The causal meaning of genomic predictors and how it affects the construction and comparison of genome-enabled selection models

• Main Authors: Valente, Bruno Dourado, Morota, Gota, Rosa, Guilherme Jordao Magalhaes, Gianola, Daniel, Weigel, Kent
• Format: Journal Article
• Language: English
• Published: 07-12-2013
• Subjects: Quantitative Biology - Quantitative Methods; Statistics - Applications
• DOI: 10.48550/arxiv.1401.1165

The additive genetic effect is arguably the most important quantity inferred
in animal and plant breeding analyses. The term effect indicates that it
represents causal information, which is different from standard statistical
concepts as regression coefficient and association. The process of inferring
causal information is also different from standard statistical learning, as the
former requires causal (i.e. non-statistical) assumptions and involves extra
complexities. Remarkably, the task of inferring genetic effects is largely seen
as a standard regression/prediction problem, contradicting its label. This
widely accepted analysis approach is by itself insufficient for causal
learning, suggesting that causality is not the point for selection. Given this
incongruence, it is important to verify if genomic predictors need to represent
causal effects to be relevant for selection decisions, especially because
applying regression studies to answer causal questions may lead to wrong
conclusions. The answer to this question defines if genomic selection models
should be constructed aiming maximum genomic predictive ability or aiming
identifiability of genetic causal effects. Here, we demonstrate that selection
relies on a causal effect from genotype to phenotype, and that genomic
predictors are only useful for selection if they distinguish such effect from
other sources of association. Conversely, genomic predictors capturing
non-causal signals provide information that is less relevant for selection
regardless of the resulting predictive ability. Focusing on covariate choice
decision, simulated examples are used to show that predictive ability, which is
the criterion normally used to compare models, may not indicate the quality of
genomic predictors for selection. Additionally, we propose using alternative
criteria to construct models aiming for the identification of the genetic
causal effects.