genetic_offset: Population genetic offset under new environments.

In LEA: LEA: an R package for Landscape and Ecological Association Studies

Description

The function returns genetic offset estimates computed from user-specified population labels and new environments based on predictions of an lfmm2 model. It takes as input an object of class lfmm2Class together with the data that were used to adjust the LFMM, and a matrix of new environmental variables in the same format as the original ones.

Usage

genetic.offset (object, input, env, new.env, pop.labels)

Arguments

object	An object of class lfmm2Class.
input	A genotypic matrix or a character string containing a path to the input file. The genotypic matrix must be in the lfmm{lfmm_format} format without missing values (9 or NA). See impute for completion based on nonnegative matrix factorization and consider R packages for reading large matrices.
env	A matrix of environmental covariates or a character string containing a path to the environmental file. The environment matrix must be in the env format without missing values. All variables must be encoded as numeric.
new.env	A matrix of new environmental covariates or a character string containing a path to the new environmental data file. The new environmental matrix must be in the env format without missing values, and of same dimension as the env matrix. All variables must be encoded as numeric.
pop.labels	A numeric or character vector providing population labels for all rows (individuals) of the response matrix.

Value

offset

A matrix of genetic offset values computed for every population in pop.labels.

Author(s)

Olivier Francois

References

Gain C, Francois O (2020). LEA 3: Factor models for population and ecological genomics in R.

See Also

lfmm.data lfmm2

Examples

### Example of offset prediction using lfmm2 ###

# Simulation with 100 target loci, with effect sizes ranging between -10 an 10 
# n = 100 individuals and L = 1000 loci

X <- as.matrix(rnorm(100)) # environmental variable
B <- rep(0, 1000) 
target <- sample(1:1000, 100) # target loci
B[target] <- runif(100, -10, +10) # effect sizes

# Creating hidden factors and loadings

U <- t(tcrossprod(as.matrix(c(-1.25,0.5,1.25)), X)) + matrix(rnorm(300), ncol = 3)
V <- matrix(rnorm(3000), ncol = 3)

# Simulating a binarized matrix containing haploid genotypes 
# Simulation performed with a generative LFMM

Y <- tcrossprod(as.matrix(X), B) + tcrossprod(U, V) + matrix(rnorm(100000, sd = .5), nrow = 100)
Y <- matrix(as.numeric(Y > 0), ncol = 1000)

######################################
# Fitting an LFMM with K = 3 factors #
######################################

mod2 <- lfmm2(input = Y, env = X, K = 3)

# Computing genetic offset statistics for 2 populations, defined from latent factor 1
pop <-  1 + (U[,1] > 0)

g.offset <- genetic.offset(object = mod2, input = Y, 
                           env = X, new.env = 2*X + 10, 
                           pop.labels = pop)
round(g.offset, digit = 3) 

#rm(list = ls())

Example output

               2      1
offset    -0.157 -0.157
F.fit      0.471  0.484
F.pred     0.462  0.475
F.fitpred  0.383  0.398

LEA documentation built on Nov. 8, 2020, 8:19 p.m.