Path Coefficient Analysis
Path coefficients online. Direct and indirect effects, residual effect and full path values.
When to use it
Use when you have replicated breeding data and want to decompose the genotypic, phenotypic, and environmental correlations of yield (or any target trait) with its components into direct and indirect effects. Estimating all three correlation senses from the same trial separates the genetic component (the breeder's lever) from the environmental component (which selection cannot move). Standard analysis in Singh and Chaudhary (1979) for advanced breeding nurseries.
When NOT to use it
Do not use when component traits are not pre-specified by biology; this is confirmatory, not exploratory. Do not use when component variables are highly collinear; path coefficients become unstable (check the determinant of each correlation matrix). Do not use unreplicated data; you cannot partition variance into G and E without replication.
What you get
Three correlation matrices (genotypic, phenotypic, environmental) computed from the variance-covariance partition of replicated trials. For each correlation matrix, the standardised partial regression (path) coefficients = direct effects on the target trait, the indirect-effect breakdown via every other component, total correlation, R-squared, and residual effect.
How to interpret the output
Compare the three correlation matrices: a strong genotypic correlation but weak phenotypic correlation indicates environment masks the genetic relationship in the field. The genotypic-path direct effect is the breeder's expected response to selection on that component. A small phenotypic correlation can mask offsetting positive and negative indirect effects, which is the central insight of path analysis (Wright 1921).
Common pitfalls
- Reporting only the phenotypic path; the genotypic path is the selectable signal.
- Selecting components after the fact based on which gave a large direct effect.
- Ignoring multicollinearity; check each matrix's determinant.
- Treating the residual effect as small without comparing to the response variance.
Try it in StatVeda
The Path Analysis on Genotypic / Phenotypic / Environmental correlations engine runs entirely in the browser. No signup, no install, no data sent to a server. Paste your data, hit Run, copy the output.
Open Path Analysis on Genotypic / Phenotypic / Environmental correlationsReferences
- Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557 to 585.
- Dewey, D. R. and Lu, K. H. (1959). A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agronomy Journal, 51(9), 515 to 518.
- Singh, R. K. and Chaudhary, B. D. (1979). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi. Chapter 5.