A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses

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  • Baranski, T.J.
    • Other Affiliation: Washington University School of Medicine
  • Kraja, A.T.
    • Other Affiliation: Washington University School of Medicine
  • Fink, J.L.
    • Other Affiliation: Washington University
  • Feitosa, M.
    • Other Affiliation: Washington University School of Medicine
  • Lenzini, P.A.
    • Other Affiliation: Washington University School of Medicine
  • Borecki, I.B.
    • Other Affiliation: University of Washington
  • Liu, C.-T.
    • Other Affiliation: Boston University School of Public Health
  • Cupples, L.A.
    • Other Affiliation: Boston University School of Public Health
  • North, K.E.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Province, M.A.
    • Other Affiliation: Washington University School of Medicine
Abstract
  • Human GWAS of obesity have been successful in identifying loci associated with adiposity, but for the most part, these are non-coding SNPs whose function, or even whose gene of action, is unknown. To help identify the genes on which these human BMI loci may be operating, we conducted a high throughput screen in Drosophila melanogaster. Starting with 78 BMI loci from two recently published GWAS meta-analyses, we identified fly orthologs of all nearby genes (± 250KB). We crossed RNAi knockdown lines of each gene with flies containing tissue-specific drivers to knock down (KD) the expression of the genes only in the brain and the fat body. We then raised the flies on a control diet and compared the amount of fat/triglyceride in the tissue-specific KD group compared to the driver-only control flies. 16 of the 78 BMI GWAS loci could not be screened with this approach, as no gene in the 500-kb region had a fly ortholog. Of the remaining 62 GWAS loci testable in the fly, we found a significant fat phenotype in the KD flies for at least one gene for 26 loci (42%) even after correcting for multiple comparisons. By contrast, the rate of significant fat phenotypes in RNAi KD found in a recent genome-wide Drosophila screen (Pospisilik et al. (2010) is ~5%. More interestingly, for 10 of the 26 positive regions, we found that the nearest gene was not the one that showed a significant phenotype in the fly. Specifically, our screen suggests that for the 10 human BMI SNPs rs11057405, rs205262, rs9925964, rs9914578, rs2287019, rs11688816, rs13107325, rs7164727, rs17724992, and rs299412, the functional genes may NOT be the nearest ones (CLIP1, C6orf106, KAT8, SMG6, QPCTL, EHBP1, SLC39A8, ADPGK /ADPGK-AS1, PGPEP1, KCTD15, respectively), but instead, the specific nearby cis genes are the functional target (namely: ZCCHC8, VPS33A, RSRC2; SPDEF, NUDT3; PAGR1; SETD1, VKORC1; SGSM2, SRR; VASP, SIX5; OTX1; BANK1; ARIH1; ELL; CHST8, respectively). The study also suggests further functional experiments to elucidate mechanism of action for genes evolutionarily conserved for fat storage.
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  • Attribution 4.0 International
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  • PLoS Genetics
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  • 14
Journal issue
  • 4
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  • English
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