Gene expression networks in the <i>Drosophila</i> Genetic Reference Panel

Everett, Logan J.; Huang, Wen; Zhou, Shanshan; Carbone, Mary Anna; Lyman, Richard F.; Arya, Gunjan H.; Geisz, Matthew; Ma, Junwu; Morgante, Fabio; Armour, Genevieve St.; Turlapati, Lavanya; Anholt, Robert R. H.; Mackay, Trudy F. C.

doi:10.1101/gr.257592.119

Cited by 64 publications

(105 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the total number of 157 genes, 11,152 were known genes (i.e. annotated in Flybase) and 4,580 were novel genes in 158 females; on the other hand, 13,249 were known genes and 7,126 were novel genes in males 159 (Everett et al 2019). 160 161 To obtain a list of genes that were highly expressed, we pruned out all the genes that had a mean 162 expression across lines < -1.8 log2(FPKM).…”

Section: Dgrp Lines Genomic Transcriptomic and Phenotypic Data 144mentioning

confidence: 99%

“…160 161 To obtain a list of genes that were highly expressed, we pruned out all the genes that had a mean 162 expression across lines < -1.8 log2(FPKM). This threshold was obtained from an analysis where 163 a mixture model was fitted to the distribution of all expression values, which was bimodal 164 (Everett et al 2019). This procedure yielded 11,562 and 15,184 highly expressed genes in 165 females and males, respectively.…”

Section: Dgrp Lines Genomic Transcriptomic and Phenotypic Data 144mentioning

confidence: 99%

“…S16). 379 380 Discussion 381 Here, we evaluated the use of transcriptomic data for the prediction of complex traits, taking 382 advantage of the unique resource of the DGRP, for which RNA-sequence data has been obtained 383 recently (Everett et al 2019). 384…”

Section: Gene Ontology (Go) Informed Prediction 350mentioning

confidence: 99%

“…In addition, TRS 124 were also able to predict disease progression whereas PRS were not (Marigorta et al 2017). 125 126 Very recently, Li et al (2019) used genotype, expression (obtained by tiling arrays; Huang et al 127 we used 200 of the 205 fully sequenced DGRP inbred lines for which gene expression levels 137 were recently obtained by RNA-seq (Everett et al 2019). Taking advantage of the optimal 138 experimental design, high quality "omic" data, and precise phenotype measurements, we sought 139 to evaluate the prediction performance of either single-omic or poly-omic models using three 140 complex traits (starvation resistance, startle response and chill coma recovery) as model traits.…”

Section: Introduction 15mentioning

confidence: 99%

“…First, the DGRP has a small sample size, which can be problematic for mapping 444 genes with smaller effects. Second, expression levels displayed a strong correlation structure 445 across genes (Everett et al 2019); this was highlighted by the fact that a few randomly sampled 446 genes could achieve very similar accuracies to all highly expressed genes. Correlations among 447 genes can be an issue for mapping; correlations may induce spurious associations if they result 448 from some type of structure (e.g.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Leveraging multiple layers of data to predict Drosophila complex traits

Morgante

Huang

Sørensen

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

1 An important challenge in genetics is to be able to predict complex traits accurately. Despite 2 recent advances, prediction accuracy for most complex traits remains low. Here, we used the 3 Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome 4 sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene 5 expression levels compared to relying on genotypes for predicting three complex traits. We 6 found that expression levels provided higher accuracy than genotypes for starvation resistance, 7 similar accuracy for chill coma recovery, and lower accuracy for startle response. Models 8 including both genotype and expressions levels did not outperform the best single component 9 model. However, accuracy increased considerably for all the three traits when we included 10 another layer of information, i.e., gene ontology (GO). We found that a limited number of GO 11 terms, some of which had a clear biological interpretation, were strongly predictive of the traits. 12In summary, this study shows that integrating different sources of information can improve 13 prediction accuracy, especially when large samples are not available. 14 2006;Hayes et al. 2009). 32 33 GS has been applied to many agricultural species, the first being livestock. Despite the fact that 34 GS has revolutionized animal breeding, the results have varied greatly between species. In dairy 35 cattle, which has the largest and highest quality reference populations, the accuracy of genomic 36 estimated breeding values (gEBVs) has been ~0.7-0.8 for many traits. This high accuracy 37combined with the reduced generation interval has doubled the rate of genetic gain since the 38 implementation of GS in breeding programs (Meuwissen et al. 2016). However, in beef cattle 39 and pigs, for example, the reference populations are much smaller within each breed, resulting in 40 lower accuracy. To overcome this issue, multi-breed reference populations have been used. 41

show abstract

Section: Dgrp Lines Genomic Transcriptomic and Phenotypic Data 144mentioning

confidence: 99%

Section: Dgrp Lines Genomic Transcriptomic and Phenotypic Data 144mentioning

confidence: 99%

Section: Gene Ontology (Go) Informed Prediction 350mentioning

confidence: 99%

Section: Introduction 15mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Leveraging multiple layers of data to predict Drosophila complex traits

Morgante

Huang

Sørensen

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Perspectives on the Drosophila melanogaster Model for Advances in Toxicological Science

Rand,

Tennessen,

Mackay

et al. 2023

Current Protocols

View full text Add to dashboard Cite

The use of Drosophila melanogaster for studies of toxicology has grown considerably in the last decade. The Drosophila model has long been appreciated as a versatile and powerful model for developmental biology and genetics because of its ease of handling, short life cycle, low cost of maintenance, molecular genetic accessibility, and availability of a wide range of publicly available strains and data resources. These features, together with recent unique developments in genomics and metabolomics, make the fly model especially relevant and timely for the development of new approach methodologies and movements toward precision toxicology. Here, we offer a perspective on how flies can be leveraged to identify risk factors relevant to environmental exposures and human health. First, we review and discuss fundamental toxicologic principles for experimental design with Drosophila. Next, we describe quantitative and systems genetics approaches to resolve the genetic architecture and candidate pathways controlling susceptibility to toxicants. Finally, we summarize the current state and future promise of the emerging field of Drosophila metabolomics for elaborating toxic mechanisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

show abstract