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Systems genetics approaches to understand complex traits

Key Points

  • Systems genetics is an approach to understand complex traits, including common diseases. It examines intermediate molecular phenotypes, such as transcript, protein or metabolite abundance, to bridge DNA variation with the traits of interest.

  • Systems genetics has been driven by the development of high-throughput technologies, which makes it possible to interrogate molecular phenotypes in populations of humans and of model organisms.

  • Genetic mapping of molecular phenotypes, correlation among the phenotypes, and statistical modelling are used to capture the interactions among these traits. This provides a broad view of information flow from the genetic variant to the trait.

  • Network modelling provides a useful approach in organizing the data into biologically meaningful units and interactions.

  • Systems genetics approaches can be integrated with genome-wide association studies to predict causal genes and their functions; for example, expression quantitative trait loci provide a measure of functional variation.

  • Animal populations have some important advantages for systems genetics studies, such as the availability of relevant tissues and the ability to control the environment of such studies.


Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

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Figure 1: Systems genetics strategies.
Figure 2: Collection and analysis of systems genetics data.
Figure 3: Genetics of gene expression and genetic interactions.
Figure 4: Predicting causal genes in GWAS loci.


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The authors thank R. Chen for assistance in the preparation of this paper. M.C. is supported by Ruth L. Kirschstein National Research Service Award T32HL69766; A.J.L. is supported by the US National Institutes of Health grants HL30568, HL28481, HL094322, HL110667 and DP3D094311, and Transatlantic Networks of Excellence Award from Foundation Leducq. They are also grateful to the detailed and critical reviewers.

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Correspondence to Aldons J. Lusis.

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Systems genetics

A global analysis of the molecular factors that underlie variability in physiological or clinical phenotypes across individuals in a population. It considers not only the underlying genetic variation but also intermediate phenotypes such as gene expression, protein levels and metabolite levels, in addition to gene-by-gene and gene-by-environment interactions.

Natural populations

Human populations, or animal populations in wild environments, that are experiencing normal selective pressures. By contrast, laboratory animal populations, such as inbred strains, can show natural genetic variation, but they have been subjected to nonrandom breeding and artificial selection.

Natural genetic variation

Genetic variation that is present in all populations as a result of mutations that occur in the germline; the frequencies of such mutations in populations are affected by selection and by random drift. This is in contrast with experimental variation that is introduced by techniques such as gene targeting and chemical mutagenesis.

Principal components

Dominant patterns in multivariate data, as extracted by the principal component analysis data reduction method.


In the context of network modelling, groups of components that are tightly connected or correlated across a set of conditions, perturbations or genetic backgrounds.

Inbred strains

Strains in which a set of naturally occurring genetic variations have been fixed by many generations of inbreeding.

Biological scales

Various levels in the flow of information from DNA to proteins to metabolites to cell structures to cell interactions.

Chromatin immunoprecipitation followed by sequencing

(ChIP–seq). A method that is used to analyse protein–DNA interactions by combining chromatin immunoprecipitation with next-generation sequencing to identify binding sites of DNA-associated proteins.


A statistical interaction between two or more genetic loci, such that their effects are non-additive.

Missing heritability

The phenomenon whereby the fraction of the heritability of a trait that is explained by a genome-wide association study is modest.


Combinations of alleles at genetic loci that are inherited together.

Recombinant inbred strains

A set of inbred strains that is generally produced by crossing two parental inbred strains and then inbreeding random intercross progeny; they provide a permanent resource for examining the segregation of traits that differ between the parental strains.

Congenic strains

Strains in which a small region of the genome from one strain has been placed, by repeated crossing, onto the genetic background of a second strain.

Linkage disequilibrium blocks

Regions of high correlation across genetic markers, which results from their linkage in cis on a chromosome and thus infrequent recombination during meiosis. LD blocks are often demarcated by recombination hot spots.

CEPH cell lines

A large set of lymphoblastoid cell lines from European pedigrees that serves as a reference collection for studies of allele frequencies, linkage mapping and the genetics of gene expression.

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Civelek, M., Lusis, A. Systems genetics approaches to understand complex traits. Nat Rev Genet 15, 34–48 (2014).

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