Multiple quantitative trait analysis using bayesian networks

Abstract

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 article 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.

Keywords: Bayesian networks; MPP; Multiparent Advanced Generation Inter-Cross (MAGIC); Multiparental populations; genome-wide predictions; multiple traits; quantitative traits.

Figure 1

Averaged network obtained from the cross-validated BNs for α = 0.10. Green nodes correspond to traits: yield (YLD), flowering time (FT), height (HT), yellow rust in the field (YR.FIELD) and in the glasshouse (YR.GLASS), mildew (MIL), and Fusarium (FUS). Blue nodes correspond to SNPs. The thickness of the arcs represents the strength of the corresponding dependence relationships as measured by their frequency in the BNs produced during cross-validation.