Reconstruction of Networks with Direct and Indirect Genetic Effects

Author:

Kruijer Willem1,Behrouzi Pariya1,Bustos-Korts Daniela1,Rodríguez-Álvarez María Xosé23,Mahmoudi Seyed Mahdi4,Yandell Brian5,Wit Ernst6,van Eeuwijk Fred A1

Affiliation:

1. Biometris, Wageningen University and Research, 6708 PB Wageningen, Netherlands

2. BCAM - Basque Center for Applied Mathematics, 48009 Bilbao, Spain

3. IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain

4. Faculty of Mathematics, Statistics and Computer Science, Semnan University, 35131-19111 Semnan, Iran

5. University of Wisconsin-Madison, Wisconsin 53706-1510

6. Università della Svizzera italiana, 6900 Lugano, Switzerland

Abstract

Abstract Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the “missing heritability” case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

Publisher

Oxford University Press (OUP)

Subject

Genetics

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