# Multiple Quantitative Trait Analysis Using Bayesian Networks

arXiv: https://arxiv.org/abs/1402.2905 · HTML: https://mlsanity.com/paper/1402.2905/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2014.

## Built on by (most cited first, 9 of 9)

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- [Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery](https://mlsanity.com/paper/2508.12650/) (2025)
- [Benchmarking Constraint-Based Bayesian Structure Learning Algorithms: Role of Network Topology](https://mlsanity.com/paper/2501.02019/) (2025)
- [Consistency evaluation of benchmarks used for causal discovery](https://mlsanity.com/paper/2606.01789/) (2026)
- [Causal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer](https://mlsanity.com/paper/2602.03914/) (2026)

## Abstract (arXiv)

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this paper we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP), and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.