Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits
Fabio Morgante; Wen Huang; Peter Sørensen; Christian Maltecca; Trudy F. C. Mackay · 2020 · G3 Genes Genomes Genetics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r
Abstract by Fabio Morgante; Wen Huang; Peter Sørensen; Christian Maltecca; Trudy F. C. Mackay, G3 Genes Genomes Genetics (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1534/g3.120.401847