Charting the genotype–phenotype map: lessons from the <i>Drosophila melanogaster</i> Genetic Reference Panel

Mackay, Trudy F. C.; Huang, Wen

doi:10.1002/wdev.289

Cited by 125 publications

(162 citation statements)

References 122 publications

(132 reference statements)

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“…The original DGRP lines were constructed such that population structure effects should be minimized, but some genetic relatedness leading to cryptic population structure might still exist (Mackay & Huang, ). In order to correct any major influence from the cryptic population structure that we identified above, a linear mixed model using the FastLMM (Lippert et al, ) program (version 0.2.32) was applied.…”

Section: Resultsmentioning

confidence: 99%

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

2020

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Pupation site choice of Drosophila third‐instar larvae is critical for the survival of individuals, as pupae are exposed to various biotic and abiotic dangers while immobilized during the 3–4 days of metamorphosis. This singular behavioural choice is sensitive to both environmental and genetic factors. Here, we developed a high‐throughput phenotyping approach to assay the variation in pupation height in Drosophila melanogaster, while controlling for possibly confounding factors. We find substantial variation of mean pupation height among sampled natural stocks and we show that the Drosophila Genetic Reference Panel (DGRP) reflects this variation. Using the DGRP stocks for genome‐wide association (GWA) mapping, 16 loci involved in determining pupation height could be resolved. The candidate genes in these loci are enriched for high expression in the larval central nervous system. A genetic network could be constructed from the candidate loci, which places scribble (scrib) at the centre, plus other genes known to be involved in nervous system development, such as Epidermal growth factor receptor (Egfr) and p53. Using gene disruption lines, we could functionally validate several of the initially identified loci, as well as additional loci predicted from network analysis. Our study shows that the combination of high‐throughput phenotyping with a genetic analysis of variation captured from the wild can be used to approach the genetic dissection of an environmentally relevant behavioural phenotype.

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Section: Resultsmentioning

confidence: 99%

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

2020

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“…Understanding how naturally occurring genetic variation affects variation in organismal quantitative traits by modifying underlying molecular networks is a key challenge in modern biology. Most traits are highly polygenic 1–3 and associated molecular variants have small additive effects on trait variation 4 . Most of these variants are in intergenic regions, up- or down- stream of coding regions, or in introns, and presumably play a regulatory role in modulating gene expression.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Networks in the Drosophila Genetic Reference Panel

Everett

Huang

Zhou

et al. 2019

Preprint

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1 2 A major challenge in modern biology is to understand how naturally occurring variation in DNA 3 sequences affects complex organismal traits through networks of intermediate molecular 4 phenotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference 5 Panel inbred lines with complete genome sequences, and mapped expression quantitative trait 6 loci for annotated genes, novel transcribed regions (most of which are long noncoding RNAs), 7transposable elements and microbial species. We identified host variants that affect expression 8 of transposable elements, independent of their copy number, as well as microbiome 9 composition. We constructed sex-specific expression quantitative trait locus regulatory 10 networks. These networks are enriched for novel transcribed regions and target genes in 11 heterochromatin and euchromatic regions of reduced recombination, and genes regulating 12 transposable element expression. This study provides new insights regarding the role of natural 13 genetic variation in regulating gene expression and generates testable hypotheses for future 14 functional analyses.15 Introduction 1 2 Understanding how naturally occurring genetic variation affects variation in organismal 3 quantitative traits by modifying underlying molecular networks is a key challenge in modern 4 biology. Most traits are highly polygenic 1-3 and associated molecular variants have small 5 additive effects on trait variation 4 . Most of these variants are in intergenic regions, up-or down-6 stream of coding regions, or in introns, and presumably play a regulatory role in modulating 7 gene expression.8 Systems genetics analysis seeks to determine how naturally occurring molecular 9 variation gives rise to genetic variation in organismal phenotypes by examining genetic variation 10 in gene expression (expression quantitative trait loci, or eQTLs) and other intermediate 11 molecular phenotypes 2, 5-13 . Polymorphic variants associated with variation in gene expression 12 are classified as cis-or trans-eQTLs depending on whether they are proximal or distal to the 13 gene encoding the transcript, respectively. Genetic variation in gene expression is pervasive; 14 cis-eQTLs can have large effects on gene expression that are detectable in small samples; and 15variants associated with human diseases and quantitative traits tend to be enriched for cis-16 eQTLs 2, 5-15 . eQTLs with both cis-and trans-effects can be assembled into directed 17 transcriptional networks of regulator and target genes [16][17][18] . Elucidating such regulatory 18 transcriptional networks will facilitate understanding how the effects of individual variants 19 propagate through the network, and how multiple variants together regulate gene expression 20 and affect complex traits 15-18 . 21 Here, we performed deep RNA sequencing of the Drosophila melanogaster Genetic 22 Reference Panel (DGRP) of inbred lines with complete DNA sequences 19,20 . We mapped eQTLs 23 for annotated genes, novel transcribed region (NTRs, which are largel...

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“…We define this type of genotype by environment interaction (GEI) as a genotype by early-life nutrition interaction (GENI) [11]. Drosophila provides exceptionally powerful tools and approaches for exploring the mechanisms underlying GENI at the single gene and genome-wide level [12]. The D. melanogaster Genetic Reference Panel (DGRP) [13] consists of sequenced inbred lines derived from a natural population that has been extensively used to chart the genotype-phenotype architecture of complex traits, including behaviors and brain morphology [12].…”

Section: Introductionmentioning

confidence: 99%

“…Drosophila provides exceptionally powerful tools and approaches for exploring the mechanisms underlying GENI at the single gene and genome-wide level [12]. The D. melanogaster Genetic Reference Panel (DGRP) [13] consists of sequenced inbred lines derived from a natural population that has been extensively used to chart the genotype-phenotype architecture of complex traits, including behaviors and brain morphology [12]. DGRP lines reared under different nutritional conditions show changes in behaviors and metabolic and transcriptional profiles, revealing a key role of GENI [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Early-life nutrition interacts with developmental genes to shape the brain and sleep behavior inDrosophila melanogaster

Olivares

Núñez

Candia

et al. 2020

Preprint

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The genetic variation of complex behaviors depends on the variation of brain structure and organization. The mechanisms by which the genome interacts with the nutritional environment during development to shape the brain and behaviors of adults are not well understood. Here we use the Drosophila Genetic Reference Panel to identify genes and pathways underlying this interaction in sleep behavior and mushroom bodies morphology.We identify genes associated with sleep sensitivity to early nutrition, from which protein networks responsible for translation, endocytosis regulation, ubiquitination, lipid metabolism, and neural development emerge. We confirm that genes regulating neural development and insulin signaling in mushroom bodies contribute to the variable response to nutrition. We propose that natural variation in genes that control the development of the brain interact with early-life malnutrition to contribute to variation of adult sleep behavior.

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Charting the genotype–phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel

Cited by 125 publications

References 122 publications

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

Identification of a genetic network for an ecologically relevant behavioural phenotype in Drosophila melanogaster

Gene Expression Networks in the Drosophila Genetic Reference Panel

Early-life nutrition interacts with developmental genes to shape the brain and sleep behavior inDrosophila melanogaster

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