tools-only Version Compatibility
Reference examples tested with: vegan 2.6+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Landscape Genomics
"Find loci associated with environmental adaptation in my populations" → Test genotype-environment associations using LFMM2 latent factor mixed models or pcadapt outlier detection while controlling for population structure, and predict climate vulnerability with gradientForest.
- R:
LEA::lfmm2()for genotype-environment association testing - R:
pcadapt::pcadapt()for selection scan without environmental data
Identifies loci under local adaptation by testing genotype-environment associations while controlling for population structure.
Population Structure Estimation with LEA
Goal: Determine the number of ancestral populations (K) as a prerequisite for genotype-environment association testing.
Approach: Run sNMF on genotype data across K=1-10 and select the K with minimum cross-entropy.
Determine K (number of ancestral populations) before running GEA:
library(LEA)
# Convert VCF to lfmm/geno format
vcf2lfmm('variants.vcf', 'genotypes.lfmm')
vcf2geno('variants.vcf', 'genotypes.geno')
# sNMF for K estimation (faster than STRUCTURE)
snmf_result <- snmf('genotypes.geno', K = 1:10, repetitions = 5,
entropy = TRUE, project = 'new')
# Select K with minimum cross-entropy across K values
# cross.entropy(obj, K) returns per-run values for that K; take min per K
ce_values <- sapply(1:10, function(k) min(cross.entropy(snmf_result, K = k)))
plot(1:10, ce_values, xlab = 'K', ylab = 'Cross-entropy', pch = 19, col = 'blue')
best_K <- which.min(ce_values)
LFMM2 Genotype-Environment Association
Goal: Identify loci significantly associated with environmental variables while controlling for population structure.
Approach: Fit LFMM2 with K latent factors, extract p-values with genomic inflation calibration, and apply Storey q-value FDR correction.
Latent factor mixed model tests association between each locus and environmental variables while correcting for population structure via latent factors:
library(LEA)
genotypes <- read.lfmm('genotypes.lfmm')
env_vars <- read.env('environment.env')
# K from sNMM cross-entropy (latent factors = population structure proxy)
# K=3: determined from sNMF analysis above
lfmm_result <- lfmm2(input = genotypes, env = env_vars, K = 3)
# Extract p-values for each environmental variable
# Genomic inflation factor (GIF) calibration corrects for residual confounding
# Target lambda ~ 1.0; >1.5 suggests insufficient structure correction
pvalues <- lfmm2.test(lfmm_result, input = genotypes, env = env_vars,
full = TRUE, genomic.control = TRUE)
gif <- median(qchisq(1 - pvalues$pvalues[, 1], df = 1)) / qchisq(0.5, df = 1)
cat('Genomic inflation factor (lambda):', gif, '\n')
# lambda should be ~1.0; if >1.5, increase K; if <0.5, decrease K
# Adjust p-values for multiple testing
# q-value < 0.05: Storey FDR control, more powerful than BH for genomic data
library(qvalue)
qvals <- qvalue(pvalues$pvalues[, 1])$qvalues
# Candidate loci under selection
# q < 0.05: 5% false discovery rate
candidates <- which(qvals < 0.05)
cat('Candidate adaptive loci:', length(candidates), '\n')
Multiple Environmental Variables
# env_vars matrix: samples x variables (e.g., temperature, precipitation, altitude)
# LFMM2 tests each variable separately but fits latent factors jointly
for (i in 1:ncol(env_vars)) {
pvals <- lfmm2.test(lfmm_result, input = genotypes, env = env_vars,
full = TRUE, genomic.control = TRUE)
qvals_i <- qvalue(pvals$pvalues[, i])$qvalues
cat('Variable', i, '- candidates (q<0.05):', sum(qvals_i < 0.05), '\n')
}
pcadapt Outlier Detection
Goal: Detect loci under selection from genotype data alone, without requiring environmental variables.
Approach: Compute Mahalanobis distances from K principal components with pcadapt, then identify outlier loci via q-value FDR control.
Detects loci under selection based on Mahalanobis distances from principal components, without requiring environmental data:
library(pcadapt)
# Read genotypes (bed/bim/fam or lfmm format)
geno <- read.pcadapt('genotypes.bed', type = 'bed')
# Select K from screeplot (number of significant PCs)
# Look for elbow where eigenvalues flatten
x_scree <- pcadapt(geno, K = 20)
plot(x_scree, option = 'screeplot')
# Run pcadapt with selected K
# K=3: number of principal components capturing population structure
x <- pcadapt(geno, K = 3)
# QQ-plot to check calibration (should follow diagonal except tail)
plot(x, option = 'qqplot')
# Manhattan-style plot of -log10(p-values)
plot(x, option = 'manhattan')
# Identify outliers using q-values
# q < 0.05: 5% FDR threshold
library(qvalue)
qvals <- qvalue(x$pvalues)$qvalues
outliers <- which(qvals < 0.05)
cat('pcadapt outlier loci:', length(outliers), '\n')
OutFLANK Fst-Based Selection Scan
Goal: Identify loci with elevated Fst consistent with diversifying selection across populations.
Approach: Fit a chi-squared distribution to the trimmed neutral Fst distribution and flag outlier loci exceeding the expected neutral envelope.
Identifies Fst outliers by fitting a chi-squared distribution to the trimmed Fst distribution:
library(OutFLANK)
# fst_data: matrix with columns FSTNoCorr, T1, T2, indexOrder, LocusName, He
# From hierfstat or custom calculation
fst_mat <- MakeDiploidFSTMat(SNPmat = genotype_matrix,
locusNames = snp_names,
popNames = pop_assignments)
# Trim extremes to estimate neutral Fst distribution
# LeftTrimFraction=0.05, RightTrimFraction=0.05: standard 5% trim on each tail
# Hmin=0.1: exclude low-heterozygosity loci (unreliable Fst estimates)
outflank_result <- OutFLANK(fst_mat, LeftTrimFraction = 0.05,
RightTrimFraction = 0.05, Hmin = 0.1,
NumberOfSamples = length(unique(pop_assignments)),
qthreshold = 0.05)
# Visualize Fst distribution and outliers
OutFLANKResultsPlotter(outflank_result, withOutliers = TRUE,
NoCorr = TRUE, Hmin = 0.1)
# Extract outlier loci
outlier_idx <- which(outflank_result$results$OutlierFlag == TRUE)
cat('OutFLANK Fst outliers:', length(outlier_idx), '\n')
RDA-Based GEA
Goal: Detect multilocus signatures of adaptation correlated with environmental gradients using constrained ordination.
Approach: Run partial RDA on allele frequencies conditioned on population structure, then identify outlier loci from RDA loading z-scores.
Redundancy analysis detects multilocus adaptive signatures correlated with environment:
library(vegan)
# allele_freq: matrix of allele frequencies (individuals x loci)
# Impute missing values with population means
allele_freq[is.na(allele_freq)] <- apply(allele_freq, 2,
function(x) mean(x, na.rm = TRUE))[col(allele_freq)[is.na(allele_freq)]]
# Condition on population structure (Q-matrix from sNMF)
# ~ env_vars + Condition(structure): partial RDA controlling for structure
rda_result <- rda(allele_freq ~ temperature + precipitation + altitude +
Condition(as.matrix(q_matrix)), data = env_data)
# Permutation test
anova(rda_result, permutations = 999)
# Identify outlier loci from RDA loadings
# z-score > 3: conservative threshold for candidate adaptive loci
loadings <- scores(rda_result, choices = 1:3, display = 'species')
zscores <- apply(loadings, 2, function(x) (x - mean(x)) / sd(x))
rda_candidates <- which(apply(abs(zscores), 1, max) > 3)
cat('RDA candidate loci:', length(rda_candidates), '\n')
gradientForest: Allele Turnover Prediction
Goal: Predict how allele frequencies change along environmental gradients and estimate genetic offset under climate change scenarios.
Approach: Fit gradient forests on candidate loci, rank environmental predictors by importance, and compute Euclidean distance in transformed space between current and future climate projections.
Predicts how allele frequencies shift along environmental gradients using random forests:
library(gradientForest)
library(terra)
# allele_data: allele frequencies at candidate loci
# env_predictors: environmental variables at sampling sites
gf <- gradientForest(cbind(env_predictors, allele_data),
predictor.vars = colnames(env_predictors),
response.vars = colnames(allele_data),
ntree = 500, trace = FALSE)
# Variable importance: which environment drives most allele turnover
plot(gf, plot.type = 'Overall.Importance')
# Cumulative importance curves for each predictor
plot(gf, plot.type = 'Cumulative.Importance')
# Predict genetic offset under climate change
# current_rasters: current bioclimatic layers
# future_rasters: projected climate layers
current <- rast('bioclim_current.tif')
future <- rast('bioclim_2070_ssp585.tif')
current_vals <- extract(current, sampling_coords)
future_vals <- extract(future, sampling_coords)
# Genetic offset = Euclidean distance in transformed environmental space
current_transformed <- predict(gf, current_vals)
future_transformed <- predict(gf, future_vals)
genetic_offset <- sqrt(rowSums((current_transformed - future_transformed)^2))
Environmental Data Extraction with terra
Goal: Extract bioclimatic variable values at sampling coordinates for genotype-environment association analysis.
Approach: Load WorldClim raster layers with terra and extract values at georeferenced sampling points.
library(terra)
# WorldClim bioclimatic variables (1 km resolution)
bioclim <- rast('wc2.1_30s_bio.tif')
# Extract values at sampling coordinates
# coords: data.frame with longitude and latitude columns
coords_vect <- vect(coords, geom = c('longitude', 'latitude'), crs = 'EPSG:4326')
env_values <- extract(bioclim, coords_vect)
# Common bioclimatic variables for GEA:
# BIO1: mean annual temperature
# BIO4: temperature seasonality
# BIO12: annual precipitation
# BIO15: precipitation seasonality
Related Skills
- conservation-genetics - Population genetic health assessment
- community-ecology - Environmental gradient analysis for species
- population-genetics/selection-statistics - Selection scans in human data
- population-genetics/population-structure - Population structure inference
- variant-calling/vcf-basics - VCF input preparation