Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX.
Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX.
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孟德尔随机化强调了临床诊断的阿尔茨海默氏病GWAS和自我报告代理表型GWAX中的显着差异和遗传异质性。
DOI：
10.1186/s13195-022-00963-3
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发表时间：
2022-01-28
期刊：
影响因子：
--
通讯作者：
Liu G
中科院分区：
文献类型：
--
作者：
Liu H;Hu Y;Zhang Y;Zhang H;Gao S;Wang L;Wang T;Han Z;Sun BL;Liu G
到目前为止，孟德尔随机化(MR)研究已经使用大规模AD全基因组关联研究(GWAX)、代理GWAX(GWAX)以及GWAX和GWAX(GWAX+GWAX)数据集的荟萃分析来调查危险因素与阿尔茨海默病(AD)的因果关系。然而，目前仍不清楚这些GWAX、GWAX和GWAX+GWAX数据集的MR估计的一致性。在这里，我们首先选择了162个独立的教育程度遗传变异作为潜在的工具变量(N=405,072)。然后，我们选择了一个AD GWAS数据集(N=63,926)，两个AD GWAX数据集(N=314,278和408,942)，以及三个GWAS+GWAX数据集(N=388,324,455,258和472,868)。最后，我们进行了MR分析，以评估教育程度对这些数据集的AD风险的影响。同时，我们测试了这些数据集上受教育程度的遗传变异的遗传异质性。在AD GWAS数据集中，MR分析显示，受教育年限(约3.6年)每增加一次SD，AD风险降低29%(OR=0.71，95%CI：0.60~0.84，P=1.02E−04)。在AD GWAX数据集中，MR分析强调，受教育年限每增加一次SD就会显著增加84%的AD风险(OR=1.84，95%CI：1.59~2.13，P=4.66E−16)。同时，MR分析在AD GWAS+GWAX数据集中发现了模棱两可的结果。异质性检验表明，AD、GWAX和GWAX数据集中存在遗传异质性。我们强调了临床诊断为阿尔茨海默病的GWAX和自我报告的代用表型GWAX的显著差异和遗传异质性。我们的MR发现与最近在AD基因变异中的发现是一致的。因此，GWAX和GWAX+GWAX发现以及GWAX和GWAX+GWAX的MR发现应该被仔细解释，并需要使用AD GWAX数据集进行进一步的研究。网上版载有补充材料，可在10.1186/s13195-022-00963-3查阅。
Until now, Mendelian randomization (MR) studies have investigated the causal association of risk factors with Alzheimer’s disease (AD) using large-scale AD genome-wide association studies (GWAS), GWAS by proxy (GWAX), and meta-analyses of GWAS and GWAX (GWAS+GWAX) datasets. However, it currently remains unclear about the consistency of MR estimates across these GWAS, GWAX, and GWAS+GWAX datasets. Here, we first selected 162 independent educational attainment genetic variants as the potential instrumental variables (N = 405,072). We then selected one AD GWAS dataset (N = 63,926), two AD GWAX datasets (N = 314,278 and 408,942), and three GWAS+GWAX datasets (N = 388,324, 455,258, and 472,868). Finally, we conducted a MR analysis to evaluate the impact of educational attainment on AD risk across these datasets. Meanwhile, we tested the genetic heterogeneity of educational attainment genetic variants across these datasets. In AD GWAS dataset, MR analysis showed that each SD increase in years of schooling (about 3.6 years) was significantly associated with 29% reduced AD risk (OR=0.71, 95% CI: 0.60–0.84, and P=1.02E−04). In AD GWAX dataset, MR analysis highlighted that each SD increase in years of schooling significantly increased 84% AD risk (OR=1.84, 95% CI: 1.59–2.13, and P=4.66E−16). Meanwhile, MR analysis suggested the ambiguous findings in AD GWAS+GWAX datasets. Heterogeneity test indicated evidence of genetic heterogeneity in AD GWAS and GWAX datasets. We highlighted significant difference and genetic heterogeneity in clinically diagnosed AD GWAS and self-report proxy phenotype GWAX. Our MR findings are consistent with recent findings in AD genetic variants. Hence, the GWAX and GWAS+GWAX findings and MR findings from GWAX and GWAS+GWAX should be carefully interpreted and warrant further investigation using the AD GWAS dataset. The online version contains supplementary material available at 10.1186/s13195-022-00963-3.
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影响因子：
2.1
作者：
Bowden J;Davey Smith G;Haycock PC;Burgess S
通讯作者：
Burgess S
影响因子：
7.7
作者：
Anderson EL;Richmond RC;Jones SE;Hemani G;Wade KH;Dashti HS;Lane JM;Wang H;Saxena R;Brumpton B;Korologou-Linden R;Nielsen JB;Åsvold BO;Abecasis G;Coulthard E;Kyle SD;Beaumont RN;Tyrrell J;Frayling TM;Munafò MR;Wood AR;Ben-Shlomo Y;Howe LD;Lawlor DA;Weedon MN;Davey Smith G
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Davey Smith G
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影响因子：
6.8
作者：
Fani L;Georgakis MK;Ikram MA;Ikram MK;Malik R;Dichgans M
通讯作者：
Dichgans M
影响因子：
30.8
作者：
Harold, Denise;Abraham, Richard;Hollingworth, Paul;Sims, Rebecca;Gerrish, Amy;Hamshere, Marian L.;Pahwa, Jaspreet Singh;Moskvina, Valentina;Dowzell, Kimberley;Williams, Amy;Jones, Nicola;Thomas, Charlene;Stretton, Alexandra;Morgan, Angharad R.;Lovestone, Simon;Powell, John;Proitsi, Petroula;Lupton, Michelle K.;Brayne, Carol;Rubinsztein, David C.;Gill, Michael;Lawlor, Brian;Lynch, Aoibhinn;Morgan, Kevin;Brown, Kristelle S.;Passmore, Peter A.;Craig, David;McGuinness, Bernadette;Todd, Stephen;Holmes, Clive;Mann, David;Smith, A. David;Love, Seth;Kehoe, Patrick G.;Hardy, John;Mead, Simon;Fox, Nick;Rossor, Martin;Collinge, John;Maier, Wolfgang;Jessen, Frank;Schuermann, Britta;van den Bussche, Hendrik;Heuser, Isabella;Kornhuber, Johannes;Wiltfang, Jens;Dichgans, Martin;Froelich, Lutz;Hampel, Harald;Huell, Michael;Rujescu, Dan;Goate, Alison M.;Kauwe, John S. K.;Cruchaga, Carlos;Nowotny, Petra;Morris, John C.;Mayo, Kevin;Sleegers, Kristel;Bettens, Karolien;Engelborghs, Sebastiaan;De Deyn, Peter P.;Van Broeckhoven, Christine;Livingston, Gill;Bass, Nicholas J.;Gurling, Hugh;McQuillin, Andrew;Gwilliam, Rhian;Deloukas, Panagiotis;Al-Chalabi, Ammar;Shaw, Christopher E.;Tsolaki, Magda;Singleton, Andrew B.;Guerreiro, Rita;Muehleisen, Thomas W.;Noethen, Markus M.;Moebus, Susanne;Joeckel, Karl-Heinz;Klopp, Norman;Wichmann, H-Erich;Carrasquillo, Minerva M.;Pankratz, V. Shane;Younkin, Steven G.;Holmans, Peter A.;O'Donovan, Michael;Owen, Michael J.;Williams, Julie
通讯作者：
Williams, Julie
影响因子：
30.8
作者：
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