Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory

ORIGINAL RESEARCH Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory Ste ´phanie M. van den Berg •Marleen H. M. de Moor •Matt McGue •Erik Pettersson • Antonio Terracciano •Karin J. H. Verweij •Najaf Amin •Jaime Derringer •To ˜nu Esko • Gerard van Grootheest •Narelle K. Hansell •Jennifer Huffman •Bettina Konte •Jari Lahti • Michelle Luciano •Lindsay K. Matteson •Alexander Viktorin •Jasper Wouda •Arpana Agrawal • Ju ¨ri Allik •Laura Bierut •Ulla Broms •Harry Campbell •George Davey Smith •Johan G. Eriksson • Luigi Ferrucci •Barbera Franke •Jean-Paul Fox •Eco J. C. de Geus •Ina Giegling • Alan J. Gow •Richard Grucza •Annette M. Hartmann •Andrew C. Heath •Kauko Heikkila ¨• William G. Iacono •Joost Janzing •Markus Jokela •Lambertus Kiemeney •Terho Lehtima ¨ki • Pamela A. F. Madden •Patrik K. E. Magnusson •Kate Northstone •Teresa Nutile •Klaasjan G. Ouwens • Aarno Palotie •Alison Pattie •Anu-Katriina Pesonen •Ozren Polasek •Lea Pulkkinen • Laura Pulkki-Ra ˚back •Olli T. Raitakari •Anu Realo •Richard J. Rose •Daniela Ruggiero • Ilkka Seppa ¨la ¨•Wendy S. Slutske •David C. Smyth •Rossella Sorice •John M. Starr • Angelina R. Sutin •Toshiko Tanaka •Josine Verhagen •Sita Vermeulen •Eero Vuoksimaa • Elisabeth Widen •Gonneke Willemsen •Margaret J. Wright •Lina Zgaga •Dan Rujescu • Andres Metspalu •James F. Wilson •Marina Ciullo •Caroline Hayward •Igor Rudan • Ian J. Deary •Katri Ra ¨ikko ¨nen •Alejandro Arias Vasquez •Paul T. Costa •Liisa Keltikangas-Ja ¨rvinen • Cornelia M. van Duijn •Brenda W. J. H. Penninx •Robert F. Krueger •David M. Evans • Jaakko Kaprio •Nancy L. Pedersen •Nicholas G. Martin •Dorret I. Boomsma Received: 21 October 2013 / Accepted: 20 March 2014 / Published online: 15 May 2014 ÓThe Author(s) 2014. This article is published with open access at Springerlink.com Abstract Megaor meta-analytic studies (e.g. genomewide 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 Edited by Kristen Jacobson. Ste ´phanie M. van den Berg and Marleen H. M. de Moor are the cofirst authors. Electronic supplementary material The online version of this article (doi:10.1007/s10519-014-9654-x) contains supplementary material, which is available to authorized users. S. M. van den Berg J.-P. Fox Department of Research Methodology, Measurement and DataAnalysis, University of Twente, Enschede, The Netherlands S. M. van den Berg (&) Department of Behavioural Sciences, OMD, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands e-mail: [email protected] M. H. M. de Moor J. Wouda E. J. C. de Geus  K. G. Ouwens G. Willemsen D. I. Boomsma Department of Biological Psychology, VU University, Amsterdam, The Netherlands M. McGue L. K. Matteson W. G. Iacono R. F. Krueger Department of Psychology, University of Minnesota, Elliott Hall, Minneapolis, MN, USA M. McGue Institute of Public Health, University of Southern Denmark, Odense, Denmark E. Pettersson A. Viktorin P. K. E. Magnusson  N. L. Pedersen Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 123 Behav Genet (2014) 44:295–313 DOI 10.1007/s10519-014-9654-x 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 nonadditive 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 megaor meta-analytic study in which itembased behavioral measures need to be harmonized. Keywords Personality Item-Response Theory  Measurement Genome-wide association studies  Consortium Meta-analysis Introduction Megaormeta-analytic studies (e.g. genome-wide association (GWA) studies) are increasingly used in behavior genetics. Because phenotypes have not always been assessed similarly across cohorts (and sometimes not even within cohorts), measures need to be harmonized, that is, phenotypic scores need to be made comparable such that data from individuals who were assessed by different inventories can be compared meaningfully. Such harmonization then enables fixed effect meta-analytic analyses (Hedges and Vevea 1998). Metaanalytic studies are required when effect sizes are small such as for complex human traits. For example, GWA studies for psychiatric disorders have led to important discoveries, but for many disorders, individual variants typically explain less than 1 % of the heritability, although in unison they can explain quite a large proportion of phenotypic variation (Craddock et al. 2008; Lee et al. 2013; Ripke et al. 2013; Sullivan et al. 2012). Sample size determines the number of significant loci discovered (Sullivan et al. 2012), so that meta-analysis of results is the gold standard. Consortium GWA studies for traits such as height and body-mass index now report sample sizes of [100,000 (Berndt et al. 2013; Lango Allen and et al. 2010; Speliotes et al. 2010). Consortia for psychiatric disorders and behavioral traits have also been formed, with sample sizes increasing rapidly to hundreds of thousands (Rietveld et al. 2012; Ripke et al. 2011;Wrayetal.2012), leading to the discovery of novel loci for psychiatric disorders and educational attainment. Thus, large sample sizes are essential for behavioral phenotypes. A meta-analysis of behavioral measures will have most power if the same reliable and valid measurement instrument is administered in all cohorts. In practice, however, different instruments are often used, and, even when the instrument is the same, translations into different languages may cause problems. To tackle the problem that different inventories A. Terracciano L. Ferrucci A. R. Sutin T. Tanaka National Institute on Aging, NIH, Baltimore, MD, USA A. Terracciano A. R. Sutin College of Medicine, Florida State University, Tallahassee, FL, USA K. J. H. Verweij N. K. Hansell D. C. Smyth  M. J. Wright N. G. Martin QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia K. J. H. Verweij Department of Developmental Psychology and EMGO Institute for Health and Care Research, VU University Amsterdam, Amsterdam, The Netherlands N. Amin C. M. van Duijn Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands J. Derringer Department of Psychology, University of Illinois at UrbanaChampaign, Champaign, IL, USA T. Esko A. Metspalu Estonian Genome Center, University of Tartu, Tartu, Estonia G. van Grootheest B. W. J. H. Penninx Department of Psychiatry, EMGO?Institute, Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands J. Huffman C. Hayward MRC Human Genetics, MRC IGMM, Western General Hospital, University of Edinburgh, Edinburgh, Scotland, UK B. Konte I. Giegling A. M. Hartmann D. Rujescu Department of Psychiatry, University of Halle, Halle, Germany J. Lahti M. Jokela A.-K. Pesonen L. Pulkki-Ra ˚back  K. Ra ¨ikko ¨nen L. Keltikangas-Ja ¨rvinen Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland J. Lahti J. G. Eriksson K. Ra ¨ikko ¨nen Folkha ¨lsan Research Center, Helsinki, Finland M. Luciano A. Pattie I. J. Deary Department of Psychology, University of Edinburgh, Edinburgh, UK M. Luciano A. Pattie J. M. Starr I. J. Deary Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK 296 Behav Genet (2014) 44:295–313 123 may not assess the same phenotype, we demonstrate how Item-Response Theory (IRT) test linking can be applied to map item data from different inventories to a common metric. We conduct such an analysis for Neuroticism and Extraversion personality traits, based on data from the Genetics of Personality Consortium (GPC). If different inventories indeed measure the same phenotype, the only requirement for this approach is that multiple inventories have been administered in at least a subset of individuals. That is, in order to be able to harmonize across different inventories, some participants must have filled in multiple inventories so that they can function as a ‘‘bridge’’ between inventories. This can be done if we assume that the true phenotype (personality) does not change between the multiple assessments. If this can be assumed, then for all individuals inthe different (sub-)cohorts, a score on the latent construct can be estimated based on all available item data for that person. The IRT-based score estimates for Neuroticism and Extraversion can subsequently be meta-analyzed to assess heritability, or can be used as phenotypes in GWA or brain-imaging studies. This IRT approach has multiple advantages. First, within each cohort there is increased measurement reliability, because when multiple inventories have been administered to the same individual, scores can be estimated using the items from all relevant inventories. In addition, items can be differentially and optimally weighted if necessary, and items that do not fit the measurement model can be identified and omitted, thereby increasing power. Subgroups of individuals that were assessed with only a subset of items can now also be included in the study. Moreover, the IRT approach can statistically evaluate the extent to which different inventories actually measure the same construct. Lastly, IRT enables researchers to determine the extent of measurement invariance across cohorts: can scores across cohorts be quantitatively compared and therefore pooled and meaningfully used in a meta-analysis? Applying the IRT method to Neuroticism and Extraversion is especially relevant for the field of behaviorgenetics, asthese personality traits are correlated with numerous other traits and disorders, not only phenotypically but also genetically (Heath et al. 1994; Hopwood et al. 2011; Klein et al. 2011;Markon et al. 2005; Samuel and Widiger 2008). For example, Neuroticism is highly related to a variety of psychiatric disorders, including major depression and borderline personality disorder (Distel et al. 2009;KendlerandMyers2009), and Extraversion is associated with alcohol use (Dick et al. 2013). Earlier GWA studies of personality (De Moor et al. 2010; Service et al. 2012; Shifman et al. 2008; Terracciano et al. 2010;vandenOordetal.2008) focused on single inventories, hence hampering sample size, and few, if any, genome-wide significant loci were detected. Large sample sizes are needed, which can be achieved by pooling results from multiple inventories. This study included data obtained from 160,958 individuals from 23 cohorts, of which 6 were twin cohorts. Neuroticism and Extraversion were assessed by 9 different personality inventories; 7 cohorts assessed more than one inventory. The first objective was to determine the feasibility of the IRT approach in linking Neuroticism and Extraversion item data from different inventories: to what extent do the different inventories measure the same constructs? For instance, Harm Avoidance correlates moderA. Agrawal L. Bierut R. Grucza A. C. Heath  P. A. F. Madden Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA J. Allik A. Realo Department of Psychology, University of Tartu, Tartu, Estonia J. Allik A. Metspalu Estonian Academy of Sciences, Tallinn, Estonia U. Broms K. Heikkila ¨E. Vuoksimaa J. Kaprio Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland U. Broms J. G. Eriksson K. Ra ¨ikko ¨nen J. Kaprio National Institute for Health and Welfare (THL), Helsinki, Finland H. Campbell L. Zgaga J. F. Wilson I. Rudan Centre for Population Health Sciences, Medical School, University of Edinburgh, Edinburgh, UK G. D. Smith K. Northstone D. M. Evans MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK J. G. Eriksson Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland J. G. Eriksson Unit of General Practice, Helsinki University Central Hospital, Helsinki, Finland J. G. Eriksson Vasa Central Hospital, Vaasa, Finland B. Franke A. Arias Vasquez Donders Institute for Cognitive Neuroscience, Radboud University Nijmegen, Nijmegen, The Netherlands B. Franke J. Janzing A. Arias Vasquez Department of Psychiatry, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands B. Franke S. Vermeulen A. Arias Vasquez Department of Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands A. J. Gow Department of Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK Behav Genet (2014) 44:295–313 297 123 ately high with Neuroticism (r =0.5–0.6) (De Fruyt et al. 2000). Therefore, we expect that mapping item data from Harm Avoidance with Neuroticism will be less perfect than mapping Neuroticism item data from other personality inventories (e.g. EPQ versus NEO neuroticism). We expect that this is even more the case for mapping Reward Dependence with Extraversion. Here we determine to what extent cross-inventory mapping is feasible, for the purpose of a GWAS meta-analysis in mind. The second objective was to test for measurement invariance across cohorts, and the third objective was to establish the heritability of the harmonized Neuroticism and Extraversion scores in the six participating twin cohorts. Sex differences in the genetic background of Neuroticism and Extraversion were studied, as well as the contribution of non-additive genetic factors. The contribution of non-additive genetic factors to variation in personality traits has been extensively discussed in the literature (Keller et al. 2005), but their assessment requires a large sample (Posthuma and Boomsma 2000). Lastly, we studied the theoretical increase in power of finding a quantitative trait locus due to the harmonization of phenotypes in two large cohorts. Materials and methods Cohorts Twenty-three cohorts of the GPC were included in this study (for detailed descriptions, see Supplementary Materials Online). Seventeen cohorts originated from Europe, 4 cohorts were from the USA and 2 cohorts from Australia. Most cohorts are large epidemiological studies. Some of the cohorts focused on specific birth cohorts and/or recruited individuals of specific regions in the country (e.g. ERF, VIS, KORCULA, NBS, LBC1921, LBC1936 and HBCS), or targeted twins and their family members (QIMR cohorts, NTR, MCTFR, STR, Finnish Twin Cohort). Three cohorts were designed to include cases and controls for Nicotine dependence, Alcoholism or Mood and Anxiety disorders (respectively, COGEND, SAGE-COGA and NESDA). The data collection in some of the cohorts is longitudinal in nature. Personality assessment Supplementary Table 1 and Supplementary Fig. 3 give an overview of the personality inventories administered in each cohort. The Supplementary Materials Online describes these inventories in detail. For the Neuroticism analysis, we included all Neuroticism items from the NEO, the International Personality Item Pool (IPIP) and Eysenck (EPQ, EPI, ABV) inventories, the Harm Avoidance (HA) items from the Temperament and Character Inventory (TCI), and the Negative Emotionality (NEM) items (excluding the aggression items) from the Multidimensional Personality Questionnaire (MPQ). The Neuroticism scales of the NEO, IPIP and Eysenck inventories consist of different items, but there is strong overlap in item content and the sum scores correlate highly across inventories (Aluja et al. 2004; Draycott and Kline 1995; Larstone et al. 2002). HA correlates most strongly with Neuroticism (as assessed with the NEO-PI-R or EPQ-R) (De Fruyt et al. 2000; Gillespie et al. 2001). NEM corresponds most closely to Neuroticism, although NEM is a broader concept because it also includes items about aggressive behavior. M. Jokela T. Lehtima ¨ki I. Seppa ¨la ¨ Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland L. Kiemeney S. Vermeulen Department of Health Evidence, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands L. Kiemeney Department of Urology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands T. Nutile D. Ruggiero R. Sorice M. Ciullo Institute of Genetics and Biophysics ‘‘A. Buzzati-Traverso’’ – CNR, Naples, Italy A. Palotie Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK A. Palotie E. Widen J. Kaprio Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland O. Polasek Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia L. Pulkkinen Department of Psychology, University of Jyva ¨skyla ¨, Jyva ¨skyla ¨, Finland O. T. Raitakari Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland O. T. Raitakari Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland R. J. Rose Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA W. S. Slutske Department of Psychological Sciences and Missouri Alcoholism Research Center, University of Missouri, Columbia, MO, USA 298 Behav Genet (2014) 44:295–313 123 For the Extraversion analysis, all Extraversion items from the NEO, IPIP and Eysenck inventories were analyzed, a selection of Reward Dependence (RD) items from the TCI, and the Positive Emotionality (PEM) items from the MPQ. Extraversion sum scores derived from the NEO, IPIP and Eysenck inventories correlate highly across inventories (Aluja et al. 2004; Draycott and Kline 1995; Larstone et al. 2002). The relationship between Extraversion and the temperament traits is less clear, but Extraversion correlates strongest with RD (De Fruyt et al. 2000; Gillespie et al. 2001). Based on the item correlations among the RD items with the Extraversion items from the NEO-PI-R and EPQ in the HBCS, PAGES and QIMR adults cohorts, we decided to include a subset of RD items that correlated strongest with the Extraversion items (see Supplementary Fig. 3 for number of items included and Supplementary Table 2 for overview of the items). Estimating Neuroticism and Extraversion scores The harmonization goal is to estimate personality scores that are not biased by the number of items and the specific inventory used. In the field of IRT, such harmonization is termed ‘test linking’. By fitting IRT models (Lord 1980)to item data, personality scores can be estimated conditional on the observed items and their respective item parameters. This leads to personality scores for individuals that are comparable irrespective of what items were assessed in a particular individual. For example, image an intelligence assessment: If we know that items 1–10 are very easy test items, and items 11–20 are very difficult, we are pretty confident that a person that scores 1 on the items 1–10 is less bright than a person that scores 9 on items 11–20. The exact knowledge of the difficulties of the 20 items allows us to estimate the difference in intelligence. A basic IRT model assumes a one-dimensional latent variable representing the trait that predicts the probability of a certain response on a particular item: the higher the latent trait value, the higher the probability of a high score on the item. Item parameters determine the exact relationship between the latent trait and the probability of the response to a particular item. The so-called difficulty parameter provides information about the general probability of a positive response to a particular item, and is very similar to the threshold parameter in liability models. The discrimination parameter value of an item indicates how strong the relationship is between the latent trait and the item response variable, and is therefore similar to a factor loading. Because latent scores are estimated conditional on the item parameters for the administered items, the scoring process becomes independent of the particular items in the test. For example, this allows the comparison of a child’s achievement on a test with easy questions with the achievement of another child on a test with difficult questions. IRT test linking was applied in each cohort separately and used to link all data from one cohort to one common metric for Neuroticism and one common metric for Extraversion. For more details, see Supplementary Materials Online. Appropriateness of Item Response Theory to harmonize Neuroticism and Extraversion scores We assessed whether the IRT Neuroticism and Extraversion scores in the 23 cohorts were truly independent of the specific inventory used. First, the appropriateness of linking tests within cohorts was investigated by testing basic assumptions of IRT models: the idea that scoring is independent of the specific item set that was administered (local independence), and unidimensionality. For every cohort and every inventory separately, item parameters were estimated based on data from individuals without missing data. Such a set of parameter values for a particular sample of items assessed in a particular sample is termed a calibration. Calibrations were also obtained for combinations of item sets from various inventories, if there was a subsample of individuals that was assessed with those inventories. Based on these calibrations, (i.e., sets of item parameter values), latent scores can be estimated for those individuals for which one has either complete data or data with some missing values, assuming these are missing at random. In order to investigate local independence, latent scores for a particular item set (say, item scores for NEOPI-R) were estimated and compared based on different calibrations: one based on the calibration of several inventories combined (e.g., NEO-PI-R and EPQ-R Neuroticism) and one based on only one inventory (NEO-PI-R items). The resulting scores were then correlated. A J. Verhagen Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands E. Vuoksimaa Department of Psychiatry, University of California, La Jolla, CA, USA L. Zgaga Department of Public Health and Primary Care, Trinity College Dublin, Dublin, Ireland A. Arias Vasquez Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands P. T. Costa Behavioral Medicine Research Center, Duke University School of Medicine, Durham, NC, USA Behav Genet (2014) 44:295–313 299 123 correlation of 1 indicates that the estimated scores are completely independent of what inventory was used for assessment (see also Supplementary Materials Online). Unidimensionality was assessed by plotting the test information curves (TICs) (Lord 1980; van den Berg and Service 2012) for inventories separately and with two or more inventories combined. If two tests measure the same underlying construct, the TIC of the tests combined should be the sum of the TICs of the two separate tests. These curves also show the increase in measurement precision for those individuals that were administered multiple inventories. The choice for the above approach to assessing model fit, which is a bit unconventional, was motivated by the fact that the personality inventories are well-developed and validated instruments. Also, from previous research we know that two-parameter models generally are more appropriate for personality data than oneand threeparameter models (Chernyshenko et al. 2001; Reise and Waller 1990). As one aim is to use as much information as possible from the personality inventories, to establish a linear relationship between personality scales and an external variable, such as a SNP, we chose to retain all items in the analyses. The above analysis determines whether within cohorts, items from inventories can be combined, that is, whether different inventories can be used to measure the same trait. In addition, it is important to assess whether across cohorts, the same trait is being measured. If Neuroticism and Extraversion were very differently expressed across cohorts, a meta-analysis is rather meaningless. Due to a host of reasons (culture, language, sample selection criteria, etc.), the same test items might have different parameters across cohorts. Ignoring these differences results in systematic bias when comparing individual sum scores from different cohorts. The assumption of equal item parameters across groups is usually termed measurement invariance (Meredith 1993). If one item has different parameter values across groups, this is called differential item functioning (DIF) (Glas 1998,2001; Speliotes et al. 2010). There are two ways of dealing with DIF, either (1) omitting the item entirely in estimating individual scores, or (2) allowing for different item parameters for that particular DIF item across groups (Weisscher et al. 2010). The first approach leads to loss of information, so that the second is generally more attractive. A new alternative Bayesian method for modeling measurement non-invariance (Verhagen and Fox 2013a,b) was applied to assess variance of item parameters across cohorts and that identifies true differences in means and variances of Neuroticism and Extraversion across cohorts, while controlling for any measurement non-invariance. The Bayesian approach allows for estimating complicated models in a straightforward way, and through hierarchical modeling one borrows statistical strength for small cohorts from information in larger cohorts. The Bayesian hierarchical approach assumes there is at least some violation of measurement invariance, and quantifies its extent. Since there are some important differences across cohorts in terms of population and language, we expect there will be at least some difference in item parameters across cohorts. In the Bayesian hierarchical approach, item and person parameters are estimated using a Markov Chain Monte Carlo procedure, in which cohort-specific item parameters are considered level-1 parameters randomly distributed around overall mean item parameters at level 2. See Fig. 1 for a graph representation of the hierarchical structure of both item and person parameters across cohorts. As the identification constraint, the average difficulty of the items is assumed equal across cohorts. That is, cohorts may differ in mean and variance of the latent trait, and particular item parameters might be different across cohorts, but the average difficulty of items is the same (for example, in case of an IQ test for males and females: the assumption is that overall the test has the same difficulty, although it can be the case that some items are relatively more difficult for Fig. 1 A graph representation of the hierarchical model for measurement variance. Item parameters n(thresholds and discrimination parameter) are allowed to vary across cohorts, but person parameters are allowed to vary both across cohorts and within cohorts. Observed response Y ijk from person iin cohort jto item kis predicted by a latent score h ij for that person and item parameters n kj for item kthat is specific for cohort j 300 Behav Genet (2014) 44:295–313 123 males, and other items are relatively more difficult for females). In addition, to identify the variance of the scale the product of the discrimination parameters was fixed at 1. Allowing for such random fluctuations in difficulty and discrimination across cohorts is also referred to as the assumption of approximate measurement invariance. This Bayesian method was only applied to NEO-FFI and EPQ-R test items, as for those tests, the numbers of cohorts were sufficiently large. We randomly selected 1,000 individuals from each cohort (or all individuals if sample size was smaller) and determined which items showed considerable DIF across cohorts by computing Bayes factors (Verhagen and Fox 2013a,b). When testing invariance hypotheses, an advantage of the Bayes factor is that you can gather evidence in favor of the (null) hypothesis of invariance. A Bayes factor smaller than 0.3 was regarded as clear evidence of DIF. A Bayes factor larger than 3 was regarded as evidence of measurement invariance (i.e., no DIF). Taking into account possible DIF, all individuals with either NEO or EPQ data were mapped to a common scale for Neuroticism and Extraversion and mean Neuroticism and Extraversion scores and variances were estimated for each cohort. Significant DIF does not imply that its effects are dramatic. To assess the extent to which DIF results in different scoring, depending on what calibration is used, Neuroticism and Extraversion scores were estimated using different cohort-specific calibrations and these were compared. For example, how much would the estimated scores for individuals in the Dutch NTR sample differ if instead of using the NTR calibration (i.e., using item parameters as estimated using NTR data), the Finnish HBCS calibration were used? If measurement invariance holds perfectly, the correlation between the different score estimates should be very close to 1. These correlations were computed for NEO-FFI, NEO-PI-R and EPQ inventories in the appropriate cohorts. Meta-analysis of heritability In each of the 6 cohorts with twin data separately, twin correlations for the IRT latent trait scores were estimated using the structural equation modeling package OpenMx within the statistical software program R (Boker et al. 2011). This was done by fitting a fully saturated model using full information likelihood to the data of twins in five sex-by-zygosity groups: monozygotic male twin pairs (MZM), dizygotic male twin pairs (DZM), monozygotic female twin pairs (MZM), dizygotic female twin pairs (DZM) and dizygotic twin pairs of opposite sex (DOS; if available in the particular cohort). Twin pairs in which Neuroticism and Extraversion scores were available for both twins were included, as well as twin pairs for which information was available for only one of the twins. In each cohort including a DOS group, 16 parameters were estimated: 5 means (5 sex by zygosity groups), 1 regression parameter for the effect of age on the means, 5 variances (5 sex by zygosity groups) and 5 covariances (for 5 sex by zygosity groups). In the cohorts without a DOS group, 4 means, 1 regression parameter for age, 4 variances and 4 covariances were estimated (13 parameters in total). The 4 or 5 covariances were standardized in each sex-by-zygosity group in order to obtain 4 or 5 twin correlations in each cohort. In addition, the 95 % confidence intervals for the twin correlations were computed. It was further tested whether the twin correlations could be constrained to be equal across sex (MZM =MZF and DZM =DZF = DOS). Under the classical twin model assumptions, the expected MZ twin correlation is a function of the proportions of variance in a trait explained by additive (h 2 ) and non-additive (d 2 ) genetic effects: r(MZ) =h 2 ?d 2 . The expected DZ twin correlation is a different function of these two types of effects: r(DZ) =h 2 ?d 2 . IRTscore-based twin correlations (Table 1) were used as the basis to assess both qualitative and quantitative sex effects. This was done by fitting the same model to data from all six cohorts simultaneously allowing for different estimates of h 2 and d 2 in each sex, and allowing the opposite-sex twin correlation to be different from its expectation, h m h f ? d m d f . The estimates of parameters (h 2 ,e 2 and d 2 by sex) thus were constrained to be the same across cohorts. First it was tested whether the correlation in opposite-sex twins could be equated to the expectation above (i.e. testing for qualitative sex effects). Next, it was tested whether the relative sizes of the genetic components could be equated across sexes, that is, whether h m 2 =h f 2 and d m 2 =d f 2 . Lastly, it was tested whether non-additive genetic effects were present, by comparing the fit of the model with a model in which d 2 =0. Power study For the NTR and the QIMR-adult cohorts, the increase in statistical power for a GWAS on Neuroticism was determined that results from the increase in sample size and measurement precision due to the IRT test linking. A baseline condition of using 12 NEO-FFI items as in a previous meta-analysis (De Moor et al. 2010) was compared with using all available data from NEO-PI-R and other available inventories. We assumed that genotype data was non-missing for all phenotypes. Power was computed for a single nucleotide polymorphism (SNP) explaining 0.1 % of true phenotypic variance (latent trait) with allele Behav Genet (2014) 44:295–313 301 123 frequency 0.5. Item data were simulated with parameter settings equal to the observed parameter estimates in the empirical data. Sample sizes were also the same as in the empirical data. For each power estimate, 100 data sets were simulated and analyzed, and the proportion of p-values smaller than 10 -8 was calculated. Table 1 Twin correlations for the IRT-based Neuroticism and Extraversion scores Cohort Twin pairs Trait r MZ N 95 % CI r DZ N95%CI 7. FINNISH TWINS M–M Neuroticism 0.43 1998 0.39–0.47 0.20 4862 0.16–0.23 Extraversion 0.44 1999 0.40–0.48 0.14 4861 0.11–0.17 F–F Neuroticism 0.48 2226 0.45–0.52 0.19 4658 0.16–0.22 Extraversion 0.52 2227 0.49–0.55 0.15 4663 0.12–0.18 All Neuroticism 0.46 4224 0.43–0.48 0.19 9520 0.17–0.21 Extraversion 0.48 4226 0.46–0.51 0.14 9524 0.12–0.17 12. MCTFR M–M Neuroticism 0.53 922 0.47–0.60 0.17 506 0.05–0.28 Extraversion 0.52 922 0.45–0.58 0.23 506 0.11–0.34 F–F Neuroticism 0.45 1054 0.38–0.52 0.26 580 0.15–0.37 Extraversion 0.51 1054 0.45–0.57 0.13 580 0.02–0.25 All Neuroticism 0.48 1976 0.44–0.53 0.22 1086 0.14–0.30 Extraversion 0.52 1976 0.47–0.56 0.17 1086 0.09–0.25 15. NTR M–M Neuroticism 0.45 1124 0.40–0.50 0.22 855 0.14–0.29 Extraversion 0.47 1123 0.42–0.52 0.13 855 0.06–0.21 F–F Neuroticism 0.51 2249 0.47–0.54 0.23 1391 0.17–0.28 Extraversion 0.49 2248 0.46–0.52 0.20 1392 0.14–0.26 M–F Neuroticism – – – 0.21 2044 0.16–0.26 Extraversion – – – 0.14 2044 0.09–0.19 All Neuroticism 0.49 3373 0.46–0.52 0.22 4290 0.18–0.25 Extraversion 0.48 3371 0.46–0.51 0.16 4291 0.13–0.19 18. QIMR adolescents M–M Neuroticism 0.51 304 0.42–0.59 0.27 252 0.15–0.38 Extraversion 0.49 304 0.40–0.57 0.18 252 0.06–0.30 F–F Neuroticism 0.39 329 0.29–0.48 0.19 268 0.07–0.30 Extraversion 0.45 329 0.36–0.53 0.19 268 0.07–0.31 M–F Neuroticism – – – 0.21 463 0.13–0.30 Extraversion – – – 0.12 463 0.03–0.21 All Neuroticism 0.44 633 0.38–0.50 0.22 983 0.16–0.28 Extraversion 0.47 633 0.40–0.53 0.16 983 0.09–0.22 19. QIMR adults M–M Neuroticism 0.45 1182 0.40–0.50 0.11 889 0.04–0.19 Extraversion 0.48 1182 0.43–0.53 0.19 889 0.11–0.26 F–F Neuroticism 0.48 2075 0.45–0.52 0.22 1435 0.17–0.28 Extraversion 0.48 2075 0.44–0.51 0.16 1435 0.11–0.21 M–F Neuroticism – – – 0.13 1827 0.08–0.18 Extraversion – – – 0.14 1827 0.09–0.19 All Neuroticism 0.47 3257 0.44–0.50 0.16 4151 0.13–0.19 Extraversion 0.48 3257 0.45–0.51 0.16 4151 0.12–0.19 21. STR M–M Neuroticism 0.54 3188 0.51–0.56 0.18 4841 0.15–0.21 Extraversion 0.54 3188 0.51–0.56 0.25 4841 0.22–0.28 F–F Neuroticism 0.45 2830 0.42–0.49 0.16 4625 0.13–0.19 Extraversion 0.44 2830 0.41–0.48 0.20 4625 0.17–0.23 All Neuroticism 0.51 6018 0.49–0.53 0.19 9466 0.17–0.21 Extraversion 0.52 6018 0.50–0.54 0.26 9466 0.23–0.28 r MZ correlation in monozygotic twin pairs, r DZ correlation in dizygotic twin pairs, Nnumber of twin pairs (pairs are included with personality data for both twins and with data for one twin), 95 % CI 95 % confidence interval, M–M male–male twin pairs, F–Ffemale–female twin pairs, M–F male–female twin pairs, All twin pairs combined across gender 302 Behav Genet (2014) 44:295–313 123 Results Estimating Neuroticism and Extraversion scores Personality scores were estimated for 160,671 (Neuroticism) and 160,713 individuals (Extraversion). Correlations between estimated latent scores and sum scores were high for Neuroticism (79 % of the correlations [0.90, and 50 % [0.95; lowest correlation 0.73) and moderately high for Extraversion (82 % of the correlations [0.80, and 48 % [0.90; lowest correlation 0.60) (Table 2). Correlations were highest with NEO, EPQ and IPIP-based sum scores, and lowest with TCI-based sum scores. Appropriateness of Item Response Theory to harmonize Neuroticism and Extraversion scores To assess whether test linking was successful within the seven cohorts that assessed more than one personality inventory, latent scores were computed based on different calibrations. In the majority of cohorts, the correlations among estimated scores were very high for most of the Table 2 Correlations between the IRT-based Neuroticism and Extraversion scores and the personality inventory-based sum scores Neuroticism Extraversion Cohort Nr Nr 1. ALSPAC 6,068 0.98 (IPIP) 6,072 0.97 (IPIP) 2. BLSA 1,917 0.96 (NEO-PI-R) 1,917 0.97 (NEO-PI-R) 3. CILENTO 800 0.97 (NEO-PI-R) 800 0.98 (NEO-PI-R) 4. COGEND 2,712 0.98 (NEO-FFI) 2,712 0.98 (NEO-FFI) 5. EGCUT 1,730 0.98 (NEO-PI-3) 1,730 0.98 (NEO-PI-3) 6. ERF 2,474 0.93 (NEO-FFI) 2,479 0.87 (NEO-FFI) 7. FINNISH TWINS 30,073 0.96 (NEO-FFI) 0.98 (EPI) 30,120 0.94 (NEO-FFI) 0.97 (EPI) 8. HBCS 1,698 0.91 (NEO-PI-R) 0.85 (TCI) 1,698 0.92 (NEO-PI-R) 0.63 (TCI) 9. KORCULA 810 0.97 (EPQ) 809 0.79 (EPQ) 10. LBC1921 478 0.96 (IPIP) 478 0.98 (IPIP) 11. LBC1936 1,032 0.92 (NEO-FFI) 0.92 (IPIP) 1,032 0.85 (NEO-FFI) 0.93 (IPIP) 12. MCTFR 9,063 0.97 (MPQ) 9,063 0.96 (MPQ) 13. NBS 1,818 0.96 (EPQ) 1,821 0.96 (EPQ) 14. NESDA 2,961 0.99 (NEO-FFI) 2,961 0.96 (NEO-FFI) 15. NTR 31,299 0.91 (NEO-FFI) 0.89 (ABV) 31,294 0.85 (NEO-FFI) 0.86 (ABV) 16. ORCADES 602 0.98 (EPQ) 602 0.88 (EPQ) 17. PAGES 476 0.95 (NEO-PI-R) 0.73 (TCI) 476 0.93 (NEO-PI-R) 0.60 (TCI) 18. QIMR-adolescents 4,100 0.93 (NEO-PI-R) 0.94 (NEO-FFI) 0.86 (JEPQ) 4,100 0.88 (NEO-PI-R) 0.77 (NEO-FFI) 0.81 (JEPQ) 19. QIMR-adults 26,681 0.94 (NEO-PI-R) 0.92 (NEO-FFI) 0.86 (EPQ) 0.88 (TCI) 0.87 (MPQ) 26,681 0.90 (NEO-PI-R) 0.89 (NEO-FFI) 0.94 (EPQ) 0.64 (TCI) 0.85 (MPQ) 20. SAGE-COGA 649 0.97 (TCI) 649 0.89 (TCI) 21. STR 30,264 0.96 (EPI) 30,253 0.97 (EPI) 22. VIS 909 0.98 (EPQ) 909 0.75 (EPQ) 23. YOUNG FINNS 2,057 0.97 (NEO-FFI) 2,057 0.96 (NEO-FFI) TOTAL 160,671 160,713 Behav Genet (2014) 44:295–313 303 123 University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Netherlands Institute of Mental Health and Addiction). NTR acknowledges financial support from the Netherlands Organization for Scientific Research (NWO) Grants No. 575-25-006, 480-04-004, 904-61-090; 904-61-193, 400-05-717, 311-60008 and Spinozapremie SPI 56-464-14192 and the European Research Council (ERC 230374). MHMdeM is financially supported by NWO VENI Grant No. 016-115-035. ORCADES was supported by the Chief Scientist Office of the Scottish Government, the Royal Society, the MRC Human Genetics Unit, Arthritis Research UK and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006018947). DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. We would like to acknowledge the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. QIMR adolescents We acknowledge financial support from the Australian Research Council (A79600334, A79906588, A79801419, DP0212016, DP0343921, DP0664638, DP1093900), Beyond Blue, and the Borderline Personality Disorder Research Foundation. QIMR adults We acknowledge financial support from NIH (DA12854, AA07728, AA10248, AA07580, AA11998, AA13320, AA13321, AA13326, DA019951, AA014041, AA07535, MH66206, AGO4954 and GM30250), the Australian National Health and Medical Research Council, Gemini Genomics Plc, the Borderline Personality Disorder Research Foundation, the Australian Associated Brewers, ADAMHA (AA06781 and MH40828), and the American Cancer Society (IRG-58-010-50). Funding support for the Study of Addiction Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004422). SAGE is one of the GWA studies funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401) and the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392). The STR is financially supported by the Swedish Ministry for Higher Education For the various projects financial support has been provided by: TwinGene; the Swedish Research Council (M-20051112), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-200201254), NIH DK U01-066134, The Swedish Foundation for Strategic Research (SSF), the Heart and Lung foundation no. 20070481. STOPPA; the Strategic Research Program in Epidemiology at Karolinska Institutet, the Swedish Research Council (grant number 2011-3060), the Swedish Asthma and Allergy Association and the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet. CATSS; support was provided by the Swedish Council for Working Life and Social Research, the Swedish Research Council, Systembolaget, the National Board of Forensic Medicine, the Swedish Prison and Probation Service, Bank of Sweden Tercentenary Foundation, the So ¨derstro ¨m–Ko ¨nigska foundation, and the Karolinska Institutet Center of Neurodevelopmental Disorders (KIND). BIRTH; supported by the Swedish Council for Working Life and Social Research (20040174 and 2007-0231), the Swedish Research Council (K2006-71X14676-04-2 and 2008-54 920638-01-3), the Swedish Cancer Society (4594-B01-01XAC and 4594-B04-04XAB), and the European Unionfunded Network of Excellence Lifespan (FP6036894). The CROATIA-Vis study was funded by grants from the Medical Research Council (UK) and Republic of Croatia Ministry of Science, Education and Sports research grants to I.R. (108-1080315-0302). We would like to acknowledge the staff of several institutions in Croatia that supported the fieldwork, including but not limited to The University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health. The Cardiovascular Risk in Young Finns Study acknowledges financial support from the Academy of Finland (grants 126925, 121584, 124282, 129378 [Salve], 117787 [Gendi], 265869 (MIND), 258711 and 41071 [Skidi]); the Social Insurance Institution of Finland; the Kuopio, Tampere, and Turku University Hospital Medical Funds (grant 9M048 and 9N035 for Dr. Lehtima ¨ki); the Juho Vainio Foundation; the Paavo Nurmi Foundation; Signe and Ane Gyllenberg’s Foundation, the Finnish Foundation of Cardiovascular Research; the Finnish Cultural Foundation; as well as the Tampere Tuberculosis Foundation and the Emil Aaltonen Foundation (Dr. Lehtima ¨ki). 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