SPACox / SPAmix / SPAGRM approaches

Main features

SPACox, SPAmix, and SPAGRM are accurate and efficient approaches to associating complex traits (including but not limited to time-to-event traits) to single-variant.

The three methods use empirical SPA approaches and share the below features.

  • Model residuals (whose sum is zero) are needed as input. Users can select appropriate statistical models depending on the type of traits in analysis.
  • High computational efficiency to analyze large-scale biobank data with millions of individuals
  • High accuracy to analyze common, low-frequency, and rare variants, even if the phenotypic distribution (or residual distribution) is highly unbalanced.

The three methods are different in terms of

  • SPACox is the basic function to analyze unrelated subjects in a homogeneous population.

  • SPAmix extends SPACox to analyze an admixture population or multiple populations. The method is still only valid to analyze unrelated subjects.

  • SPAGRM extends SPACox to analyze a study cohort in which subjects can be genetically related to each other. The method is still only valid to analyze a homogeneous population.

Important notes about function GRAB.NullModel

  • If the left side of argument formula is model residual, please specify the argument traitType = "Residuals". Otherwise, the argument traitType can be specified based on the type of trait in analysis.

  • For method SPAmix, the top SNP-derived PC and related information is required in the arguments formula and control.

  • For method SPAGRM, arguments GenoFile, GenoFileIndex, and SparseGRMFile are required to characterize the family relatedness.

Quick Start-up Guide

The below gives examples to demonstrate the usage of SPACox, SPAmix, and SPAGRM approaches.

Step 1. Read in data and fit a null model 

library(GRAB)
library(survival)

PhenoFile = system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData = data.table::fread(PhenoFile, header = T)

obj.SPACox = GRAB.NullModel(
  Surv(SurvTime, SurvEvent) ~ AGE + GENDER, 
  data = PhenoData, 
  subjData = IID, 
  method = "SPACox", 
  traitType = "time-to-event"
)

SPACox method can also support model residuals as input. The above codes are the same as below.

obj.coxph = coxph(
  Surv(SurvTime, SurvEvent) ~ AGE + GENDER, 
  data = PhenoData
)

obj.SPACox = GRAB.NullModel(
  obj.coxph$residuals ~ AGE + GENDER, 
  data = PhenoData, 
  subjData = IID, 
  method = "SPACox", 
  traitType = "Residual"
)

SPAmix method also support both original trait or model residuals as input. For SPAmix, the confounding factors of SNP-derived PCs are required and should be specified in control.

PhenoFile = system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData = data.table::fread(PhenoFile, header = T)

obj.SPAmix = GRAB.NullModel(
  Surv(SurvTime, SurvEvent) ~ AGE + GENDER + PC1 + PC2, 
  data = PhenoData, 
  subjData = IID, 
  method = "SPAmix", 
  traitType = "time-to-event", 
  control = list(PC_columns = "PC1,PC2")
)

The same results can be obtained via using model residuals

obj.coxph = coxph(
  Surv(SurvTime, SurvEvent ) ~ AGE + GENDER + PC1 + PC2, 
  data = PhenoData
)

obj.SPACox = GRAB.NullModel(
  obj.coxph$residuals ~ AGE + GENDER + PC1 + PC2, 
  data = PhenoData, 
  subjData = IID, 
  method = "SPAmix", 
  traitType = "Residual", 
  control = list(PC_columns = "PC1,PC2")
)

Step 2. Conduct genome-wide association studies

For different types of traits and methods, the step 2 is the same as below.

GenoFile = system.file("extdata", "simuPLINK.bed", package = "GRAB")
OutputDir = tempdir()

# step 2 for SPACox method
OutputFile = file.path(OutputDir, "Results_SPACox.txt")
GRAB.Marker(obj.SPACox, GenoFile = GenoFile, OutputFile = OutputFile)

# step 2 for SPAmix method
OutputFile = file.path(OutputDir, "Results_SPAmix.txt")
GRAB.Marker(obj.SPAmix, GenoFile = GenoFile, OutputFile = OutputFile)

Detailed documentation about how to use SPAGRM is available at SPAGRM online tutorial.

Citation

  • SPACox: Wenjian Bi, Lars G. Fritsche, Bhramar Mukherjee, Sehee Kim, and Seunggeun Lee. A fast and accurate method for genome-wide time-to-event data analysis and its application to UK Biobank. The American Journal of Human Genetics 107, no. 2 (2020): 222-233.

  • SPAmix: Yuzhuo Ma, He Xu, Ying Li, Hyesung Kim, Lin-lin Xu, Lin Miao, Peng Xu, Fengbiao Mao, Xu-jie Zhou, Wei Zhou, Seunggeun Lee, Ji-Feng Zhang, Peipei Zhang, Wenjian Bi (2025). A scalable, accurate, and universal analysis framework using individual-specific allele frequency for large-scale genetic association studies in an admixed population. Genome Biology in press

  • SPAGRM: He Xu, Yuzhuo Ma, Lin-lin Xu, Yin Li, Yufei Liu, Ying Li, Xu-jie Zhou, Wei Zhou, Seunggeun Lee, Peipei Zhang, Weihua Yue and Wenjian Bi (2025). SPA(GRM): effectively controlling for sample relatedness in large-scale genome-wide association studies of longitudinal traits. Nature Communications 16(1): 1413.