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      Polymorphisms in early neurodevelopmental genes affect natural variation in alcohol sensitivity in adult drosophila

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          Abstract

          Background

          Alcohol abuse and alcoholism are significant public health problems, but the genetic basis for individual variation in alcohol sensitivity remains poorly understood. Drosophila melanogaster presents a powerful model system for dissecting the genetic underpinnings that determine individual variation in alcohol-related phenotypes. We performed genome wide association analyses for alcohol sensitivity using the sequenced, inbred lines of the D. melanogaster Genetic Reference Panel (DGRP) together with extreme QTL mapping in an advanced intercross population derived from sensitive and resistant DGRP lines.

          Results

          The DGRP harbors substantial genetic variation for alcohol sensitivity and tolerance. We identified 247 candidate genes affecting alcohol sensitivity in the DGRP or the DGRP-derived advanced intercross population, some of which met a Bonferroni-corrected significance threshold, while others occurred among the top candidate genes associated with variation in alcohol sensitivity in multiple analyses. Among these were candidate genes associated with development and function of the nervous system, including several genes in the Dopamine decarboxylase ( Ddc) cluster involved in catecholamine synthesis. We found that 58 of these genes formed a genetic interaction network. We verified candidate genes using mutational analysis, targeted gene disruption through RNAi knock-down and transcriptional profiling. Two-thirds of the candidate genes have been implicated in previous Drosophila, mouse and human studies of alcohol-related phenotypes.

          Conclusions

          Individual variation in alcohol sensitivity in Drosophila is highly polygenic and in part determined by variation in evolutionarily conserved signaling pathways that are associated with catecholamine neurotransmitter biosynthesis and early development of the nervous system.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-015-2064-5) contains supplementary material, which is available to authorized users.

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          Most cited references93

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          Introduction to Quantitative Genetics

          D. S. Falconer (1996)
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            Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines

            Wen Huang, Andreas Massouras, Yutaka Inoue … (2014)
            The Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource of 205 sequenced inbred lines, derived to improve our understanding of the effects of naturally occurring genetic variation on molecular and organismal phenotypes. We used an integrated genotyping strategy to identify 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants. Our molecular population genomic analyses show higher deletion than insertion mutation rates and stronger purifying selection on deletions. Weaker selection on insertions than deletions is consistent with our observed distribution of genome size determined by flow cytometry, which is skewed toward larger genomes. Insertion/deletion and single nucleotide polymorphisms are positively correlated with each other and with local recombination, suggesting that their nonrandom distributions are due to hitchhiking and background selection. Our cytogenetic analysis identified 16 polymorphic inversions in the DGRP. Common inverted and standard karyotypes are genetically divergent and account for most of the variation in relatedness among the DGRP lines. Intriguingly, variation in genome size and many quantitative traits are significantly associated with inversions. Approximately 50% of the DGRP lines are infected with Wolbachia , and four lines have germline insertions of Wolbachia sequences, but effects of Wolbachia infection on quantitative traits are rarely significant. The DGRP complements ongoing efforts to functionally annotate the Drosophila genome. Indeed, 15% of all D. melanogaster genes segregate for potentially damaged proteins in the DGRP, and genome-wide analyses of quantitative traits identify novel candidate genes. The DGRP lines, sequence data, genotypes, quality scores, phenotypes, and analysis and visualization tools are publicly available.
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              Dissection of genetically complex traits with extremely large pools of yeast segregants

              Ian M. Ehrenreich, Noorossadat Torabi, Yue Jia … (2010)
              Most heritable traits, including many human diseases 1, are caused by multiple loci. Studies in both humans and model organisms, such as yeast, have failed to detect a large fraction of the loci that underlie such complex traits 2,3. A lack of statistical power to identify multiple loci with small effects is undoubtedly one of the primary reasons for this problem. We have developed a method in yeast that allows the use of dramatically larger sample sizes than previously possible and hence permits the detection of multiple loci with small effects. The method involves generating very large numbers of progeny from a cross between two strains and then phenotyping and genotyping pools of these offspring. We applied the method to 17 chemical resistance traits and mitochondrial function, and identified loci for each of these phenotypes. We show that the range of genetic complexity underlying these quantitative traits is highly variable, with some traits influenced by one major locus and others due to at least 20 loci. Our results provide an empirical demonstration of the genetic complexity of many traits and show that it is possible to identify many of the underlying factors using straightforward techniques. Our method should have broad applications in yeast and can be extended to other organisms.
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                Author and article information

                Contributors
                Trudy F. C. Mackay: trudy_mackay@ncsu.edu
                Journal
                Journal ID (nlm-ta): BMC Genomics
                Journal ID (iso-abbrev): BMC Genomics
                Title: BMC Genomics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2164
                Publication date (Electronic): 26 October 2015
                Publication date PMC-release: 26 October 2015
                Publication date Collection: 2015
                Volume: 16
                Electronic Location Identifier: 865
                Affiliations
                [ ]Department of Biological Sciences, W. M. Keck Center for Behavioral Biology and Program in Genetics, North Carolina State University, Box 7614, Raleigh, NC 27695 USA
                [ ]Department of Biochemistry and Physiology, School of Bioscience and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH UK
                Article
                Publisher ID: 2064
                DOI: 10.1186/s12864-015-2064-5
                PMC ID: 4624176
                PubMed ID: 26503115
                SO-VID: e1a857b6-35da-47f4-b8dc-cae03a89dec0
                Copyright © © Morozova et al. 2015
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 23 July 2015
                Date accepted : 13 October 2015
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2015

                ScienceOpen disciplines: Genetics
                Keywords: drosophila genetic reference panel,genome-wide association analysis,extreme qtl mapping,alcohol tolerance,genetic networks
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: drosophila genetic reference panel, genome-wide association analysis, extreme qtl mapping, alcohol tolerance, genetic networks

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