Genetic dissection of a model complex trait using the <i>Drosophila</i> Synthetic Population Resource

King, Elizabeth G.; Merkes, Christopher M.; McNeil, Casey Lee; Hoofer, Steven R.; Sen, Sáunak; Broman, Karl W.; Long, Anthony D.; Macdonald, Stuart J.

doi:10.1101/gr.134031.111

Cited by 204 publications

(405 citation statements)

References 47 publications

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“…These loci may have been influenced by unintended selection for alleles influencing viability, rather than for wing phenotypes. Indeed, such unanticipated genetic effects have been observed in other studies (e.g., Seidel et al 2008;Ross et al 2011;King et al 2012). Moreover, there was some inconsistency among introgression lines.…”

Section: Mapping By Introgressionmentioning

confidence: 80%

“…These two parental strains are commonly used wild-types for Drosophila research, one primarily in molecular biology (ORE), and the other (SAM) in quantitative genetics, aging, and studies of recombination (Hofmanova 1975;Hofmann et al 1987;Lyman et al 1996;Lyman and Mackay 1998;Leips and Mackay 2002;Mackenzie et al 2011). More recently it was used as the common line for an advanced intercross design for the Drosophila synthetic population resource (King et al 2012). Thus, identifying sequence polymorphisms and differences in gene expression between these two strains will facilitate studies of background dependence in other Drosophila traits.…”

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confidence: 99%

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Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

et al. 2014

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The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist or about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scalloped E3 allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-byintrogression experiment and a tagged RNA gene expression dataset. In addition we used whole genome resequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative noncomplementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well. GENETICISTS often strictly control their organisms' wildtype genetic backgrounds when experimentally dissecting genetic pathways. Although this tight control is necessary to avoid faulty inferences caused by confounding variables (e.g., Burnett et al. 2011), it can often paint an incomplete or even incorrect picture; no genetic pathway or network exists in a vacuum. Instead, these networks occur in the context of all the alleles in the genome, which usually vary among individuals. There is substantial evidence that wild-type genetic background almost always modulates the phenotypic effects of mutations (e.g., McKenzie et al. 1982;Threadgill et al. 1995;Atallah et al. 2004;Milloz et al. 2008;Chandler 2010;Dowell et al. 2010;Gerke et al. 2010). The influence of wild-type genetic backgrounds also extends to interactions among mutations (Remold and Lenski 2004;Dworkin et al. 2009;Wang et al. 2013b), altering patterns of epistasis, and these complex interactions are likely widespread . Alleles that influence many mutant phenotypes segregate in most natural populations, representing a potential source of cryptic genetic variation Félix 2007;Vaistij et al. 2013). In many cases, this cryptic variation has been described phenomenologically, or via the partitioning of genetic variance components (Gibson et al. 1999;Dworkin et al. 2003;McGuigan e...

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Section: Mapping By Introgressionmentioning

confidence: 80%

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confidence: 99%

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

et al. 2014

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“…Following King et al . 2012; we first ranked the founder genotype at each QTL according to their mean phenotype. We then split this ranked list into all possible classes (‘resistant’ and ‘susceptible’).…”

Section: Methodsmentioning

confidence: 99%

“…2009) and Drosophila (King et al . 2012). They are formed by crossing several inbred founder lines for multiple generations to create a population whose genomes are fine‐scale mosaics of the original founder lines’ genomes.…”

Section: Introductionmentioning

confidence: 99%

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The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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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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