Introduction
Schizophrenia (SCZ) is a debilitating psychiatric disorder that is associated with substantial health and economic burden for patients, families, and society (Chong et al. Reference Chong, Teoh, Wu, Kotirum, Chiou and Chaiyakunapruk2016). Its genetic component is substantial with estimated heritability of 77% from twin studies (Polderman et al. Reference Polderman, Benyamin, De Leeuw, Sullivan, Van Bochoven, Visscher and Posthuma2015) and shared environment accounts for a significant fraction of liability to SCZ (~10%) (Sullivan et al. Reference Sullivan, Kendler and Neale2003). These estimates are broadly consistent with national pedigree studies from Denmark and Sweden (Lichtenstein et al. Reference Lichtenstein, Yip, Bjork, Pawitan, Cannon, Sullivan and Hultman2009; Wray & Gottesman, Reference Wray and Gottesman2012). These results underscore the importance of both genes and environment in the etiology of SCZ (Mortensen et al. Reference Mortensen, Pedersen, Westergaard, Wohlfahrt, Ewald, Mors, Andersen and Melbye1999; van Os et al. Reference Van Os, Krabbendam, Myin-Germeys and Delespaul2005; March & Susser, Reference March and Susser2006).
A family history of SCZ in first-degree relatives is a strong and robust risk factor for SCZ (Mortensen et al. Reference Mortensen, Pedersen, Westergaard, Wohlfahrt, Ewald, Mors, Andersen and Melbye1999), with relative risk estimates ranging from 7.0 to 9.3 (Mortensen et al. Reference Mortensen, Pedersen, Westergaard, Wohlfahrt, Ewald, Mors, Andersen and Melbye1999). However, given its low lifetime risk (<1%) (Lichtenstein et al. Reference Lichtenstein, Yip, Bjork, Pawitan, Cannon, Sullivan and Hultman2009), the absolute risks of a positive family history are low and the vast majority of SCZ cases (>95%) (Lichtenstein et al. Reference Lichtenstein, Bjork, Hultman, Scolnick, Sklar and Sullivan2006) do not have an immediate family history of SCZ (Yang et al. Reference Yang, Visscher and Wray2010).
Knowledge of the genetic basis of SCZ has advanced markedly in the past decade (Ripke et al. Reference Ripke, O'dushlaine, Chambert, Moran, Kähler, Akterin, Bergen, Collins, Crowley, Fromer, Kim, Lee, Magnusson, Sanchez, Stahl, Williams, Wray, Xia, Bettella, Børglum, Bulik-Sullivan, Cormican, Craddock, De Leeuw, Durmishi, Gill, Golimbet, Hamshere, Holmans, Hougaard, Kendler, Lin, Morris, Mors, Mortensen, Neale, O'neill, Owen, Pejovic Milovancevic, Posthuma, Powell, Richards, Riley, Ruderfer, Rujescu, Sigurdsson, Silagadze, Smit, Stefansson, Steinberg, Suvisaari, Tosato, Walters, Verhage, Bramon, Corvin, O'donovan, Stefansson, Scolnick, Purcell, Mccarroll, Sklar, Hultman and Sullivan2013). A substantial fraction of the heritability of SCZ is due to numerous common genetic variants each carrying small changes in risk (Lee et al. Reference Lee, Decandia, Ripke, Yang, Sullivan, Goddard, Keller, Visscher and Wray2012a). One implication of these discoveries is the capacity to estimate individual genetic risk scores (GRS) (International Schizophrenia Consortium, 2009) which, for the first time, quantify genetic risks for an individual (McGrath et al. Reference Mcgrath, Mortensen, Visscher and Wray2013). Case–control differences in SCZ GRS have been widely replicated (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Agerbo et al. Reference Agerbo, Sullivan, Vilhjalmsson, Pedersen, Mors, Borglum, Hougaard, Hollegaard, Meier, Mattheisen, Ripke, Wray and Mortensen2015). GRS are a continuous measure of genetic liability and have been suggested to be a more powerful and more direct assessment of liability compared with a dichotomous family history measure (McGrath et al. Reference Mcgrath, Mortensen, Visscher and Wray2013). Unlike family history, GRS does not suffer from information loss due to recall bias, reduced fertility, or incomplete knowledge of family history.
A major distinction between family history and GRS is the extent of information captured. Family history captures the overt results of shared genetics (common and rare genetic variants) and shared environmental risks among family members. GRS are comprised of common but probably non-causal genetic markers. Indeed, there may be low correlations between these two measures (Belsky et al. Reference Belsky, Moffitt and Caspi2013a, Reference Belsky, Sears, Hancox, Harrington, Houts, Moffitt, Sugden, Williams, Poulton and Caspib), which could be due to the small proportion of variation explained by current GRS for most traits. One study found that SCZ cases with a positive family history had higher GRS compared with family history negative cases (Bigdeli et al. Reference Bigdeli, Ripke, Bacanu, Lee, Wray, Gejman, Rietschel, Cichon, St Clair, Corvin, Kirov, Mcquillin, Gurling, Rujescu, Andreassen, Werge, Blackwood, Pato, Pato, Malhotra, O'donovan, Kendler and Fanous2016). However, there has been little research on the joint contribution of family history and GRS to liability for SCZ. One study found that the effect of family history was mediated through GRS (Agerbo et al. Reference Agerbo, Sullivan, Vilhjalmsson, Pedersen, Mors, Borglum, Hougaard, Hollegaard, Meier, Mattheisen, Ripke, Wray and Mortensen2015).
In this study, we aimed to investigate the interplay of the conventional measure of family history and GRS. The Nordic countries have multiple advantages for population-based research. These countries have universal health care and a long tradition of collecting high-quality and comprehensive health care data (i.e. diagnostic data from hospital admissions and specialist outpatient treatment contacts). This makes it possible to recruit representative samples, unbiased by sociodemographic factors or insurance plans. These factors are important for generating large samples and facilitating psychiatric genetics research. The current study builds on these advantages by combining large samples across the Nordic countries. We expand upon prior research by combining data across four Nordic countries, incorporating systematically captured family history, and the use of GRS for multiple traits. We sought to determine if the effects of family history could be reduced or eradicated by adding GRS based on direct measures obtained from the genomes of cases and controls. In other words, can a direct measure of genetic liability like GRS replace cruder family history measures used in epidemiological and clinical studies?
Methods
Samples
We evaluated data from Denmark, Estonia, Norway, and Sweden (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), all of which have universal public health care systems. Case/control status and family history of SCZ and other psychiatric disorders were extracted from national health registers that systematically capture International Classification of Diseases (ICD) diagnoses from inpatient admissions and outpatient specialist treatment contacts. As in previous studies (Schizophrenia Psychiatric Genome-Wide Association Study Consortium, 2011; Ripke et al. Reference Ripke, O'dushlaine, Chambert, Moran, Kähler, Akterin, Bergen, Collins, Crowley, Fromer, Kim, Lee, Magnusson, Sanchez, Stahl, Williams, Wray, Xia, Bettella, Børglum, Bulik-Sullivan, Cormican, Craddock, De Leeuw, Durmishi, Gill, Golimbet, Hamshere, Holmans, Hougaard, Kendler, Lin, Morris, Mors, Mortensen, Neale, O'neill, Owen, Pejovic Milovancevic, Posthuma, Powell, Richards, Riley, Ruderfer, Rujescu, Sigurdsson, Silagadze, Smit, Stefansson, Steinberg, Suvisaari, Tosato, Walters, Verhage, Bramon, Corvin, O'donovan, Stefansson, Scolnick, Purcell, Mccarroll, Sklar, Hultman and Sullivan2013; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), the case definition included individuals with SCZ or schizoaffective disorder. For family history, the first-degree relatives of cases and controls were identified via linkage with multi-generational registers.
Danish cases were ascertained through psychiatric departments in the Copenhagen area and were of Danish parentage. Controls of Danish parentage were collected from the Danish Blood Donor Corps in the same catchment area. All subjects gave written informed consent and the Danish Data Protection Agency and the ethics committees of Denmark approved the human subjects protocol. The Estonian cohort comes from the population-based Estonian Genome Project (Metspalu et al. Reference Metspalu, Kohler, Laschinski, Ganten and Roots2004). The project was conducted according to the Estonian Gene Research Act and all participants provided informed consent. Participation was voluntary and no gender balance was controlled for. In total, 52 000 individuals aged 18 years or older participated. General practitioners and physicians in hospitals recruited participants but SCZ was diagnosed by psychiatrists according to ICD-10 criteria. Controls were drawn from a larger pool of genotyped biobank samples by matching on sex, age, and genetic ancestry. All controls were population-based and have not been sampled for any specific disease. Norwegian cases of European ancestry, born in Norway, were recruited from psychiatric hospitals in the Oslo region and diagnosed using the SCID (Athanasiu et al. Reference Athanasiu, Mattingsdal, Kahler, Brown, Gustafsson, Agartz, Giegling, Muglia, Cichon, Rietschel, Pietilainen, Peltonen, Bramon, Collier, Clair, Sigurdsson, Petursson, Rujescu, Melle, Steen, Djurovic and Andreassen2010). Healthy controls were randomly selected from statistical records of persons from the same catchment area as cases. Controls were required to have no family history of any psychiatric disorder. All participants provided written informed consent and the human subjects protocol was approved by the Norwegian Scientific-Ethical Committee and the Norwegian Data Protection Agency. Cases and controls from the Swedish Schizophrenia Study (Ripke et al. Reference Ripke, O'dushlaine, Chambert, Moran, Kähler, Akterin, Bergen, Collins, Crowley, Fromer, Kim, Lee, Magnusson, Sanchez, Stahl, Williams, Wray, Xia, Bettella, Børglum, Bulik-Sullivan, Cormican, Craddock, De Leeuw, Durmishi, Gill, Golimbet, Hamshere, Holmans, Hougaard, Kendler, Lin, Morris, Mors, Mortensen, Neale, O'neill, Owen, Pejovic Milovancevic, Posthuma, Powell, Richards, Riley, Ruderfer, Rujescu, Sigurdsson, Silagadze, Smit, Stefansson, Steinberg, Suvisaari, Tosato, Walters, Verhage, Bramon, Corvin, O'donovan, Stefansson, Scolnick, Purcell, Mccarroll, Sklar, Hultman and Sullivan2013) were identified via the Swedish Hospital Discharge Register which captures all public and private inpatient hospitalizations. Case inclusion criteria included ⩾2 hospitalizations with a discharge diagnosis of SCZ or schizoaffective disorder and age ⩾18 years. Case exclusion criteria included hospital register diagnosis of any medical or psychiatric disorder mitigating a confident diagnosis of SCZ as determined by expert review. The validity of this case definition of SCZ was strongly supported by clinical, epidemiological, genetic epidemiological and genetic evidence (Ripke et al. Reference Ripke, O'dushlaine, Chambert, Moran, Kähler, Akterin, Bergen, Collins, Crowley, Fromer, Kim, Lee, Magnusson, Sanchez, Stahl, Williams, Wray, Xia, Bettella, Børglum, Bulik-Sullivan, Cormican, Craddock, De Leeuw, Durmishi, Gill, Golimbet, Hamshere, Holmans, Hougaard, Kendler, Lin, Morris, Mors, Mortensen, Neale, O'neill, Owen, Pejovic Milovancevic, Posthuma, Powell, Richards, Riley, Ruderfer, Rujescu, Sigurdsson, Silagadze, Smit, Stefansson, Steinberg, Suvisaari, Tosato, Walters, Verhage, Bramon, Corvin, O'donovan, Stefansson, Scolnick, Purcell, Mccarroll, Sklar, Hultman and Sullivan2013). Controls were selected at random from Swedish population registers. Control inclusion criteria included never being hospitalized for SCZ or bipolar disorder (BIP) and age ⩾18 years. All procedures were approved by ethical committees and all subjects provided written informed consent (or legal guardian consent and subject assent).
GRS were estimated for all target Nordic subjects using best practices (online Supplementary Fig. S1) (International Schizophrenia Consortium, 2009; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). We derived GRS for SCZ, BIP, major depressive disorder (MDD), autism spectrum disorder (ASD), body mass index (BMI), and educational attainment (EDU). These traits all have significant genetic correlations with SCZ (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013; Okbay et al. Reference Okbay, Beauchamp, Fontana, Lee, Pers, Rietveld, Turley, Chen, Emilsson, Meddens, Oskarsson, Pickrell, Thom, Timshel, De Vlaming, Abdellaoui, Ahluwalia, Bacelis, Baumbach, Bjornsdottir, Brandsma, Pina Concas, Derringer, Furlotte, Galesloot, Girotto, Gupta, Hall, Harris, Hofer, Horikoshi, Huffman, Kaasik, Kalafati, Karlsson, Kong, Lahti, Van Der Lee, Deleeuw, Lind, Lindgren, Liu, Mangino, Marten, Mihailov, Miller, Van Der Most, Oldmeadow, Payton, Pervjakova, Peyrot, Qian, Raitakari, Rueedi, Salvi, Schmidt, Schraut, Shi, Smith, Poot, St Pourcain, Teumer, Thorleifsson, Verweij, Vuckovic, Wellmann, Westra, Yang, Zhao, Zhu, Alizadeh, Amin, Bakshi, Baumeister, Biino, Bonnelykke, Boyle, Campbell, Cappuccio, Davies, De Neve, Deloukas, Demuth, Ding, Eibich, Eisele, Eklund, Evans, Faul, Feitosa, Forstner, Gandin, Gunnarsson, Halldorsson, Harris, Heath, Hocking, Holliday, Homuth and Horan2016; Zheng et al. Reference Zheng, Erzurumluoglu, Elsworth, Kemp, Howe, Haycock, Hemani, Tansey, Laurin, Early, Pourcain, Warrington, Finucane, Price, Bulik-Sullivan, Anttila, Paternoster, Gaunt, Evans and Neale2016). We obtained genome-wide association study (GWAS) results for SCZ, BIP, ASD, and MDD from the Psychiatric Genomics Consortium (PGC) (URLs), and reran meta-analysis of case–control data from European samples but excluding Nordic sites. The results of meta-analyses from these non-Nordic samples were considered as discovery sets to select SNPs and to weight the risk alleles during the calculation of GRS. We found limited evidence for sample overlap with our target sets (online Supplementary Table S1). Genome-wide association (GWA) summary statistics for BMI were obtained from GIANT (Locke et al. Reference Locke, Kahali, Berndt, Justice, Pers, Day, Powell, Vedantam, Buchkovich, Yang, Croteau-Chonka, Esko, Fall, Ferreira, Gustafsson, Kutalik, Luan, Magi, Randall, Winkler, Wood, Workalemahu, Faul, Smith, Hua Zhao, Zhao, Chen, Fehrmann, Hedman, Karjalainen, Schmidt, Absher, Amin, Anderson, Beekman, Bolton, Bragg-Gresham, Buyske, Demirkan, Deng, Ehret, Feenstra, Feitosa, Fischer, Goel, Gong, Jackson, Kanoni, Kleber, Kristiansson, Lim, Lotay, Mangino, Mateo Leach, Medina-Gomez, Medland, Nalls, Palmer, Pasko, Pechlivanis, Peters, Prokopenko, Shungin, Stancakova, Strawbridge, Ju Sung, Tanaka, Teumer, Trompet, Van Der Laan, Van Setten, Van Vliet-Ostaptchouk, Wang, Yengo, Zhang, Isaacs, Albrecht, Arnlov, Arscott, Attwood, Bandinelli, Barrett, Bas, Bellis, Bennett, Berne, Blagieva, Bluher, Bohringer, Bonnycastle, Bottcher, Boyd, Bruinenberg, Caspersen, Ida Chen, Clarke, Daw, De Craen, Delgado, Dimitriou, Doney, Eklund, Estrada, Eury, Folkersen, Fraser, Garcia, Geller, Giedraitis, Gigante, Go, Golay, Goodall, Gordon, Gorski, Grabe, Grallert, Grammer, Grassler, Gronberg, Groves, Gusto, Haessler, Hall, Haller, Hallmans, Hartman, Hassinen, Hayward, Heard-Costa, Helmer, Hengstenberg, Holmen, Hottenga, James, Jeff, Johansson, Jolley, Juliusdottir, Kinnunen, Koenig, Koskenvuo, Kratzer, Laitinen, Lamina, Leander, Lee, Lichtner, Lind, Lindstrom, Lo, Lobbens, Lorbeer, Lu, Mach, Magnusson, Mahajan, McArdle, McLachlan, Menni, Merger, Mihailov, Milani, Moayyeri, Monda, Morken, Mulas, Muller, Muller-Nurasyid, Musk, Nagaraja, Nothen, Nolte, Pilz, Rayner, Renstrom, Rettig, Ried, Ripke, Robertson, Rose, Sanna, Scharnagl, Scholtens, Schumacher, Scott, Seufferlein, Shi, Smith, Smolonska, Stanton, Steinthorsdottir, Stirrups, Stringham, Sundstrom, Swertz, Swift, Syvanen, Tan, Tayo, Thorand, Thorleifsson, Tyrer, Uh, Vandenput, Verhulst, Vermeulen, Verweij, Vonk, Waite, Warren, Waterworth, Weedon, Wilkens, Willenborg, Wilsgaard, Wojczynski, Wong, Wright, Zhang, Brennan, Choi, Dastani, Drong, Eriksson, Franco-Cereceda, Gadin, Gharavi, Goddard, Handsaker, Huang, Karpe, Kathiresan, Keildson, Kiryluk, Kubo, Lee, Liang, Lifton, Ma, McCarroll, McKnight, Min, Moffatt, Montgomery, Murabito, Nicholson, Nyholt, Okada, Perry, Dorajoo, Reinmaa, Salem, Sandholm, Scott, Stolk, Takahashi, Tanaka, van'tHooft, Vinkhuyzen, Westra, Zheng, Zondervan, Heath, Arveiler, Bakker, Beilby, Bergman, Blangero, Bovet, Campbell, Caulfield, Cesana, Chakravarti, Chasman, Chines, Collins, Crawford, Cupples, Cusi, Danesh, deFaire, denRuijter, Dominiczak, Erbel, Erdmann, Eriksson, Farrall, Felix, Ferrannini, Ferrieres, Ford, Forouhi, Forrester, Franco, Gansevoort, Gejman, Gieger, Gottesman, Gudnason, Gyllensten, Hall, Harris, Hattersley, Hicks, Hindorff, Hingorani, Hofman, Homuth, Hovingh, Humphries, Hunt, Hypponen, Illig, Jacobs, Jarvelin, Jockel, Johansen, Jousilahti, Jukema, Jula, Kaprio, Kastelein, Keinanen-Kiukaanniemi, Kiemeney, Knekt, Kooner, Kooperberg, Kovacs, Kraja, Kumari, Kuusisto, Lakka, Langenberg, Marchand, Lehtimaki, Lyssenko, Mannisto, Marette, Matise, McKenzie, McKnight, Moll, Morris, Morris, Murray, Nelis, Ohlsson, Oldehinkel, Ong, Madden, Pasterkamp, Peden, Peters, Postma, Pramstaller, Price, Qi, Raitakari, Rankinen, Rao, Rice, Ridker, Rioux, Ritchie, Rudan, Salomaa, Samani, Saramies, Sarzynski, Schunkert, Schwarz, Sever, Shuldiner, Sinisalo, Stolk, Strauch, Tonjes, Tregouet, Tremblay, Tremoli, Virtamo, Vohl, Volker, Waeber, Willemsen, Witteman, Zillikens, Adair, Amouyel, Asselbergs, Assimes, Bochud, Boehm, Boerwinkle, Bornstein, Bottinger, Bouchard, Cauchi, Chambers, Chanock, Cooper, deBakker, Dedoussis, Ferrucci, Franks, Froguel, Groop, Haiman, Hamsten, Hui, Hunter, Hveem, Kaplan, Kivimaki, Kuh, Laakso, Liu, Martin, Marz, Melbye, Metspalu, Moebus, Munroe, Njolstad, Oostra, Palmer, Pedersen, Perola, Perusse, Peters, Power, Quertermous, Rauramaa, Rivadeneira, Saaristo, Saleheen, Sattar, Schadt, Schlessinger, Slagboom, Snieder, Spector, Thorsteinsdottir, Stumvoll, Tuomilehto, Uitterlinden, Uusitupa, vanderHarst, Walker, Wallaschofski, Wareham, Watkins, Weir, Wichmann, Wilson, Zanen, Borecki, Deloukas, Fox, Heid, O'Connell, Strachan, Stefansson, vanDuijn, Abecasis, Franke, Frayling, McCarthy, Visscher, Scherag, Willer, Boehnke, Mohlke, Lindgren, Beckmann, Barroso, North, Ingelsson, Hirschhorn, Loos and Speliotes2015). GWA summary statistics for the total number of years of education (a proxy for general intelligence) from SSGAC were used to derive GRS (after excluding Swedish studies) (Okbay et al. Reference Okbay, Beauchamp, Fontana, Lee, Pers, Rietveld, Turley, Chen, Emilsson, Meddens, Oskarsson, Pickrell, Thom, Timshel, De Vlaming, Abdellaoui, Ahluwalia, Bacelis, Baumbach, Bjornsdottir, Brandsma, Pina Concas, Derringer, Furlotte, Galesloot, Girotto, Gupta, Hall, Harris, Hofer, Horikoshi, Huffman, Kaasik, Kalafati, Karlsson, Kong, Lahti, Van Der Lee, Deleeuw, Lind, Lindgren, Liu, Mangino, Marten, Mihailov, Miller, Van Der Most, Oldmeadow, Payton, Pervjakova, Peyrot, Qian, Raitakari, Rueedi, Salvi, Schmidt, Schraut, Shi, Smith, Poot, St Pourcain, Teumer, Thorleifsson, Verweij, Vuckovic, Wellmann, Westra, Yang, Zhao, Zhu, Alizadeh, Amin, Bakshi, Baumeister, Biino, Bonnelykke, Boyle, Campbell, Cappuccio, Davies, De Neve, Deloukas, Demuth, Ding, Eibich, Eisele, Eklund, Evans, Faul, Feitosa, Forstner, Gandin, Gunnarsson, Halldorsson, Harris, Heath, Hocking, Holliday, Homuth and Horan2016). Meta-analyses were performed using METAL (Willer et al. Reference Willer, Li and Abecasis2010). The genotype data in the Nordic target sets were processed using the PGC Ricopilli pipeline for quality control (QC) and imputation. Cryptic relatedness was checked in the Ricopilli pipeline, where one in each pair of related individuals 
${\rm (}\hat \pi \,{\rm \gt}\, 0.2)$ was excluded. Variants were excluded for the following reasons: not biallelic, lacking The Short Genetic Variations database (dbSNP) name, strand ambiguous, allele frequency <0.05 or >0.95, or poor imputation quality (R 2 from MACH (Scott et al. Reference Scott, Mohlke, Bonnycastle, Willer, Li, Duren, Erdos, Stringham, Chines, Jackson, Prokunina-Olsson, Ding, Swift, Narisu, Hu, Pruim, Xiao, Li, Conneely, Riebow, Sprau, Tong, White, Hetrick, Barnhart, Bark, Goldstein, Watkins, Xiang, Saramies, Buchanan, Watanabe, Valle, Kinnunen, Abecasis, Pugh, Doheny, Bergman, Tuomilehto, Collins and Boehnke2007) or INFO from IMPUTE (Howie et al. Reference Howie, Donnelly and Marchini2009) < 0.8). To select a relatively independent set of SNPs for the calculation of GRS, we ran linkage disequilibrium (LD clumping) (r 2 < 0.1 in 1Mb window) on the overlapping SNPs using 1000 Genomes Project European samples as LD reference. Online Supplementary Table S2 has details on the number of SNPs used for calculation of GRS.
We calculated GRS in the target sets as the sum of the SNP dosages weighted by the effect from the discovery set (logistic regression β for the case/control psychiatric disorders or multiple regression β for BMI and EDU) across all SNPs under a specified p value threshold. All β values were recoded to be positive. GRS were calculated for eight p value cutoffs (P T): ⩽5 × 10−8, ⩽1 × 10−5, ⩽1 × 10−3, ⩽0.01, ⩽0.05, ⩽0.1, ⩽0.5, and ⩽1. We used PLINK (v1.9) for the calculation of GRS (Chang et al. Reference Chang, Chow, Tellier, Vattikuti, Purcell and Lee2015).
Statistical methods
We used logistic regression to test the association of family history and different GRS on SCZ case/control status. Family history was dichotomized as negative (i.e. no first-degree relative with SCZ or schizoaffective disorder) or positive (i.e. at least one first-degree relative with the condition).
The associations between the GRS of four psychiatric disorders with SCZ were first tested within each Nordic dataset. We included 20 ancestry principal components (PCs). The variance in SCZ [Nagelkerke's (Nagelkerke, Reference Nagelkerke1991) pseudo-R 2] explained by GRS was calculated as the difference in R 2 from the full model including the GRS and the baseline model with PCs only (online Supplementary Fig. S2). Assuming a life-time risk of 1%, we reported variance explained on the liability scale adjusted for case–control ascertainment (Lee et al. Reference Lee, Goddard, Wray and Visscher2012b; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). GRS were standardized within each target set prior to the main analyses to account for variation in numbers of SNPs used for GRS calculation across studies. We used the GRS from the SNP set that was selected from each of the discovery sets at P T ⩽ 0.05, given that variance in SCZ explained by most of GRS plateaued at this cut-off (online Supplementary Fig. S2). We included the GRS of all four psychiatric disorders due to the known moderate to strong genetic overlap among these disorders (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013) (online Supplementary Table S3). We included GRS for BMI and EDU given their genetic correlations with SCZ (Zheng et al. Reference Zheng, Erzurumluoglu, Elsworth, Kemp, Howe, Haycock, Hemani, Tansey, Laurin, Early, Pourcain, Warrington, Finucane, Price, Bulik-Sullivan, Anttila, Paternoster, Gaunt, Evans and Neale2016). GRS were included as continuous variables. To aid the comparison of ORs from GRS and from family history, we also divided the GRS of SCZ into three subgroups: low genetic risk (GRS in the first quintile), intermediate genetic risk (second to fourth quintile), and high genetic risk (fifth quintile).
The main analyses included: (a) fitting separate logistic regression models for family history (adjusting for study: SCZ diagnosis ~ family history + study indicator) and for the GRS of four psychiatric disorders and of BMI and EDU (adjusting for study and PCs: SCZ diagnosis ~ GRS_SCZ + GRS_BIP + GRS_MDD + GRS_ASD + GRS_BMI + GRS_EDU + study indicator + PCs); and (b) joint estimation of family history and all GRS (adjusting for the studies and PCs: SCZ diagnosis ~ family history + GRS_SCZ + GRS_BIP + GRS_MDD + GRS_ASD + GRS_BMI + GRS_EDU + study indicator + PCs). We also tested the significance of the interaction term of family history and the GRS of SCZ in addition to the main effects. All analyses were performed using R (v3.3.2).
Results
This study included 5959 SCZ cases and 8717 controls from four Nordic countries. Table 1 shows sample characteristics for individual studies. The prevalence of a positive family history was ~1% in controls (except for Norwegian controls ascertained for the absence of family history) and 10–14% in cases (Table 1). SCZ cases had more first-degree relatives with BIP or other mental disorders than controls (Table 1). Consistent with epidemiological estimates (Lichtenstein et al. Reference Lichtenstein, Bjork, Hultman, Scolnick, Sklar and Sullivan2006), a positive family history was associated with an OR of 9.70 (95% CI 7.99–11.79, Table 2), which explained 3.5% of variance on the liability scale of SCZ calculated from a logistic regression model and adjusted for case–control ascertainment.
Table 1. Sample characteristics
The percentages in family history measures do not add up to 1 due to missing data; no sufficient information to determine the number of first-degree relatives with psychiatric disorders in the Estonian cohort; controls in the Norwegian cohort were required to have no family history of any psychiatric disorder.
Table 2. Separate and joint estimations of family history and GRS
Results based on the dataset excluding Norwegian study were provided in online Supplementary Table S6.
The mean values of GRS for all four psychiatric disorders were higher among cases than controls within each study site (Fig. 1, online Supplementary Table S4). Case–control differences were most pronounced for GRS for SCZ and BIP. Across all four study sites, the variance on the liability scale explained by single GRS was 5.3, 1.8, 0.2, and 0.02% for SCZ, BIP, MDD, and ASD. When GRS for all four psychiatric disorders were included in the model, the total variance explained was 6.0%, which was less than the sum of variances explained by individual GRS due to significant correlations among the GRS (online Supplementary Table S5). An increase in SCZ GRS of one standard deviation was associated with 83% greater risk for SCZ (OR 1.83, 95% CI 1.76–1.91), and the corresponding figures were 26% for BIP GRS, 3% for ASD GRS, and 3% for MDD GRS (Table 2). GRS for BMI and EDU were associated with SCZ at borderline significance (OR 0.97 for GRS of BMI, and 1.04 for GRS of EDU).
Fig. 1. Boxplots for GRS mean differences between cases/controls (P T ⩽ 0.05) (a) GRS of schizophrenia; (b) GRS of bipolar disorder; (c) GRS of major depressive disorder; (d) GRS of autism spectrum disorder in the four Nordic studies (‘DENM’ Denmark, ‘ESTO’ Estonia, ‘NORW’ Norway, and ‘SWE’ Sweden). Controls in white and cases in grey.
Cases with a positive family history had a significantly higher mean value of the GRS of SCZ when compared with those with a negative family history (mean = 0.47 as compared with 0.32; p = 7 × 10−5; online Supplementary Fig. S3). In the absence of a family history, the risk to SCZ was increased by 2.48-fold (95% CI 2.21–2.77) and by 5.49-fold (95% CI 4.79–6.30) for individuals with intermediate and high genetic risk when compared with the low GRS group.
Joint estimation of the risks of family history and all GRS yielded a lower OR associated with family history (OR 8.37, 95% CI 6.85–10.23; Table 2) as compared with the results from the separate models. The effects from all the GRS were essentially unchanged. Collinearity was not severe owing to a low correlation between GRS and family history (Spearman correlation of 0.11 among all samples). Family history and multiple GRS, together, explained 8.9% of variance on the liability scale. The interaction between family history and the GRS was not significant (p = 0.11) when tested together with the main effects. Thus, in the joint model, the effect of family history remained significant despite inclusion of multiple GRS that assessed common variant liability directly.
Discussion
We leveraged the unique resources of genomic and medical register data from four Nordic countries, and provided evidence that genetic risk profile and family history are strong predictors of SCZ, even when considered jointly.
We first replicated previous findings that GRS of SCZ and related disorders were robustly associated with SCZ, together accounting for 6% of variation on the liability scale after adjustment of case–control ascertainment (Lee et al. Reference Lee, Goddard, Wray and Visscher2012b). The variance explained was smaller than reported before (Schizophrenia Working Group of the Psychiatric Genomics, 2014) because, by necessity, we had to exclude all Nordic samples from the discovery samples. This highlighted that the accuracy of GRS will improve as sample sizes amassed for discovery continue to increase. We used Nordic registers to accurately identify family history, and found that a positive family history (defined as ⩾1 first-degree relatives with SCZ or schizoaffective disorder) was associated with a nearly 10-fold risk to SCZ, and subjects with a positive family history carried higher GRS burden compared with those without family history. The risk estimate was highly consistent with the estimates of relative risks in the literature (Lichtenstein et al. Reference Lichtenstein, Bjork, Hultman, Scolnick, Sklar and Sullivan2006). However, a comprehensive measure of family history explained around half of the variance explained by directly measured genetic risk. In accordance with simulations in a previous report (Do et al. Reference Do, Hinds, Francke and Eriksson2012), GRS outperforms traditional family history, and will likely improve further with larger studies now in analysis (Sullivan et al. Reference Sullivan, Agrawal, Bulik, Andreassen, Borglum, Breen, Cichon, Edenberg, Faraone, Gelernter, Mathews, Nievergelt, Smoller and O'donovan2017).
The effect of family history was attenuated but remained significant when jointly analyzed with GRS measures for SCZ, BIP, MDD, ASD, EDU, and BMI. This indicates that family history contains information not captured by these multiple GRS. Intuitively, the reduction in the effect of family history was due to shared genetic information captured by both measures. We expect that the evolving GRS with better accuracy will continue to capture a higher proportion of SCZ risk associated with family history, although it will be inherently capped at a level determined by the SNP heritability, i.e. the phenotypic variance attributable to common genetic variants that are tagged on the genotyping arrays. The independent effects captured by family history could be due to common or rare genetic variation not well captured by the SNP arrays used in these GWAS as well as shared environmental risk factors. On the other hand, few individuals would have a positive family history, which may limit its utility as a predictive measure.
We note that, in the absence of a family history, there is a risk gradient associated with different profiles of GRS. The combination of GRS and comprehensive family history explained 9% of variance in SCZ liability – better discrimination accuracy can be achieved by jointly considering GRS and family history. This has proven useful in other diseases. For prostate cancer, integrating family history, prostate cancer GRS, and protein biomarkers led to a clinically important prediction algorithm that is now being implemented in routine clinical care (Gronberg et al. Reference Gronberg, Adolfsson, Aly, Nordstrom, Wiklund, Brandberg, Thompson, Wiklund, Lindberg, Clements, Egevad and Eklund2015). In our data, joint modeling both measures yielded an area under the receiver operating characteristic (AUC) curve of 0.73. This level of discriminative accuracy is close to the AUC threshold of 0.75, which is regarded as the minimum for clinical utility (Janssens et al. Reference Janssens, Moonesinghe, Yang, Steyerberg, Van Duijn and Khoury2007; Wray et al. Reference Wray, Yang, Goddard and Visscher2010). Future studies should evaluate the impact of additional epidemiological and genetic risk factors (including the burden of rare copy number variants and exon variation). Together with other risk factors, a reasonable predictive utility may be attainable.
In conclusion, by integrating register data and carefully curated common-variant SNP data from multiple Nordic countries, we determined that ‘family history’ contains information that is not captured by GRS for multiple psychiatric disorders and EDU. By combining family history with these multiple GRS, we obtained levels of discriminative accuracy that – with replication in other studies and in samples with a lower prior probability of SCZ than in these case–control cohorts – could prove to be clinically useful.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291717002665.
Acknowledgements
This project was supported by ‘Tryggve’ project (https://wiki.neic.no/wiki/Tryggve) designed to establish a Nordic platform for collaboration on sensitive data. Support from the NeIC (Nordic e-Infrastructure Collaboration, https://neic.nordforsk.org) Tryggve project and NBIS (National Bioinformatics Infrastructure Sweden) for the use of the Mosler system for sensitive data is gratefully acknowledged. PFS gratefully acknowledges support from the Swedish Research Council (Vetenskapsrådet, award D0886501). Creation of the Sweden schizophrenia study data was supported by NIMH R01 MH077139 and the Stanley Center of the Broad Institute. The PGC is supported by NIMH U01 MH109528. The Norwegian sample was included with support from Research Council of Norway (RCN 223273, 213837) and KG Jebsen Stiftelsen.
Declaration of Interest
PFS reports the following potentially competing financial interests: Lundbeck (advisory committee), Pfizer (Scientific Advisory Board member), and Roche (grant recipient, speaker reimbursement). PFS is a scientific advisor for Pfizer, Inc. OAA received speaker honoraria from Lundbeck.
URLs
MaTCH, http://match.ctglab.nl. Ricopili, https://github.com/Nealelab/ricopili. Psychiatric Genomics Consortium, https://pgc.unc.edu
