A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses.
A high throughput, functional screen of human Body Mass Index GWAS loci using tissue-specific RNAi Drosophila melanogaster crosses.
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DOI：
10.1371/journal.pgen.1007222
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发表时间：
2018-04
期刊：
影响因子：
4.5
通讯作者：
Province MA
中科院分区：
文献类型：
--
作者：
Baranski TJ;Kraja AT;Fink JL;Feitosa M;Lenzini PA;Borecki IB;Liu CT;Cupples LA;North KE;Province MA
人类肥胖 GWAS 已成功识别与肥胖相关的位点，但在大多数情况下，这些都是非编码 SNP，其功能甚至其作用基因尚不清楚。为了帮助识别这些人类 BMI 基因座可能起作用的基因，我们在果蝇中进行了高通量筛选。从最近发表的两项 GWAS 荟萃分析中的 78 个 BMI 位点开始，我们确定了所有附近基因的果蝇直向同源物 (± 250KB)。我们将每个基因的 RNAi 敲低系与含有组织特异性驱动程序的果蝇杂交，仅敲低 (KD) 大脑和脂肪体中基因的表达。然后，我们以对照饮食饲养果蝇，并比较组织特异性 KD 组与仅驱动对照果蝇的脂肪/甘油三酯含量。 78 个 BMI GWAS 位点中的 16 个无法用这种方法筛选，因为 500 kb 区域中没有基因具有果蝇直向同源物。在果蝇中可测试的剩余 62 个 GWAS 位点中，即使在校正多重比较后，我们也发现 KD 果蝇中 26 个位点（42%）的至少一个基因具有显着的脂肪表型。相比之下，在最近的全基因组果蝇筛选（Pospisilik et al. (2010)）中发现的 RNAi KD 中显着脂肪表型的比率约为 5%。更有趣的是，对于 26 个阳性区域中的 10 个，我们发现最近的基因并不是在果蝇中显示显着表型的基因。具体来说，我们的筛选表明，对于 10 个人类 BMI SNP rs11057405， rs205262、rs9925964、rs9914578、rs2287019、rs11688816、rs13107325、rs7164727、rs17724992 和 rs299412，功能基因可能不是最近的基因（CLIP1、C6orf106、 KAT8, SMG6、QPCTL、EHBP1、SLC39A8、ADPGK /ADPGK-AS1、PGPEP1、KCTD15），但附近的特定顺式基因是功能靶标（即：ZCCHC8、VPS33A、RSRC2；SPDEF、NUDT3；PAGR1；SETD1、VKORC1； SGSM2、SRR； VASP，SIX5； OTX1;银行1； ARIH1；英语水平；分别为 CHST8）。该研究还建议进行进一步的功能实验，以阐明进化上保守的脂肪储存基因的作用机制。人类全基因组关联研究已成功发现与许多疾病相关的数千种新型遗传变异。虽然这些无疑指向新的生物学，但该领域在利用这些方面进展缓慢 新的发现可以更好地理解它们到底如何增加风险。许多（如果不是大多数）似乎是监管变体而不是编码变体，因此它们的直接后果并不明显。真正的速率限制步骤甚至是确定这些变体可能调节哪些基因，以及它们在哪些组织中起作用以增加疾病风险。在没有任何其他信息的情况下，第一顺序假设是它们可能更有可能调节附近的基因，这样 变体通常最初由“最近”的基因注释，直到它们的功能得到更明确的验证。利用许多基因可能在物种之间具有保守功能的想法，我们对 78 个经过充分验证的人类肥胖 GWAS 变体附近的人类基因的果蝇直系同源物进行了高通量筛选，以便更精确地识别作用基因。我们系统地消除了附近每一个的功能 果蝇大脑和脂肪体中的基因，以标准饮食饲养它们，并将它们的体脂百分比与对照果蝇进行比较，以验证哪些基因表现出脂肪反应。当该地区存在果蝇直系同源物时，我们能够在 43% 的情况下识别出致病基因。有趣的是，近一半的情况（46%），调节脂肪的不是最近的基因，而是附近的另一个基因。
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. Human Genome Wide Association Studies have successfully found thousands of novel genetic variants associated with many diseases. While these undoubtedly point to new biology, the field has been slowed in exploiting these new findings to reach a better understanding of exactly how they confer increased risk. Many, if not most, appear to be regulatory not coding variants, so their immediate consequence is not obvious. A real rate limiting step is even identifying which gene these variants might be regulating, and in what tissues they are operating to increase disease risk. In the absence of any other information, a first order assumption is that they may be more likely to be regulating a nearby gene, and such variants are often initially annotated by the “nearest” gene until their function is more definitively validated. Exploiting the idea that many genes may have conserved function across species, we conducted a high-throughput screen of fruit-fly orthologs of human genes nearby 78 well validated GWAS variants for human obesity, in order to more precisely identify the gene(s) of action. We systematically knocked down the function of each of these nearby genes in the brain and fat-body of the flies, raised them on a standard diet, and compared their percent body fat with control flies, in order to validate which genes showed a fat response. 43% of the time when fly orthologs existed in the region, we were able to identify the causal gene. Interestingly, nearly half the time (46%), it was not the nearest gene but another nearby one that regulated fat.
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30.8
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影响因子：
30.8
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Westra, Harm-Jan;Peters, Marjolein J.;Esko, Tonu;Yaghootkar, Hanieh;Schurmann, Claudia;Kettunen, Johannes;Christiansen, Mark W.;Fairfax, Benjamin P.;Schramm, Katharina;Powell, Joseph E.;Zhernakova, Alexandra;Zhernakova, Daria V.;Veldink, Jan H.;Van den Berg, Leonard H.;Karjalainen, Juha;Withoff, Sebo;Uitterlinden, Andre G.;Hofman, Albert;Rivadeneira, Fernando;'t Hoen, Peter A. C.;Reinmaa, Eva;Fischer, Krista;Nelis, Mari;Milani, Lili;Melzer, David;Ferrucci, Luigi;Singleton, Andrew B.;Hernandez, Dena G.;Nalls, Michael A.;Homuth, Georg;Nauck, Matthias;Radke, Doerte;Voelker, Uwe;Perola, Markus;Salomaa, Veikko;Brody, Jennifer;Suchy-Dicey, Astrid;Gharib, Sina A.;Enquobahrie, Daniel A.;Lumley, Thomas;Montgomery, Grant W.;Makino, Seiko;Prokisch, Holger;Herder, Christian;Roden, Michael;Grallert, Harald;Meitinger, Thomas;Strauch, Konstantin;Li, Yang;Jansen, Ritsert C.;Visscher, Peter M.;Knight, Julian C.;Psaty, Bruce M.;Ripatti, Samuli;Teumer, Alexander;Frayling, Timothy M.;Metspalu, Andres;van Meurs, Joyce B. J.;Franke, Lude
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Franke, Lude
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影响因子：
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作者：
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64.8
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