GWASAnalysen

GWASAnalysen describe analyses conducted in genome-wide association studies to identify genetic variants associated with traits or diseases across the genome. The primary unit of analysis is the SNP, though other variants and haplotypes can be considered. These analyses rely on large cohorts with genome-wide genotype data and measured phenotypes.

Typical workflows include rigorous quality control, adjustments for population structure and relatedness, and variant-wise statistical tests. For binary outcomes, logistic regression is common; for quantitative traits, linear regression is used. Because millions of tests are performed, genome-wide significance thresholds (about p<5×10^-8) and thorough checks for inflation via QQ plots are standard.

Outputs comprise effect estimates, standard errors, p-values, and summary statistics; visualizations include Manhattan and QQ plots. Downstream analyses may involve fine-mapping, gene-based tests, pathway analyses, and the construction of polygenic risk scores. Findings can illuminate biology and point to potential drug targets, though translation requires replication and functional validation.

Limitations include small effect sizes, confounding by population structure, phenotypic heterogeneity, and limited transferability across populations. Large sample sizes and replication in independent cohorts are essential. Common tools include PLINK, BOLT-LMM, and SAIGE; data are typically hosted in biobanks or research consortia with ethical data-sharing frameworks.