singlemarker

Singlemarker is a term used in genetics to refer to the practice of testing a single genetic marker, typically a single nucleotide polymorphism (SNP), for association with a phenotype. It is the simplest and most common form of association testing used in genome-wide association studies (GWAS).

Methodologically, singlemarker analyses test each marker independently. For quantitative traits, linear regression is used with the genotype dosage (0, 1, 2 copies of the reference allele) as the predictor, adjusting for covariates such as age, sex, and population structure. For binary traits, logistic regression is employed. The additive genetic model is standard, but alternative models (dominant or recessive) can be explored. Output includes an effect size (beta or odds ratio), standard error, and a p-value. Quality control steps often exclude markers with low minor allele frequency, high missingness, or deviation from Hardy–Weinberg equilibrium.

Statistical considerations are central to singlemarker testing. The large number of markers tested in GWAS requires stringent multiple testing correction, commonly Bonferroni correction or false discovery rate control. Population stratification can confound results and is addressed by covariates or mixed-model approaches that account for relatedness and structure.

Data and tools underpinning singlemarker tests come from genotyping arrays or sequencing, frequently with genotype imputation to increase marker density. Widely used software includes PLINK, SNPTEST, and mixed-model tools like GEMMA or SAIGE, which support single-marker tests and covariate adjustment.

Limitations of singlemarker analyses include the potential to miss combined effects across neighboring markers or interactions. They may also be influenced by linkage disequilibrium, making fine-mapping and multi-marker approaches important follow-ups. See also GWAS, haplotype tests, fine-mapping, and imputation.