Unlocking Rare Diseases Genetics: Insights from Genome-Wide Association Studies and Single Nucleotide Polymorphisms
Keywords:GWAS, Chronic diseases, Rare diseases, Genetic basis, Complex diseases, Personalized medicine, Genetic variants, Disease mechanisms.
Genome-wide association studies (GWAS) are powerful tools for identifying genetic variants associated with complex diseases. However, their utility is limited in elucidating the genetics of rare diseases due to these disorders typically involving low-frequency gene mutations. Single nucleotide polymorphisms (SNPs), which represent single base-pair variations in the genome, can provide valuable insights into the genetic architecture of rare diseases. Notably, specific SNPs within genes such as APP, PSEN1, PSEN2, APOE, TREM2, and ABCA7 have shed light on the molecular underpinnings of Alzheimer's disease (AD). An SNP in the APP gene, rs429358, correlates with increased Alzheimer's risk by altering amyloid beta production. Similarly, SNPs discovered via GWAS have linked loci to chronic obstructive pulmonary disease susceptibility (COPD), Fibrodysplasia ossificans progressive (FOP) and Hutchinson-Gilford progeria syndrome (HGPS) also demonstrate disease-causing mutations in ACVR1 and LMNA, respectively. However, conducting well-powered rare disease, GWAS presents difficulties due to challenges in recruiting large cohorts. The standard GWAS workflow involves patient enrollment, genomic DNA extraction, genotyping, and stringent quality control. Cases and controls are matched and analyzed using logistic regression or chi-squared tests, with corrections for multiple testing. Rare variant methods and imputation aim to bolster statistical power. Key obstacles include insufficient sample sizes, genetic heterogeneity, and rare causative variants. Mitigation strategies incorporate transnational consortia, family-based designs, functional analyses, next-generation sequencing, customized gene panels, and machine-learning approaches. Advancing GWAS will require increasingly large and diverse datasets, alongside novel statistical and high-throughput omic technologies to decipher the genetic roots of rare and complex pathologies.
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