Nested genetic variation simulation (Myrtaceae)

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Method synopsis

Simulates trait data with hierarchical genetic structure where genotypes are nested within species, allowing comparison of nested vs non-nested statistical approaches. Uses allele matrices to generate additive genetic effects with environmental noise.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Accounting for the nested nature of genetic variation across levels of organization improves our understanding of biodiversity and community ecology (2016), Oikos. Implementations in other papers (listed below) may differ.

1. Generate simulated allele matrix
   Generated an allele matrix M with variable numbers of binary loci with values of either 1 or -1 that together represent a simulated genotype for each individual within a species. Matrix M is m × n where m is number of individuals and n is number of genetic loci.
   Quantities: 100 datasets for each parameter combination, with 10, 50, and 100 individuals per species; 1, 2, and 3 loci (yielding 2, 4, and 8 possible genotypes per species)Duration: Single generation per simulation runConditions: Computational simulation environment
   Equipment: R 3.0.1 statistical programming language, MATLAB v8.4 programming language
2. Calculate genetic variation
   Total additive genetic variation G for each individual calculated by taking cross product of transposed matrix containing allele values and a vector with length m of normally distributed random variables U representing variation across genotypes: G = M^T·U; U ~ N(0, σg)
   Quantities: Heritability values H² = {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}; species means μ₁ = 0 and μ₂ = {0, 0.1, 0.25, 0.5, 1, 2, 5, 10}; genotype standard deviation σg = {0.1, 0.25, 0.5, 1, 2, 5, 10}Duration: Per simulation replicateConditions: Computational simulation
   Equipment: R 3.0.1 statistical programming language
3. Assign phenotype values
   Each individual assigned phenotype or trait value Y by adding normally distributed environmental noise term V to genotype value: Y = G + V; V ~ N(μs, (1 - sH²) / sg ); where individuals of same genotype may have differing phenotypes

Typical Equipment

• R 3.0.1 statistical programming language
• MATLAB v8.4 programming language

Output Measurements

• simulated trait values
• genotype classifications
• species classifications

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used