Title:	Testing Neighbor Effects in Marker-Based Regressions
Version:	1.2.5
Description:	To incorporate neighbor genotypic identity into genome-wide association studies, the package provides a set of functions for variation partitioning and association mapping. The theoretical background of the method is described in Sato et al. (2021) <doi:10.1038/s41437-020-00401-w>.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Maintainer:	Yasuhiro Sato <sato.yasuhiro.36c@kyoto-u.jp>
Suggests:	knitr, rmarkdown, testthat
VignetteBuilder:	knitr
Imports:	gaston, Matrix, RcppParallel, stats, graphics
NeedsCompilation:	no
Packaged:	2025-04-23 03:18:03 UTC; yasuhirosato
Author:	Yasuhiro Sato [aut, cre], Eiji Yamamoto [aut], Kentaro K. Shimizu [aut], Atsushi J. Nagano [aut]
Repository:	CRAN
Date/Publication:	2025-04-23 03:30:02 UTC

rNeigborGWAS: Testing Neighbor Effects in Marker-based Regressions

Description

This package provides a set of functions to test neighbor effects in genome-wide association studies. The neighbor effects are estimated using the Ising model of ferromagnetism. See Sato et al. (2021) for motivation and modeling.

Details

The flow of neighbor GWAS consists of two steps, (i) variation partitioning and (ii) association mapping. In the first step, we compute proportion of phenotypic variation explained by neighbor effects, and estimate their effective area. In the second step, we test neighbor effects, and map their association score on a genome. In addition to standard GWAS inputs, spatial information of individuals is required to run these analyses. See vignette("rNeighborGWAS") for how to use this package.

Note

A developer version of rNeighborGWAS is available at the GitHub reporsitory (https://github.com/yassato/rNeighborGWAS). If the CRAN version is out of work or you want to use the latest methods, you may install the package via GitHub.

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

References

Sato Y, Yamamoto E, Shimizu KK, Nagano AJ (2021) Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory. Heredity 126(4):597-614. https://doi.org/10.1038/s41437-020-00401-w

Calculating phenotypic variation explained by neighbor effects

Description

A function to calculate PVE by neighbor effects for a series of neighbor distance using a mixed model.

Usage

calc_PVEnei(
  pheno,
  geno,
  smap,
  scale_seq,
  addcovar = NULL,
  grouping = rep(1, nrow(smap)),
  response = c("quantitative", "binary"),
  n_core = 1L
)

Arguments

Details

This function uses mixed models via the gaston package (Perdry & Dandine-Roulland 2020). If "binary" is selected, logistic.mm.aireml() is called via the gaston package. In such a case, PVEnei below is replaced by the ratio of phenotypic variation explained (RVE) by neighbor effects as RVE_nei = \sigma_2^2/\sigma_1^2 and p-values are not provided.

Value

A numeric matrix including a given spatial scale, PVE by neighbor effects, and p-values.

• scale Maximum neighbor distance given as an argument

• PVEself Proportion of phenotypic variation explained (PVE) by self effects. RVE is returned when response = "binary"

• PVEnei Proportion of phenotypic variation explained (PVE) by neighbor effects. RVE is returned when response = "binary"

• p-value p-value by a likelihood ratio test between models with or without neighbor effects (when s is not zero); or between a null model and model with self effects alone (when s = 0). NA when response = "binary"

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

References

Perdry H, Dandine-Roulland C. (2020) gaston: Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models. https://CRAN.R-project.org/package=gaston

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2),rep(2,nrow(g)/2))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

min_s <- min_dist(fake_nei$smap, fake_nei$pheno$grouping)
scale_seq <- c(min_s, quantile(dist(fake_nei$smap),c(0.2*rep(1:5))))

pve_out <- calc_PVEnei(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                       smap=fake_nei$smap, scale_seq=scale_seq,
                       addcovar=as.matrix(fake_nei$pheno$grouping),
                       grouping=fake_nei$pheno$grouping)
delta_PVE(pve_out)

Estimating the effective scale of neighbor effects

Description

A function to calculate \DeltaPVE that estimates the effective scale of neighbor effects.

Usage

delta_PVE(res, fig = TRUE, ...)

Arguments

Value

Estimated effective scale and proportion of phenotypic variation explained by neighbor effects at that scale.

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

See Also

calc_PVEnei

Convert 'gaston' package's bed.matrix data to rNeighborGWAS genotype data.

Description

A function convert a bed.matrix dataset to rNeighborGWAS genotype data.

Usage

gaston2neiGWAS(x)

Arguments

Details

This function converts genotype data into -1, 0, or 1 digit as the rNeighborGWAS format. Zero indicates heterozygotes.

Value

A list including an individual x marker matrix, a data.frame including chromosome numbers in the first column, and SNP positions in the second column, and a numeric vector including phenotypes for individuals.

• geno Individual x marker matrix

• gmap Data.frame including chromosome numbers in the first column, and SNP positions in the second column

• pheno Numeric vector including phenotypes for individuals

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

References

Perdry H, Dandine-Roulland C. (2020) gaston: Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models. https://CRAN.R-project.org/package=gaston

Examples

data("TTN", package="gaston")
x <- gaston::as.bed.matrix(TTN.gen, TTN.fam, TTN.bim)
g <- gaston2neiGWAS(x)

Calculating the minimum distance

Description

A function to calculate a Euclidian distance including at least one neighbor for all individuals.

Usage

min_dist(smap, grouping = rep(1, nrow(smap)))

Arguments

Value

Return a scalar of the minimum Euclidian distance that allows all individuals to have at least one neighbor.

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap = cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2), rep(2,nrow(g)/2))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

min_s <- min_dist(fake_nei$smap, fake_nei$pheno$grouping)

Genome-wide association mapping of neighbor effects

Description

A function to test neighbor effects for each marker and to calculate p-values at a given reference scale.

Usage

neiGWAS(
  geno,
  pheno,
  gmap,
  smap,
  scale,
  addcovar = NULL,
  grouping = rep(1, nrow(smap)),
  response = c("quantitative", "binary"),
  model = c("lmm", "lm"),
  n_core = 1L
)

Arguments

Details

This function calls a mixed model via the gaston package. If "lmm" with "binary" is selected, p-values are based on Wald tests. This is because the logistic mixed model is based on a pseudo-likelihood and thus likelihood ratio tests are not applicable. See Chen et al. (2016) for the theory.

Value

A data.frame including the chromosome number, marker position, and p-values.

• chr Chromosome number

• pos Marker position

• p p-value by a likelihood ratio test between models with or without neighbor effects

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

References

Chen H, Wang C, Conomos M. et al. (2016) Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. The American Journal of Human Genetics 98: 653-666.

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2),rep(2,nrow(g)/2))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

scale <- 43
gwas_out <- neiGWAS(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                    gmap=fake_nei$gmap, smap=fake_nei$smap,
                    scale=scale, addcovar=as.matrix(fake_nei$pheno$grouping),
                    grouping=fake_nei$pheno$grouping)

gaston::manhattan(gwas_out)
gaston::qqplot.pvalues(gwas_out$p)

Calculating neighbor genotypic identity

Description

A function to calculate neighbor genotypic identity, with a given reference scale and a degree of distance decay.

Usage

nei_coval(
  geno,
  smap,
  scale,
  alpha = Inf,
  kernel = c("exp", "gaussian"),
  grouping = rep(1, nrow(smap)),
  n_core = 1L
)

Arguments

Details

Default setting is recommended for alpha and kernel arguments unless spatial distance decay of neighbor effects needs to be modeled. If alpha is not Inf, output variables are weighted by a distance decay from a focal individual to scale. For the type of dispersal kernel in the distance decay, we can choose a negative exponential or Gaussian kernel as a fat-tailed or thin-tailed distribution, respectively. See Nathan et al. (2012) for detailed characteristics of the two dispersal kernels.

Value

A numeric matrix for neighbor covariates, with no. of individuals x markers.

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

References

Nathan R, Klein E, Robledo-Arnuncio JJ, Revilla E. (2012) Dispersal kernels: review. In: Clobert J, Baguette M, Benton TG, Bullock JM (Eds.), Dispersal Ecology and Evolution. Oxford University Press, pp.186-210.

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2), rep(2,nrow(g)/2))

g_nei <- nei_coval(g,smap,44,grouping = grouping)

Standard linear models for testing self and neighbor effects

Description

A function to provide coefficients and p-values of self and neighbor effects for each marker.

Usage

nei_lm(
  geno,
  g_nei,
  pheno,
  addcovar = NULL,
  response = c("quantitative", "binary"),
  n_core = 1L,
  asym = FALSE
)

Arguments

Details

This function is a subset of neiGWAS(). nei_lm() gives detailed results when the option model="lm" is selected in neiGWAS().

Value

A data.frame including coefficients and p-values of self and neighbor effects, without the chromosome numbers and marker position.

• beta_self coefficient for self effects

• beta_self coefficient for neighbor effects

• p_self p-value for self effects by a likelihood ratio test between a null and standard GWAS model

• p_nei p-value for neighbor effects by a likelihood ratio test between models with or without neighbor effects

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

See Also

neiGWAS

Mixed models for testing self and neighbor effects

Description

A function to provide coefficients and p-values of self and neighbor effects for each marker.

Usage

nei_lmm(
  geno,
  g_nei,
  pheno,
  addcovar = NULL,
  response = c("quantitative", "binary"),
  n_core = 1L,
  asym = FALSE
)

Arguments

Details

This function is a subset of neiGWAS(). nei_lmm() gives detailed results but requires more computational time.

Value

A data.frame including coefficients and p-values of self and neighbor effects, without the chromosome numbers and marker position.

• beta_self coefficient for self effects

• beta_self coefficient for neighbor effects

• p_self p-value for self effects by a likelihood ratio test between a null and standard GWAS model

• p_nei p-value for neighbor effects by a likelihood ratio test between models with or without neighbor effects

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

See Also

neiGWAS

Simulating phenotypes with self and neighbor effects

Description

A function to simulate phenotypes caused by self and neighbor effects, with the proportion of phenotypic variation explained (PVE) by fixed and random effects controlled.

Usage

nei_simu(
  geno,
  smap,
  scale,
  alpha = Inf,
  grouping = rep(1, nrow(smap)),
  kernel = c("exp", "gaussian"),
  n_causal,
  pveB,
  pve,
  b_ratio = c(1, 1)
)

Arguments

Value

A vector of simulated phenotype values for all individuals

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2),rep(2,nrow(g)/2))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

Simulating phenotype values with neighbor effects.

Description

A function to simulate phenotype values with multiple sources of variation controlled

Usage

qtl_pheno_simu(
  b_self,
  b_nei,
  eigenK_self,
  eigenK_nei,
  b_ratio = c(1, 1),
  pveB,
  pve
)

Arguments

Value

A list of simulated phenotypes

• y Simulated phenotype values

• beta_self major self-genetic effects

• beta_nei major neighbor effects

• sigma_self self polygenic effects

• sigma_nei neighbor polygenic effects

• epsilon residuals

• res_pveB realized proportion of variation explained by major-effect genes

• res_pve realized proportion of variation explained by major-effect genes and polygenic effects

Author(s)

Eiji Yamamoto, and Yasuhiro Sato

Calculating a distance decay weight

Description

A function to calculate, with a negative exponential or Gaussian dispersal kernel.

Usage

w(s, a, kernel = c("exp", "gaussian"))

Arguments

Value

A numeric scalar for a distance decay weight.

Author(s)

Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)