The Collaborative Cross, a community resource for the genetic analysis of complex traits

Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host–pathogen co‐evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major‐effect loci. Sigma virus resistance involves more genes – we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co‐evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major‐effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near‐Mendelian trait.

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“…These panels have been developed for mouse (Churchill et al . 2004), Arabidopsis (Kover et al . 2009; Huang et al .…”

Section: Introductionmentioning

confidence: 99%

The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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“…Thus far, it has not been feasible to combine eQTL perspectives with population genetics perspectives. Recently, the mouse and fly communities (Churchill et al 2004;Macdonald & Long 2007) have been working towards design of RILs constructed from a large number of founder individuals. Such a design is quite amenable to population genetic analysis, provided that the demography of individuals making up the founders can be modelled using current population genetics methodologies.…”

Section: Prospects: Investigating the Action Of Natural Selection On mentioning

confidence: 99%

The genetic basis of evolutionary change in gene expression levels

Emerson

2010

Phil. Trans. R. Soc. B

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The regulation of gene expression is an important determinant of organismal phenotype and evolution. However, the widespread recognition of this fact occurred long after the synthesis of evolution and genetics. Here, we give a brief sketch of thoughts regarding gene regulation in the history of evolution and genetics. We then review the development of genome-wide studies of gene regulatory variation in the context of the location and mode of action of the causative genetic changes. In particular, we review mapping of the genetic basis of expression variation through expression quantitative trait locus studies and measuring the cis/trans component of expression variation in allele-specific expression studies. We conclude by proposing a systematic integration of ideas that combines global mapping studies, cis/trans tests and modern population genetics methodologies, in order to directly estimate the forces acting on regulatory variation within and between species.

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“…The major genetic resources used currently include recombinant inbred lines (RIL) (Broman 2005), recombinant congenic strains (RCS) (Broman 2005), Genometagged or congenic (CON) lines (Iakoubova et al 2001), chromosome substitution strains (CSS) (Hudgins et al 1985;Nadeau et al 2000), heterogeneous stocks (HS) (Hitzemann et al 1994), and Laboratory Strain Diversity Panels (LSDP) (Paigen and Eppig 2000). Recombinant inbred strains and other reference panels of inbred strains are powerful tools for performing a genome-wide dissection of complex biological traits that are the result of multiple, quantitative, and often highly interacting genes (Churchill et al 2004;Flint et al 2005;Zou et al 2005;Hill et al 2006;Peters et al 2007). Unfortunately, the use of recombinant inbred strains does not fall under the category ''quick and easy'' but requires a fair amount of logistics, infrastructure, and an appreciation for the power of genetics.…”

Section: Main Challenges and Resolved Approachesmentioning

confidence: 99%

Systems genetics, bioinformatics and eQTL mapping

Deng

2010

Genetica

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Jansen and Nap (Trends Genet 17(7):388-391, 2001) and Jansen (Nat Rev Genet 4:145-151, 2003) first proposed the concept of genetical genomics, or genome-wide genetic analysis of gene expression data, which is also called transcriptome mapping. In this approach, microarrays are used for measuring gene expression levels across genetic mapping populations. These gene expression patterns have been used for genome-wide association analysis, an analysis referred to as expression QTL (eQTL) mapping. Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. Bioinformatics is essential to genome-wide analysis of gene expression data and used as an effective tool for eQTL mapping. The use of Plabsoft database, EcoTILLING, GNARE and FastMap allowed for dramatic reduction of time in genome analysis. Some web-based tools (e.g., Lirnet, eQTL Viewer) provide efficient and intuitive ways for biologists to explore transcriptional regulation patterns, and to generate hypotheses on the genetic basis of transcriptional regulations. Expression quantitative trait loci (eQTL) mapping concerns finding genomic variation to elucidate variation of expression traits. This problem poses significant challenges due to high dimensionality of both the gene expression and the genomic marker data. The core challenges in understanding and explaining eQTL associations are the fine mapping and the lack of mechanistic explanation. But with the development of genetical genomics and computer technology, many new approaches for eQTL mapping will emerge. The statistical methods used for the analysis of expression QTL will become mature in the future.

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The Collaborative Cross, a community resource for the genetic analysis of complex traits

Cited by 1,024 publications

References 15 publications

The genetic architecture of resistance to virus infection in Drosophila

The genetic architecture of resistance to virus infection in Drosophila

The genetic basis of evolutionary change in gene expression levels

Systems genetics, bioinformatics and eQTL mapping

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