Genome-wide polygenic scoring for a 14-year long-term average depression phenotype.
Genome-wide polygenic scoring for a 14-year long-term average depression phenotype.
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DOI：
10.1002/brb3.205
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发表时间：
2014-03
影响因子：
3.1
通讯作者：
Kubzansky, Laura D.
中科院分区：
文献类型：
--
作者：
Chang, Shun-Chiao;Glymour, M. Maria;Walter, Stefan;Liang, Liming;Koenen, Karestan C.;Tchetgen, Eric J.;Cornelis, Marilyn C.;Kawachi, Ichiro;Rimm, Eric;Kubzansky, Laura D.
尽管对抑郁症相关表型的遗传力进行了适度的估计，但几乎没有确定可靠的遗传预测因子。这种差异的潜在解释包括使用从单个时间点采取的表型测量，而不是通过多个评估整合较长时间段的信息，以及遗传风险可能是由多个影响较小的基因座决定的。我们根据护士健康研究(NHS；N=26989名女性)14年的跟踪调查，制定了14年长期平均抑郁测量标准。我们用内部全基因组评分(NHS-GWAS-PS)来估计多基因评分(PS)。我们还从独立的样本中应用了两个外部PS加权算法来构建PS，其中一个先前被证明可以预测抑郁症(GAIN-MDD-PS)，另一个来自目前可用的最大的全基因组分析(PGC-MDD-PS)。我们通过线性回归、Logistic回归和分位数回归来评估这三种PS与我们的长期平均抑郁表型的相关性。在这项研究中，三种PS方法最多可以解释长期平均表型的0.2%的变异。分位数回归表明，PS对抑郁症状的分位数越高，影响越大。在所有三种多基因评分算法中，第75个百分位数的分位数回归系数至少比第25个百分位数的回归系数大40%。四分位数间比较显示，PGC-MDD-PS在长期抑郁表型的第25百分位数和第75百分位数上PS的作用有显著差异(P<0.05)，对于GAIN-MDD-PS，这种差异也达到临界性统计学意义(P<0.05)。整合跨越14年的多种表型评估，并应用不同的多基因评分方法，并没有实质性地改善抑郁症的遗传预测。分位数回归表明，PS的影响可能在抑郁症状评分的高分位数时最大，大概是在有额外的、未观察到的抑郁症易感性来源的人中。
Despite moderate heritability estimates for depression-related phenotypes, few robust genetic predictors have been identified. Potential explanations for this discrepancy include the use of phenotypic measures taken from a single time point, rather than integrating information over longer time periods via multiple assessments, and the possibility that genetic risk is shaped by multiple loci with small effects. We developed a 14-year long-term average depression measure based on 14 years of follow-up in the Nurses' Health Study (NHS; N = 6989 women). We estimated polygenic scores (PS) with internal whole-genome scoring (NHS-GWAS-PS). We also constructed PS by applying two external PS weighting algorithms from independent samples, one previously shown to predict depression (GAIN-MDD-PS) and another from the largest genome-wide analysis currently available (PGC-MDD-PS). We assessed the association of all three PS with our long-term average depression phenotype using linear, logistic, and quantile regressions. In this study, the three PS approaches explained at most 0.2% of variance in the long-term average phenotype. Quantile regressions indicated PS had larger impacts at higher quantiles of depressive symptoms. Quantile regression coefficients at the 75th percentile were at least 40% larger than at the 25th percentile in all three polygenic scoring algorithms. The interquartile range comparison suggested the effects of PS significantly differed at the 25th and 75th percentiles of the long-term depressive phenotype for the PGC-MDD-PS (P = 0.03), and this difference also reached borderline statistical significance for the GAIN-MDD-PS (P = 0.05). Integrating multiple phenotype assessments spanning 14 years and applying different polygenic scoring approaches did not substantially improve genetic prediction of depression. Quantile regressions suggested the effects of PS may be largest at high quantiles of depressive symptom scores, presumably among people with additional, unobserved sources of vulnerability to depression.
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影响因子：
10.6
作者：
Hek, Karin;Demirkan, Ayse;Lahti, Jari;Terracciano, Antonio;Teumer, Alexander;Cornelis, Marilyn C.;Amin, Najaf;Bakshis, Erin;Baumert, Jens;Ding, Jingzhong;Liu, Yongmei;Marciante, Kristin;Meirelles, Osorio;Nalls, Michael A.;Sun, Yan V.;Vogelzangs, Nicole;Yu, Lei;Bandinelli, Stefania;Benjamin, Emelia J.;Bennett, David A.;Boomsma, Dorret;Cannas, Alessandra;Coker, Laura H.;de Geus, Eco;De Jager, Philip L.;Diez-Roux, Ana V.;Purcell, Shaun;Hu, Frank B.;Rimm, Eric B.;Hunter, David J.;Jensen, Majken K.;Curhan, Gary;Rice, Kenneth;Penman, Alan D.;Rotter, Jerome I.;Sotoodehnia, Nona;Emeny, Rebecca;Eriksson, Johan G.;Evans, Denis A.;Ferrucci, Luigi;Fornage, Myriam;Gudnason, Vilmundur;Hofman, Albert;Illig, Thomas;Kardia, Sharon;Kelly-Hayes, Margaret;Koenen, Karestan;Kraft, Peter;Kuningas, Maris;Massaro, Joseph M.;Melzer, David;Mulas, Antonella;Mulder, Cornelis L.;Murray, Anna;Oostra, Ben A.;Palotie, Aarno;Penninx, Brenda;Petersmann, Astrid;Pilling, Luke C.;Psaty, Bruce;Rawal, Rajesh;Reiman, Eric M.;Schulz, Andrea;Shulman, Joshua M.;Singleton, Andrew B.;Smith, Albert V.;Sutin, Angelina R.;Uitterlinden, Andre G.;Voelzke, Henry;Widen, Elisabeth;Yaffe, Kristine;Zonderman, Alan B.;Cucca, Francesco;Harris, Tamara;Ladwig, Karl-Heinz;Llewellyn, David J.;Raikkonen, Katri;Tanaka, Toshiko;van Duijn, Cornelia M.;Grabe, Hans J.;Launer, Lenore J.;Lunetta, Kathryn L.;Mosley, Thomas H., Jr.;Newman, Anne B.;Tiemeier, Henning;Murabito, Joanne
通讯作者：
Murabito, Joanne
影响因子：
1.9
作者：
Jamshed, Shazia Qasim;Elkalmi, Ramadan;Zamri, Siti Maryam Bt.
通讯作者：
Zamri, Siti Maryam Bt.
影响因子：
3
作者：
Mägi R;Morris AP
通讯作者：
Morris AP
影响因子：
2.1
作者：
Li, Yun;Willer, Cristen J.;Ding, Jun;Scheet, Paul;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
3.5
作者：
Evans, David M.;Visscher, Peter M.;Wray, Naomi R.
通讯作者：
Wray, Naomi R.