SPA_GRM is now implemented in the GRAB package. Please click here for download.

SPA_GRM is a scalable, accurate, and universal analysis framework to control for sample relatedness in large-scale genome-wide association studies (GWAS). In the paper A scalable, accurate, and universal analysis framework to control for sample relatedness in large-scale genome-wide association studies and its application to 79 longitudinal traits in UK Biobank (to be updated), we applied SPA_GRM to analyze 79 longitudinal traits extracted from UK Biobank primary care data. As a universal analysis framework, we also evaluated SPA_GRM's performance in quantitative and binary trait analysis.

Like many other popular methods, such as BOLT-LMM, SAIGE, fastGWA, REGENIE, GATE, and POLMM, SPA_GRM is also a two-step method to control for sample relatedness in large-scale cohort. It consists of:

• In step 1, SPA_GRM fits a null model to adjust for the effect of covariates on phenotypes and calculate model residuals. It is optional, rather than required, to incorporate the random effect into null model fitting to characterize the sample relatedness.

• In step 2, SPA_GRM associates the trait of interest to a single genetic variant and obtain GWAS results by a retrospective strategy: that is treating the genotypes as random variables. Unlike regular retrospective methods which solely rely on the genetic relationship matrix (GRM), SPA_GRM calculates identity by descent (IBD)-sharing probabilities and then employs the Chow-Liu algorithm to approximate the joint distribution of genotypes. Saddlepoint approximation is applied in SPA_GRM that can greatly increase the accuracy to analyze low-frequency and rare variants, especially if the phenotypic distribution is unbalanced.

• To avoid redundant computations in step 2, SPA_GRM uses the IBD-sharing probabilities and Chow-Liu algorithm to approximate the discrete joint distribution of genotype vectors in advance (illustrated as step 0).

See A scalable, accurate, and universal analysis framework to control for sample relatedness in large-scale genome-wide association studies and its application to 79 longitudinal traits in UK Biobank (to be updated) for more details about the workflow of SPA_GRM.