Studying complex biological systems using multifactorial perturbation


High-throughput genomics, transcriptomics, proteomics and metabolomics have the potential to identify the functional consequences of induced and natural genetic variation. Surprisingly, the experiments of most genomics researchers still mainly involve perturbing a biological system of interest by modifying either one factor or one gene at a time. By contrast, this article argues that multifactorial experimentation would allow the study of many more biologically relevant questions in parallel at the same or lower cost.

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Figure 1: Two strategies for perturbing biological systems.


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This article is dedicated to my former room-mate and bioinformatics colleague J. (Hans) M. Sandbrink, who recently passed away. I am grateful to M. P. de Boer for carrying out the simulations presented in Box 1, to R. W. Williams for providing early access to his mouse work, and to three reviewers for their constructive comments.

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cystic fibrosis

Huntington disease

Saccharomyces Genome Database






List of gene and QTL mapping software

Ritsert Jansen's laboratory

The Complex Trait Consortium



Subsequent generations (F3, F4, and so on) of an intercross pedigree that are used for the high-resolution mapping of trait loci.


(CSS). Each CSS contains an entire chromosome of a donor parent placed in the genetic background of the recipient parent.


A computational strategy that consists of pooling genotypes from multiple loci into a smaller number of classes, thereby avoiding the increased dimensionality that is associated with modelling interactions between loci or between loci and the environment.


The study of traits that are determined by many genes, which almost always interact with environmental factors.


In the context of quantitative genetics, epistasis refers to any genetic interaction in which the combined phenotypic effect of two or more loci is less than (negative epistasis) or greater than (positive epistasis) the sum of the effects at individual loci.


A numerical optimization procedure that is based on evolutionary principles such as mutation, deletion and selection.


The process that uses gene expression profiling and marker-based fingerprinting of each individual in a segregating population to analyse the cis- and trans-acting factors that underlie variation in gene expression. This information can then be used to reconstruct a gene network.


A randomized computational approach for identifying the most likely among many possible models.


The situation in which two or more predictors (or subsets of predictors) are strongly (but not perfectly) correlated to one other, making it difficult to interpret the strength of the effect of each predictor (or predictor subset). For example, it would be hard to detect a gene if its effect is 'absorbed' (or masked) by combinations of genetic background action/interaction parameters in the model.


(QTL). Genetic loci or chromosomal regions that contribute to variability in complex quantitative traits (such as plant height or body weight), as identified by statistical analysis. Quantitative traits are typically affected by several genes and by the environment.


(RCS). A population of fully homozygous individuals, each of which contains a restricted part of one of the two genomes from which the inbred lines were created.


(RILs). A population of fully homozygous individuals that is obtained through the repeated selfing of an F1 hybrid, and that comprises 50% of each parental genome in different combinations.


The study of the complex interactions that occur at all levels of biological information — from whole-genome sequence interactions to developmental and biochemical networks — and their functional relationship to organism-level phenotypes.


A statistic that quantifies the dispersion of data about the mean.

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Jansen, R. Studying complex biological systems using multifactorial perturbation. Nat Rev Genet 4, 145–151 (2003).

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