Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information

Angli Xue,Hongcheng Wang,Jun Zhu

doi:10.1038/s41598-017-11676-1

Angli Xue, Hongcheng Wang + Show 1 more

Open Access

https://doi.org/10.1038/s41598-017-11676-1

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Journal: Scientific Reports	Publication Date: Sep 28, 2017
Citations: 6	License type: open-access

Affiliation: Zhejiang University

Abstract

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

Highlights

With the diminishing cost of high-throughput technologies, including rapid genome-sequencing methods and whole genome transcript profiling, the focus of genomic sciences is shifting from data production to data interpretation[1]
Drosophila melanogaster is able to serve as a valuable model to assess the genetic architecture of complex traits and, based on evolutionary conservation of fundamental principles, insights from this model system can be applied to human quantitative traits and diseases[5]
We found that several quantitative trait SNPs (QTSs) were associated with quantitative trait transcripts (QTTs), and some QTTs were associated with other QTTs, which provided more information of the regulatory process of startle response

Summary

Introduction

With the diminishing cost of high-throughput technologies, including rapid genome-sequencing methods and whole genome transcript profiling, the focus of genomic sciences is shifting from data production to data interpretation[1]. GWASs have identified hundreds of common variants associated with complex traits and susceptibility loci have been reported for many diseases, the overall genetic risk explained by these loci remains modest[11]. This could be attributed to a preponderance of a vast number of rare alleles that evade detection even with large sample sizes. A simple, high-throughput and reproducible assay to quantify startle-induced locomotion has been developed and used to map candidate genes corresponding to QTLs. Transcriptional analysis indicated that a large fraction of the genome affects this trait[12] and a previous study showed evidence for epistasis both at the level of QTLs and P-element-induced mutations[13]. Taking into account regulatory networks will provide a better understanding of the genetic mechanisms that orchestrate complex traits[20]

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