Heritability Estimate and Computing Genetic Correlation with GXwasR

This document provides a tutorial for heritability estimate and computing genetic correlation utilizing GXwasR R package.

## Call some libraries
library(GXwasR)
#> 
#> GXwasR: Genome-wide and x-chromosome wide association analyses applying
#> best practices of quality control over genetic data
#> Version 0.99.0 () installed
#> Author: c(
#>     person(given = "Banabithi",
#>            family = "Bose",
#>            role = c("cre", "aut"),
#>            email = "banabithi.bose@gmail.com",
#>            comment = c(ORCID = "0000-0003-0842-8768"))
#>            )
#> Maintainer: Banabithi Bose <banabithi.bose@gmail.com>
#> Tutorial: https://github.com
#> Use citation("GXwasR") to know how to cite this work.
library(printr)
#> Registered S3 method overwritten by 'printr':
#>   method                from     
#>   knit_print.data.frame rmarkdown
library(rmarkdown)
#> 
#> Attaching package: 'rmarkdown'
#> The following objects are masked from 'package:BiocStyle':
#> 
#>     html_document, md_document, pdf_document

Computing heritability estimate using EstimateHerit():

help(EstimateHerit, package = GXwasR)

EstimateHerit

R Documentation

EstimateHerit: Computing SNP heritability i.e., the proportion of phenotypic variance explained by SNPs.

Description

This functions performs two types of heritability estimation, (i)GREML:Genomic relatedness matrix (GRM) restricted maximum likelihood-based method following GCTA (Yang et al. 2011) and (ii)LDSC: LD score regression-based method following (Bulik-Sullivan et al. 2014; Privé et al. 2020). For the details, please follow the associated paper.

Prior to using this function, it is recommended to apply QCsnp and QCsample to ensure data quality control.

Usage

EstimateHerit(
  DataDir = NULL,
  ResultDir = tempdir(),
  finput = NULL,
  precomputedLD = NULL,
  indepSNPs = NULL,
  summarystat = NULL,
  ncores = 2,
  model = c("LDSC", "GREML"),
  computeGRM = TRUE,
  grmfile_name = NULL,
  byCHR = FALSE,
  r2_LD = 0,
  LDSC_blocks = 20,
  intercept = NULL,
  chi2_thr1 = 30,
  chi2_thr2 = Inf,
  REMLalgo = c(0, 1, 2),
  nitr = 100,
  cat_covarfile = NULL,
  quant_covarfile = NULL,
  prevalence = NULL,
  partGRM = FALSE,
  autosome = TRUE,
  Xsome = TRUE,
  nGRM = 3,
  cripticut = 0.025,
  minMAF = NULL,
  maxMAF = NULL,
  hg = c("hg19", "hg38"),
  PlotIndepSNP = TRUE,
  IndepSNP_window_size = 50,
  IndepSNP_step_size = 5,
  IndepSNP_r2_threshold = 0.02,
  highLD_regions = NULL,
  plotjpeg = TRUE,
  plotname = "Heritability_Plots"
)

Arguments

DataDir

A character string for the file path of the all the input files. The default is NULL.

ResultDir

A character string for the file path where all output files will be stored. The default is tempdir().

finput

Character string, specifying the prefix of the input PLINK binary files for the genotype data. This file needs to be in DataDir. For LDSC model, if the original genotype data is not available, Hapmap 3 or 1000Genome data can be used. If use NULL, then you need to provide precomputedLD argument. See below.

precomputedLD

A dataframe object as LD matrix with columns: CHR, SNP, BP, ld_size, MAF, ld_score. . The default is NULL.

indepSNPs

A dataframe with independent SNP ids with column name "rsid". The default is NULL.

summarystat

A dataframe object with GWAS summary statistics. The mandatory column headers in this dataframe are

chr (Chromosome code),
pos (Basepair position)
a1 (First allele code)
rsid (i.e., SNP identifier)
beta (i.e., effect-size or logarithm of odds ratio)
beta_se (i.e., standard error of beta)
P (i.e., p-values)
n_eff (i.e., effective sample size)

For case-control study, effective sample size should be 4 / (1/<# of cases> + 1/<# of controls>). The default is NULL.

ncores

Integer value, specifying the number of cores to be used for running LDSC model. The default is 2.

model

Character string, specifying the heritability estimation model. There are two options, “GREML” or “LDSC”. The default is “GREML”.

Note: argument For LDSC, DataDir and finput can be NULL.

computeGRM

Boolean value, TRUE or FALSE, specifying whether to compute GRM matrices or not. The default is TRUE.

grmfile_name

A string of characters specifying the prefix of autosomal .grm.bin file. Users need to provide separate GRM files for autosomes and X chromosome in ResultDir.

The X chromosomal GRM file should have "x" added in the autosomal prefix as file name. For instance, if autosomal file is "ABC.grm.bin", then X chromosomal file should be "xABC.grm.bim".

If you are providing chromosome-wise GRMs, then the prefix should add "ChrNumber_" at the start of the prefix like, "Chr1_ABC.grm.bin". The default is NULL.

byCHR

Boolean value, TRUE or FALSE, specifying whether the analysis will be performed chromosome wise or not. The default is FALSE.

r2_LD

Numeric value, specifying the LD threshold for clumping in LDSC model. The default is 0.

LDSC_blocks

Integer value, specifying the block size for performing jackknife variance estimator in LDSC model following (Privé et al. 2020). The default is 200.

intercept

Numeric value to constrain the intercept to some value (e.g. 1) in LDSC regression. Default is NULL.

chi2_thr1

Numeric value for threshold on chi2 in step 1 of LDSC regression. Default is 30.

chi2_thr2

Numeric value for threshold on chi2 in step 2. Default is Inf (none).

REMLalgo

Integer value of 0, 1 or 2, specifying the algorithm to run REML iterations, 0 for average information (AI), 1 for Fisher-scoring and 2 for EM. The default option is 0, i.e. AI-REML (Yang et al. 2011).

nitr

Integer value, specifying the number of iterations for performing the REML. The default is 100.

cat_covarfile

A character string, specifying the name of the categorical covariate file which is a plain text file with no header line; columns are family ID, individual ID and discrete covariates. The default is NULL. This file needs to be in DataDir.

quant_covarfile

A character string, specifying the name of the quantitative covariate file which is a plain text file with no header line; columns are family ID, individual ID and continuous covariates. The default is NULL. This file needs to be in DataDir.

prevalence

Numeric value, specifying the disease prevalence. The default is NULL.

Note: for the continuous trait value, users should use the default.

partGRM

Boolean value, TRUE or FALSE, specifying whether the GRM will be partitioned into n parts (by row) in GREML model. The default is FALSE.

autosome

Boolean value, TRUE or FALSE, specifying whether estimate of heritability will be done for autosomes or not. The default is TRUE.

Xsome

Boolean value, TRUE or FALSE, specifying whether estimate of heritability will be done for X chromosome or not. The default is TRUE.

nGRM

Integer value, specifying the number of the partition of the GRM in GREML model. The default is 3.

cripticut

Numeric value, specifying the threshold to create a new GRM of "unrelated" individuals in GREML model. The default is arbitrary chosen as 0.025 following (Yang et al. 2011).

minMAF

Positive numeric value (0,1), specifying the minimum threshold for the MAF filter of the SNPs in the GREML model. This value cannot be greater than maxMAF parameter. The default is NULL. For NULL, maximum MAF value of the genotype data will be computed and printed on the plot.

maxMAF

Positive numeric value (0,1), specifying the maximum threshold for the MAF filter of the SNPs in the GREML model. This value cannot be less than minMAF parameter. The default is NULL. For NULL, minimum MAF value of the genotype data will be computed and printed on the plot.

hg

Boolean value, specifying the genome built, “hg19” or “hg38” to use chromosome length from UCSC genome browser and getting genes and proteins according to this built. The default is “hg19”.

PlotIndepSNP

Boolean value, TRUE or FALSE, specifying whether to use independent SNPs i.e., chromosome-wise LD pruned SNPs in the plots or not. The default is TRUE.

IndepSNP_window_size

Integer value, specifying a window size in variant count or kilobase for LD-based filtering. The default is 50.

IndepSNP_step_size

Integer value, specifying a variant count to shift the window at the end of each step for LD filtering for pruned SNPs in the plots. The default is 5.

IndepSNP_r2_threshold

Numeric value between 0 to 1 of pairwise r^2 threshold for LD-based filtering for pruned SNPs in the plots. The default is 0.02.

highLD_regions

Character string, specifying the .txt file name with genomic regions with high LD for using in finding pruned SNPs in the plots. This file needs to be in DataDir.

plotjpeg

Boolean value, TRUE or FALSE, specifying whether to save the plots in jpeg file in ResultDir. The default is TRUE.

plotname

String of character value specifying the name of the jpeg file with the plots. The default is "Heritability_Plots".

Value

A dataframe with maximum eight columns for GREML (here, three columns if running genome-wide) and ten columns for LDSC model if byCHR is TRUE. The columns, such as, "chromosome"(i.e., chromosome code),"snp_proportion" (i.e.,chromosome-wise SNP proportion)", "no.of.genes" (i.e., number of genes per chromosome), "no.of.proteins" (i.e., number of genes per chromosome),"size_mb" (i.e., chromosome length), "Source" (i.e., source of heritability), "Variance" (i.e., estimated heritability), and "SE" (i.e., standard error of the estimated heritability) are common for both GREML and LDSC model. The column, "Intercept" (i.e., LDSC regression intercept) and "Int_SE" (i.e., standard error of the intercept) will be two extra columns for LDSC models. Source column will have rows, such as V(1) (i.e., name of genetic variance), V(e) (i.e., residual variance), V(p) (i.e., phenotypic variance), V(1)/Vp (i.e., ratio of genetic variance to phenotypic variance), and V(1)/Vp_L (i.e., ratio of genetic variance to phenotypic variance in liability scale for binary phenotypes). If byCHR is FALSE, then the first five columns will not be reported in the dataframe.

Author(s)

Banabithi Bose

References

Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P, Consortium R, Consortium PG, of the Wellcome Trust Case Control Consortium GCfAN, Perry JRB, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM (2014). “LD score regression distinguishes confounding from polygenicity in genome-wide association studies.” Nature Genetics, 47, 291–295.

Privé F, Arbel J, Vilhjálmsson BJ (2020). “LDpred2: better, faster, stronger.” Bioinformatics, 36(22-23), 5424–5431. doi:10.1093/bioinformatics/btaa1029.

Yang J, Lee SH, Goddard ME, Visscher PM (2011). “GCTA: a tool for Genome-wide Complex Trait Analysis.” American Journal of Human Genetics, 88(1), 76–82.

Examples

data("Summary_Stat_Ex1", package = "GXwasR")
data("highLD_hg19", package = "GXwasR")
DataDir <- GXwasR:::GXwasR_data()
ResultDir <- tempdir()
precomputedLD <- NULL
finput <- "GXwasR_example"
test.sumstats <- na.omit(Summary_Stat_Ex1[Summary_Stat_Ex1$TEST == "ADD", c(seq_len(4), 6:8)])
colnames(test.sumstats) <- c("chr", "rsid", "pos", "a1", "n_eff", "beta", "beta_se")
summarystat <- test.sumstats
ncores <- 3
model <- "GREML"
byCHR <- FALSE
r2_LD <- 0
LDSC_blocks <- 20
REMLalgo <- 0
nitr <- 3
cat_covarfile <- NULL
quant_covarfile <- NULL
prevalence <- 0.01
partGRM <- FALSE
autosome <- TRUE
Xsome <- TRUE
nGRM <- 3
cripticut <- 0.025
minMAF <- NULL
maxMAF <- NULL
hg <- "hg19"
PlotIndepSNP <- TRUE
IndepSNP_window_size <- 50
IndepSNP_step_size <- 5
IndepSNP_r2_threshold <- 0.02
highLD_regions <- highLD_hg19
H2 <- EstimateHerit(
    DataDir = DataDir, ResultDir = ResultDir, finput = finput,
    summarystat = NULL, ncores, model = "GREML", byCHR = TRUE, r2_LD = 0,
    LDSC_blocks = 20, REMLalgo = 0, nitr = 100, cat_covarfile = NULL, quant_covarfile = NULL,
    prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,
    cripticut = 0.025, minMAF = NULL, maxMAF = NULL, hg = "hg19", PlotIndepSNP = TRUE,
    IndepSNP_window_size = 50, IndepSNP_step_size = 5, IndepSNP_r2_threshold = 0.02,
    highLD_regions = highLD_hg19
)

Running EstimateHerit() with model = “GREML”

# Running
data("highLD_hg19", package = "GXwasR")
DataDir <- GXwasR:::GXwasR_data()
ResultDir = tempdir()
finput <- "GXwasR_example"
ncores = 3
model = "GREML"
byCHR = FALSE
r2_LD = 0
LDSC_blocks = 20
REMLalgo = 0
nitr = 100
cat_covarfile = NULL
quant_covarfile = NULL
prevalence = 0.01
partGRM = FALSE
autosome = TRUE
Xsome = TRUE
nGRM = 3
cripticut = 0.025
minMAF = NULL
maxMAF = NULL
hg = "hg19"
byCHR = TRUE
PlotIndepSNP = TRUE
IndepSNP_window_size = 50
IndepSNP_step_size = 5
IndepSNP_r2_threshold = 0.02

# For saving the plot in .jpeg file, make plotjpeg = TRUE.

H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = finput, summarystat = NULL, ncores = 5, model = model, byCHR = TRUE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = nitr, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3, computeGRM = TRUE, grmfile_name = NULL, cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19, plotjpeg = TRUE, plotname = "Heritability_Plots")
#> ✔ Input genotype files are present in specified directory.
#> Using PLINK v1.9.0-b.7.7 64-bit (22 Oct 2024)
#> Processing chromosome 1
#> Using GCTA version v1.94.4 Mac
#> Processing chromosome 2
#> Processing chromosome 3
#> Processing chromosome 4
#> Processing chromosome 5
#> Processing chromosome 6
#> Processing chromosome 7
#> Processing chromosome 8
#> Processing chromosome 9
#> Processing chromosome 10
#> Processing chromosome 23
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.N.bin]
#> 
#> 
#> 
#> Processing chromosome 24
#> ✔ Plots are saved in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2 with name Heritability_Plots.jpeg
#> ✔ All GRM related files are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2

#Heritability
H2

Chromosome	SNP_proportion	No.of.genes	No.of.proteins	Size_mb	Source	Variance	SE
1	0.0277453	2177	1936	249.25062	V(G)	0.000000	0.054312
1	0.0277453	2177	1936	249.25062	V(e)	0.239076	0.058127
1	0.0277453	2177	1936	249.25062	Vp	0.239076	0.020390
1	0.0277453	2177	1936	249.25062	V(G)/Vp	0.000001	0.227173
1	0.0277453	2177	1936	249.25062	V(G)/Vp_L	0.000001	0.131597
2	0.0275568	1377	1188	243.19937	V(G)	0.000000	0.050757
2	0.0275568	1377	1188	243.19937	V(e)	0.239068	0.054764
2	0.0275568	1377	1188	243.19937	Vp	0.239068	0.020389
2	0.0275568	1377	1188	243.19937	V(G)/Vp	0.000001	0.212310
2	0.0275568	1377	1188	243.19937	V(G)/Vp_L	0.000001	0.122987
3	0.0237494	1205	1029	198.02243	V(G)	0.000000	0.049616
3	0.0237494	1205	1029	198.02243	V(e)	0.239070	0.053909
3	0.0237494	1205	1029	198.02243	Vp	0.239070	0.020391
3	0.0237494	1205	1029	198.02243	V(G)/Vp	0.000001	0.207539
3	0.0237494	1205	1029	198.02243	V(G)/Vp_L	0.000001	0.120224
4	0.0235986	824	720	191.15428	V(G)	0.000000	0.050377
4	0.0235986	824	720	191.15428	V(e)	0.238750	0.053826
4	0.0235986	824	720	191.15428	Vp	0.238750	0.020355
4	0.0235986	824	720	191.15428	V(G)/Vp	0.000001	0.211004
4	0.0235986	824	720	191.15428	V(G)/Vp_L	0.000001	0.122231
5	0.0217137	958	839	180.91526	V(G)	0.000000	0.047760
5	0.0217137	958	839	180.91526	V(e)	0.238982	0.051750
5	0.0217137	958	839	180.91526	Vp	0.238982	0.020378
5	0.0217137	958	839	180.91526	V(G)/Vp	0.000001	0.199849
5	0.0217137	958	839	180.91526	V(G)/Vp_L	0.000001	0.115769
6	0.0200550	922	826	171.11507	V(G)	0.000000	0.044094
6	0.0200550	922	826	171.11507	V(e)	0.237174	0.047108
6	0.0200550	922	826	171.11507	Vp	0.237175	0.020203
6	0.0200550	922	826	171.11507	V(G)/Vp	0.000001	0.185916
6	0.0200550	922	826	171.11507	V(G)/Vp_L	0.000001	0.107697
7	0.0206959	997	855	159.13866	V(G)	0.000000	0.046032
7	0.0206959	997	855	159.13866	V(e)	0.238933	0.050098
7	0.0206959	997	855	159.13866	Vp	0.238934	0.020373
7	0.0206959	997	855	159.13866	V(G)/Vp	0.000001	0.192657
7	0.0206959	997	855	159.13866	V(G)/Vp_L	0.000001	0.111602
8	0.0186225	761	642	146.36402	V(G)	0.000000	0.045378
8	0.0186225	761	642	146.36402	V(e)	0.232560	0.046633
8	0.0186225	761	642	146.36402	Vp	0.232560	0.019797
8	0.0186225	761	642	146.36402	V(G)/Vp	0.000001	0.195125
8	0.0186225	761	642	146.36402	V(G)/Vp_L	0.000001	0.113032
9	0.0164361	861	732	141.21343	V(G)	0.000000	0.037296
9	0.0164361	861	732	141.21343	V(e)	0.238767	0.042010
9	0.0164361	861	732	141.21343	Vp	0.238767	0.020357
9	0.0164361	861	732	141.21343	V(G)/Vp	0.000001	0.156201
9	0.0164361	861	732	141.21343	V(G)/Vp_L	0.000001	0.090484
10	0.0175293	820	696	135.53475	V(G)	0.000000	0.043767
10	0.0175293	820	696	135.53475	V(e)	0.234920	0.045847
10	0.0175293	820	696	135.53475	Vp	0.234920	0.020010
10	0.0175293	820	696	135.53475	V(G)/Vp	0.000001	0.186305
10	0.0175293	820	696	135.53475	V(G)/Vp_L	0.000001	0.107923
23	0.0057677	0	0	155.27056	V(G)	0.161447	0.035905
23	0.0057677	0	0	155.27056	V(e)	0.074652	0.025725
23	0.0057677	0	0	155.27056	Vp	0.236099	0.021754
23	0.0057677	0	0	155.27056	V(G)/Vp	0.683811	0.116600
23	0.0057677	0	0	155.27056	V(G)/Vp_L	0.396119	0.067544
24	0.0001131	0	0	59.37357	V(G)	0.000000	0.043767
24	0.0001131	0	0	59.37357	V(e)	0.234920	0.045847
24	0.0001131	0	0	59.37357	Vp	0.234920	0.020010
24	0.0001131	0	0	59.37357	V(G)/Vp	0.000001	0.186305
24	0.0001131	0	0	59.37357	V(G)/Vp_L	0.000001	0.107923

Running the EstimateHerit() by providing chromosome-wise GRM files in ResultDir

Set:

computeGRM = FALSE

grmfile_name = “GXwasR”…… This is the prefix. These files are created in the previous run in ResultDir. Users are encouraged to check that folder.


H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = finput, summarystat = NULL, ncores = 5, model = model, byCHR = TRUE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = nitr, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3, computeGRM = FALSE, grmfile_name = "GXwasR", cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19, plotjpeg = FALSE, plotname = "Heritability_Plots")
#> ✔ Input genotype files are present in specified directory.
#> Processing chromosome 1
#> Processing chromosome 2
#> Processing chromosome 3
#> Processing chromosome 4
#> Processing chromosome 5
#> Processing chromosome 6
#> Processing chromosome 7
#> Processing chromosome 8
#> Processing chromosome 9
#> Processing chromosome 10
#> Processing chromosome 23
#> Processing chromosome 24
#> ✔ All GRM related files are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2

#Heritability
H2

Chromosome	SNP_proportion	No.of.genes	No.of.proteins	Size_mb	Source	Variance	SE
1	0.0277453	2177	1936	249.25062	V(G)	0.000000	0.054312
1	0.0277453	2177	1936	249.25062	V(e)	0.239076	0.058127
1	0.0277453	2177	1936	249.25062	Vp	0.239076	0.020390
1	0.0277453	2177	1936	249.25062	V(G)/Vp	0.000001	0.227173
1	0.0277453	2177	1936	249.25062	V(G)/Vp_L	0.000001	0.131597
2	0.0275568	1377	1188	243.19937	V(G)	0.000000	0.050757
2	0.0275568	1377	1188	243.19937	V(e)	0.239068	0.054764
2	0.0275568	1377	1188	243.19937	Vp	0.239068	0.020389
2	0.0275568	1377	1188	243.19937	V(G)/Vp	0.000001	0.212310
2	0.0275568	1377	1188	243.19937	V(G)/Vp_L	0.000001	0.122987
3	0.0237494	1205	1029	198.02243	V(G)	0.000000	0.049616
3	0.0237494	1205	1029	198.02243	V(e)	0.239070	0.053909
3	0.0237494	1205	1029	198.02243	Vp	0.239070	0.020391
3	0.0237494	1205	1029	198.02243	V(G)/Vp	0.000001	0.207539
3	0.0237494	1205	1029	198.02243	V(G)/Vp_L	0.000001	0.120224
4	0.0235986	824	720	191.15428	V(G)	0.000000	0.050377
4	0.0235986	824	720	191.15428	V(e)	0.238750	0.053826
4	0.0235986	824	720	191.15428	Vp	0.238750	0.020355
4	0.0235986	824	720	191.15428	V(G)/Vp	0.000001	0.211004
4	0.0235986	824	720	191.15428	V(G)/Vp_L	0.000001	0.122231
5	0.0217137	958	839	180.91526	V(G)	0.000000	0.047760
5	0.0217137	958	839	180.91526	V(e)	0.238982	0.051750
5	0.0217137	958	839	180.91526	Vp	0.238982	0.020378
5	0.0217137	958	839	180.91526	V(G)/Vp	0.000001	0.199849
5	0.0217137	958	839	180.91526	V(G)/Vp_L	0.000001	0.115769
6	0.0200550	922	826	171.11507	V(G)	0.000000	0.044094
6	0.0200550	922	826	171.11507	V(e)	0.237174	0.047108
6	0.0200550	922	826	171.11507	Vp	0.237175	0.020203
6	0.0200550	922	826	171.11507	V(G)/Vp	0.000001	0.185916
6	0.0200550	922	826	171.11507	V(G)/Vp_L	0.000001	0.107697
7	0.0206959	997	855	159.13866	V(G)	0.000000	0.046032
7	0.0206959	997	855	159.13866	V(e)	0.238933	0.050098
7	0.0206959	997	855	159.13866	Vp	0.238934	0.020373
7	0.0206959	997	855	159.13866	V(G)/Vp	0.000001	0.192657
7	0.0206959	997	855	159.13866	V(G)/Vp_L	0.000001	0.111602
8	0.0186225	761	642	146.36402	V(G)	0.000000	0.045378
8	0.0186225	761	642	146.36402	V(e)	0.232560	0.046633
8	0.0186225	761	642	146.36402	Vp	0.232560	0.019797
8	0.0186225	761	642	146.36402	V(G)/Vp	0.000001	0.195125
8	0.0186225	761	642	146.36402	V(G)/Vp_L	0.000001	0.113032
9	0.0164361	861	732	141.21343	V(G)	0.000000	0.037296
9	0.0164361	861	732	141.21343	V(e)	0.238767	0.042010
9	0.0164361	861	732	141.21343	Vp	0.238767	0.020357
9	0.0164361	861	732	141.21343	V(G)/Vp	0.000001	0.156201
9	0.0164361	861	732	141.21343	V(G)/Vp_L	0.000001	0.090484
10	0.0175293	820	696	135.53475	V(G)	0.000000	0.043767
10	0.0175293	820	696	135.53475	V(e)	0.234920	0.045847
10	0.0175293	820	696	135.53475	Vp	0.234920	0.020010
10	0.0175293	820	696	135.53475	V(G)/Vp	0.000001	0.186305
10	0.0175293	820	696	135.53475	V(G)/Vp_L	0.000001	0.107923
23	0.0057677	0	0	155.27056	V(G)	0.161447	0.035905
23	0.0057677	0	0	155.27056	V(e)	0.074652	0.025725
23	0.0057677	0	0	155.27056	Vp	0.236099	0.021754
23	0.0057677	0	0	155.27056	V(G)/Vp	0.683811	0.116600
23	0.0057677	0	0	155.27056	V(G)/Vp_L	0.396119	0.067544
24	0.0001131	0	0	59.37357	V(G)	0.000000	0.043767
24	0.0001131	0	0	59.37357	V(e)	0.234920	0.045847
24	0.0001131	0	0	59.37357	Vp	0.234920	0.020010
24	0.0001131	0	0	59.37357	V(G)/Vp	0.000001	0.186305
24	0.0001131	0	0	59.37357	V(G)/Vp_L	0.000001	0.107923

Running the EstimateHerit() for genome-wide heritability estimate by creating GRM files

Set:

computeGRM = TRUE

byCHR = FALSE

grmfile_name = NULL…….if computeGRM = TRUE the grmfile_name is by default NULL.

In genome-wide case, there no plot will be generated.


H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = finput, summarystat = NULL, ncores = 5, model = model, byCHR = FALSE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = nitr, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3, computeGRM = TRUE, grmfile_name = NULL, cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19, plotjpeg = FALSE, plotname = "Heritability_Plots")
#> ✔ Input genotype files are present in specified directory.
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.N.bin]
#> 
#> 
#> 
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.N.bin]
#> 
#> 
#> 
#> ℹ computeGRM is set to: TRUE
#> • Creating multi_GRMs.txt because computeGRM is TRUE
#> ✖ Convergence issue occurred, please:
#>   - verify the model used
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> Log output from 'Reading IDs':
#> • Reading IDs of the GRM from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.id].
#> • 276 IDs are read from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.id].
#> • Reading the GRM from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.bin].
#> • GRM for 276 individuals are included from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.bin].
#> • Reading the GRM from the 2th file ...
#> • Reading IDs of the GRM from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.id].
#> • 276 IDs are read from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.id].
#> • Reading the GRM from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin].
#> • GRM for 276 individuals are included from [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin].
#> • 276 individuals are in common in these files.
#> • 
#> • Performing  REML analysis ... (Note: may take hours depending on sample size).
#> • 276 observations, 1 fixed effect(s), and 3 variance component(s)(including residual variance).
#> • Calculating prior values of variance components by EM-REML ...
#> • Updated prior values: 0.0766102 0.0812353 0.0774914
#> • logL: 65.5415
#> • Running AI-REML algorithm ...
#> • Iter.    logL    V(G1)   V(G2)   V(e)    
#> • 1    65.89   0.00000 0.07619 0.15118 (1 component(s) constrained)
#> • 2    67.21   0.00000 0.16240 0.21670 (1 component(s) constrained)
#> • 3    53.24   0.00000 0.05086 0.24361 (1 component(s) constrained)
#> • 4    59.08   0.00000 0.22012 0.23090 (1 component(s) constrained)
#> • 5    43.58   0.00000 0.03335 0.27444 (1 component(s) constrained)
#> • 6    55.91   0.00000 0.23059 0.22225 (1 component(s) constrained)
#> • 7    43.71   0.00000 0.03591 0.27305 (1 component(s) constrained)
#> • 8    56.00   0.00000 0.23306 0.22260 (1 component(s) constrained)
#> • 9    43.33   0.00000 0.03483 0.27436 (1 component(s) constrained)
#> • 10   55.88   0.00000 0.23293 0.22218 (1 component(s) constrained)
#> • 11   43.42   0.00000 0.03517 0.27401 (1 component(s) constrained)
#> • 12   55.91   0.00000 0.23306 0.22229 (1 component(s) constrained)
#> • 13   43.39   0.00000 0.03505 0.27414 (1 component(s) constrained)
#> • 14   55.90   0.00000 0.23302 0.22225 (1 component(s) constrained)
#> • 15   43.40   0.00000 0.03509 0.27410 (1 component(s) constrained)
#> • 16   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 17   43.39   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 18   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 19   43.40   0.00000 0.03509 0.27411 (1 component(s) constrained)
#> • 20   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 21   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 22   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 23   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 24   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 25   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 26   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 27   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 28   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 29   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 30   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 31   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 32   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 33   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 34   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 35   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 36   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 37   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 38   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 39   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 40   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 41   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 42   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 43   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 44   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 45   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 46   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 47   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 48   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 49   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 50   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 51   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 52   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 53   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 54   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 55   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 56   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 57   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 58   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 59   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 60   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 61   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 62   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 63   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 64   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 65   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 66   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 67   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 68   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 69   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 70   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 71   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 72   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 73   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 74   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 75   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 76   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 77   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 78   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 79   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 80   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 81   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 82   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 83   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 84   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 85   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 86   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 87   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 88   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 89   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 90   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 91   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 92   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 93   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 94   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 95   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 96   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 97   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • 98   55.90   0.00000 0.23303 0.22226 (1 component(s) constrained)
#> • 99   43.40   0.00000 0.03508 0.27411 (1 component(s) constrained)
#> • Error: Log-likelihood not converged (stop after 100 iteractions). 
#> • You can specify the option --reml-maxit to allow for more iterations.
#> • 
#> • An error occurs, please check the options or data
#> ✔ All GRM related files are in /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2

#Heritability
H2

Source	Variance	SE
NA	NA	NA

Running the EstimateHerit() for genome-wide heritability estimate by utilizing the pre-computed GRM files in ResultDir

Set:

computeGRM = FALSE

byCHR = FALSE

grmfile_name = “GXwasR”

In genome-wide case, there no plot will be generated.


H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = finput, summarystat = NULL, ncores = 5, model = model, byCHR = FALSE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = nitr, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3, computeGRM = FALSE, grmfile_name = "GXwasR", cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19, plotjpeg = FALSE, plotname = "Heritability_Plots")
#> ✔ Input genotype files are present in specified directory.
#> ℹ Skipping GRM computation.
#> ℹ computeGRM is set to: FALSE
#> • computeGRM is FALSE, checking grmfile_name
#> Warning: path[1]="/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR": No such file or directory

#Heritability
H2
#> NULL

Running EstimateHerit() genome-wide with model = “LDSC”

data("Summary_Stat_Ex1", package = "GXwasR")
model <- "LDSC"
data("GXwasRData")
#> Warning in data("GXwasRData"): data set 'GXwasRData' not found
DataDir <- GXwasR:::GXwasR_data()
ResultDir = tempdir()
finput <- "GXwasR_example"
test.sumstats <- na.omit(Summary_Stat_Ex1[Summary_Stat_Ex1$TEST=="ADD",c(1:4,6:8)])
colnames(test.sumstats) <- c("chr","rsid","pos","a1","n_eff","beta","beta_se")
summarystat = test.sumstats
highLD_regions = highLD_hg19

H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = "GXwasR_example", summarystat = summarystat, ncores, model = model, byCHR = FALSE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = 100, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19)
#> An error occurred: incorrect number of dimensions
#Heritability
H2
#> NULL

Running EstimateHerit() chromosome-wise with model = “LDSC”

model <- "LDSC"
DataDir <- GXwasR:::GXwasR_data()
ResultDir = tempdir()
finput <- "GXwasR_example"
test.sumstats <- na.omit(Summary_Stat_Ex1[Summary_Stat_Ex1$TEST=="ADD",c(1:4,6:8)])
colnames(test.sumstats) <- c("chr","rsid","pos","a1","n_eff","beta","beta_se")
summarystat = test.sumstats
highLD_regions = highLD_hg19

H2 <- EstimateHerit(DataDir = DataDir, ResultDir = ResultDir, finput = "GXwasR_example", summarystat = summarystat, ncores, model = model, byCHR = TRUE, r2_LD = 0, LDSC_blocks = 20,REMLalgo = 0, nitr = 100, cat_covarfile = NULL, quant_covarfile = NULL,prevalence = 0.01, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,hg = "hg19",PlotIndepSNP = TRUE, IndepSNP_window_size = 50,IndepSNP_step_size = 5,IndepSNP_r2_threshold = 0.02,highLD_regions = highLD_hg19)
#> ℹ IndepSNPs needed to be supplied else all SNPs are being used.
#> Processing chromosome 1
#> An error occurred: incorrect number of dimensions

# Chromosome-wise Heritability Estimate
H2
#> NULL

Computing genetic correlation between traits using GeneticCorrBT()

help(GeneticCorrBT, package = GXwasR)

GeneticCorrBT

R Documentation

GeneticCorrBT: Computing genetic correlation between two traits.

Description

This function computes genetic correlation, a quantitative genetic measure that describes the genetic link between two traits and has been predicted to indicate pleiotropic gene activity or correlation between causative loci in two traits. For example, it does a bivariate GREML analysis to determine the genetic association between two quantitative traits, two binary disease traits from case-control studies, and between a quantitative trait and a binary disease trait following (Yang et al. 2011; Lee et al. 2012). If users want, this function gives the flexibility to compute the genetic correlation chromosome-wise.

Usage

GeneticCorrBT(
  DataDir,
  ResultDir,
  finput,
  byCHR = FALSE,
  REMLalgo = c(0, 1, 2),
  nitr = 100,
  phenofile,
  cat_covarfile = NULL,
  quant_covarfile = NULL,
  computeGRM = TRUE,
  grmfile_name = NULL,
  partGRM = FALSE,
  autosome = TRUE,
  Xsome = TRUE,
  nGRM = 3,
  cripticut = 0.025,
  minMAF = NULL,
  maxMAF = NULL,
  excludeResidual = FALSE,
  ncores = 2
)

Arguments

DataDir

A character string for the file path of the all the input files.

ResultDir

A character string for the file path where all output files will be stored. The default is tempdir().

finput

Character string, specifying the prefix of the input PLINK binary files for the genotype data. This file needs to be in DataDir.

byCHR

Boolean value, TRUE or FALSE, specifying whether the analysis will be performed chromosome wise or not. The default is FALSE.

REMLalgo

Integer value of 0, 1 or 2, specifying the algorithm to run REML iterations, 0 for average information (AI), 1 for Fisher-scoring and 2 for EM. The default option is 0, i.e. AI-REML (1).

nitr

Integer value, specifying the number of iterations for performing the REML. The default is 100.

phenofile

A dataframe for Bivar RELM has four columns ⁠family ID⁠, ⁠individual ID⁠ and two trait columns. For binary trait, the phenotypic value should be coded as 0 or 1, then it will be recognized as a case-control study (0 for controls and 1 for cases). Missing value should be represented by "-9" or "NA".

cat_covarfile

A character string, specifying the name of the categorical covariate file which is a plain text file with no header line; columns are ⁠family ID⁠, ⁠individual ID⁠ and discrete covariates. The default is NULL. This file needs to be in DataDir.

quant_covarfile

A character string, specifying the name of the quantitative covariate file which is a plain text file with no header line; columns are ⁠family ID⁠, ⁠individual ID⁠ and continuous covariates. The default is NULL. This file needs to be in DataDir.

computeGRM

Boolean value, TRUE or FALSE, specifying whether to compute GRM matrices or not. The default is TRUE.

grmfile_name

A string of characters specifying the prefix of autosomal .grm.bin file. Users need to provide separate GRM files for autosomes and X chromosome in ResultDir. The X chromosomal GRM file should have "x" added in the autosomal prefix as file name.

For instance, if autosomal file is "ABC.grm.bin", then X chromosomal file should be "xABC.grm.bim". If you are providing chromosome-wise GRMs, then the prefix should add "ChrNumber_" at the starting of the prefix like, "Chr1_ABC.grm.bin". The default is NULL.

partGRM

Boolean value, TRUE or FALSE, specifying whether the GRM will be partitioned into n parts (by row) in GREML model. The default is FALSE.

autosome

Boolean value, TRUE or FALSE, specifying whether estimate of heritability will be done for autosomes or not. The default is TRUE.

Xsome

Boolean value, TRUE or FALSE, specifying whether estimate of heritability will be done for X chromosome or not. The default is TRUE.

nGRM

Integer value, specifying the number of the partition of the GRM in GREML model. The default is 3.

cripticut

Numeric value, specifying the threshold to create a new GRM of "unrelated" individuals in GREML model. The default is arbitrary chosen as 0.025 following (Yang et al. 2011).

minMAF

Positive numeric value (< maxMAF), specifying the minimum threshold for the MAF filter of the SNPs in the Bivariate GREML model.

maxMAF

Positive numeric value (minMAF,1), specifying the maximum threshold for the MAF filter of the SNPs in the Bivariate GREML model.

excludeResidual

Boolean value, TRUE or FALSE, specifying whether to drop the residual covariance from the model. Recommended to set this TRUE if the traits were measured on different individuals. The default is FALSE.

ncores

Integer value, specifying the number of cores to be used.

Value

A dataframe with minimum three columns:

Source" (i.e., source of heritability)
Variance" (i.e. estimated heritability)
SE" (i.e., standard error of the estimated heritability)

Source column will have rows, such as V(G)_tr1 (genetic variance for trait 1), V(G)_tr2 (genetic variance for trait 2), C(G)_tr12 (genetic covariance between traits 1 and 2),V(e)_tr1 (residual variance for trait 1), V(e)_tr2 (residual variance for trait 2), C(e)_tr12 (residual covariance between traits 1 and 2), Vp_tr1 (proportion of variance explained by all SNPs for trait 1), Vp_tr2 (proportion of variance explained by all SNPs for trait 2), V(G)/Vp_tr1 (phenotypic variance for trait 1), V(G)/Vp_tr2 (phenotypic variance for trait 2), rG (genetic correlation) and n (sample size). In case of chromosome-wise analysis, there will be 'chromosome' column for chromosome code.

References

Lee SH, Wray NR, Goddard ME, Visscher PM (2012). “Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood.” Bioinformatics, 28, 2540–2542. doi:10.1093/bioinformatics/bts474, http://www.ncbi.nlm.nih.gov/pubmed/22843982.

Yang J, Lee SH, Goddard ME, Visscher PM (2011). “GCTA: a tool for Genome-wide Complex Trait Analysis.” American Journal of Human Genetics, 88(1), 76–82.

Examples

data("Example_phenofile", package = "GXwasR")
DataDir <- GXwasR:::GXwasR_data()
ResultDir <- tempdir()
finput <- "GXwasR_example"
byCHR <- TRUE
REMLalgo <- 0
nitr <- 3
ncores <- 3
phenofile <- Example_phenofile # Cannot be NULL
cat_covarfile <- NULL
quant_covarfile <- NULL
partGRM <- FALSE # Partition the GRM into m parts (by row),
autosome <- TRUE
Xsome <- TRUE
cripticut <- 0.025
minMAF <- 0.01 # if MAF filter apply
maxMAF <- 0.04
excludeResidual <- TRUE

genetic_correlation <- GeneticCorrBT(
    DataDir = DataDir, ResultDir = ResultDir, finput = finput, byCHR = byCHR,
    REMLalgo = 0, nitr = 10, phenofile = phenofile, cat_covarfile = NULL, quant_covarfile = NULL,
    partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,
    cripticut = 0.025, minMAF = NULL, maxMAF = NULL, excludeResidual = TRUE, ncores = ncores
)

Running GeneticCorrBT() by computing GRM genome-wide


# Running
DataDir <- GXwasR:::GXwasR_data()
ResultDir <- tempdir()
finput <- "GXwasR_example"
byCHR = FALSE
REMLalgo = 0
nitr <- 3
ncores <- 3
data("Example_phenofile", package = "GXwasR")
phenofile <- Example_phenofile #Cannot be NULL, the interested phenotype column should be labeled as
cat_covarfile <- NULL
quant_covarfile <- NULL
partGRM <- FALSE #Partition the GRM into m parts (by row),
autosome = TRUE
Xsome <- TRUE
cripticut = 0.025
minMAF <- 0.01 # if MAF filter apply
maxMAF <- 0.04
excludeResidual = TRUE
GC <- GeneticCorrBT(DataDir = DataDir, ResultDir = ResultDir, finput = finput, byCHR = byCHR,
                    REMLalgo = 0, nitr = nitr, phenofile = phenofile, cat_covarfile = NULL, quant_covarfile = NULL, computeGRM = TRUE, grmfile_name = NULL, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,excludeResidual = TRUE, ncores = ncores)
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/GXwasR.grm.N.bin]
#> 
#> 
#> 
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.N.bin]
#> 
#> 
#> 
#> ℹ computeGRM is set to: TRUE
#> ℹ Creating multi_GRMs.txt because computeGRM is TRUE
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ℹ Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite. In this case, the SE is unreliable.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
##Genetic correlation from the last iteration. In this example there was no rg value since the program stopped after nitr-1 = 2 iteration due to convergence issue.
GC

	Source	Variance
2	logL	347.85
3	V(G1)_tr1	0.00000
4	V(G1)_tr2	0.00000
5	C(G1)_tr12	0.00000
6	V(G2)_tr1	0.04243
7	V(G2)_tr2	0.00547
8	C(G2)_tr12	-0.00060
9	V(e)_tr1	0.17746
10	V(e)_tr2	0.00470

Running GeneticCorrBT() using precomputed genome-wide GRM


byCHR = FALSE
computeGRM = FALSE
grmfile_name = "GXwasR"

GC <- GeneticCorrBT(DataDir = DataDir, ResultDir = ResultDir, finput = finput, byCHR = byCHR,
                    REMLalgo = 0, nitr = nitr, phenofile = phenofile, cat_covarfile = NULL, quant_covarfile = NULL, computeGRM = FALSE, grmfile_name = grmfile_name, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,excludeResidual = TRUE, ncores = ncores)
#> ℹ Skipping GRM computation.
#> ℹ computeGRM is set to: FALSE
#> ℹ computeGRM is FALSE, checking grmfile_name
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ℹ Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite. In this case, the SE is unreliable.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
##Genetic correlation from the last iteration. In this example there was no rg value since the program stopped after nitr-1 = 2 iteration due to convergence issue.
GC

	Source	Variance
2	logL	347.85
3	V(G1)_tr1	0.00000
4	V(G1)_tr2	0.00000
5	C(G1)_tr12	0.00000
6	V(G2)_tr1	0.04243
7	V(G2)_tr2	0.00547
8	C(G2)_tr12	-0.00060
9	V(e)_tr1	0.17746
10	V(e)_tr2	0.00470

Running GeneticCorrBT() by computing GRM chromosome-wise

byCHR = TRUE
computeGRM = TRUE
grmfile_name = NULL

GC <- GeneticCorrBT(DataDir = DataDir, ResultDir = ResultDir, finput = finput, byCHR = byCHR,
                    REMLalgo = 0, nitr = nitr, phenofile = phenofile, cat_covarfile = NULL, quant_covarfile = NULL, computeGRM = computeGRM, grmfile_name = grmfile_name, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,excludeResidual = TRUE, ncores = ncores)
#> Processing chromosome 1
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 2
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ℹ Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite. In this case, the SE is unreliable.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 3
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 4
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 5
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 6
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 7
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 8
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ℹ Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite. In this case, the SE is unreliable.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 9
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 10
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 23
#> ✔ GRM has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.bin]
#> ℹ Number of SNPs in each pair of individuals has been saved in the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.N.bin]
#> 
#> 
#> 
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 24
#> ✖ Error: Log-likelihood not converged (stop after 3 iteractions). 
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
GC

chromosome	Source	Variance
1	V.G._tr1	0.05138
1	V.G._tr2	0.00213
1	C.G._tr12	0.00469
1	V.e._tr1	0.18285
1	V.e._tr2	0.03878
1	X	NA
2	V.G._tr1	0.04528
2	V.G._tr2	0.00082
2	C.G._tr12	0.00608
2	V.e._tr1	0.18797
2	V.e._tr2	0.04005
2	X	NA
3	V.G._tr1	0.04650
3	V.G._tr2	0.01572
3	C.G._tr12	0.00651
3	V.e._tr1	0.18726
3	V.e._tr2	0.02617
3	X	NA
4	V.G._tr1	0.04069
4	V.G._tr2	0.01256
4	C.G._tr12	0.00562
4	V.e._tr1	0.19310
4	V.e._tr2	0.02917
4	X	NA
5	V.G._tr1	0.04711
5	V.G._tr2	0.00633
5	C.G._tr12	0.00598
5	V.e._tr1	0.18671
5	V.e._tr2	0.03486
5	X	NA
6	V.G._tr1	0.02092
6	V.G._tr2	0.01238
6	C.G._tr12	0.00481
6	V.e._tr1	0.20929
6	V.e._tr2	0.02924
6	X	NA
7	V.G._tr1	0.04212
7	V.G._tr2	0.00460
7	C.G._tr12	0.00347
7	V.e._tr1	0.19032
7	V.e._tr2	0.03621
7	X	NA
8	V.G._tr1	0.01087
8	V.G._tr2	0.00261
8	C.G._tr12	0.00533
8	V.e._tr1	0.21702
8	V.e._tr2	0.03861
8	X	NA
9	V.G._tr1	0.03287
9	V.G._tr2	0.00896
9	C.G._tr12	0.00466
9	V.e._tr1	0.19717
9	V.e._tr2	0.03229
9	X	NA
10	V.G._tr1	0.01271
10	V.G._tr2	0.00711
10	C.G._tr12	0.00667
10	V.e._tr1	0.21588
10	V.e._tr2	0.03385
10	X	NA
23	V.G._tr1	0.13520
23	V.G._tr2	0.00746
23	C.G._tr12	0.00435
23	V.e._tr1	0.09522
23	V.e._tr2	0.03324
23	X	NA
24	V.G._tr1	0.01271
24	V.G._tr2	0.00711
24	C.G._tr12	0.00667
24	V.e._tr1	0.21588
24	V.e._tr2	0.03385
24	X	NA

Running GeneticCorrBT() using chromosome-wide pre-computed GRM

byCHR = TRUE
computeGRM = FALSE
grmfile_name = "GXwasR"

GC <- GeneticCorrBT(DataDir = DataDir, ResultDir = ResultDir, finput = finput, byCHR = byCHR,
                    REMLalgo = 0, nitr = nitr, phenofile = phenofile, cat_covarfile = NULL, quant_covarfile = NULL, computeGRM = computeGRM, grmfile_name = grmfile_name, partGRM = FALSE, autosome = TRUE, Xsome = TRUE, nGRM = 3,cripticut = 0.025, minMAF = NULL, maxMAF = NULL,excludeResidual = TRUE, ncores = ncores)
#> Processing chromosome 1
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr1_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 2
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr2_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 3
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr3_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 4
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr4_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 5
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr5_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 6
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr6_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 7
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr7_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 8
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr8_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 9
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr9_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 10
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr10_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 23
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/xGXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
#> 
#> 
#> 
#> Processing chromosome 24
#> ✖ Error: cannot open the file [/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpXETxP2/Chr24_GXwasR.grm.id] to read.
#> ✖ Convergence issue occurred, please:
#>   - set byCHR = TRUE
#>   - set different options
#>   - verify SNP partitioning or quality of the data
#> ℹ The result will be provided for the last iteration.
GC

chromosome	Source	Variance
1	NA	NA
2	NA	NA
3	NA	NA
4	NA	NA
5	NA	NA
6	NA	NA
7	NA	NA
8	NA	NA
9	NA	NA
10	NA	NA
23	NA	NA
24	NA	NA

Note: When computeGRM = FALSE, the function assumes that all required GRM files already exist in ResultDir with the expected naming convention (e.g., Chr1_GXwasR.grm.*). If these files are not present, the analysis will fail because GCTA will be unable to locate the input GRM files.

Citing `GXwasR`

We hope that GXwasR will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("GXwasR")
#> To cite package 'GXwasR' in publications use:
#> 
#>   Bose B, Blostein F, Kim J, Winters J, Actkins KV, Mayer D, Congivaram
#>   H, Niarchou M, Edwards DV, Davis LK, Stranger BE (2025). "GXwasR: A
#>   Toolkit for Investigating Sex-Differentiated Genetic Effects on
#>   Complex Traits." _medRxiv 2025.06.10.25329327_.
#>   doi:10.1101/2025.06.10.25329327
#>   <https://doi.org/10.1101/2025.06.10.25329327>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {GXwasR: A Toolkit for Investigating Sex-Differentiated Genetic Effects on Complex Traits},
#>     author = {Banabithi Bose and Freida Blostein and Jeewoo Kim and Jessica Winters and Ky’Era V. Actkins and David Mayer and Harrsha Congivaram and Maria Niarchou and Digna Velez Edwards and Lea K. Davis and Barbara E. Stranger},
#>     journal = {medRxiv 2025.06.10.25329327},
#>     year = {2025},
#>     doi = {10.1101/2025.06.10.25329327},
#>   }

Reproducibility

The GXwasR package (Bose, Blostein, Kim, Winters, Actkins, Mayer, Congivaram, Niarchou, Edwards, Davis, and Stranger, 2025) was made possible thanks to:

R (R Core Team, 2025)
BiocStyle (Oleś, 2025)
knitr (Xie, 2025)
RefManageR (McLean, 2017)
rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2025)
sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2025)
testthat (Wickham, 2011)

This package was developed using biocthis.

R session information.

#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.5.1 (2025-06-13)
#>  os       macOS Sequoia 15.7.1
#>  system   aarch64, darwin24.4.0
#>  ui       unknown
#>  language en-US
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Bibliography

This vignette was generated using BiocStyle (Oleś, 2025) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux et al., 2025) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.30. 2025. URL: https://github.com/rstudio/rmarkdown.

[2] B. Bose, F. Blostein, J. Kim, et al. “GXwasR: A Toolkit for Investigating Sex-Differentiated Genetic Effects on Complex Traits”. In: medRxiv 2025.06.10.25329327 (2025). DOI: 10.1101/2025.06.10.25329327.

[3] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.36.0. 2025. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.

[5] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2025. URL: https://www.R-project.org/.

[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.3. 2025. DOI: 10.32614/CRAN.package.sessioninfo. URL: https://CRAN.R-project.org/package=sessioninfo.

[8] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50. 2025. URL: https://yihui.org/knitr/.

chromosome	Source	Variance
1	NA	NA
2	NA	NA
3	NA	NA
4	NA	NA
5	NA	NA
6	NA	NA
7	NA	NA
8	NA	NA
9	NA	NA
10	NA	NA
23	NA	NA
24	NA	NA

chromosome	Source	Variance
1	NA	NA
2	NA	NA
3	NA	NA
4	NA	NA
5	NA	NA
6	NA	NA
7	NA	NA
8	NA	NA
9	NA	NA
10	NA	NA
23	NA	NA
24	NA	NA

Banabithi Bose

16 October 2025

Computing heritability estimate using EstimateHerit():

EstimateHerit: Computing SNP heritability i.e., the proportion of phenotypic variance explained by SNPs.

Description

Usage

Arguments

Value

Author(s)

References

Examples

Running EstimateHerit() with model = “GREML”

Running the EstimateHerit() by providing chromosome-wise GRM files in ResultDir

Running the EstimateHerit() for genome-wide heritability estimate by creating GRM files

Running the EstimateHerit() for genome-wide heritability estimate by utilizing the pre-computed GRM files in ResultDir

Running EstimateHerit() genome-wide with model = “LDSC”

Running EstimateHerit() chromosome-wise with model = “LDSC”

Computing genetic correlation between traits using GeneticCorrBT()

GeneticCorrBT: Computing genetic correlation between two traits.

Description

Usage

Arguments

Value

References

Examples

Running GeneticCorrBT() by computing GRM genome-wide

Running GeneticCorrBT() using precomputed genome-wide GRM

Running GeneticCorrBT() by computing GRM chromosome-wise

Running GeneticCorrBT() using chromosome-wide pre-computed GRM

Citing GXwasR

Reproducibility

Bibliography

Citing `GXwasR`

chromosome	Source	Variance
1	NA	NA
2	NA	NA
3	NA	NA
4	NA	NA
5	NA	NA
6	NA	NA
7	NA	NA
8	NA	NA
9	NA	NA
10	NA	NA
23	NA	NA
24	NA	NA