A new method to infer causal phenotype networks using QTL and phenotypic information.

Huange Wang,Fred A Van Eeuwijk

doi:10.1371/journal.pone.0103997

Abstract

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.

Highlights

In animal and plant breeding, selection of superior genotypes for further crossing is an important objective
Neto et al [10] proposed a QTL-directed dependency graph (QDG) approach that requires a priori estimation of QTLs for the traits and executes the following two steps: 1) learn an undirected network from phenotypic data; 2) infer causal direction for every edge in the undirected phenotype network by conditioning on detected QTLs
To construct directed phenotype networks, especially when some traits come without QTLs, we present in this paper a QTL+ phenotype supervised orientation (QPSO) algorithm

Summary

Introduction

In animal and plant breeding, selection of superior genotypes for further crossing is an important objective To achieve this objective, identification of quantitative trait loci (QTLs) can be a first step in the development of a breeding strategy; alternatively nowadays, estimation of genomic breeding values can be considered to form another initial step. An adaptive lasso (AL) based method was presented to infer a gene regulatory network from gene expression and expression quantitative trait loci (eQTLs) data [12] In their simulation studies, Logsdon and Mezey [12] compared the performance of five algorithms, i.e. the PC algorithm [13], the NEO algorithm, the QDG algorithm, the QTLnet algorithm and the AL algorithm. The QDG and the AL algorithms will be regarded as two state-of-the-art methods in this field

Methods

Results

Conclusion