van den Berg Stéphanie M, de Moor Marleen H M, McGue Matt, Pettersson Erik, Terracciano Antonio, Verweij Karin J H, Amin Najaf, Derringer Jaime, Esko Tõnu, van Grootheest Gerard, Hansell Narelle K, Huffman Jennifer, Konte Bettina, Lahti Jari, Luciano Michelle, Matteson Lindsay K, Viktorin Alexander, Wouda Jasper, Agrawal Arpana, Allik Jüri, Bierut Laura, Broms Ulla, Campbell Harry, Smith George Davey, Eriksson Johan G, Ferrucci Luigi, Franke Barbera, Fox Jean-Paul, de Geus Eco J C, Giegling Ina, Gow Alan J, Grucza Richard, Hartmann Annette M, Heath Andrew C, Heikkilä Kauko, Iacono William G, Janzing Joost, Jokela Markus, Kiemeney Lambertus, Lehtimäki Terho, Madden Pamela A F, Magnusson Patrik K E, Northstone Kate, Nutile Teresa, Ouwens Klaasjan G, Palotie Aarno, Pattie Alison, Pesonen Anu-Katriina, Polasek Ozren, Pulkkinen Lea, Pulkki-Råback Laura, Raitakari Olli T, Realo Anu, Rose Richard J, Ruggiero Daniela, Seppälä Ilkka, Slutske Wendy S, Smyth David C, Sorice Rossella, Starr John M, Sutin Angelina R, Tanaka Toshiko, Verhagen Josine, Vermeulen Sita, Vuoksimaa Eero, Widen Elisabeth, Willemsen Gonneke, Wright Margaret J, Zgaga Lina, Rujescu Dan, Metspalu Andres, Wilson James F, Ciullo Marina, Hayward Caroline, Rudan Igor, Deary Ian J, Räikkönen Katri, Arias Vasquez Alejandro, Costa Paul T, Keltikangas-Järvinen Liisa, van Duijn Cornelia M, Penninx Brenda W J H, Krueger Robert F, Evans David M, Kaprio Jaakko, Pedersen Nancy L, Martin Nicholas G, Boomsma Dorret I
Department of Research Methodology, Measurement and Data-Analysis, University of Twente, Enschede, The Netherlands,
Behav Genet. 2014 Jul;44(4):295-313. doi: 10.1007/s10519-014-9654-x. Epub 2014 May 15.
Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.
大型或元分析研究(如全基因组关联研究)在行为遗传学中的应用越来越广泛。这类研究中的一个问题是,不同研究队列中表型通常由不同的工具测量,这就需要对测量方法进行协调,以便能够采用更强大的固定效应元分析。在人格遗传学联盟中,我们针对两种临床相关的人格特质——神经质和外向性,展示了如何应用项目反应理论(IRT)将来自不同量表的项目数据映射到相同的潜在结构上。对来自欧洲、美国和澳大利亚23个队列的16万多人的人格项目数据进行了分析,其中神经质和外向性由九种不同的人格量表进行评估。结果表明,对于大多数人格量表,协调非常成功,对一些量表则取得了一定成功。神经质和外向性量表在不同队列中基本具有测量不变性,特别是在比较使用相同语言的国家的队列时。基于IRT的神经质和外向性得分具有遗传性(分别为48%和49%,基于对六个双胞胎队列的元分析,双胞胎总数分别为29496对和29501对),遗传性的很大一部分归因于非加性遗传因素。对于外向性,这些遗传因素在性别上存在质的差异。我们表明,我们的IRT方法可以大幅增加样本量,从而提高统计效力。IRT方法可应用于任何需要协调基于项目的行为测量的大型或元分析研究。
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