Click on a button corresponding to a genome-wide association studies (GWAS) process. Refer to the table below and the Comparison of Association Testing Processes for guidance.
|
Quickly and easily performing association mapping of a binary trait (with two generic levels case and control) using a chi-square test on genetic marker data
|
|
|
Exploring and revealing population structure based on SNP genotypes for a sample; or, adjusting for population stratification or allele frequency variation due to ancestral differences in association tests
|
|
|
Testing association between various types of traits and individual SNPs, in a modeling framework allowing fixed and random effect adjustment, in large whole genomes or other very large data sets
Tip: For studies involving multi-allelic markers, use Marker-Trait Association. Where SNP genotypes are imputed, use Imputed SNP-Trait Association. For complex survey designs, choose Survey SNP-Trait Association.
|
|
|
Testing association between various types of traits and individual SNPs, taking into account the uncertainty of SNP genotype imputation
Note: Unlike other genetic association processes, this process requires a stacked input data set.
|
|
|
Testing association between various types of traits and SNP genotypes or alleles from a single SNP at a time, taking into account complex survey designs
|
|
|
Performing association analysis of family data, using one or more quantitative transmission disequilibrium tests, to map binary and quantitative traits
|
|
|
Performing association analysis of family data, using one or more family association tests, to map binary traits, and to facilitate the analysis of X-linked markers
|
|
|
Performing a meta-analysis of genome-wide association studies by combining p-values or effects for a SNP from multiple studies and calculating a combined p-value
|
See Other Association Testing for additional association testing processes.