Estimates of molecular genetic variation are often used as a cheap and simple surrogate for a population's adaptive potential, yet empirical evidence suggests they are unlikely to be a valid proxy. However, this evidence is based on molecular genetic variation poorly predicting estimates of adaptive potential rather than how well it predicts true values. As a consequence, the relationship has been systematically underestimated and the precision with which it could be measured severely overstated. By collating a large database, and using suitable statistical methods, we obtain a 95% upper bound of 0.26 for the proportion of variance in quantitative genetic variation explained by molecular diversity. The relationship is probably too weak to be useful, but this conclusion must be taken as provisional: less noisy estimates of quantitative genetic variation are required. In contrast, and perhaps surprisingly, current sampling strategies appear sufficient for characterising a population's molecular genetic variation at comparable markers.
Loci
Loci contains information from both single locus and multiple loci estimates with the following fields: # Study: Reference # Population: Population description given in the source paper # Species: Species # Year: Year measurements made # Season: Season measurements made # Age.Class: Adults or chicks # Sample.size: Number of individuals the estimate is based on # Type.of.loci: Molecular marker used # Total.loci: Number of loci the estimate is based on # Number of sites: Only relevant for nucleotide diversity estimates # Locus: Name of the locus # Mean.diversity: Average diversity for either single or multilocus estimates # SE.Mean.Diversity: Any standard errors reported # SD.Mean.Diversity: Any standard deviations reported # Diversity.Measure: Heterozygosity (Microsatellite, Allozyme, AFLP) or pi (Nucleotide diversities) # Type.of.chromosome: Autosome or sex # Type.of.site: For nucleotide diversities, i.e. silent, intronic, non-coding, synonymous, mixed sites # Sampling strategy: Estimate from single or multiple populations
Quan
Quan contains the heritability and coefficients of additive genetic variance estimates with the following fields: # Study: Reference # Population: Population description given in the source paper # Type.of.relative: The type of relatives used in the estimation. AM = animal model, C = clonal, FS = full-sib, HS = half-sib, MPO = mid-parent-offspring, SPO = single-parent-offspring # Male.Female: Was a maternal or paternal parent used in SPO or forming a HS family # Lab.Field: Lab = laboratory estimates, Field = wild estimates, Field/Lab = parental measurements made in the wild and F1 estimates made in a laboratory # Species: Species # Trait: The trait measured (this is copied and pasted from the original study removing case and white space is necessary) # Group: Information on the experimental groups used. E.g. experimentally different temperatures # Trait.From.Hansen.et.al: Is the initial information for an estimate sourced from Hansen et al. (2011)? Additional information has been added here. # Trait.Type: Classification of the trait into B = behaviour, LH = life-history, M = morphological, P = physiological # Sample.size: Number of individuals used in the estimation # Trait.Mean: Average value for the trait # SD.Trait.Mean: Any standard deviations reported # Precision.Trait.Mean: Any precision reported # Precision.Measure.Trait.Mean: SE = standard error, CI - Confidence intervals, Range # Heritability: Heritability estimate reported # SD.Heritability: Any standard deviations reported # Precision.Heritability: Any precision reported # Precision.Measure.Heritability: SE = standard error, CI - Confidence intervals # Var.H2: Variation in heritability estimate # Cva: Coefficient of additive genetic variance # SE.ia: Standard error reported for CVa
Script
R script to process data and run models.
QuantitativeVsMolecular.R