associated with diseases and traits.
Pharmacogenomics is an application of genomics focused on understanding how an individual's genetic make-up affects their response to drugs. GPS findings provide insights that help tailor medication to maximize efficacy and minimize side effects. Moreover, the genetic basis of polygenic diseases like heart disease or cystic fibrosis can be explored through this method, driving further medical research and therapy development targeted at these complex illnesses.