Compute prediction accuracy for a quantitative or binary trait

Description

Compute prediction accuracy for a quantitative or binary trait

Usage

acc(yobs = NULL, ypred = NULL, typeoftrait = "quantitative")

Arguments

LD pruning of summary statistics

Description

Perform LD pruning of summary statistics before they are used in gene set enrichment analyses.

Usage

adjLD(
  stat = NULL,
  Glist = NULL,
  chr = NULL,
  statistics = "p-value",
  r2 = 0.9,
  ldSets = NULL,
  threshold = 1,
  method = "pruning"
)

Arguments

Check concordance between marker effect and sparse LD matrix.

Description

Check concordance between predicted and observed marker effect. Marker effect is predicted based on sparse LD matrix in Glist.

Usage

adjLDStat(
  stat = NULL,
  Glist = NULL,
  chr = NULL,
  region = NULL,
  msize = NULL,
  threshold = 1e-05,
  overlap = NULL,
  niter = 5
)

Arguments

Adjustment of marker summary statistics using clumping and thresholding

Description

Adjust marker summary statistics using linkage disequilibrium information from Glist.

Usage

adjStat(
  stat = NULL,
  Glist = NULL,
  chr = NULL,
  statistics = "b",
  r2 = 0.9,
  ldSets = NULL,
  threshold = 1,
  header = NULL,
  method = "pruning"
)

Arguments

Details

Required input format for summary statistics:

stat can be a data.frame(rsids, chr, pos, ea, nea, eaf, b, seb, stat, p, n) (single trait)

stat can be a list(marker=(rsids, chr, pos, ea, nea, eaf), b, seb, stat, p, n) (multiple trait)

For details about the summary statistics format, see the main function description.

Author(s)

Peter Soerensen

Adjust B-values

Description

This function adjusts the B-values based on the LD structure and other parameters. The adjustment is done in subsets, and a plot of observed vs. predicted values is produced for each subset.

Usage

adjustB(
  b = NULL,
  seb = NULL,
  LD = NULL,
  msize = 100,
  overlap = 50,
  nrep = 20,
  shrink = 0.001,
  threshold = 1
)

Arguments

Value

A list containing the adjusted B-values.

Adjust Linkage Disequilibrium (LD) Using Map Information

Description

Adjusts the linkage disequilibrium (LD) values based on map information, effective population size, and sample size used for map construction.

Usage

adjustMapLD(LD = NULL, map = NULL, neff = 11600, nmap = 186, threshold = 0.001)

Arguments

Value

A matrix where each value is the adjusted LD based on the input map, effective population size, and map construction sample size.

Compute AUC

Description

Compute Area Under the Curve (AUC)

Usage

auc(yobs = NULL, ypred = NULL)

Arguments

Quality Control of Marker Summary Statistics

Description

Quality control is a fundamental step in GWAS summary statistics analysis. The function is equipped to handle various tasks including mapping marker ids, checking the effect allele and its frequency, determining build versions, and excluding data based on multiple criteria.

Usage

checkStat(
  Glist = NULL,
  stat = NULL,
  excludeMAF = 0.01,
  excludeMAFDIFF = 0.05,
  excludeINFO = 0.8,
  excludeCGAT = TRUE,
  excludeINDEL = TRUE,
  excludeDUPS = TRUE,
  excludeMHC = FALSE,
  excludeMISS = 0.05,
  excludeHWE = 1e-12
)

Arguments

Details

Performs quality control on GWAS summary statistics, which includes: - Mapping marker ids to LD reference panel data. - Checking effect allele, frequency, and build version. - Excluding based on various criteria like MAF, HWE, INDELS, and more.

The function works with both "internal" and "external" formats of summary statistics. When the summary statistics format is "external", the function maps marker ids based on chr-pos-ref-alt information. It also aligns the effect allele with the LD reference panel and flips effect sizes if necessary. When allele frequencies are not provided, it uses the frequencies from the genotype data.

Required headers for external summary statistics: marker, chr, pos, ea, nea, eaf, b, seb, stat, p, n

Required headers for internal summary statistics: rsids, chr, pos, ea, nea, eaf, b, seb, stat, p, n

Value

A data frame with processed and quality-controlled summary statistics.

Author(s)

Peter Soerensen

Compute Receiver Operating Curve statistics

Description

Compute ROC

Usage

computeROC(yobs = NULL, ypred = NULL)

Arguments

Author(s)

Palle Duun Rohde

Create Linkage Disequilibrium (LD) Sets

Description

This function partitions a vector of LD scores into sets or blocks based on cumulative LD scores, with constraints on block size and separation. The method ensures that blocks are evenly distributed and reduces overlap based on LD patterns.

Usage

createLDsets(
  ldscores = NULL,
  msize = 200,
  maxsize = 2000,
  nsplit = 200,
  verbose = FALSE
)

Arguments

Details

The function uses cumulative sums of LD scores to create blocks of markers that minimize overlap while satisfying block size constraints. Blocks are defined iteratively by identifying regions with low cumulative LD scores and expanding them until they reach the defined block size limits.

- **Cumulative LD Calculation:** The function calculates the average cumulative LD scores over sliding windows of size msize. - **Block Splitting:** Regions with the lowest cumulative LD scores are selected iteratively to define block boundaries. - **Plotting:** If verbose = TRUE, the function generates two diagnostic plots: - Block sizes for each LD set. - Cumulative LD scores across genome positions, highlighting split positions.

Value

A list where each element is a vector of marker names corresponding to a block of LD scores.

Bayesian linear regression models

Description

Bayesian linear regression (BLR) models:

- unified mapping of genetic variants, estimation of genetic parameters (e.g. heritability) and prediction of disease risk)

- handles different genetic architectures (few large, many small effects)

- scale to large data (e.g. sparse LD)

In the Bayesian multiple regression model the posterior density of the model parameters depend on the likelihood of the data given the parameters and a prior probability for the model parameters

The prior density of marker effects defines whether the model will induce variable selection and shrinkage or shrinkage only. Also, the choice of prior will define the extent and type of shrinkage induced. Ideally the choice of prior for the marker effect should reflect the genetic architecture of the trait, and will vary (perhaps a lot) across traits.

The following prior distributions are provided:

Bayes N: Assigning a Gaussian prior to marker effects implies that the posterior means are the BLUP estimates (same as Ridge Regression).

Bayes L: Assigning a double-exponential or Laplace prior is the density used in the Bayesian LASSO

Bayes A: similar to ridge regression but t-distribution prior (rather than Gaussian) for the marker effects ; variance comes from an inverse-chi-square distribution instead of being fixed. Estimation via Gibbs sampling.

Bayes C: uses a “rounded spike” (low-variance Gaussian) at origin many small effects can contribute to polygenic component, reduces the dimensionality of the model (makes Gibbs sampling feasible).

Bayes R: Hierarchical Bayesian mixture model with 4 Gaussian components, with variances scaled by 0, 0.0001 , 0.001 , and 0.01 .

Usage

gbayes(
  y = NULL,
  X = NULL,
  W = NULL,
  stat = NULL,
  covs = NULL,
  trait = NULL,
  fit = NULL,
  Glist = NULL,
  chr = NULL,
  rsids = NULL,
  b = NULL,
  bm = NULL,
  seb = NULL,
  LD = NULL,
  n = NULL,
  formatLD = "dense",
  vg = NULL,
  vb = NULL,
  ve = NULL,
  ssg_prior = NULL,
  ssb_prior = NULL,
  sse_prior = NULL,
  lambda = NULL,
  scaleY = TRUE,
  h2 = NULL,
  pi = 0.001,
  updateB = TRUE,
  updateG = TRUE,
  updateE = TRUE,
  updatePi = TRUE,
  adjustE = TRUE,
  models = NULL,
  nug = 4,
  nub = 4,
  nue = 4,
  verbose = FALSE,
  msize = 100,
  mask = NULL,
  GRMlist = NULL,
  ve_prior = NULL,
  vg_prior = NULL,
  tol = 0.001,
  nit = 100,
  nburn = 0,
  nthin = 1,
  nit_local = NULL,
  nit_global = NULL,
  method = "mixed",
  algorithm = "mcmc"
)

Arguments

Value

Returns a list structure including

Author(s)

Peter Sørensen

Examples

# Simulate data and test functions

W <- matrix(rnorm(100000),nrow=1000)
set1 <- sample(1:ncol(W),5)
set2 <- sample(1:ncol(W),5)
sets <- list(set1,set2)
g <- rowSums(W[,c(set1,set2)])
e <- rnorm(nrow(W),mean=0,sd=1)
y <- g + e


fitM <- gbayes(y=y, W=W, method="bayesN")
fitA <- gbayes(y=y, W=W, method="bayesA")
fitL <- gbayes(y=y, W=W, method="bayesL")
fitC <- gbayes(y=y, W=W, method="bayesC")

Compute Genomic BLUP values

Description

Compute Genomic BLUP values based on linear mixed model fit output from greml

Usage

gblup(
  GRMlist = NULL,
  GRM = NULL,
  fit = NULL,
  ids = NULL,
  idsCLS = NULL,
  idsRWS = NULL
)

Arguments

Get elements from genotype matrix stored in PLINK bedfiles

Description

Extracts specific rows (based on ids or row numbers) and columns (based on rsids or column numbers) from a genotype matrix stored on disk. The extraction is based on provided arguments such as chromosome number, ids, rsids, etc. Genotypes can be optionally scaled and imputed.

Usage

getG(
  Glist = NULL,
  chr = NULL,
  bedfiles = NULL,
  bimfiles = NULL,
  famfiles = NULL,
  ids = NULL,
  rsids = NULL,
  rws = NULL,
  cls = NULL,
  impute = TRUE,
  scale = FALSE
)

Arguments

Details

This function facilitates the extraction of specific genotype data from storage based on various criteria. The extracted genotype data can be optionally scaled or imputed. If rsids are provided that are not found in the 'Glist', a warning is raised.

Value

A matrix with extracted genotypic data. Rows correspond to individuals, and columns correspond to SNPs. Row names are set to individual IDs, and column names are set to rsids.

Extract elements from genomic relationship matrix (GRM) stored on disk

Description

Extract elements from genomic relationship matrix (GRM) (whole or subset) stored on disk.

Usage

getGRM(
  GRMlist = NULL,
  ids = NULL,
  idsCLS = NULL,
  idsRWS = NULL,
  cls = NULL,
  rws = NULL
)

Arguments

Retrieve Sparse LD Matrix for a Given Chromosome

Description

Extracts and returns a sparse LD (Linkage Disequilibrium) matrix for the specified chromosome based on genotypic data provided in 'Glist'.

Usage

getLD(Glist = NULL, chr = NULL, rsids = NULL)

Arguments

Details

The function constructs the LD matrix by reading LD values from binary files stored in 'Glist$ldfiles'. Each column of the matrix represents a SNP from 'rsidsLD', and rows represent LD values for surrounding SNPs. The main diagonal (msize + 1 row) is set to 1 for all SNPs.

Value

A matrix containing LD values. The matrix is of size (msize * 2 + 1) x mchr, where mchr is the number of rsids in the chromosome. The returned matrix has column names corresponding to rsids in the chromosome, and row names representing relative positions to the current SNP, from -msize to msize.

Extract Linkage Disequilibrium (LD) Scores

Description

This function retrieves LD scores from a structured list ('Glist') that includes LD scores, chromosome information, and SNP identifiers. You can extract all LD scores or filter them by a specific chromosome and/or SNP identifiers (rsids).

Usage

getLDscores(Glist = NULL, chr = NULL, rsids = NULL)

Arguments

Value

A vector containing LD scores.

Get marker LD sets

Description

Extracts marker LD sets based on a sparse LD matrix stored in the Glist object.

Usage

getLDsets(Glist = NULL, chr = NULL, r2 = 0.5)

Arguments

Retrieve the map for specified rsids on a given chromosome.

Description

Fetch the map associated with provided rsids for a given chromosome from the list 'Glist'.

Usage

getMap(Glist = NULL, chr = NULL, rsids = NULL)

Arguments

Value

A vector containing the map corresponding to the specified rsids on the given chromosome.

Retrieve marker rsids in a specified genome region.

Description

Get marker rsids (reference SNP cluster IDs) from a specified genome region based on markers present in 'Glist'.

Usage

getMarkers(Glist = NULL, chr = NULL, region = NULL)

Arguments

Value

A vector of rsids that fall within the specified region on the given chromosome.

Retrieve the positions for specified rsids on a given chromosome.

Description

Fetch the genomic positions associated with provided rsids for a given chromosome from the list 'Glist'.

Usage

getPos(Glist = NULL, chr = NULL, rsids = NULL)

Arguments

Value

A vector containing the positions corresponding to the specified rsids on the given chromosome.

Extract Sparse Linkage Disequilibrium (LD) Information

Description

Retrieves and formats linkage disequilibrium (LD) data from binary files based on a specified chromosome and LD threshold. It provides options for returning the data in sparse or dense format.

Usage

getSparseLD(
  Glist = NULL,
  chr = NULL,
  r2 = 0,
  onebased = FALSE,
  rsids = NULL,
  format = "sparse"
)

Arguments

Value

If 'format' is "sparse", a list with two components: 'indices' and 'values'. Each component is a list of length equal to the number of rsids in the specified chromosome. If 'format' is "dense", a matrix with rows and columns named after the rsids is returned.

Filter genetic marker data based on different quality measures

Description

Quality control is a critical step for working with summary statistics (in particular for external). Processing and quality control of GWAS summary statistics includes:

- map marker ids (rsids/cpra (chr, pos, ref, alt)) to LD reference panel data - check effect allele (flip EA, EAF, Effect) - check effect allele frequency - thresholds for MAF and HWE - exclude INDELS, CG/AT and MHC region - remove duplicated marker ids - check which build version - check for concordance between marker effect and LD data

External summary statistics format: marker, chr, pos, effect_allele, non_effect_allele, effect_allele_freq, effect, effect_se, stat, p, n

Internal summary statistics format: rsids, chr, pos, a1, a2, af, b, seb, stat, p, n

Usage

gfilter(
  Glist = NULL,
  excludeMAF = 0.01,
  excludeMISS = 0.05,
  excludeINFO = NULL,
  excludeCGAT = TRUE,
  excludeINDEL = TRUE,
  excludeDUPS = TRUE,
  excludeHWE = 1e-12,
  excludeMHC = FALSE,
  assembly = "GRCh37"
)

Arguments

Author(s)

Peter Soerensen

Single marker association analysis using linear models or linear mixed models

Description

The function glma performs single marker association analysis between genotype markers and the phenotype either based on linear model analysis (LMA) or mixed linear model analysis (MLMA).

The basic MLMA approach involves 1) building a genetic relationship matrix (GRM) that models genome-wide sample structure, 2) estimating the contribution of the GRM to phenotypic variance using a random effects model (with or without additional fixed effects) and 3) computing association statistics that account for this component on phenotypic variance.

MLMA methods are the method of choice when conducting association mapping in the presence of sample structure, including geographic population structure, family relatedness and/or cryptic relatedness. MLMA methods prevent false positive associations and increase power. The general recommendation when using MLMA is to exclude candidate markers from the GRM. This can be efficiently implemented via a leave-one-chromosome-out analysis. Further, it is recommend that analyses of randomly ascertained quantitative traits should include all markers (except for the candidate marker and markers in LD with the candidate marker) in the GRM, except as follows. First, the set of markers included in the GRM can be pruned by LD to reduce running time (with association statistics still computed for all markers). Second, genome-wide significant markers of large effect should be conditioned out as fixed effects or as an additional random effect (if a large number of associated markers). Third, when population stratification is less of a concern, it may be useful using the top associated markers selected based on the global maximum from out-of sample predictive accuracy.

Usage

glma(
  y = NULL,
  X = NULL,
  W = NULL,
  Glist = NULL,
  chr = NULL,
  fit = NULL,
  verbose = FALSE,
  statistic = "mastor",
  ids = NULL,
  rsids = NULL,
  msize = 100,
  scale = TRUE
)

Arguments

Value

Returns a dataframe (if number of traits = 1) else a list including

Author(s)

Peter Soerensen

References

Chen, W. M., & Abecasis, G. R. (2007). Family-based association tests for genomewide association scans. The American Journal of Human Genetics, 81(5), 913-926.

Loh, P. R., Tucker, G., Bulik-Sullivan, B. K., Vilhjalmsson, B. J., Finucane, H. K., Salem, R. M., ... & Patterson, N. (2015). Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nature genetics, 47(3), 284-290.

Kang, H. M., Sul, J. H., Zaitlen, N. A., Kong, S. Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010). Variance component model to account for sample structure in genome-wide association studies. Nature genetics, 42(4), 348-354.

Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011). FaST linear mixed models for genome-wide association studies. Nature methods, 8(10), 833-835.

Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. (2012). Improved linear mixed models for genome-wide association studies. Nature methods, 9(6), 525-526.

Listgarten, J., Lippert, C., & Heckerman, D. (2013). FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nature Genetics, 45(5), 470-471.

Lippert, C., Quon, G., Kang, E. Y., Kadie, C. M., Listgarten, J., & Heckerman, D. (2013). The benefits of selecting phenotype-specific variants for applications of mixed models in genomics. Scientific reports, 3.

Zhou, X., & Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nature genetics, 44(7), 821-824.

Svishcheva, G. R., Axenovich, T. I., Belonogova, N. M., van Duijn, C. M., & Aulchenko, Y. S. (2012). Rapid variance components-based method for whole-genome association analysis. Nature genetics, 44(10), 1166-1170.

Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014). Advantages and pitfalls in the application of mixed-model association methods. Nature genetics, 46(2), 100-106.

Bulik-Sullivan, B. K., Loh, P. R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., ... & Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics, 47(3), 291-295.

Examples

# Simulate data
W <- matrix(rnorm(1000000), ncol = 1000)
	colnames(W) <- as.character(1:ncol(W))
	rownames(W) <- as.character(1:nrow(W))
y <- rowSums(W[, 1:10]) + rowSums(W[, 501:510]) + rnorm(nrow(W))

# Create model
data <- data.frame(y = y, mu = 1)
fm <- y ~ 0 + mu
X <- model.matrix(fm, data = data)

# Linear model analyses and single marker association test
stat <- glma(y=y,X=X,W = W)

head(stat)


# Compute GRM
GRM <- grm(W = W)

# Estimate variance components using REML analysis
fit <- greml(y = y, X = X, GRM = list(GRM), verbose = TRUE)

# Single marker association test
stat <- glma(fit = fit, W = W)

head(stat)

Finemapping using Bayesian Linear Regression Models

Description

In the Bayesian multiple regression model, the posterior density of the model parameters depends on the likelihood of the data given the parameters and a prior probability for the model parameters. The choice of the prior for marker effects can influence the type and extent of shrinkage induced in the model.

Usage

gmap(
  Glist = NULL,
  stat = NULL,
  sets = NULL,
  models = NULL,
  rsids = NULL,
  ids = NULL,
  mask = NULL,
  lambda = NULL,
  vb = NULL,
  vg = NULL,
  ve = NULL,
  vy = NULL,
  pi = NULL,
  gamma = NULL,
  mc = 5000,
  h2 = 0.5,
  nub = 4,
  nug = 4,
  nue = 4,
  ssb_prior = NULL,
  ssg_prior = NULL,
  sse_prior = NULL,
  vb_prior = NULL,
  vg_prior = NULL,
  ve_prior = NULL,
  updateB = TRUE,
  updateG = TRUE,
  updateE = TRUE,
  updatePi = TRUE,
  formatLD = "dense",
  checkLD = FALSE,
  shrinkLD = FALSE,
  shrinkCor = FALSE,
  pruneLD = FALSE,
  checkConvergence = FALSE,
  critVe = 3,
  critVg = 3,
  critVb = 3,
  critPi = 3,
  critB = 3,
  critB1 = 0.5,
  critB2 = 3,
  verbose = FALSE,
  eigen_threshold = 0.995,
  cs_threshold = 0.9,
  cs_r2 = 0.5,
  nit = 1000,
  nburn = 100,
  nthin = 1,
  output = "summary",
  method = "bayesR",
  algorithm = "mcmc-eigen",
  seed = 10
)

Arguments

Details

This function implements Bayesian linear regression models to provide unified mapping of genetic variants, estimate genetic parameters (e.g. heritability), and predict disease risk. It is designed to handle various genetic architectures and scale efficiently with large datasets.

Value

Returns a list structure including the following components:

Author(s)

Peter Sørensen

Prepare genotype data for all statistical analyses

Description

All functions in qgg relies on a simple data infrastructure that takes five main input sources; phenotype data (y), covariate data (X), genotype data (G or Glist), a genomic relationship matrix (GRM or GRMlist) and genetic marker sets (sets).

The genotypes are stored in a matrix (n x m (individuals x markers)) in memory (G) or in a binary file on disk (Glist).

It is only for small data sets that the genotype matrix (G) can stored in memory. For large data sets the genotype matrix has to stored in a binary file on disk (Glist). Glist is as a list structure that contains information about the genotypes in the binary file.

The gprep function prepares the Glist, and is required for downstream analyses of large-scale genetic data. Typically, the Glist is prepared once, and saved as an *.Rdata-file.

The gprep function reads genotype information from binary PLINK files, and creates the Glist object that contains general information about the genotypes such as reference alleles, allele frequencies and missing genotypes, and construct a binary file on the disk that contains the genotypes as allele counts of the alternative allele (memory usage = (n x m)/4 bytes).

The gprep function can also be used to prepare sparse ld matrices. The r2 metric used is the pairwise correlation between markers (allele count alternative allele) in a specified region of the genome. The marker genotype is allele count of the alternative allele which is assumed to be centered and scaled.

The Glist structure is used as input parameter for a number of qgg core functions including: 1) construction of genomic relationship matrices (grm), 2) construction of sparse ld matrices, 3) estimating genomic parameters (greml), 4) single marker association analyses (glma), 5) gene set enrichment analyses (gsea), and 6) genomic prediction from genotypes and phenotypes (gsolve) or genotypes and summary statistics (gscore).

Usage

gprep(
  Glist = NULL,
  task = "prepare",
  study = NULL,
  fnBED = NULL,
  ldfiles = NULL,
  bedfiles = NULL,
  bimfiles = NULL,
  famfiles = NULL,
  mapfiles = NULL,
  ids = NULL,
  rsids = NULL,
  assembly = NULL,
  overwrite = FALSE,
  msize = 100,
  r2 = NULL,
  kb = NULL,
  cm = NULL,
  ncores = 1
)

Arguments

Value

Returns a list structure (Glist) with information about the genotypes.

Author(s)

Peter Soerensen

Examples

bedfiles <- system.file("extdata", "sample_chr1.bed", package = "qgg")
bimfiles <- system.file("extdata", "sample_chr1.bim", package = "qgg")
famfiles <- system.file("extdata", "sample_chr1.fam", package = "qgg")

Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles,
             famfiles=famfiles)

Genomic rescticted maximum likelihood (GREML) analysis

Description

The greml function is used for the estimation of genomic parameters (co-variance, heritability and correlation) for linear mixed models using restricted maximum likelihood estimation (REML) and genomic prediction using best linear unbiased prediction (BLUP).

The linear mixed model can account for multiple genetic factors (fixed and random genetic marker effects), adjust for complex family relationships or population stratification and adjust for other non-genetic factors including lifestyle characteristics. Different genetic architectures (infinitesimal, few large and many small effects) is accounted for by modeling genetic markers in different sets as fixed or random effects and by specifying individual genetic marker weights. Different genetic models (e.g. additive and non-additive) can be specified by providing additive and non-additive genomic relationship matrices (GRMs) (constructed using grm). The GRMs can be accessed from the R environment or from binary files stored on disk facilitating the analyses of large-scale genetic data.

The output contains estimates of variance components, fixed and random effects, first and second derivatives of log-likelihood and the asymptotic standard deviation of parameter estimates.

Assessment of predictive accuracy (including correlation and R2, and AUC for binary phenotypes) can be obtained by providing greml with a data frame, or a list that contains sample IDs used in the validation (see examples for details).

Genomic parameters can also be estimated with DMU (http://www.dmu.agrsci.dk/DMU/) if interface =”DMU”. This option requires DMU to be installed locally, and the path to the DMU binary files has to be specified (see examples below for details).

Usage

greml(
  y = NULL,
  X = NULL,
  GRMlist = NULL,
  GRM = NULL,
  theta = NULL,
  ids = NULL,
  validate = NULL,
  maxit = 100,
  tol = 1e-05,
  bin = NULL,
  ncores = 1,
  wkdir = getwd(),
  verbose = FALSE,
  interface = "R",
  fm = NULL,
  data = NULL
)

Arguments

Value

returns a list structure including:

Author(s)

Peter Soerensen

References

Lee, S. H., & van der Werf, J. H. (2006). An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree. Genetics Selection Evolution, 38(1), 25.

Examples

# Simulate data
W <- matrix(rnorm(1000000), ncol = 1000)
	colnames(W) <- as.character(1:ncol(W))
	rownames(W) <- as.character(1:nrow(W))
y <- rowSums(W[, 1:10]) + rowSums(W[, 501:510]) + rnorm(nrow(W))

# Create model
data <- data.frame(y = y, mu = 1)
fm <- y ~ 0 + mu
X <- model.matrix(fm, data = data)

# Compute GRM
GRM <- grm(W = W)

# REML analyses
fitG <- greml(y = y, X = X, GRM = list(GRM))


# REML analyses and cross validation

# Create marker sets
setsGB <- list(A = colnames(W)) # gblup model
setsGF <- list(C1 = colnames(W)[1:500], C2 = colnames(W)[501:1000]) # gfblup model
setsGT <- list(C1 = colnames(W)[1:10], C2 = colnames(W)[501:510]) # true model

GB <- lapply(setsGB, function(x) {grm(W = W[, x])})
GF <- lapply(setsGF, function(x) {grm(W = W[, x])})
GT <- lapply(setsGT, function(x) {grm(W = W[, x])})

n <- length(y)
fold <- 10
nvalid <- 5

validate <- replicate(nvalid, sample(1:n, as.integer(n / fold)))
cvGB <- greml(y = y, X = X, GRM = GB, validate = validate)
cvGF <- greml(y = y, X = X, GRM = GF, validate = validate)
cvGT <- greml(y = y, X = X, GRM = GT, validate = validate)

cvGB$accuracy
cvGF$accuracy
cvGT$accuracy

Computing the genomic relationship matrix (GRM)

Description

The grm function is used to compute a genomic relationship matrix (GRM) based on all, or a subset of marker genotypes. GRM for additive, and non-additive (dominance and epistasis) genetic models can be constructed. The output of the grm function can either be a within-memory GRM object (n x n matrix), or a GRM-list which is a list structure that contains information about the GRM stored in a binary file on the disk.

Usage

grm(
  Glist = NULL,
  GRMlist = NULL,
  ids = NULL,
  rsids = NULL,
  rws = NULL,
  cls = NULL,
  W = NULL,
  method = "add",
  scale = TRUE,
  msize = 100,
  ncores = 1,
  fnG = NULL,
  overwrite = FALSE,
  returnGRM = FALSE,
  miss = NA,
  impute = TRUE,
  pedigree = NULL,
  task = "grm"
)

Arguments

Value

Returns a genomic relationship matrix (GRM) if returnGRM=TRUE else a list structure (GRMlist) with information about the GRM stored on disk

Author(s)

Peter Soerensen

Examples

# Simulate data
W <- matrix(rnorm(1000000), ncol = 1000)
	colnames(W) <- as.character(1:ncol(W))
	rownames(W) <- as.character(1:nrow(W))

# Compute GRM
GRM <- grm(W = W)



# Eigen value decompostion GRM
eig <- grm(GRM=GRM, task="eigen")

Genomic scoring based on single marker summary statistics

Description

Computes genomic predictions using single marker summary statistics and observed genotypes.

Usage

gscore(
  Glist = NULL,
  chr = NULL,
  bedfiles = NULL,
  bimfiles = NULL,
  famfiles = NULL,
  stat = NULL,
  fit = NULL,
  ids = NULL,
  scaleMarker = TRUE,
  scaleGRS = TRUE,
  impute = TRUE,
  msize = 100,
  ncores = 1,
  verbose = FALSE
)

Arguments

Value

Returns the genomic scores based on the provided parameters.

Author(s)

Peter Soerensen

Examples

 ## Plink bed/bim/fam files
 bedfiles <- system.file("extdata", paste0("sample_chr",1:2,".bed"), package = "qgg")
 bimfiles <- system.file("extdata", paste0("sample_chr",1:2,".bim"), package = "qgg")
 famfiles <- system.file("extdata", paste0("sample_chr",1:2,".fam"), package = "qgg")
 
 # Summarize bed/bim/fam files
 Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles, famfiles=famfiles)
 
 # Simulate phenotype
 sim <- gsim(Glist=Glist, chr=1, nt=1)
 
 # Compute single marker summary statistics
 stat <- glma(y=sim$y, Glist=Glist, scale=FALSE)
 
 # Compute genomic scores
 gsc <- gscore(Glist = Glist, stat = stat)
 

Gene set enrichment analysis

Description

The function gsea can perform several different gene set enrichment analyses. The general procedure is to obtain single marker statistics (e.g. summary statistics), from which it is possible to compute and evaluate a test statistic for a set of genetic markers that measures a joint degree of association between the marker set and the phenotype. The marker set is defined by a genomic feature such as genes, biological pathways, gene interactions, gene expression profiles etc.

Currently, four types of gene set enrichment analyses can be conducted with gsea; sum-based, count-based, score-based, and our own developed method, the covariance association test (CVAT). For details and comparisons of test statistics consult doi:10.1534/genetics.116.189498.

The sum test is based on the sum of all marker summary statistics located within the feature set. The single marker summary statistics can be obtained from linear model analyses (from PLINK or using the qgg glma approximation), or from single or multiple component REML analyses (GBLUP or GFBLUP) from the greml function. The sum test is powerful if the genomic feature harbors many genetic markers that have small to moderate effects.

The count-based method is based on counting the number of markers within a genomic feature that show association (or have single marker p-value below a certain threshold) with the phenotype. Under the null hypothesis (that the associated markers are picked at random from the total number of markers, thus, no enrichment of markers in any genomic feature) it is assumed that the observed count statistic is a realization from a hypergeometric distribution.

The score-based approach is based on the product between the scaled genotypes in a genomic feature and the residuals from the liner mixed model (obtained from greml).

The covariance association test (CVAT) is derived from the fit object from greml (GBLUP or GFBLUP), and measures the covariance between the total genomic effects for all markers and the genomic effects of the markers within the genomic feature.

The distribution of the test statistics obtained from the sum-based, score-based and CVAT is unknown, therefore a circular permutation approach is used to obtain an empirical distribution of test statistics.

Usage

gsea(
  stat = NULL,
  sets = NULL,
  Glist = NULL,
  W = NULL,
  fit = NULL,
  g = NULL,
  e = NULL,
  threshold = 0.05,
  method = "sum",
  nperm = 1000,
  ncores = 1
)

Arguments

Value

Returns a dataframe or a list including

Author(s)

Peter Soerensen

Examples

 # Simulate data
 W <- matrix(rnorm(1000000), ncol = 1000)
 colnames(W) <- as.character(1:ncol(W))
 rownames(W) <- as.character(1:nrow(W))
 y <- rowSums(W[, 1:10]) + rowSums(W[, 501:510]) + rnorm(nrow(W))

 # Create model
 data <- data.frame(y = y, mu = 1)
 fm <- y ~ 0 + mu
 X <- model.matrix(fm, data = data)

 # Single marker association analyses
 stat <- glma(y=y,X=X,W=W)

 # Create marker sets
 f <- factor(rep(1:100,each=10), levels=1:100)
 sets <- split(as.character(1:1000),f=f)

 # Set test based on sums
 b2 <- stat[,"stat"]**2
 names(b2) <- rownames(stat)
 mma <- gsea(stat = b2, sets = sets, method = "sum", nperm = 100)
 head(mma)

 # Set test based on hyperG
 p <- stat[,"p"]
 names(p) <- rownames(stat)
 mma <- gsea(stat = p, sets = sets, method = "hyperg", threshold = 0.05)
 head(mma)


 G <- grm(W=W)
 fit <- greml(y=y, X=X, GRM=list(G=G), theta=c(10,1))

 # Set test based on cvat
 mma <- gsea(W=W,fit = fit, sets = sets, nperm = 1000, method="cvat")
 head(mma)

 # Set test based on score
 mma <- gsea(W=W,fit = fit, sets = sets, nperm = 1000, method="score")
 head(mma)

Genomic simulation

Description

Simulate Genotype and Phenotype Data

Usage

gsim(Glist = NULL, chr = 1, nt = 1, W = NULL, n = 1000, m = 1000, rsids = NULL)

Arguments

Details

This function simulates genotype and phenotype data based on the 'Glist', which is information about the genotype matrix.

Value

A list containing:

• y: Phenotypes.

• W: Matrix of centered and scaled genotypes.

• e: Errors.

• g: Genotype effect.

• b0, b1: Coefficients.

• set0, set1: Selected markers.

• causal: Causal markers.

Author(s)

Peter Soerensen

Examples

## Plink bed/bim/fam files
bedfiles <- system.file("extdata", paste0("sample_chr",1:2,".bed"), package = "qgg")
bimfiles <- system.file("extdata", paste0("sample_chr",1:2,".bim"), package = "qgg")
famfiles <- system.file("extdata", paste0("sample_chr",1:2,".fam"), package = "qgg")

# Summarize bed/bim/fam files
Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles, famfiles=famfiles)

# Simulate phenotype
sim <- gsim(Glist=Glist, chr=1, nt=1)
head(sim$y)
head(sim$e)
head(sim$causal)

Simulate Genetic Data Based on Given Parameters

Description

This function simulates phenotype data by random sampling of markers available on 'Glist'. Default parameters for the simulated phenotype reflect the genetic architecture assumed by BayesC prior(Habier et al., 2011). This function is under active development.

Usage

gsimC(Glist = NULL, h2 = NULL, m = NULL, prp.cau = NULL, n = NULL)

Arguments

Value

A list containing:

• y: Vector of simulated phenotypes.

• g: Vector of simulated genetic values.

• e: Vector of simulated residual effects.

• b: Vector of effect sizes of the simulated causal markers.

• causal: Vector of ids for the simulated causal markers.

• h2: Estimated heritability of the simulated phenotype.

Author(s)

Peter Soerensen

Merina Shrestha

Simulate Genetic Data Based on Given Parameters

Description

This function simulates phenotype data by random sampling of markers available on 'Glist'. Default parameters for the simulated phenotype reflect the genetic architecture assumed by BayesR prior(Erbe et al., 2012). Marker effect are sampled from mixture distributions leading to small, moderate and large effect sizes. This function is under active development.

Usage

gsimR(Glist = NULL, h2 = NULL, m = NULL, prp.cau = NULL, n = NULL)

Arguments

Value

A list containing:

• y: Vector of simulated phenotypes.

• g: Vector of simulated genetic values.

• e: Vector of simulated residual effects.

• b: list of vectors for effect sizes of the simulated causal markers in three classes.

• causal: list of vectors for ids for the simulated causal markers in three classes.

• h2: Estimated heritability of the simulated phenotype.

Author(s)

Peter Soerensen

Merina Shrestha

Solve linear mixed model equations

Description

The gsolve function is used for solving of linear mixed model equations. The algorithm used to solve the equation system is based on a Gauss-Seidel (GS) method (matrix-free with residual updates) that handles large data sets.

The linear mixed model fitted can account for multiple traits, multiple genetic factors (fixed or random genetic marker effects), adjust for complex family relationships or population stratification, and adjust for other non-genetic factors including lifestyle characteristics. Different genetic architectures (infinitesimal, few large and many small effects) is accounted for by modeling genetic markers in different sets as fixed or random effects and by specifying individual genetic marker weights.

Usage

gsolve(
  y = NULL,
  X = NULL,
  GRM = NULL,
  va = NULL,
  ve = NULL,
  Glist = NULL,
  W = NULL,
  ids = NULL,
  rsids = NULL,
  sets = NULL,
  scale = TRUE,
  lambda = NULL,
  weights = FALSE,
  maxit = 500,
  tol = 1e-05,
  method = "gsru",
  ncores = 1
)

Arguments

Author(s)

Peter Soerensen

Examples

# Simulate data
W <- matrix(rnorm(1000000), ncol = 1000)
	colnames(W) <- as.character(1:ncol(W))
	rownames(W) <- as.character(1:nrow(W))
m <- ncol(W)
causal <- sample(1:ncol(W),50)
y <- rowSums(W[,causal]) + rnorm(nrow(W),sd=sqrt(50))

X <- model.matrix(y~1)

Sg <- 50
Se <- 50
h2 <- Sg/(Sg+Se)
lambda <- Se/(Sg/m)
lambda <- m*(1-h2)/h2

# BLUP of single marker effects and total genomic effects based on Gauss-Seidel procedure
fit <- gsolve( y=y, X=X, W=W, lambda=lambda)

Perform Hardy Weinberg Equilibrium Test

Description

Perform Hardy Weinberg Equilibrium Test ££(HWE)

Usage

hwe(Glist = NULL)

Arguments

LD score regression

Description

The ldsc function is used for LDSC analysis

Usage

ldsc(
  Glist = NULL,
  ldscores = NULL,
  sets = NULL,
  method = "regression",
  residual = FALSE,
  z = NULL,
  b = NULL,
  seb = NULL,
  af = NULL,
  stat = NULL,
  tol = 1e-08,
  n = NULL,
  intercept = TRUE,
  what = "h2",
  maxZ2 = NULL,
  SE.h2 = FALSE,
  SE.rg = FALSE,
  blk = 200
)

Arguments

Value

Returns a matrix of heritability estimates when what="h2", and if SE.h2=TRUE standard errors (SE) and significance levels (P) are returned. If what="rg" an n-by-n matrix of correlations is returned where the diagonal elements being h2 estimates. If SE.rg=TRUE a list is returned with n-by-n matrices of genetic correlations, estimated standard errors and significance levels.

Author(s)

Peter Soerensen

Palle Duun Rohde

Examples

# Plink bed/bim/fam files
 #bedfiles <- system.file("extdata", paste0("sample_chr",1:2,".bed"), package = "qgg")
 #bimfiles <- system.file("extdata", paste0("sample_chr",1:2,".bim"), package = "qgg")
 #famfiles <- system.file("extdata", paste0("sample_chr",1:2,".fam"), package = "qgg")
 #
 ## Summarize bed/bim/fam files
 #Glist <- gprep(study="Example", bedfiles=bedfiles, bimfiles=bimfiles, famfiles=famfiles)

 #
 ## Filter rsids based on MAF, missingness, HWE
 #rsids <-  gfilter(Glist = Glist, excludeMAF=0.05, excludeMISS=0.05, excludeHWE=1e-12) 
 #
 ## Compute sparse LD (msize=size of LD window)
 ##ldfiles <- system.file("extdata", paste0("sample_chr",1:2,".ld"), package = "qgg")
 ##Glist <- gprep(Glist, task="sparseld", msize=200, rsids=rsids, ldfiles=ldfiles, overwrite=TRUE)
 #
 #
 ##Simulate data
 #W1 <- getG(Glist, chr=1, scale=TRUE)
 #W2 <- getG(Glist, chr=2, scale=TRUE)

 #W <- cbind(W1,W2)
 #causal <- sample(1:ncol(W),5)

 #b1 <- rnorm(length(causal))
 #b2 <- rnorm(length(causal))
 #y1 <- W[, causal]%*%b1 + rnorm(nrow(W))
 #y2 <- W[, causal]%*%b2 + rnorm(nrow(W))

 #data1 <- data.frame(y = y1, mu = 1)
 #data2 <- data.frame(y = y2, mu = 1)
 #X1 <- model.matrix(y ~ 0 + mu, data = data1)
 #X2 <- model.matrix(y ~ 0 + mu, data = data2)

 ## Linear model analyses and single marker association test
 #maLM1 <- lma(y=y1, X=X1,W = W)
 #maLM2 <- lma(y=y2,X=X2,W = W)
 #
 ## Compute heritability and genetic correlations for trait 1 and 2
 #z1 <- maLM1[,"stat"]
 #z2 <- maLM2[,"stat"]

 #z <- cbind(z1=z1,z2=z2)

 #h2 <- ldsc(Glist, z=z, n=c(500,500), what="h2")
 #rg <- ldsc(Glist, z=z, n=c(500,500), what="rg")

Compute LD (Linkage Disequilibrium) Scores for a Given Chromosome.

Description

This function calculates LD scores for the specified chromosome(s) based on genotypic data provided in 'Glist'. The LD score quantifies the amount of Linkage Disequilibrium at a given SNP.

Usage

ldscore(
  Glist = NULL,
  chr = NULL,
  onebased = TRUE,
  nbytes = 4,
  cm = NULL,
  kb = NULL
)

Arguments

Details

The function computes the LD scores for each SNP by reading LD values from binary files stored in 'Glist$ldfiles'. It can filter SNPs based on physical distance ('kb') or genetic map distance ('cm'). If both 'cm' and 'kb' are NULL, all LD values are used in computation.

Value

A list containing computed LD scores for each chromosome in the input.

Bayesian Multi-marker Analysis of Genomic Annotation (Bayesian MAGMA)

Description

This function analyzes feature sets using MAGMA or Bayesian methods for association testing. It supports joint or marginal testing, as well as Bayesian linear regression using different priors ('bayesC', 'bayesR').

Usage

magma(
  stat = NULL,
  sets = NULL,
  method = "magma",
  type = "joint",
  test = "one-sided",
  pi = 0.001,
  nit = 5000,
  nburn = 1000
)

Arguments

Details

The function uses either the MAGMA approach for set-based testing or Bayesian linear regression to estimate effect sizes and probabilities of association for feature sets. For Bayesian methods, a spike-and-slab prior is applied.

The 'stat' input must have row names corresponding to feature identifiers. The 'sets' input must be a named list, where each element corresponds to a feature set.

Value

A data frame or list with analysis results.

Map Sets to rsids

Description

This function maps sets to rsids. If a 'Glist' is provided, 'rsids' are extracted from the 'Glist'. It returns a list of matched RSIDs for each set.

Usage

mapSets(sets = NULL, rsids = NULL, Glist = NULL, index = TRUE)

Arguments

Value

A list where each element represents a set and contains matched RSIDs or their indices.

Map marker summary statistics to Glist

Description

Quality control is a critical step for working with summary statistics (in particular for external). Processing and quality control of GWAS summary statistics includes:

- map marker ids (rsids/cpra (chr, pos, ref, alt)) to LD reference panel data

- check effect allele (flip EA, EAF, Effect)

- check effect allele frequency

- thresholds for MAF and HWE

- exclude INDELS, CG/AT and MHC region

- remove duplicated marker ids

- check which build version

- check for concordance between marker effect and LD data

Usage

mapStat(
  Glist = NULL,
  stat = NULL,
  excludeMAF = 0.01,
  excludeMAFDIFF = 0.05,
  excludeINFO = 0.8,
  excludeCGAT = TRUE,
  excludeINDEL = TRUE,
  excludeDUPS = TRUE,
  excludeMHC = FALSE,
  excludeMISS = 0.05,
  excludeHWE = 1e-12
)

Arguments

Author(s)

Peter Soerensen

Merge multiple GRMlist objects

Description

Merge multiple GRMlist objects each with information about a genomic rfelationship matrix stored on disk

Usage

mergeGRM(GRMlist = NULL)

Arguments

Adjustment of marker effects using correlated trait information

Description

The 'mtadj' function uses selection index theory to determine the optimal weights across 'n' traits. These weights are then used to adjust marker effects by 'n' correlated traits. More details can be found [here](https://www.nature.com/articles/s41467-017-02769-6).

Usage

mtadj(
  h2 = NULL,
  rg = NULL,
  stat = NULL,
  b = NULL,
  z = NULL,
  n = NULL,
  mtotal = NULL,
  meff = 60000,
  method = "ols",
  statistics = "z"
)

Arguments

Value

A matrix of adjusted marker effects for each trait.

Author(s)

Palle Duun Rohde and Peter Soerensen

Examples

 #bedfiles <- system.file("extdata", "sample_22.bed", package = "qgg")
 #bimfiles <- system.file("extdata", "sample_22.bim", package = "qgg")
 #famfiles <- system.file("extdata", "sample_22.fam", package = "qgg")
 #Glist <- gprep(study="1000G", bedfiles=bedfiles, bimfiles=bimfiles,famfiles=famfiles)
 #Glist <- gprep(Glist, task="sparseld",  msize=200)
 #
 ##Simulate data
 #set.seed(23)
 #
 #W <- getG(Glist, chr=1, scale=TRUE)
 #causal <- sample(1:ncol(W),50)
 #set1 <- c(causal, sample(c(1:ncol(W))[-causal],10))
 #set2 <- c(causal, sample(c(1:ncol(W))[-set1],10))
 #
 #b1 <- rnorm(length(set1))
 #b2 <- rnorm(length(set2))
 #y1 <- W[, set1]%*%b1 + rnorm(nrow(W))
 #y2 <- W[, set2]%*%b2 + rnorm(nrow(W))
 #
 ## Create model
 #data1 <- data.frame(y = y1, mu = 1)
 #data2 <- data.frame(y = y2, mu = 1)
 #X1 <- model.matrix(y ~ 0 + mu, data = data1)
 #X2 <- model.matrix(y ~ 0 + mu, data = data2)
 #
 ## Linear model analyses and single marker association test
 #maLM1 <- glma(y=y1, X=X1,W = W)
 #maLM2 <- glma(y=y2,X=X2,W = W)
 #
 ## Compute genetic parameters
 #z1 <- maLM1[,"stat"]
 #z2 <- maLM2[,"stat"]
 #
 #z <- cbind(z1=z1,z2=z2)
 #
 #h2 <- ldsc(Glist, z=z, n=c(500,500), what="h2")
 #rg <- ldsc(Glist, z=z, n=c(500,500), what="rg")
 #
 ## Adjust summary statistics using estimated genetic parameters
 #b <- cbind(b1=maLM1[,"b"],b2=maLM2[,"b"])
 #bm <- mtadj( h2=h2, rg=rg, b=b, n=c(500,500), method="ols")
 
 

Plot fit from gbayes

Description

Summary plots from gbayes fit

Usage

plotBayes(fit = NULL, causal = NULL, what = "bm", chr = 1)

Arguments

Forest plot

Description

This function generates a forest plot, which is commonly used to visualize effect sizes and their confidence intervals.

Usage

plotForest(
  x = NULL,
  sd = NULL,
  cex = 1,
  mar = NULL,
  mai = NULL,
  xlim = NULL,
  pos = NULL,
  reorder = TRUE,
  xaxis = TRUE,
  main = NULL,
  xlab = "x"
)

Arguments

Plot LD Matrix

Description

Visualizes the linkage disequilibrium (LD) matrix using a color gradient. The function produces an image plot with custom color mapping.

Usage

plotLD(LD = NULL, cols = NULL)

Arguments

Value

A plot visualizing the LD matrix. Row and column names of the LD matrix are used as labels on the x and y axes, respectively.

Plot Receiver Operating Curves

Description

Plot ROC

Usage

plotROC(roc.data = NULL, cols = NULL)

Arguments

Author(s)

Palle Duun Rohde

Bayesian Polygenic Prioritisation Scoring (Bayesian POPS)

Description

This function performs Polygenic Prioritisation Scoring (POPS) using Bayesian regression ('bayesC' or 'bayesR') or ridge regression ('rr'). It maps features to sets, performs optional feature selection based on p-value thresholds, and calculates predictive scores for prioritisation.

Usage

pops(
  stat = NULL,
  sets = NULL,
  validate = NULL,
  threshold = NULL,
  method = "bayesC",
  pi = 0.001,
  nit = 5000,
  nburn = 1000,
  updateB = TRUE,
  updateE = TRUE,
  updatePi = TRUE,
  updateG = TRUE
)

Arguments

Value

A matrix of predicted prioritisation scores ('ypred') for each feature, ordered by their predictive values. If a validation set is provided, cross-validation results are returned instead.

Expected AUC for prediction of a binary trait using information on correlated binary trait

Description

Computes the expected Area Under the Curve (AUC) for predicting a binary trait using information on a correlated binary trait.

Usage

predict_auc_mt_cc(h2x, Nx, Kx, Px, h2y, Ny, Ky, Py, rg, Me, M)

Arguments

Value

A numeric value representing the expected AUC.

Expected AUC for prediction of a binary trait using information on a correlated continuous trait

Description

Computes the expected Area Under the Curve (AUC) for predicting a binary trait using information from a correlated continuous trait.

Usage

predict_auc_mt_continuous(h2x, Nx, Kx, Px, h2y, Ny, rg, Me, M)

Arguments

Value

A numeric value representing the expected AUC.

Expected AUC for prediction of a binary trait

Description

Computes the expected Area Under the Curve (AUC) for predicting a binary trait.

Usage

predict_auc_st(h2x, Nx, Kx, Px, Me, M)

Arguments

Value

A numeric value representing the expected AUC.

Expected R2 for multiple trait prediction of continuous traits

Description

Computes the expected R2 value for the multiple trait prediction of continuous traits.

Usage

predict_r2_mt(h2x, Nx, h2y, Ny, rg, Me, M)

Arguments

Value

A numeric value representing the expected R2 for the multiple trait prediction.

Expected R2 for single trait prediction of a continuous trait

Description

Computes the expected R2 value for the single trait prediction of a continuous trait.

Usage

predict_r2_st(h2x, Nx, Me, M)

Arguments

Value

A numeric value representing the expected R2 for the single trait prediction.

Compute Nagelkerke R2

Description

Compute Nagelkerke R2 ££ perhaps add: r2nag?

Usage

rnag(yobs = NULL, ypred = NULL)

Arguments

Split Vector with Overlapping Segments

Description

Splits a vector into segments of a specified length with a specified overlap. The function returns a list where each element contains a segment of the vector.

Usage

splitWithOverlap(vec, seg.length, overlap)

Arguments

Value

A list where each element is a segment of the input vector. The segments can overlap based on the specified overlap.

Perform VEGAS Gene-Based Association Analysis

Description

This function performs VEGAS (Versatile Gene-based Association Study) to analyze gene-level associations using marker statistics and linkage disequilibrium (LD) structure from a reference panel.

Usage

vegas(
  Glist = NULL,
  sets = NULL,
  stat = NULL,
  p = NULL,
  threshold = 1e-10,
  tol = 1e-07,
  minsize = 2,
  verbose = FALSE
)

Arguments

Details

The function uses marker-level statistics to compute gene-level association statistics, accounting for LD structure among markers. The LD structure is retrieved from 'Glist', which should include precomputed LD matrices or genotype data for the markers.

Two modes are supported: - **'stat' Mode**: Uses marker statistics (e.g., p-values) from a single study to compute gene-level statistics. - **'p' Mode**: Uses marker p-values across multiple studies for meta-analysis of gene-level statistics.

Value

A data frame with the results

Write a subset of data from a BED file

Description

This function reads a BED file and writes a subset of it based on a list of SNP (rsids) identifiers to output BED, BIM, and FAM files.

Usage

writeBED(
  bedRead = NULL,
  bimRead = NULL,
  famRead = NULL,
  bedWrite = NULL,
  bimWrite = NULL,
  famWrite = NULL,
  rsids = NULL,
  endian = .Platform$endian,
  useBytes = TRUE
)

Arguments

Value

No return value. Files are written to the specified output paths.