Final answer:
The use of independently associated SNPs versus SNPs in linkage disequilibrium in GWAS depends on the research focus. Pruning SNPs in LD is common to reduce data redundancy, whereas using SNPs in LD can be useful for identifying inherited groups of SNPs and wider genomic regions of interest. The choice is guided by whether the aim is fine-mapping genetic variants, assessing disease susceptibility, or studying evolutionary patterns.
Step-by-step explanation:
The choice between using independently associated SNPs (single nucleotide polymorphisms) and SNPs in linkage disequilibrium (LD) in genome-wide association studies (GWAS) depends on the scientific question at hand. When studying complex diseases that might be influenced by multiple genetic variations, GWAS use SNPs to pinpoint the differences between affected individuals and control groups. Researchers may prune SNPs in LD to reduce redundancy and focus on the most information-rich SNPs when looking for associations that might be directly causative of disease traits.
However, when there is a need to capture the full genetic variation and structure within the genome, SNPs in LD can be very informative. Linkage disequilibrium is helpful for identifying haplotypes, groups of SNPs that are inherited together, which can point to regions of the genome that contribute to the disease but may not be directly causative. In contrast, for fine-mapping studies aiming to identify the specific loci contributing to a trait or disease, researchers might focus on independently associated SNPs to pinpoint the exact genetic variants involved.
Finally, the selection of SNPs may also be influenced by whether the goal is to assess the genetic susceptibility to certain diseases for diagnostics or therapeutic developments, or to identify evolutionary patterns in a population. Therefore, the usage of SNPs in LD or those independently associated is not a one-size-fits-all approach but should be tailored to the study's goals.