bio-ecological-genomics-landscape-genomics

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_name to 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.