Literature DB >> 22949659

Epistasis dominates the genetic architecture of Drosophila quantitative traits.

Wen Huang1, Stephen Richards, Mary Anna Carbone, Dianhui Zhu, Robert R H Anholt, Julien F Ayroles, Laura Duncan, Katherine W Jordan, Faye Lawrence, Michael M Magwire, Crystal B Warner, Kerstin Blankenburg, Yi Han, Mehwish Javaid, Joy Jayaseelan, Shalini N Jhangiani, Donna Muzny, Fiona Ongeri, Lora Perales, Yuan-Qing Wu, Yiqing Zhang, Xiaoyan Zou, Eric A Stone, Richard A Gibbs, Trudy F C Mackay.   

Abstract

Epistasis-nonlinear genetic interactions between polymorphic loci-is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agriculture.

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Year:  2012        PMID: 22949659      PMCID: PMC3465439          DOI: 10.1073/pnas.1213423109

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  24 in total

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7.  A statistical method for the detection of variants from next-generation resequencing of DNA pools.

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Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

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Authors:  Trudy F C Mackay; Stephen Richards; Eric A Stone; Antonio Barbadilla; Julien F Ayroles; Dianhui Zhu; Sònia Casillas; Yi Han; Michael M Magwire; Julie M Cridland; Mark F Richardson; Robert R H Anholt; Maite Barrón; Crystal Bess; Kerstin Petra Blankenburg; Mary Anna Carbone; David Castellano; Lesley Chaboub; Laura Duncan; Zeke Harris; Mehwish Javaid; Joy Christina Jayaseelan; Shalini N Jhangiani; Katherine W Jordan; Fremiet Lara; Faye Lawrence; Sandra L Lee; Pablo Librado; Raquel S Linheiro; Richard F Lyman; Aaron J Mackey; Mala Munidasa; Donna Marie Muzny; Lynne Nazareth; Irene Newsham; Lora Perales; Ling-Ling Pu; Carson Qu; Miquel Ràmia; Jeffrey G Reid; Stephanie M Rollmann; Julio Rozas; Nehad Saada; Lavanya Turlapati; Kim C Worley; Yuan-Qing Wu; Akihiko Yamamoto; Yiming Zhu; Casey M Bergman; Kevin R Thornton; David Mittelman; Richard A Gibbs
Journal:  Nature       Date:  2012-02-08       Impact factor: 49.962

10.  FlyBase 101--the basics of navigating FlyBase.

Authors:  Peter McQuilton; Susan E St Pierre; Jim Thurmond
Journal:  Nucleic Acids Res       Date:  2011-11-29       Impact factor: 16.971

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  174 in total

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2.  Genetic Control of Environmental Variation of Two Quantitative Traits of Drosophila melanogaster Revealed by Whole-Genome Sequencing.

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Journal:  Genetics       Date:  2015-08-12       Impact factor: 4.562

Review 3.  Genetic Effects on the Correlation Structure of CVD Risk Factors: Exome-Wide Data From a Ghanaian Population.

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4.  Genomics and the nature of behavioral and social risk.

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6.  Genome-Wide Analysis of Starvation-Selected Drosophila melanogaster-A Genetic Model of Obesity.

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7.  Biased estimates of diminishing-returns epistasis? Empirical evidence revisited.

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8.  Variable selection method for the identification of epistatic models.

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9.  How consistent are the transcriptome changes associated with cold acclimation in two species of the Drosophila virilis group?

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10.  Genetic interactions improve models of quantitative traits.

Authors:  Anna L Tyler; Gregory W Carter
Journal:  Nat Genet       Date:  2017-03-30       Impact factor: 38.330

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