1.
J Gerontol A Biol Sci Med Sci. 2015 Dec;70(12):1470-8. doi: 10.1093/gerona/glv047. Epub 2015 Apr 28.

Longevity GWAS Using the Drosophila Genetic Reference Panel.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. divanov@ebi.ac.uk.
2
Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK.
3
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. Department of Genetics Evolution and Environment, The Institute of Healthy Ageing, University College London, UK.
4
Department of Biological Sciences, Program in Genetics and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh. Syngenta, Research Triangle Park, North Carolina.
5
Department of Biological Sciences, Program in Genetics and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh.
6
Department of Genetics Evolution and Environment, The Institute of Healthy Ageing, University College London, UK. Max Planck Institute for Biology of Ageing, Cologne, Germany.
7
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

Abstract

We used 197 Drosophila melanogaster Genetic Reference Panel (DGRP) lines to perform a genome-wide association analysis for virgin female lifespan, using ~2M common single nucleotide polymorphisms (SNPs). We found considerable genetic variation in lifespan in the DGRP, with a broad-sense heritability of 0.413. There was little power to detect signals at a genome-wide level in single-SNP and gene-based analyses. Polygenic score analysis revealed that a small proportion of the variation in lifespan (~4.7%) was explicable in terms of additive effects of common SNPs (≥2% minor allele frequency). However, several of the top associated genes are involved in the processes previously shown to impact ageing (eg, carbohydrate-related metabolism, regulation of cell death, proteolysis). Other top-ranked genes are of unknown function and provide promising candidates for experimental examination. Genes in the target of rapamycin pathway (TOR; Chrb, slif, mipp2, dredd, RpS9, dm) contributed to the significant enrichment of this pathway among the top-ranked 100 genes (p = 4.79×10(-06)). Gene Ontology analysis suggested that genes involved in carbohydrate metabolism are important for lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. This analysis suggests that our understanding of the genetic basis of natural variation in lifespan from induced mutations is incomplete.

KEYWORDS:

Ageing; Gene ontology; Gene-based analysis; Insulin signaling pathway; Polygenic score analysis; Target of rapamycin

PMID:
25922346
PMCID:
PMC4631106
DOI:
10.1093/gerona/glv047
[Indexed for MEDLINE]
Free PMC Article
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