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 homozygous 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 homozygous 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)
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.

library(GRAB)
PhenoFile = system.file("extdata", "simuPHENO.txt", package = "GRAB")
PhenoData = data.table::fread(PhenoFile, header = T)
N = nrow(PhenoData)
PhenoData = PhenoData %>% mutate(PC1 = rnorm(N), PC2 = rnorm(N))  # add two PCs
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 = system.file("results", package = "GRAB")
# step 2 for SPACox method
OutputFile = paste0(OutputDir, "/Results_SPACox.txt")
GRAB.Marker(obj.SPACox, GenoFile = GenoFile, OutputFile = OutputFile)
# step 2 for SPAmix method
OutputFile = paste0(OutputDir, "/Results_SPAmix.txt")
GRAB.Marker(obj.SPAmix, GenoFile = GenoFile, OutputFile = OutputFile)

Citation

  • SPACox: Bi, Wenjian, 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: to be submitted.

  • SPAGRM: to be submitted.