| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on traits influenced by many genes of small effect plus environmental variation. Includes quantitative trait variation, resemblance among relatives, heritability, genetic variance components, breeder’s equation, genomic prediction, and selection response. Excludes single-gene Mendelian inheritance and molecular mechanisms unless they contribute to variance decomposition. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at phenotypic, population, and generational scales; temporal scales include short-term selection response and long-term trait evolution; quantitative resolution spans continuous phenotypes rather than discrete trait categories. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Quantitative traits, genetic values, breeding values, environmental effects, additive/dominance/epistatic components, variance components (VA, VD, VI), heritability measures, selection differentials, phenotypic distributions. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Trait means, variances, covariances, heritabilities (h², H²), genetic correlations, selection gradients, breeding values, residual variance, additive effect sizes, epistatic interactions, G-matrix structure. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Trait types (continuous, threshold), variance components (additive, dominance, epistatic, environmental), selection regimes (directional, stabilizing, disruptive), genetic architectures (polygenic, oligogenic), covariance structures (phenotypic, genetic). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Phenotypic values, additive genetic values, dominance deviations, environmental deviations, variance components (VA, VD, VI, VE), trait means, heritability estimates, selection differential (S), response to selection (R), G-matrix elements. |
| | Parameterization | How variables encode and represent the system’s state. | System encoded via variance decompositions, covariance matrices, breeder’s equation (R = h²S), linear mixed models, polygenic scores, quantitative trait distribution models, and parent–offspring regression parameters. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Assuming infinite loci with small independent additive effects; ignoring dominance or epistasis; treating environments as uniform; assuming constant variance components; linearity in trait–gene relationships; treating G-matrix as stable. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | These assumptions fail under strong epistasis, major-effect loci, G×E interactions, shifting environments, unstable G-matrices, non-linear genotype–phenotype maps, or when selection changes variance structure. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Trait variation can be decomposed into additive genetic and environmental components; genetic resemblance reflects shared alleles; selection response is predictable; trait distributions approximate normality under polygenic architecture. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes stable developmental environments, consistent trait measurement, approximate normality via central limit theorem, constant allele effects over the modeled interval, and minimal confounding between genetic and environmental sources of variation. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Variance components must sum to phenotypic variance; heritability estimates must align with observed parent–offspring resemblance; predicted selection response must match estimated genetic variance and selection differential. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Genetic values, environmental effects, variance components, selection parameters, and trait distributions must integrate into a unified quantitative framework describing polygenic trait behavior across generations. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Continuous phenotypic variation, parent–offspring resemblance, sibling resemblance, trait heritability patterns, response to selection, variance shifts across generations, genetic correlations among traits, distributional changes after selection. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by measurement precision of quantitative traits, inability to detect tiny genetic effects, insufficient sample sizes, environmental noise masking genetic signals, and difficulty measuring rare or extreme phenotypes. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Trait units (cm, g, counts, scores), variance units, covariance values, correlation coefficients, heritability estimates (h², H²), selection differential (S), response to selection (R), additive genetic variance (VA), dominance variance (VD). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Phenotyping tools (calipers, scales, imaging systems), quantitative trait assays, pedigree data, genomic markers (SNP arrays, sequencing), statistical software for LMMs, trait-distribution measurement platforms, environmental monitoring instruments. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Heritability defined as the proportion of phenotypic variance due to additive genetic variance; selection differential defined as mean trait difference between selected and overall population; breeding value defined by expected genetic contribution to offspring; G-matrix defined as genetic covariance structure. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Measuring phenotypes across relatives, constructing pedigrees or genomic-relationship matrices, estimating variance components using mixed models, applying standardized trait assays, calculating parent–offspring regression slopes, computing selection gradients. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Standardized environmental conditions, replicated trait measurements, controlled breeding or selection programs, consistent phenotyping protocols, large population sampling, time-series data collection across generations. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling across families, across genotypes, and across environments; ensuring sufficient sample size for variance estimation; avoiding biased selection; sampling multiple generations for selection-response tracking. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Trait measurements, phenotype distributions, pedigree matrices, genomic relationship matrices, variance–covariance matrices, heritability estimates, selection-response curves, QTL summaries, reaction norms. |
| | Resolution | The granularity or precision with which data is captured. | Determined by trait-measurement precision, sample size, genotyping resolution, environmental uniformity, and ability to resolve small additive effects or weak genetic correlations. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of phenotyping instruments, validation of trait assays, consistency checks in repeated measures, genomic-marker quality control, environmental calibration, and cross-validation of variance-component estimates. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Identification of measurement error, environmental noise, sampling bias, pedigree inaccuracies, misestimated variance components, confounding between genetic and environmental effects, and quantification of random vs systematic error. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Trait means and variances follow predictable responses to selection; the breeder’s equation (R = h²S) governs generational change; phenotypes approximate normal distributions under polygenic control; additive genetic variance contributes linearly to heritability; covariance among relatives reflects shared alleles. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable relationships between additive genetic variance and heritability; conserved structure of variance decomposition (VP = VA + VD + VI + VE); consistent forms of parent–offspring regression; stable proportionality between selection differential and response when assumptions hold. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Many loci of small effect combine additively to generate phenotypes; allele substitution contributes incremental additive effects; environmental inputs contribute independent variance; selection shifts allele-frequency distributions; covariance among relatives arises from genetic relatedness. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Locus effects → additive/dominance/epistatic components → genetic values → phenotypic variance → selection differential → response to selection across generations; environmental influences → residual variance → phenotypic expression. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Additive genetic variance, dominance variance, epistatic variance, heritability (h², H²), breeding value, genetic correlation, phenotypic variance, G-matrix, selection differential, selection gradient, polygenic trait architecture. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Variance components (VA, VD, VI, VE), trait types (continuous, threshold), selection regimes (directional, stabilizing, disruptive), genetic architectures (polygenic, oligogenic), covariance structures (genetic vs environmental; multivariate G-matrices). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Breeder’s equation (R = h²S); variance decomposition (VP = VA + VD + VI + VE); multivariate response equation (Δz = Gβ); covariance equations (Cov = VA × relatedness); regression models for heritability; mixed-model equations for variance estimation. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Linear mixed models (LMMs), infinitesimal model, polygenic-score models, quantitative-trait distribution models, animal models for variance estimation, G-matrix evolution models, multivariate selection models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Infinitesimal model assuming infinite loci of tiny additive effect; constant variance components; linear genotype–phenotype mapping; stable G-matrix; ignoring dominance, epistasis, or G×E; treating trait distributions as perfectly normal. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Break down when major-effect loci dominate, epistasis is strong, environments fluctuate, G-matrices change over time, selection alters variance components, or genotype–phenotype maps become nonlinear. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Polygenic traits viewed as the product of additive genetic variance, environmental variance, and selection pressures; the breeder’s equation integrates heritability with selection; G-matrix provides a unified multivariate framework; quantitative genetics forms the bridge between Mendelian inheritance and evolutionary dynamics. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to evolutionary biology (trait evolution, adaptive landscapes), animal/plant breeding (selection programs), genomics (GWAS, PGS), statistics (mixed models, multivariate analysis), ecology (environmental variance), and developmental biology (phenotype formation). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating selection intensity, designing controlled breeding programs, altering environmental conditions to partition variance components, establishing replicated family structures (full-sib, half-sib), and creating artificial polygenic populations to test predicted selection responses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Measuring natural phenotypic distributions, tracking trait change across generations, observing natural selection gradients, and quantifying resemblance among relatives in unmanaged populations. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing additive vs. non-additive variance models, validating heritability estimates with parent–offspring regression, comparing expected vs. observed selection responses, and applying likelihood-ratio tests for model components. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating quantitative measurements, re-estimating variance components with independent datasets, replicating selection experiments, validating trait assays across observers, and verifying pedigree or genomic-relationship accuracy. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating heritability (h², H²), decomposing phenotypic variance, fitting linear mixed models, estimating genetic correlations, calculating selection gradients (β), and quantifying uncertainty via confidence intervals or Bayesian posteriors. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing additive-only vs. additive+dominance vs. epistatic models, evaluating G×E models, comparing environmental-variance structures, testing stability of the G-matrix, and assessing fit of multivariate selection models. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying measurement error, environmental confounding, pedigree errors, genotyping inaccuracies, overfitting in genomic prediction, and instability of variance-component estimates; partitioning systematic and random error sources. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Standardizing phenotyping protocols, controlling environmental conditions, blinding observers, correcting for confounders, validating pedigree accuracy, filtering low-quality genomic markers, and ensuring representative sampling. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Reviewing variance-component models, cross-checking heritability estimates, validating selection-response data, comparing G-matrix estimates across studies, and replicating quantitative-trait analyses in independent populations. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating models when major-effect loci emerge, when G-matrices shift across environments, when epistasis or G×E must be incorporated, or when real-world selection responses violate additive expectations. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Fully disclosing phenotyping methods, environmental conditions, variance-component assumptions, model structures, genotyping quality filters, and the limitations of all quantitative estimates. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ensuring humane treatment of organisms in breeding/selection experiments, careful handling of genomic data, accurate reporting of quantitative results, and avoidance of selective omission or manipulation. |