Author: "Peyrot, WJ" - Searchworks@Jio Institute Digital Library Search Results

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1. Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression

Author: Glanville, KP, Coleman, JR, Hanscombe, KB, Euesden, J, Choi, SW, Purves, KL, Breen, G, Air, TM, Andlauer, TFM, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Buttenschon, HN, Colodro-Conde, L, Dannlowski, U, Direk, N, Dunn, EC, Forstner, AJ, de Geus, EJC, Grabe, HJ, Hamilton, SP, Jones, I, Jones, LA, Knowles, JA, Kutalik, Z, Levinson, DF, Lewis, G, Lind, PA, Lucae, S, Magnusson, PK, McGuffin, P, McIntosh, AM, Milaneschi, Y, Mors, O, Mostafavi, S, Mueller-Myhsok, B, Pedersen, NL, Penninx, BWJH, Potash, JB, Preisig, M, Ripke, S, Shi, J, Shyn, S, Smoller, JW, Streit, F, Sullivan, PF, Tiemeier, H, Uher, R, Van der Auwera, S, Weissman, MM, O'Reilly, PF, Lewis, CM, Wray, NR, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Bryois, J, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Dolan, C, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jorgenson, E, Kohane, IS, Kraft, J, Kretzschmar, WW, Li, Y, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milani, L, Mondimore, FM, Montgomery, GW, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Cichon, S, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Li, QS, Madden, PAF, Martin, NG, Metspalu, A, Mortensen, PB, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Perlis, RH, Porteous, DJ, Rietschel, M, Schaefer, C, Schulze, TG, Stefansson, K, Voelzke, H, Werge, T, Borglum, AD, Glanville, KP, Coleman, JR, Hanscombe, KB, Euesden, J, Choi, SW, Purves, KL, Breen, G, Air, TM, Andlauer, TFM, Baune, BT, Binder, EB, Blackwood, DHR, Boomsma, D, Buttenschon, HN, Colodro-Conde, L, Dannlowski, U, Direk, N, Dunn, EC, Forstner, AJ, de Geus, EJC, Grabe, HJ, Hamilton, SP, Jones, I, Jones, LA, Knowles, JA, Kutalik, Z, Levinson, DF, Lewis, G, Lind, PA, Lucae, S, Magnusson, PK, McGuffin, P, McIntosh, AM, Milaneschi, Y, Mors, O, Mostafavi, S, Mueller-Myhsok, B, Pedersen, NL, Penninx, BWJH, Potash, JB, Preisig, M, Ripke, S, Shi, J, Shyn, S, Smoller, JW, Streit, F, Sullivan, PF, Tiemeier, H, Uher, R, Van der Auwera, S, Weissman, MM, O'Reilly, PF, Lewis, CM, Wray, NR, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Bryois, J, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Dolan, C, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jorgenson, E, Kohane, IS, Kraft, J, Kretzschmar, WW, Li, Y, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milani, L, Mondimore, FM, Montgomery, GW, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Cichon, S, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Li, QS, Madden, PAF, Martin, NG, Metspalu, A, Mortensen, PB, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Perlis, RH, Porteous, DJ, Rietschel, M, Schaefer, C, Schulze, TG, Stefansson, K, Voelzke, H, Werge, T, and Borglum, AD
Abstract: BACKGROUND: The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. METHODS: We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4). RESULTS: No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99). CONCLUSIONS: We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.
Published: 2020

2. Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Author: Coleman, JRI, Peyrot, WJ, Purves, KL, Davis, KAS, Rayner, C, Choi, SW, Hubel, C, Gaspar, HA, Kan, C, Van der Auwera, S, Adams, MJ, Lyall, DM, Choi, KW, Dunn, EC, Vassos, E, Danese, A, Maughan, B, Grabe, HJ, Lewis, CM, O'Reilly, PF, McIntosh, AM, Smith, DJ, Wray, NR, Hotopf, M, Eley, TC, Breen, G, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Volzke, H, Weissman, MM, Werge, T, Levinson, DF, Borglum, AD, Sullivan, PF, Coleman, JRI, Peyrot, WJ, Purves, KL, Davis, KAS, Rayner, C, Choi, SW, Hubel, C, Gaspar, HA, Kan, C, Van der Auwera, S, Adams, MJ, Lyall, DM, Choi, KW, Dunn, EC, Vassos, E, Danese, A, Maughan, B, Grabe, HJ, Lewis, CM, O'Reilly, PF, McIntosh, AM, Smith, DJ, Wray, NR, Hotopf, M, Eley, TC, Breen, G, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Howard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Volzke, H, Weissman, MM, Werge, T, Levinson, DF, Borglum, AD, and Sullivan, PF
Abstract: Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8 × 10-7 versus rg = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10-4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.
Published: 2020

3. Evidence for increased genetic risk load for major depression in patients assigned to electroconvulsive therapy

Author: Foo, JC, Streit, F, Frank, J, Witt, SH, Treutlein, J, Baune, BT, Moebus, S, Joeckel, K-H, Forstner, AJ, Noethen, MM, Rietschel, M, Sartorius, A, Kranaster, L, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JRI, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, Sullivan, PF, Foo, JC, Streit, F, Frank, J, Witt, SH, Treutlein, J, Baune, BT, Moebus, S, Joeckel, K-H, Forstner, AJ, Noethen, MM, Rietschel, M, Sartorius, A, Kranaster, L, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JRI, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, and Sullivan, PF
Abstract: Electroconvulsive therapy (ECT) is the treatment of choice for severe and treatment-resistant depression; disorder severity and unfavorable treatment outcomes are shown to be influenced by an increased genetic burden for major depression (MD). Here, we tested whether ECT assignment and response/nonresponse are associated with an increased genetic burden for major depression (MD) using polygenic risk score (PRS), which summarize the contribution of disease-related common risk variants. Fifty-one psychiatric inpatients suffering from a major depressive episode underwent ECT. MD-PRS were calculated for these inpatients and a separate population-based sample (n = 3,547 healthy; n = 426 self-reported depression) based on summary statistics from the Psychiatric Genomics Consortium MDD-working group (Cases: n = 59,851; Controls: n = 113,154). MD-PRS explained a significant proportion of disease status between ECT patients and healthy controls (p = .022, R2 = 1.173%); patients showed higher MD-PRS. MD-PRS in population-based depression self-reporters were intermediate between ECT patients and controls (n.s.). Significant associations between MD-PRS and ECT response (50% reduction in Hamilton depression rating scale scores) were not observed. Our findings indicate that ECT cohorts show an increased genetic burden for MD and are consistent with the hypothesis that treatment-resistant MD patients represent a subgroup with an increased genetic risk for MD. Larger samples are needed to better substantiate these findings.
Published: 2019

4. Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

Author: Arnau-Soler, A, Macdonald-Dunlop, E, Adams, MJ, Clarke, T-K, MacIntyre, DJ, Milburn, K, Navrady, L, Hayward, C, McIntosh, AM, Thomson, PA, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenscon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Rawford, GEC, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, Macintyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, ASD, Sullivan, PF, Arnau-Soler, A, Macdonald-Dunlop, E, Adams, MJ, Clarke, T-K, MacIntyre, DJ, Milburn, K, Navrady, L, Hayward, C, McIntosh, AM, Thomson, PA, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenscon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Rawford, GEC, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Foo, JC, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, Macintyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, ASD, and Sullivan, PF
Abstract: Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 × 10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53-kb downstream PIWIL4; p = 4.95 × 10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46 × 10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00 × 10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 × 10-6). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 × 10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.
Published: 2019

5. Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Author: Czamara, D, Eraslan, G, Page, CM, Lahti, J, Lahti-Pulkkinen, M, Hamalainen, E, Kajantie, E, Laivuori, H, Villa, PM, Reynolds, RM, Nystad, W, Haberg, SE, London, SJ, O'Donnell, KJ, Garg, E, Meaney, MJ, Entringer, S, Wadhwa, PD, Buss, C, Jones, MJ, Lin, DTS, MacIsaac, JL, Kobor, MS, Koen, N, Zar, HJ, Koenen, KC, Dalvie, S, Stein, DJ, Kondofersky, I, Mueller, NS, Theis, FJ, Raikkonen, K, Binder, EB, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, Sullivan, PF, Czamara, D, Eraslan, G, Page, CM, Lahti, J, Lahti-Pulkkinen, M, Hamalainen, E, Kajantie, E, Laivuori, H, Villa, PM, Reynolds, RM, Nystad, W, Haberg, SE, London, SJ, O'Donnell, KJ, Garg, E, Meaney, MJ, Entringer, S, Wadhwa, PD, Buss, C, Jones, MJ, Lin, DTS, MacIsaac, JL, Kobor, MS, Koen, N, Zar, HJ, Koenen, KC, Dalvie, S, Stein, DJ, Kondofersky, I, Mueller, NS, Theis, FJ, Raikkonen, K, Binder, EB, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD, and Sullivan, PF
Abstract: Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike's information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.
Published: 2019

6. Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder

Author: Trzaskowski, M, Mehta, D, Peyrot, WJ, Hawkes, D, Davies, D, Howard, DM, Kemper, KE, Sidorenko, J, Maier, R, Ripke, S, Mattheisen, M, Baune, BT, Grabe, HJ, Heath, AC, Jones, L, Jones, I, Madden, PAF, McIntosh, AM, Breen, G, Lewis, CM, Borglum, AD, Sullivan, PF, Martin, NG, Kendler, KS, Levinson, DF, Wray, NR, Trzaskowski, M, Mehta, D, Peyrot, WJ, Hawkes, D, Davies, D, Howard, DM, Kemper, KE, Sidorenko, J, Maier, R, Ripke, S, Mattheisen, M, Baune, BT, Grabe, HJ, Heath, AC, Jones, L, Jones, I, Madden, PAF, McIntosh, AM, Breen, G, Lewis, CM, Borglum, AD, Sullivan, PF, Martin, NG, Kendler, KS, Levinson, DF, and Wray, NR
Abstract: Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between-cohort and between-sex heterogeneity. First, we used genome-wide association study (GWAS) summary statistics to investigate between-cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; N cases = 16,823, N controls = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between-sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD.
Published: 2019

7. Collaborative meta-Analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression

Author: Culverhouse, RC, Saccone, NL, Horton, AC, Ma, Y, Anstey, KJ, Banaschewski, T, Burmeister, M, Cohen-Woods, S, Etain, B, Fisher, HL, Goldman, N, Guillaume, S, Horwood, J, Juhasz, G, Lester, KJ, Mandelli, L, Middeldorp, CM, Olié, E, Villafuerte, S, Air, TM, Araya, R, Bowes, L, Burns, R, Byrne, EM, Coffey, C, Coventry, WL, Gawronski, KAB, Glei, D, Hatzimanolis, A, Hottenga, JJ, Jaussent, I, Jawahar, C, Jennen-Steinmetz, C, Kramer, JR, Lajnef, M, Little, K, Zu Schwabedissen, HM, Nauck, M, Nederhof, E, Petschner, P, Peyrot, WJ, Schwahn, C, Sinnamon, G, Stacey, D, Tian, Y, Toben, C, Van Der Auwera, S, Wainwright, N, Wang, JC, Willemsen, G, Anderson, IM, Arolt, V, Aslund, C, Bagdy, G, Baune, BT, Bellivier, F, Boomsma, DI, Courtet, P, Dannlowski, U, De Geus, EJC, Deakin, JFW, Easteal, S, Eley, T, Fergusson, DM, Goate, AM, Gonda, X, Grabe, HJ, Holzman, C, Johnson, EO, Kennedy, M, Laucht, M, Martin, NG, Munafò, MR, Nilsson, KW, Oldehinkel, AJ, Olsson, CA, Ormel, J, Otte, C, Patton, GC, Penninx, BWJH, Ritchie, K, Sarchiapone, M, Scheid, JM, Serretti, A, Smit, JH, Stefanis, NC, Surtees, PG, Völzke, H, Weinstein, M, Whooley, M, Nurnberger, JI, Breslau, N, Bierut, LJ, Culverhouse, RC, Saccone, NL, Horton, AC, Ma, Y, Anstey, KJ, Banaschewski, T, Burmeister, M, Cohen-Woods, S, Etain, B, Fisher, HL, Goldman, N, Guillaume, S, Horwood, J, Juhasz, G, Lester, KJ, Mandelli, L, Middeldorp, CM, Olié, E, Villafuerte, S, Air, TM, Araya, R, Bowes, L, Burns, R, Byrne, EM, Coffey, C, Coventry, WL, Gawronski, KAB, Glei, D, Hatzimanolis, A, Hottenga, JJ, Jaussent, I, Jawahar, C, Jennen-Steinmetz, C, Kramer, JR, Lajnef, M, Little, K, Zu Schwabedissen, HM, Nauck, M, Nederhof, E, Petschner, P, Peyrot, WJ, Schwahn, C, Sinnamon, G, Stacey, D, Tian, Y, Toben, C, Van Der Auwera, S, Wainwright, N, Wang, JC, Willemsen, G, Anderson, IM, Arolt, V, Aslund, C, Bagdy, G, Baune, BT, Bellivier, F, Boomsma, DI, Courtet, P, Dannlowski, U, De Geus, EJC, Deakin, JFW, Easteal, S, Eley, T, Fergusson, DM, Goate, AM, Gonda, X, Grabe, HJ, Holzman, C, Johnson, EO, Kennedy, M, Laucht, M, Martin, NG, Munafò, MR, Nilsson, KW, Oldehinkel, AJ, Olsson, CA, Ormel, J, Otte, C, Patton, GC, Penninx, BWJH, Ritchie, K, Sarchiapone, M, Scheid, JM, Serretti, A, Smit, JH, Stefanis, NC, Surtees, PG, Völzke, H, Weinstein, M, Whooley, M, Nurnberger, JI, Breslau, N, and Bierut, LJ
Abstract: The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research and controversy since first proposed in 2003. Multiple meta-Analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 data sets containing 38 802 European ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-Analysed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis) with qualifying unpublished data, were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction and subsequent meta-Analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalisable, but must be of modest effect size and only observable in limited situations.
Published: 2018

8. Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Author: de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, Shi, J, de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, and Shi, J
Abstract: Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.
Published: 2018

9. Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD

Author: Van der Auwera, S, Peyrot, WJ, Milaneschi, Y, Hertel, J, Baune, B, Breen, G, Byrne, E, Dunn, EC, Fisher, H, Homuth, G, Levinson, D, Lewis, C, Mills, N, Mullins, N, Nauck, M, Pistis, G, Preisig, M, Rietschel, M, Ripke, S, Sullivan, P, Teumer, A, Voelzke, H, Boomsma, DI, Wray, NR, Penninx, B, Grabe, H, Van der Auwera, S, Peyrot, WJ, Milaneschi, Y, Hertel, J, Baune, B, Breen, G, Byrne, E, Dunn, EC, Fisher, H, Homuth, G, Levinson, D, Lewis, C, Mills, N, Mullins, N, Nauck, M, Pistis, G, Preisig, M, Rietschel, M, Ripke, S, Sullivan, P, Teumer, A, Voelzke, H, Boomsma, DI, Wray, NR, Penninx, B, and Grabe, H
Abstract: Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.
Published: 2018

10. Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Author: Graff, M, Scott, RA, Justice, AE, Young, KL, Feitosa, MF, Barata, L, Winkler, TW, Chu, AY, Mahajan, A, Hadley, D, Xue, LT, Workalemahu, T, Heard-Costa, NL, den Hoed, M, Ahluwalia, TS, Qi, QB, Ngwa, JS, Renstrom, F, Quaye, L, Eicher, JD, Hayes, J E, Cornelis, M, Kutalik, Z, Lim, E, Luan, J, Huffman, JE, Zhang, WH, Zhao, W, Griffin, PJ, Haller, T, Ahmad, Shahzad, Marques-Vidal, PM, Bien, S, Yengo, L, Teumer, A, Smith, AV, Kumari, M, Harder, MN, Justesen, JM, Kleber, ME, Hollensted, M, Lohman, K, Rivera, NV, Whitfield, JB, Zhao, JH, Stringham, HM, Lyytikainen, LP, Huppertz, C, Willemsen, G, Peyrot, WJ, Wu, Fenny, Kristiansson, K, Demirkan, Ayse, Fornage, M, Hassinen, M, Bielak, LF, Cadby, G, Tanaka, T, Magl, R, van der Most, PJ, Jackson, AU, Bragg-Gresham, JL, Vitart, V, Marten, J, Navarro, P, Bellis, C, Pasko, D, Johansson, A, Snitker, S, Cheng, YC, Eriksson, J, Lim, U, Aadahl, M, Adair, LS, Amin, Najaf, Balkau, B, Auvinen, J, Beilby, J, Bergman, RN, Bergmann, S, Bertoni, AG, Blangero, J, Bonnefond, A, Bonnycastle, LL, Borja, JB, Brage, S, Busonero, F, Buyske, S, Campbell, H, Chines, PS, Collins, FS, Corre, T, Smith, GD, Delgado, GE, Dueker, N, Dorr, M, Ebeling, T, Eiriksdottir, G, Esko, T, Faul, JD, Fu, M, Faerch, K, Gieger, C, Glaser, S, Gong, J, Gordon-Larsen, P, Grallert, H, Grammer, TB, Grarup, N, Van Grootheest, G, Harald, K, Hastie, ND, Havulinna, AS, Hernandez, D, Hindorff, L, Hocking, LJ, Holmens, OL, Holzapfel, C, Hottenga, JJ, Huang, J, Huang, T, Hui, J, Huth, C, Hutri-Kahonen, N, James, AL, Jansson, JO, Jhun, MA, Juonala, M, Kinnunen, L, Koistinen, HA, Kolcic, I, Komulainen, P, Kuusisto, J, Kvaloy, K, Kahonen, M, Lakka, TA, Launer, LJ, Lehne, B, Lindgren, CM, Lorentzon, M, Luben, R, Marre, M (Michel), Milaneschi, Y, Monda, KL, Montgomery, GW, de Moor, MHM, Mulas, A, Muller-Nurasyid, M, Musk, AW, Mannikko, R, Mannisto, S, Narisu, N, Nauck, M, Nettleton, JA, Nolte, IM, Oldehinkel, AJ (A.), Olden, M, Ong, KK, Padmanabhan, S, Paternoster, L, Perez, J, Perola, M, Peters, A, Peters, U, Peyser, PA, Prokopenko, I, Puolijoki, H, Raitakari, OT, Rankinen, T, Rasmussen-Torvik, LJ, Rawal, R, Ridker, PM, Rose, LM, Rudan, I, Sarti, C, Sarzynski, MA, Savonen, K, Scott, WR, Sanna, S, Shuldiner, AR, Sidney, S, Silbernagel, G, Smith, BH, Smith, JA, Snieder, H, Stancakova, A, Sternfeld, B, Swift, AJ, Tammelin, T, Tan, ST, Thorand, B, Thuillier, D, Vandenput, L, Vestergaard, H, van Vliet-Ostaptchouk, JV, Vohl, MC, Volker, U, Waeber, G, Walker, M, Wild, S, Wong, A, Wright, AF, Zillikens, M.C., Zubair, N, Haiman, CA, LeMarchand, L, Gyllensten, U, Ohlsson, C, Ohlsson, CC, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Perusse, L, Wilson, JF, Hayward, C, Polasek, O, Cucca, F, Hveem, K, Hartman, CA, Tonjes, A, Bandinelli, S, Palmer, LJ, Kardia, SLR, Rauramaa, R, Sorensen, TIA, Tuomilehto, J, Salomaa, V, Penninx, B, de Geus, EJC, Boomsma, DI, Lehtimaki, T, Mangino, M, Laakso, M, Bouchard, C, Martin, NG, Kuh, D, Liu, YM, Linneberg, A, Marz, W, Strauch, K, Kivimaki, M, Harris, TB, Gudnason, V, Volzke, H, Qi, L, Jarvelin, MR, Chambers, JC, Kooner, JS, Froguel, P, Kooperberg, C, Vollenweider, P, Hallmans, G, Hansen, T, Pedersen, O, Metspalu, A, Wareham, NJ, Langenberg, C, Weir, DR, Porteous, DJ, Boerwinkle, E, Chasman, DI, Abecasis, GR, Barroso, I, McCarthy, MI, Frayling, TM, O'Connell, JR, Duijn, Cornelia, Boehnke, M, Heid, IM, Mohlke, KL, Strachan, DP, Fox, CS, Liu, CT, Hirschhorn, JN, Klein, RJ, Johnson, AD, Borecki, IB, Franks, PW, North, KE, Cupples, LA, Loos, RJF, Kilpelainen, TO, Graff, M, Scott, RA, Justice, AE, Young, KL, Feitosa, MF, Barata, L, Winkler, TW, Chu, AY, Mahajan, A, Hadley, D, Xue, LT, Workalemahu, T, Heard-Costa, NL, den Hoed, M, Ahluwalia, TS, Qi, QB, Ngwa, JS, Renstrom, F, Quaye, L, Eicher, JD, Hayes, J E, Cornelis, M, Kutalik, Z, Lim, E, Luan, J, Huffman, JE, Zhang, WH, Zhao, W, Griffin, PJ, Haller, T, Ahmad, Shahzad, Marques-Vidal, PM, Bien, S, Yengo, L, Teumer, A, Smith, AV, Kumari, M, Harder, MN, Justesen, JM, Kleber, ME, Hollensted, M, Lohman, K, Rivera, NV, Whitfield, JB, Zhao, JH, Stringham, HM, Lyytikainen, LP, Huppertz, C, Willemsen, G, Peyrot, WJ, Wu, Fenny, Kristiansson, K, Demirkan, Ayse, Fornage, M, Hassinen, M, Bielak, LF, Cadby, G, Tanaka, T, Magl, R, van der Most, PJ, Jackson, AU, Bragg-Gresham, JL, Vitart, V, Marten, J, Navarro, P, Bellis, C, Pasko, D, Johansson, A, Snitker, S, Cheng, YC, Eriksson, J, Lim, U, Aadahl, M, Adair, LS, Amin, Najaf, Balkau, B, Auvinen, J, Beilby, J, Bergman, RN, Bergmann, S, Bertoni, AG, Blangero, J, Bonnefond, A, Bonnycastle, LL, Borja, JB, Brage, S, Busonero, F, Buyske, S, Campbell, H, Chines, PS, Collins, FS, Corre, T, Smith, GD, Delgado, GE, Dueker, N, Dorr, M, Ebeling, T, Eiriksdottir, G, Esko, T, Faul, JD, Fu, M, Faerch, K, Gieger, C, Glaser, S, Gong, J, Gordon-Larsen, P, Grallert, H, Grammer, TB, Grarup, N, Van Grootheest, G, Harald, K, Hastie, ND, Havulinna, AS, Hernandez, D, Hindorff, L, Hocking, LJ, Holmens, OL, Holzapfel, C, Hottenga, JJ, Huang, J, Huang, T, Hui, J, Huth, C, Hutri-Kahonen, N, James, AL, Jansson, JO, Jhun, MA, Juonala, M, Kinnunen, L, Koistinen, HA, Kolcic, I, Komulainen, P, Kuusisto, J, Kvaloy, K, Kahonen, M, Lakka, TA, Launer, LJ, Lehne, B, Lindgren, CM, Lorentzon, M, Luben, R, Marre, M (Michel), Milaneschi, Y, Monda, KL, Montgomery, GW, de Moor, MHM, Mulas, A, Muller-Nurasyid, M, Musk, AW, Mannikko, R, Mannisto, S, Narisu, N, Nauck, M, Nettleton, JA, Nolte, IM, Oldehinkel, AJ (A.), Olden, M, Ong, KK, Padmanabhan, S, Paternoster, L, Perez, J, Perola, M, Peters, A, Peters, U, Peyser, PA, Prokopenko, I, Puolijoki, H, Raitakari, OT, Rankinen, T, Rasmussen-Torvik, LJ, Rawal, R, Ridker, PM, Rose, LM, Rudan, I, Sarti, C, Sarzynski, MA, Savonen, K, Scott, WR, Sanna, S, Shuldiner, AR, Sidney, S, Silbernagel, G, Smith, BH, Smith, JA, Snieder, H, Stancakova, A, Sternfeld, B, Swift, AJ, Tammelin, T, Tan, ST, Thorand, B, Thuillier, D, Vandenput, L, Vestergaard, H, van Vliet-Ostaptchouk, JV, Vohl, MC, Volker, U, Waeber, G, Walker, M, Wild, S, Wong, A, Wright, AF, Zillikens, M.C., Zubair, N, Haiman, CA, LeMarchand, L, Gyllensten, U, Ohlsson, C, Ohlsson, CC, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Perusse, L, Wilson, JF, Hayward, C, Polasek, O, Cucca, F, Hveem, K, Hartman, CA, Tonjes, A, Bandinelli, S, Palmer, LJ, Kardia, SLR, Rauramaa, R, Sorensen, TIA, Tuomilehto, J, Salomaa, V, Penninx, B, de Geus, EJC, Boomsma, DI, Lehtimaki, T, Mangino, M, Laakso, M, Bouchard, C, Martin, NG, Kuh, D, Liu, YM, Linneberg, A, Marz, W, Strauch, K, Kivimaki, M, Harris, TB, Gudnason, V, Volzke, H, Qi, L, Jarvelin, MR, Chambers, JC, Kooner, JS, Froguel, P, Kooperberg, C, Vollenweider, P, Hallmans, G, Hansen, T, Pedersen, O, Metspalu, A, Wareham, NJ, Langenberg, C, Weir, DR, Porteous, DJ, Boerwinkle, E, Chasman, DI, Abecasis, GR, Barroso, I, McCarthy, MI, Frayling, TM, O'Connell, JR, Duijn, Cornelia, Boehnke, M, Heid, IM, Mohlke, KL, Strachan, DP, Fox, CS, Liu, CT, Hirschhorn, JN, Klein, RJ, Johnson, AD, Borecki, IB, Franks, PW, North, KE, Cupples, LA, Loos, RJF, and Kilpelainen, TO
Published: 2017

11. Risk of psychiatric illness from advanced paternal age is not predominantly from de novo mutations

Author: Gratten, J, Wray, NR, Peyrot, WJ, McGrath, JJ, Visscher, PM, Goddard, ME, Gratten, J, Wray, NR, Peyrot, WJ, McGrath, JJ, Visscher, PM, and Goddard, ME
Abstract: The offspring of older fathers have higher risk of psychiatric disorders such as schizophrenia and autism. Paternal-age-related de novo mutations are widely assumed to be the underlying causal mechanism, and, although such mutations must logically make some contribution, there are alternative explanations (for example, elevated liability to psychiatric illness may delay fatherhood). We used population genetic models based on empirical observations of key parameters (for example, mutation rate, prevalence, and heritability) to assess the genetic relationship between paternal age and risk of psychiatric illness. These models suggest that age-related mutations are unlikely to explain much of the increased risk of psychiatric disorders in children of older fathers. Conversely, a model incorporating a weak correlation between age at first child and liability to psychiatric illness matched epidemiological observations. Our results suggest that genetic risk factors shared by older fathers and their offspring are a credible alternative explanation to de novo mutations for risk to children of older fathers.
Published: 2016

12. Genome-wide association study identifies 74 loci associated with educational attainment

Author: Okbay, Aysu, Beauchamp, JP, Fontana, MA, Lee, JJ, Pers, TH, Rietveld, Niels, Turley, P, Chen, GB, Emilsson, V, Meddens, SFW, Oskarsson, S, Pickrell, JK, Thom, K, Timshel, P, Vlaming, Ronald, Abdellaoui, A, Ahluwalia, TS, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Johan, Concas, MP, Derringer, J, Furlotte, NA, Galesloot, TE, Girotto, G, Gupta, R, Hall, LM, Harris, SE, Hofer, E, Horikoshi, M, Huffman, JE, Kaasik, K, Kalafati, IP, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sven, de Leeuw, C, Lind, PA, Lindgren, KO, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, MB, van der Most, PJ, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, WJ, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, KE, Shi, JX, Smith, AV, Poot, Raymond, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N (Niek), Vuckovic, D, Wellmann, J, Westra, HJ, Yang, JY, Zhao, W, Zhu, ZH, Alizadeh, BZ, Amin, Najaf, Bakshi, A, Baumeister, SE, Biino, G, Bonnelykke, K, Boyle, PA, Campbell, H, Cappuccio, FP, Davies, G, De Neve, JE, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, DM, Faul, JD, Feitosa, MF, Forstner, AJ, Gandin, I, Gunnarsson, B, Halldorsson, BV, Harris, TB, Heath, AC, Hocking, LJ, Holliday, EG, Homuth, G, Horan, MA, Hottenga, JJ, De Jager, PL, Joshi, PK, Jugessur, A, Kaakinen, MA, Kahonen, M, Kanoni, S, Keltigangas-Jarvinen, L, Kiemeney, LALM, Kolcic, I, Koskinen, S, Kraja, AT, Kroh, M, Kutalik, Z, Latvala, A, Launer, LJ, Lebreton, MP, Levinson, DF, Lichtenstein, P, Lichtner, P, Liewald, DCM, Loukola, A, Madden, PA, Magi, R, Maki-Opas, T, Marioni, RE, Marques-Vidal, P, Meddens, GA, McMahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, GW, Myhre, R, Nelson, CP, Nyholt, DR, Ollier, WER, Palotie, A, Paternoster, L, Pedersen, NL, Petrovic, KE, Porteous, DJ, Raikkonen, K, Ring, SM, Robino, A, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, AR, Sarin, AP, Schmidt, Heléna, Scott, RJ, Smith, BH, Smith, JA, Staessen, JA, Steinhagen-Thiessen, E, Strauch, K, Terracciano, A, Tobin, MD, Ulivi, S, Vaccargiu, S, Quaye, L, van Rooij, FJA, Venturini, C, Vinkhuyzen, AAE, Volker, U, Volzke, H, Vonk, JM, Vozzi, D, Waage, J, Ware, EB, Willemsen, G, Attia, JR, Bennett, DA, Berger, K, Bertram, L, Bisgaard, H, Boomsma, DI, Borecki, IB, Bultmann, U, Chabris, CF, Cucca, F, Cusi, D, Deary, IJ, Dedoussis, GV, Duijn, Cornelia, Eriksson, JG, Franke, B, Franke, L, Gasparini, P, Gejman, PV, Gieger, C, Grabe, HJ, Gratten, J, Groenen, Patrick, Gudnason, V, van der Harst, P, Hayward, C, Hinds, DA, Hoffmann, W, Hyppnen, E, Iacono, WG, Jacobsson, B, Jarvelin, MR, Jockel, KH, Kaprio, J, Kardia, SLR, Lehtimaki, T, Lehrer, SF, Magnusson, PKE, Martin, NG, McGue, M, Metspalu, A, Pendleton, N, Penninx, BWJH, Perola, M, Pirastu, N, Pirastu, M, Polasek, O, Posthuma, Daniëlle, Power, C, Province, MA, Samani, NJ, Schlessinger, D, Schmidt, R, Sorensen, TIA, Spector, TD, Stefansson, K, Thorsteinsdottir, U, Thurik, Roy, Timpson, NJ, Tiemeier, Henning, Tung, JY, Uitterlinden, André, Vitart, V, Vollenweider, P, Weir, DR, Wilson, JF, Wright, AF, Conley, DC, Krueger, RF, Smith, GD, Hofman, Bert, Laibson, DI, Medland, SE, Meyer, MN, Yang, Jiaqi, Johannesson, M, Visscher, PM, Esko, T, Koellinger, PD, Cesarini, D, Benjamin, DJ, Okbay, Aysu, Beauchamp, JP, Fontana, MA, Lee, JJ, Pers, TH, Rietveld, Niels, Turley, P, Chen, GB, Emilsson, V, Meddens, SFW, Oskarsson, S, Pickrell, JK, Thom, K, Timshel, P, Vlaming, Ronald, Abdellaoui, A, Ahluwalia, TS, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Johan, Concas, MP, Derringer, J, Furlotte, NA, Galesloot, TE, Girotto, G, Gupta, R, Hall, LM, Harris, SE, Hofer, E, Horikoshi, M, Huffman, JE, Kaasik, K, Kalafati, IP, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sven, de Leeuw, C, Lind, PA, Lindgren, KO, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, MB, van der Most, PJ, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, WJ, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, KE, Shi, JX, Smith, AV, Poot, Raymond, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N (Niek), Vuckovic, D, Wellmann, J, Westra, HJ, Yang, JY, Zhao, W, Zhu, ZH, Alizadeh, BZ, Amin, Najaf, Bakshi, A, Baumeister, SE, Biino, G, Bonnelykke, K, Boyle, PA, Campbell, H, Cappuccio, FP, Davies, G, De Neve, JE, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, DM, Faul, JD, Feitosa, MF, Forstner, AJ, Gandin, I, Gunnarsson, B, Halldorsson, BV, Harris, TB, Heath, AC, Hocking, LJ, Holliday, EG, Homuth, G, Horan, MA, Hottenga, JJ, De Jager, PL, Joshi, PK, Jugessur, A, Kaakinen, MA, Kahonen, M, Kanoni, S, Keltigangas-Jarvinen, L, Kiemeney, LALM, Kolcic, I, Koskinen, S, Kraja, AT, Kroh, M, Kutalik, Z, Latvala, A, Launer, LJ, Lebreton, MP, Levinson, DF, Lichtenstein, P, Lichtner, P, Liewald, DCM, Loukola, A, Madden, PA, Magi, R, Maki-Opas, T, Marioni, RE, Marques-Vidal, P, Meddens, GA, McMahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, GW, Myhre, R, Nelson, CP, Nyholt, DR, Ollier, WER, Palotie, A, Paternoster, L, Pedersen, NL, Petrovic, KE, Porteous, DJ, Raikkonen, K, Ring, SM, Robino, A, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, AR, Sarin, AP, Schmidt, Heléna, Scott, RJ, Smith, BH, Smith, JA, Staessen, JA, Steinhagen-Thiessen, E, Strauch, K, Terracciano, A, Tobin, MD, Ulivi, S, Vaccargiu, S, Quaye, L, van Rooij, FJA, Venturini, C, Vinkhuyzen, AAE, Volker, U, Volzke, H, Vonk, JM, Vozzi, D, Waage, J, Ware, EB, Willemsen, G, Attia, JR, Bennett, DA, Berger, K, Bertram, L, Bisgaard, H, Boomsma, DI, Borecki, IB, Bultmann, U, Chabris, CF, Cucca, F, Cusi, D, Deary, IJ, Dedoussis, GV, Duijn, Cornelia, Eriksson, JG, Franke, B, Franke, L, Gasparini, P, Gejman, PV, Gieger, C, Grabe, HJ, Gratten, J, Groenen, Patrick, Gudnason, V, van der Harst, P, Hayward, C, Hinds, DA, Hoffmann, W, Hyppnen, E, Iacono, WG, Jacobsson, B, Jarvelin, MR, Jockel, KH, Kaprio, J, Kardia, SLR, Lehtimaki, T, Lehrer, SF, Magnusson, PKE, Martin, NG, McGue, M, Metspalu, A, Pendleton, N, Penninx, BWJH, Perola, M, Pirastu, N, Pirastu, M, Polasek, O, Posthuma, Daniëlle, Power, C, Province, MA, Samani, NJ, Schlessinger, D, Schmidt, R, Sorensen, TIA, Spector, TD, Stefansson, K, Thorsteinsdottir, U, Thurik, Roy, Timpson, NJ, Tiemeier, Henning, Tung, JY, Uitterlinden, André, Vitart, V, Vollenweider, P, Weir, DR, Wilson, JF, Wright, AF, Conley, DC, Krueger, RF, Smith, GD, Hofman, Bert, Laibson, DI, Medland, SE, Meyer, MN, Yang, Jiaqi, Johannesson, M, Visscher, PM, Esko, T, Koellinger, PD, Cesarini, D, and Benjamin, DJ
Abstract: Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals(1). Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample(1,2) of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.
Published: 2016

13. Genetic variants linked to education predict longevity

Author: Chris Power, Gail Davies, Ilaria Gandin, Panagiotis Deloukas, Jennifer E. Huffman, Pascal Timshel, Albert V. Smith, A. Kong, Paul Lichtenstein, Joseph K. Pickrell, Philipp Koellinger, P. L. De Jager, Reedik Mägi, G. B. Chen, Neil Pendleton, B. V. Halldórsson, George Dedoussis, Antti-Pekka Sarin, Natalia Pervjakova, Veikko Salomaa, Simona Vaccargiu, Ozren Polasek, K. H. Jöckel, Elisabeth Steinhagen-Thiessen, Y. Milaneschi, Jessica D. Faul, Patricia A. Boyle, Patrik K. E. Magnusson, Igor Rudan, Christopher P. Nelson, Vilmundur Gudnason, John Attia, Jürgen Wellmann, Kristi Läll, Konstantin Strauch, Stuart J. Ritchie, Markus Perola, Nicola Pirastu, Klaus Bønnelykke, Robert Karlsson, R. de Vlaming, Liisa Keltigangas-Jarvinen, Thomas Meitinger, Riccardo E. Marioni, Anu Loukola, Barbera Franke, Reinhold Schmidt, Maël Lebreton, Sven Oskarsson, E. Mihailov, Harm-Jan Westra, David R. Weir, Aldi T. Kraja, Niek Verweij, Peter M. Visscher, Hans-Jörgen Grabe, Johannes H. Brandsma, Mark Adams, R. J. Scott, G. Thorleifsson, Tõnu Esko, Mika Kähönen, Saskia P. Hagenaars, Patrick Turley, Johannes Waage, Peter Lichtner, Dragana Vuckovic, Antonietta Robino, Henry Völzke, Lydia Quaye, C. de Leeuw, Marika Kaakinen, Wei Zhao, Abdel Abdellaoui, Reka Nagy, Pedro Marques-Vidal, Johan G. Eriksson, Alan F. Wright, Andres Metspalu, Lavinia Paternoster, Momoko Horikoshi, Jan A. Staessen, Tarunveer S. Ahluwalia, Tian Liu, Martin Kroh, Aldo Rustichini, Giorgia Girotto, Cristina Venturini, Lili Milani, Jennifer A. Smith, Ginevra Biino, Tessel E. Galesloot, Michael A. Horan, Gerardus A. Meddens, James F. Wilson, Francesco Cucca, Peter Vollenweider, Erika Salvi, P. J. van der Most, Jari Lahti, Campbell A, David Laibson, Andrew Bakshi, Wolfgang Hoffmann, Tomi Mäki-Opas, Andreas J. Forstner, C M van Duijn, Nicholas G. Martin, Jonathan Marten, Ute Bültmann, Olli T. Raitakari, David A. Bennett, A.G. Uitterlinden, J. E. De Neve, Ingrid B. Borecki, WD Hill, Bo Jacobsson, Antti Latvala, Katri Räikkönen, Michael B. Miller, Jonathan P. Beauchamp, S. J. van der Lee, Ilja Demuth, Stavroula Kanoni, Veronique Vitart, Elina Hyppönen, N. Eklund, Francesco P. Cappuccio, Robert F. Krueger, Maria Pina Concas, Jaime Derringer, F. J.A. Van Rooij, Helena Schmidt, Patrick J. F. Groenen, Valur Emilsson, Rico Rueedi, Aysu Okbay, Georg Homuth, Edith Hofer, W. E. R. Ollier, Hannah Campbell, Paolo Gasparini, Mark Alan Fontana, Magnus Johannesson, Seppo Koskinen, Christopher F. Chabris, Jouke-Jan Hottenga, Christine Meisinger, Kari Stefansson, Jun Ding, Tia Sorensen, Brenda W.J.H. Penninx, Michelle N. Meyer, James J. Lee, Diego Vozzi, Gonneke Willemsen, K. Petrovic, Sarah E. Medland, Mary F. Feitosa, Henning Tiemeier, L. J. Launer, William G. Iacono, Massimo Mangino, Tune H. Pers, S. E. Baumeister, Christopher Oldmeadow, Grant W. Montgomery, Marjo-Riitta Järvelin, Jaakko Kaprio, Catharine R. Gale, S.F.W. Meddens, Kevin Thom, Klaus Berger, Pablo V. Gejman, Lude Franke, Gyda Bjornsdottir, Daniel J. Benjamin, Steven F. Lehrer, Krista Fischer, Alan R. Sanders, S. Ulivi, Katharina E. Schraut, Tim D. Spector, Amy Hofman, Matt McGue, Terho Lehtimäki, D. C. Liewald, Hans Bisgaard, L. Eisele, Astanand Jugessur, George Davey Smith, T.B. Harris, A.R. Thurik, Cornelius A. Rietveld, David Schlessinger, Z. Kutalik, David J. Porteous, Lynne J. Hocking, N J Timpson, A. Palotie, Lambertus A. Kiemeney, Ian J. Deary, Sharon L.R. Kardia, Peter K. Joshi, Nilesh J. Samani, Michael A. Province, Börge Schmidt, Richa Gupta, Carmen Amador, Erin B. Ware, Joyce Y. Tung, Ioanna-Panagiota Kalafati, Lars Bertram, Caroline Hayward, P. van der Harst, Penelope A. Lind, Kadri Kaasik, N.A. Furlotte, Sarah E. Harris, B. St Pourcain, Susan M. Ring, Zhihong Zhu, Alexander Teumer, Behrooz Z. Alizadeh, Judith M. Vonk, Blair H. Smith, A Payton, Wouter J. Peyrot, Jacob Gratten, Douglas F. Levinson, C Gieger, Leanne M. Hall, Andrew Heath, Mario Pirastu, Peter Eibich, Nancy L. Pedersen, Ronny Myhre, Antonio Terracciano, David M. Evans, Raymond A. Poot, Uwe Völker, Dorret I. Boomsma, Clemens Baumbach, Unnur Thorsteinsdottir, Ivana Kolcic, Jia-Shu Yang, Dalton Conley, A. A. Vinkhuyzen, Danielle Posthuma, Karl-Oskar Lindgren, Olga Rostapshova, Jonas Bacelis, Daniele Cusi, Yong Qian, Bjarni Gunnarsson, George McMahon, Elizabeth G. Holliday, Pamela A. F. Madden, David A. Hinds, David Cesarini, Jianxin Shi, Najaf Amin, Dale R. Nyholt, Applied Economics, Epidemiology, Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Research Institute for Asthma and COPD (GRIAC), Aletta Jacobs School of Public Health, Public Health Research (PHR), Stem Cell Aging Leukemia and Lymphoma (SALL), Cardiovascular Centre (CVC), Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, EMGO - Mental health, Complex Trait Genetics, Biological Psychology, Marioni, RE, Ritchie, SJ, Joshi, PK, Hagenaars, SP, Hypponen, E, Benjamin, DJ, Social Science Genetic Association Consortium, Marioni, Re, Ritchie, Sj, Joshi, Pk, Hagenaars, Sp, Okbay, A, Fischer, K, Adams, Mj, Hill, Wd, Davies, G, Nagy, R, Amador, C, Läll, K, Metspalu, A, Liewald, Dc, Campbell, A, Wilson, Jf, Hayward, C, Esko, T, Porteous, Dj, Gale, Cr, Deary, Ij, Beauchamp, Jp, Fontana, Ma, Lee, Jj, Pers, Th, Rietveld, Ca, Turley, P, Chen, Gb, Emilsson, V, Meddens, Sf, Oskarsson, S, Pickrell, Jk, Thom, K, Timshel, P, de Vlaming, R, Abdellaoui, A, Ahluwalia, T, Bacelis, J, Baumbach, C, Bjornsdottir, G, Brandsma, Jh, Concas, MARIA PINA, Derringer, J, Furlotte, Na, Galesloot, Te, Girotto, Giorgia, Gupta, R, Hall, Lm, Harris, Se, Hofer, E, Horikoshi, M, Huffman, Je, Kaasik, K, Kalafati, Ip, Karlsson, R, Kong, A, Lahti, J, van der Lee, Sj, de Leeuw, C, Lind, Pa, Lindgren, Ko, Liu, T, Mangino, M, Marten, J, Mihailov, E, Miller, Mb, van der Most, Pj, Oldmeadow, C, Payton, A, Pervjakova, N, Peyrot, Wj, Qian, Y, Raitakari, O, Rueedi, R, Salvi, E, Schmidt, B, Schraut, Ke, Shi, J, Smith, Av, Poot, Ra, St Pourcain, B, Teumer, A, Thorleifsson, G, Verweij, N, Vuckovic, Dragana, Wellmann, J, Westra, Hj, Yang, J, Zhao, W, Zhu, Z, Alizadeh, Bz, Amin, N, Bakshi, A, Baumeister, Se, Biino, G, Bønnelykke, K, Boyle, Pa, Campbell, H, Cappuccio, Fp, De Neve, Je, Deloukas, P, Demuth, I, Ding, J, Eibich, P, Eisele, L, Eklund, N, Evans, Dm, Faul, Jd, Feitosa, Mf, Forstner, Aj, Gandin, Ilaria, Gunnarsson, B, Halldórsson, Bv, Harris, Tb, Heath, Ac, Hocking, Lj, Holliday, Eg, Homuth, G, Horan, Ma, Hottenga, Jj, de Jager, Pl, Jugessur, A, Kaakinen, Ma, Kähönen, M, Kanoni, S, Keltigangas Järvinen, L, Kiemeney, La, Kolcic, I, Koskinen, S, Kraja, At, Kroh, M, Kutalik, Z, Latvala, A, Launer, Lj, Lebreton, Mp, Levinson, Df, Lichtenstein, P, Lichtner, P, Loukola, A, Madden, Pa, Mägi, R, Mäki Opas, T, Marques Vidal, P, Meddens, Ga, Mcmahon, G, Meisinger, C, Meitinger, T, Milaneschi, Y, Milani, L, Montgomery, Gw, Myhre, R, Nelson, Cp, Nyholt, Dr, Ollier, We, Palotie, A, Paternoster, L, Pedersen, Nl, Petrovic, Ke, Räikkönen, K, Ring, Sm, Robino, Antonietta, Rostapshova, O, Rudan, I, Rustichini, A, Salomaa, V, Sanders, Ar, Sarin, Ap, Schmidt, H, Scott, Rj, Smith, Bh, Smith, Ja, Staessen, Ja, Steinhagen Thiessen, E, Strauch, K, Terracciano, A, Tobin, Md, Ulivi, Sheila, Vaccargiu, S, Quaye, L, van Rooij, Fj, Venturini, C, Vinkhuyzen, Aa, Völker, U, Völzke, H, Vonk, Jm, Vozzi, Diego, Waage, J, Ware, Eb, Willemsen, G, Attia, Jr, Bennett, Da, Berger, K, Bertram, L, Bisgaard, H, Boomsma, Di, Borecki, Ib, Bultmann, U, Chabris, Cf, Cucca, F, Cusi, D, Dedoussis, Gv, van Duijn, Cm, Eriksson, Jg, Franke, B, Franke, L, Gasparini, Paolo, Gejman, Pv, Gieger, C, Grabe, Hj, Gratten, J, Groenen, Pj, Gudnason, V, van der Harst, P, Hinds, Da, Hoffmann, W, Iacono, Wg, Jacobsson, B, Järvelin, Mr, Jöckel, Kh, Kaprio, J, Kardia, Sl, Lehtimäki, T, Lehrer, Sf, Magnusson, Pk, Martin, Ng, Mcgue, M, Pendleton, N, Penninx, Bw, Perola, M, Pirastu, Nicola, Pirastu, M, Polasek, O, Posthuma, D, Power, C, Province, Ma, Samani, Nj, Schlessinger, D, Schmidt, R, Sørensen, Ti, Spector, Td, Stefansson, K, Thorsteinsdottir, U, Thurik, Ar, Timpson, Nj, Tiemeier, H, Tung, Jy, Uitterlinden, Ag, Vitart, V, Vollenweider, P, Weir, Dr, Wright, Af, Conley, Dc, Krueger, Rf, Smith, Gd, Hofman, A, Laibson, Di, Medland, Se, Meyer, Mn, Johannesson, M, Visscher, Pm, Koellinger, Pd, Cesarini, D, and Benjamin, Dj
Subjects: Netherlands Twin Register (NTR), 0301 basic medicine, Male, Parents, education: longevity: prediction: polygenic score [genetics], Multifactorial Inheritance, polygenic, Lebenserwartung, Cohort Studies, 0302 clinical medicine, Databases, Genetic, Medicine, genetics, polygenic score, longevity, education, gene, Soziales und Gesundheit, media_common, Aged, 80 and over, education, Multidisciplinary, Longevity, Middle Aged, Biobank, humanities, 3. Good health, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Cohort, Educational Status, Female, Cohort study, Estonia, education, longevity, polygenic, Offspring, media_common.quotation_subject, Kultursektor, Prognose, Lernen, Lower risk, Education, 03 medical and health sciences, longevity, SDG 3 - Good Health and Well-being, Commentaries, Polygenic score, Journal Article, Genetics, Humans, Non-Profit-Sektor, Genetic Association Studies, Aged, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, ta1184, Genetic Variation, prediction, Educational attainment, United Kingdom, Gesundheitsstatistik, 030104 developmental biology, Genetic epidemiology, Scotland, Gesundheitszustand, Genetische Forschung, business, Prediction, Bildung, 030217 neurology & neurosurgery, Demography
Abstract: Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity. Marioni RE, Ritchie SJ, Joshi PK, Hagenaars SP, Okbay A, Fischer K, Adams MJ, Hill WD, Davies G, Social Science Genetic Association Consortium, Nagy R, Amador C, Läll K, Metspalu A, Liewald DC, Campbell A, Wilson JF, Hayward C, Esko T, Porteous DJ, Proceedings of the National Academy of Sciences of the United States of America, 2016, vol. 113, no. 47, pp. 13366-13371, 2016 Refereed/Peer-reviewed
Published: 2016

14. The association between lower educational attainment and depression owing to shared genetic effects?: Results in ~25,000 subjects

Author: Patricia A. Peyser, Jessica D. Faul, Patrik K. E. Magnusson, Nicholas D. Hastie, Beate St Pourcain, Marcus Ising, Gérard Waeber, Behrooz Z. Alizadeh, Judith M. Vonk, Lawrence F. Bielak, Sang Hong Lee, Wouter J. Peyrot, Thomas Illig, M. M. Weissman, Nicholas J. Timpson, George Dedoussis, Nicholas G. Martin, Tomi E. Mäkinen, Jorma Viikari, Lili Milani, Harold Snieder, Laura J. Bierut, A. C. Heath, Reinhold E. Schmidt, Mariza de Andrade, Vilmundur Gudnason, K. Petrovic, Robert M. Kirkpatrick, Marcela González Gross, William G. Iacono, Michelle N. Meyer, Henry Völzke, Marisa Loitfelder, Maria Dimitriou, Lude Franke, Robert F. Krueger, E. J. C. G. van den Oord, Sven Cichon, Michael Conlon O'Donovan, Ian W. Craig, Shawn N. Murphy, Danielle Posthuma, Brenda W.J.H. Penninx, Aarno Palotie, Roy Thurik, Panos Deloukas, Matt McGue, M. Preisig, Patricia A. Boyle, Osorio Meirelles, Ben A. Oostra, Klaus Berger, G. M. Montgomery, Sharon L.R. Kardia, Peter K. Joshi, K. Stefansson, Paul Lichtenstein, Andrew Heath, Andrea Schulz, Dena G. Hernandez, Debbie A Lawlor, S. P. Hamilton, James B. Potash, Z. Kutalik, Elisabeth Widen, Emil L. Sigurdsson, Rudolf S N Fehrmann, Matthias Nauck, Mikael Landén, Kurt Lohman, S.D. Gordon, Lefkos T. Middleton, Caroline Hayward, Anjali K. Henders, Philipp Koellinger, Jeffrey A. Boatman, G van Grootheest, M. Daly, Jian Yang, Peter Vollenweider, Penelope A. Lind, Stacy Steinberg, Frank J. A. van Rooij, Florian Holsboer, Hkon K. Gjessing, Erkki Vartiainen, Magnus Johannesson, Jingmei Li, David Laibson, Henrik Grönberg, Tõnu Esko, Ivana Kolcic, Niina Eklund, Kelly S. Benke, Henning Tiemeier, Isaac S. Kohane, Nicolas W. Martin, Ronny Myhre, Frans G. Zitman, Arpana Agrawal, James F. Wilson, Michael R. Barnes, Lei Yu, Thorgeir E. Thorgeirsson, Franois Bastardot, Katri Räikkönen, William Lawson, Willem A. Nolen, M. Rietschel, René Breuer, Bertram Müller-Myhsok, James A. Knowles, Grant W. Montgomery, Eva Reinmaa, Rudolf Uher, Andreas Mielck, Luigi Ferrucci, S. E. Medland, Yuri Milaneschi, Philip L. De Jager, Manfred Uhr, A. E. Farmer, Cornelia M. van Duijn, Samuli Ripatti, Marja-Liisa Nuotio, Manuel Mattheisen, Sebastian E. Baumeister, David R. Van Wagoner, Martin Preisig, Fernando Rivadeneira, Peter Lichtner, Christopher Oldmeadow, Hreinn Stefansson, Ian B. Hickie, Darina Czamara, Elizabeth G. Holliday, Astanand Jugessur, Carla A. Ibrahim-Verbaas, Jaime Derringer, Vivian S. Gainer, P. Muglia, Daniel J. Benjamin, Patrick K.E. Magnusson, Patience J. Gallagher, Jennifer A. Smith, Lynn Cherkas, Pamela A. F. Madden, David A. Bennett, Zoltán Kutalik, George Davey-Smith, Gudny Eiriksdottir, Jens Treutlein, N. Craddock, Juliette Harris, Antti Latvala, Roy H. Perlis, Markus M. Noethen, Jan-Emmanuel De Neve, Stanley I. Shyn, J.H. Smit, Dalton Conley, Adriaan Hofman, Jari Lahti, Patrick J. F. Groenen, Jüri Allik, Albert V. Smith, Ozren Polasek, Susan M. Ring, Thomas Bettecken, Michele L. Pergadia, Patrick J. McGrath, Katherine E. Tansey, Stephan Ripke, Hogni Oskarsson, Peng Lin, Douglas F. Levinson, Matthijs J. H. M. van der Loos, Melissa E. Garcia, Jonathan P. Beauchamp, Rodney J. Scott, Zhihong Zhu, Michel Guipponi, Lyle J. Palmer, Alexander Teumer, William Coryell, Stefan Kloiber, Gonneke Willemsen, John Frank, Victor M. Castro, Andrew M. McIntosh, John M. Starr, Antonio Terracciano, Mika Kähönen, Marco Masala, Markus Perola, André G. Uitterlinden, Sutapa Mukherjee, Alexander Viktorin, Lenore J. Launer, Elisabeth B. Binder, William A. Scheftner, Christel M. Middeldorp, D. H. R. Blackwood, I. Jones, Thais S. Rizzi, A. Teumer, Cornelius A. Rietveld, Aldo Rustichini, Guy Lewis, Susan L. Slager, David M. Evans, Dorret I. Boomsma, Harry Campbell, Susanne Churchill, Johan G. Eriksson, Alan F. Wright, Dan V. Iosifescu, W. Maier, Francesco Cucca, Federica Tozzi, David R. Weir, Eva Albrecht, L. Milani, Jennifer R. Harris, Min A. Jhun, Marjo-Riitta Järvelin, Martin F. Elderson, Ute Bültmann, Olli T. Raitakari, Konstantin Shakhbazov, Krista Fischer, Thomas G. Schulze, T. Jung-Ying, P. Lichtenstein, Terho Lethimäki, Jeffrey B. Weilburg, Rolf Holle, Bo Jacobsson, Pedro Marques Vidal, Jordan W. Smoller, Stavroula Kanoni, Kati Kristiansson, Sergey Goryachev, Michael Steffens, Peter M. Visscher, Toshiko Tanaka, Donald J. MacIntyre, Witte J.G. Hoogendijk, David Schlessinger, Ian J. Deary, Harm-Jan Westra, Erik Ingelsson, E.J.C. de Geus, Franziska Degenhardt, Lydia Quaye, John Barnard, David C. Liewald, John P. Rice, Christopher F. Chabris, P. McGuffin, Tamara B. Harris, C. M. Lewis, Gail Davies, Enda M. Byrne, H.-Erich Wichmann, Sara Hägg, David Cesarini, Najaf Amin, Juha Karjalainen, Dale R. Nyholt, Christian Gieger, Per Hall, Ania Korszun, Neale Bm, Wei Zhao, Abdel Abdellaoui, Andres Metspalu, Christina Holzapfel, Jae Hoon Sul, Christiaan de Leeuw, Antti-Pekka Sarin, Ida Surakka, Veikko Salomaa, Mina K. Chung, N. L. Pedersen, Gerome Breen, P. A. F. Madden, Martin A. Kohli, J Kaprio, John Attia, Jing Shi, Gibran Hemani, Rauli Svento, Veronique Vitart, Susanne Lucae, L. A. Jones, Jouke-Jan Hottenga, Daniel S. Evans, Hans-Jörgen Grabe, Yongmei Liu, Danyu Lin, Albert Hofman, George McMahon, Naomi R. Wray, Stefan Herms, Stefania Bandinelli, W. Hoffmann, P.F. Sullivan, Susanne Hoefels, Michael B. Miller, Alan W. McLean, Igor Rudan, Jürgen Wellmann, Anu Realo, Maurizio Fava, Matthew Kowgier, Marika Kaakinen, Helena Schmidt, Faculteit Medische Wetenschappen/UMCG, Peyrot, WJ, Lee, SH, Milaneschi, Y, Abdellaoui, A, Penninx, BWJH, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Social Science Genetic Association Consortium, Psychiatry, NCA - Neurobiology of mental health, EMGO - Mental health, Applied Economics, Biological Psychology, Complex Trait Genetics, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Mental Health, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Corporate Collaborator), Social Science Genetic Association Consortium Corporate Collaborator, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Corporate Collaborator, Lewis, C.M., Hamilton, S.P., Weissman, M.M., Breen, G., Blackwood, D.H., Cichon, S., Heath, A.C., Holsboer, F., Madden, P.A., McGuffin, P., Muglia, P., Pergadia, M.L., Lin, D., Müller-Myhsok, B., Steinberg, S., Grabe, H.J., Lichtenstein, P., Magnusson, P., Perlis, R.H., Preisig, M., Smoller, J.W., Stefansson, K., Uher, R., Kutalik, Z., Tansey, K.E., Teumer, A., Viktorin, A., Barnes, M.R., Bettecken, T., Binder, E.B., Breuer, R., Castro, V.M., Churchill, S.E., Coryell, W.H., Craddock, N., Craig, I.W., Czamara, D., Degenhardt, F., Farmer, A.E., Fava, M., Frank, J., Gainer, V.S., Gallagher, P.J., Gordon, S.D., Goryachev, S., Gross, M., Guipponi, M., Henders, A.K., Herms, S., Hickie, I.B., Hoefels, S., Hoogendijk, W., Iosifescu, D.V., Ising, M., Jones, I., Jones, L., Jung-Ying, T., Knowles, J.A., Kohane, I.S., Kohli, M.A., Korszun, A., Landen, M., Lawson, W.B., Lewis, G., Macintyre, D., Maier, W., Mattheisen, M., McGrath, P.J., McIntosh, A., McLean, A., Middeldorp, C.M., Middleton, L., Montgomery, G.M., Murphy, S.N., Nauck, M., Nolen, W.A., Nyholt, D.R., O'Donovan, M., Oskarsson, H., Pedersen, N., Scheftner, W.A., Schulz, A., Schulze, T.G., Shyn, S.I., Sigurdsson, E., Slager, S.L., Smit, J.H., Stefansson, H., Steffens, M., Thorgeirsson, T., Tozzi, F., Treutlein, J., Uhr, M., van den Oord, E.J., Van Grootheest, G., Völzke, H., Weilburg, J.B., Willemsen, G., Zitman, F.G., Neale, B., Daly, M., Sullivan, P.F., Agrawal, A., Albrecht, E., Alizadeh, B.Z., Allik, J., Amin, N., Attia, J.R., Bandinelli, S., Barnard, J., Bastardot, F., Baumeister, S.E., Beauchamp, J., Benjamin, D.J., Benke, K.S., Bennett, D.A., Berger, K., Bielak, L.F., Bierut, L.J., Boatman, J.A., Boyle, P.A., Bültmann, U., Campbell, H., Cesarini, D., Chabris, C.F., Cherkas, L., Chung, M.K., Conley, D., Cucca, F., Davey-Smith, G., Davies, G., de Andrade, M., De Jager, P.L., de Leeuw, C., De Neve, J.E., Deary, I.J., Dedoussis, G.V., Deloukas, P., Derringer, J., Dimitriou, M., Eiriksdottir, G., Eklund, N., Elderson, M.F., Eriksson, J.G., Evans, D.S., Evans, D.M., Faul, J.D., Fehrmann, R., Ferrucci, L., Fischer, K., Franke, L., Garcia, M.E., Gieger, C., Gjessing, H.K., Groenen, P.J., Grönberg, H., Gudnason, V., Hägg, S., Hall, P., Harris, J.R., Harris, J.M., Harris, T.B., Hastie, N.D., Hayward, C., Hernandez, D.G., Hoffmann, W., Hofman, A., Holle, R., Holliday, E.G., Holzapfel, C., Iacono, W.G., Ibrahim-Verbaas, C.A., Illig, T., Ingelsson, E., Jacobsson, B., Järvelin, M.R., Jhun, M.A., Johannesson, M., Joshi, P.K., Jugessur, A., Kaakinen, M., Kähönen, M., Kanoni, S., Kaprio, J., Kardia, S.L., Karjalainen, J., Kirkpatrick, R.M., Koellinger, P.D., Kolcic, I., Kowgier, M., Kristiansson, K., Krueger, R.F., Lahti, J., Laibson, D., Latvala, A., Launer, L.J., Lawlor, D.A., Lethimäki, T., Li, J., Lichtner, P.K., Liewald, D.C., Lin, P., Lind, P.A., Liu, Y., Lohman, K., Loitfelder, M., Magnusson, P.K., Mäkinen, T.E., Vidal, P.M., Martin, N.W., Masala, M., McGue, M., McMahon, G., Meirelles, O., Meyer, M.N., Mielck, A., Milani, L., Miller, M.B., Montgomery, G.W., Mukherjee, S., Myhre, R., Nuotio, M.L., Oldmeadow, C.J., Oostra, B.A., Palmer, L.J., Palotie, A., Perola, M., Petrovic, K.E., Peyser, P.A., Polašek, O., Posthuma, D., Quaye, L., Räikkönen, K., Raitakari, O.T., Realo, A., Reinmaa, E., Rice, J.P., Ring, S.M., Ripatti, S., Rivadeneira, F., Rizzi, T.S., Rudan, I., Rustichini, A., Salomaa, V., Sarin, A.P., Schlessinger, D., Schmidt, H., Schmidt, R., Scott, R.J., Shakhbazov, K., Smith, A.V., Smith, J.A., Snieder, H., Pourcain, B.S., Starr, J.M., Sul, J.H., Surakka, I., Svento, R., Tanaka, T., Terracciano, A., Thurik, A.R., Tiemeier, H., Timpson, N.J., Uitterlinden, A.G., van der Loos, M.J., van Duijn, C.M., van Rooij, F.J., Van Wagoner, D.R., Vartiainen, E., Viikari, J., Visscher, P.M., Vitart, V., Vollenweider, P.K., Vonk, J.M., Waeber, G., Weir, D.R., Wellmann, J., Westra, H.J., Wichmann, H.E., Widen, E., Wilson, J.F., Wright, A.F., Yang, J., Yu, L., and Zhao, W.
Subjects: Netherlands Twin Register (NTR), Male, Genome-wide association study, Logistic regression, Cohort Studies, Odds Ratio, pleiotropic genetic effects, Netherlands, Psychiatry, education.field_of_study, Likelihood Functions, Single Nucleotide, Middle Aged, Psychiatry and Mental health, educational attainment, depression, Major depressive disorder, Educational Status, Regression Analysis, Female, Psychology, Adult, Estonia, medicine.medical_specialty, Biochemistry & Molecular Biology, Aged, Depressive Disorder, Major/epidemiology, Depressive Disorder, Major/genetics, Depressive Disorder, Major/psychology, Estonia/epidemiology, Gene-Environment Interaction, Genetic Association Studies, Genotype, Humans, Netherlands/epidemiology, Polymorphism, Single Nucleotide/genetics, Psychiatric Status Rating Scales, Concordance, Population, SNP, Single-nucleotide polymorphism, Genetic correlation, behavioral disciplines and activities, Polymorphism, Single Nucleotide, Article, Cellular and Molecular Neuroscience, mental disorders, medicine, Polymorphism, education, Molecular Biology, Depressive Disorder, Depressive Disorder, Major, major depressive disorder, ta1184, Neurosciences, Major, Odds ratio, medicine.disease, ta3124, Neurosciences & Neurology, polymorphisms, Demography
Abstract: An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on similar to 120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status. Refereed/Peer-reviewed
Published: 2015

15. Distinguishing different psychiatric disorders using DDx-PRS.

Author: Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, and Price AL
Abstract: Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
Published: 2024
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16. Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach.

Author: Uffelmann E, Price AL, Posthuma D, and Peyrot WJ
Abstract: Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach., Competing Interests: Declaration of interests The authors declare no competing interests.
Published: 2024
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17. Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up.

Author: Habets PC, Thomas RM, Milaneschi Y, Jansen R, Pool R, Peyrot WJ, Penninx BWJH, Meijer OC, van Wingen GA, and Vinkers CH
Subjects: Humans, Follow-Up Studies, Depression, Proteomics, Disease Progression, Depressive Disorder, Major diagnosis
Abstract: Background: The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level., Methods: Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82)., Results: Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%)., Conclusions: This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2023
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18. On the interpretation of transcriptome-wide association studies.

Author: de Leeuw C, Werme J, Savage JE, Peyrot WJ, and Posthuma D
Subjects: Chromosome Mapping, Computer Simulation, Data Analysis, Genome-Wide Association Study, Transcriptome genetics
Abstract: Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 de Leeuw et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Published: 2023
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19. Genome-wide association studies of polygenic risk score-derived phenotypes may lead to inflated false positive rates.

Author: Uffelmann E, Posthuma D, and Peyrot WJ
Subjects: Humans, Biological Specimen Banks, Phenotype, Risk Factors, United Kingdom, Genome-Wide Association Study, Alzheimer Disease
Published: 2023
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20. Associations Between Polygenic Risk Score Loading, Psychosis Liability, and Clozapine Use Among Individuals With Schizophrenia.

Author: Lin BD, Pinzón-Espinosa J, Blouzard E, van der Horst MZ, Okhuijsen-Pfeifer C, van Eijk KR, Guloksuz S, Peyrot WJ, and Luykx JJ
Subjects: Humans, Female, Adult, Risk Factors, Multifactorial Inheritance genetics, Schizophrenia drug therapy, Schizophrenia genetics, Schizophrenia diagnosis, Clozapine adverse effects, Antipsychotic Agents adverse effects, Psychotic Disorders drug therapy, Psychotic Disorders genetics, Psychotic Disorders diagnosis
Abstract: Importance: Predictors consistently associated with psychosis liability and course of illness in schizophrenia (SCZ) spectrum disorders (SSD), including the need for clozapine treatment, are lacking. Longitudinally ascertained medication use may empower studies examining associations between polygenic risk scores (PRSs) and pharmacotherapy choices., Objective: To examine associations between PRS-SCZ loading and groups with different liabilities to SSD (individuals with SSD taking clozapine, individuals with SSD taking other antipsychotics, their parents and siblings, and unrelated healthy controls) and between PRS-SCZ and the likelihood of receiving a prescription of clozapine relative to other antipsychotics., Design, Setting, and Participants: This genetic association study was a multicenter, observational cohort study with 6 years of follow-up. Included were individuals diagnosed with SSD who were taking clozapine or other antipsychotics, their parents and siblings, and unrelated healthy controls. Data were collected from 2004 until 2021 and analyzed between October 2021 and September 2022., Exposures: Polygenic risk scores for SCZ., Main Outcomes and Measures: Multinomial logistic regression was used to examine possible differences between groups by computing risk ratios (RRs), ie, ratios of the probability of pertaining to a particular group divided by the probability of healthy control status. We also computed PRS-informed odd ratios (ORs) for clozapine use relative to other antipsychotics., Results: Polygenic risk scores for SCZ were generated for 2344 participants (mean [SD] age, 36.95 years [14.38]; 994 female individuals [42.4%]) who remained after quality control screening (557 individuals with SSD taking clozapine, 350 individuals with SSD taking other antipsychotics during the 6-year follow-up, 542 parents and 574 siblings of individuals with SSD, and 321 unrelated healthy controls). All RRs were significantly different from 1; RRs were highest for individuals with SSD taking clozapine (RR, 3.24; 95% CI, 2.76-3.81; P = 2.47 × 10-46), followed by individuals with SSD taking other antipsychotics (RR, 2.30; 95% CI, 1.95-2.72; P = 3.77 × 10-22), parents (RR, 1.44; 95% CI, 1.25-1.68; P = 1.76 × 10-6), and siblings (RR, 1.40; 95% CI, 1.21-1.63; P = 8.22 × 10-6). Polygenic risk scores for SCZ were positively associated with clozapine vs other antipsychotic use (OR, 1.41; 95% CI, 1.22-1.63; P = 2.98 × 10-6), suggesting a higher likelihood of clozapine prescriptions among individuals with higher PRS-SCZ., Conclusions and Relevance: In this study, PRS-SCZ loading differed between groups of individuals with SSD, their relatives, and unrelated healthy controls, with patients taking clozapine at the far end of PRS-SCZ loading. Additionally, PRS-SCZ was associated with a higher likelihood of clozapine prescribing. Our findings may inform early intervention and prognostic studies of the value of using PRS-SCZ to personalize antipsychotic treatment.
Published: 2023
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21. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores.

Author: Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ, Khera AV, Okada Y, Martin AR, Finucane HK, and Price AL
Subjects: Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide genetics, Risk Factors, Genome-Wide Association Study, Multifactorial Inheritance genetics
Abstract: Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred + , which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred + to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred + attained similar improvements., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2022
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22. [What can genetic research teach us about the overlap and differences between psychiatric disorders?]

Author: Peyrot WJ, Nivard MG, and de Leeuw CA
Subjects: Genetic Research, Humans, Mental Disorders genetics
Abstract: Background: Genetic research has made continuous progress in the past years, as a results of the rapidly growing sample sizes and development of novel analytical tools.
AIM: To present the latest developments in research into the genetic overlap and differences between psychiatric disorders.
METHOD: Description of findings from recent literature.
RESULTS: First of all, psychiatric disorders differ in their heritability, and consequently impact of environmental factors. Second, some disorders show high concordance in direction of genetic effects, while other disorders have rather different effects. Using these genetic (dis)similarities, subgroups of psychiatric disorders can also be defined.
CONCLUSION: Genetic research is developing at rapid pace, and much more will be learned about the genetic overlap and differences between psychiatric disorders in the next years. The big promise -and challenge- will be to integrate genetic research with research of environmental factors, other biological measures and more detailed phenotypic information.
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Published: 2022

23. Correction: Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.

Author: Coleman JRI, Peyrot WJ, Purves KL, Davis KAS, Rayner C, Choi SW, Hübel C, Gaspar HA, Kan C, Van der Auwera S, Adams MJ, Lyall DM, Choi KW, Dunn EC, Vassos E, Danese A, Maughan B, Grabe HJ, Lewis CM, O'Reilly PF, McIntosh AM, Smith DJ, Wray NR, Hotopf M, Eley TC, and Breen G
Published: 2021
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24. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS.

Author: Peyrot WJ and Price AL
Subjects: Alleles, Axons metabolism, Axons pathology, Bipolar Disorder metabolism, Bipolar Disorder physiopathology, Case-Control Studies, Datasets as Topic, Depressive Disorder, Major metabolism, Depressive Disorder, Major physiopathology, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Kruppel-Like Factor 6 metabolism, Kruppel-Like Transcription Factors metabolism, Neuronal Outgrowth genetics, Polymorphism, Single Nucleotide, Schizophrenia metabolism, Schizophrenia physiopathology, Bipolar Disorder genetics, Depressive Disorder, Major genetics, Gene Frequency, Genetic Loci, Kruppel-Like Factor 6 genetics, Kruppel-Like Transcription Factors genetics, Schizophrenia genetics
Abstract: Psychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case-case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case-control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case-case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case-control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data.
Published: 2021
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25. Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank.

Author: Coleman JRI, Peyrot WJ, Purves KL, Davis KAS, Rayner C, Choi SW, Hübel C, Gaspar HA, Kan C, Van der Auwera S, Adams MJ, Lyall DM, Choi KW, Dunn EC, Vassos E, Danese A, Maughan B, Grabe HJ, Lewis CM, O'Reilly PF, McIntosh AM, Smith DJ, Wray NR, Hotopf M, Eley TC, and Breen G
Subjects: Adult, Aged, Female, Humans, Male, Middle Aged, United Kingdom epidemiology, Waist Circumference, Databases, Factual, Depressive Disorder, Major epidemiology, Depressive Disorder, Major genetics, Gene-Environment Interaction, Genetic Predisposition to Disease genetics, Genome-Wide Association Study, Psychological Trauma epidemiology, Self Report
Abstract: Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (r g = 0.24, p = 1.8 × 10 -7 versus r g = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10 -4 ). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.
Published: 2020
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26. Genetic correlations of polygenic disease traits: from theory to practice.

Author: van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, and Wray NR
Subjects: Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Disease, Genome-Wide Association Study
Abstract: The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
Published: 2019
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27. A role for vitamin D and omega-3 fatty acids in major depression? An exploration using genomics.

Author: Milaneschi Y, Peyrot WJ, Nivard MG, Mbarek H, Boomsma DI, and W J H Penninx B
Subjects: Adult, Depressive Disorder, Major drug therapy, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Multifactorial Inheritance, Pharmacogenetics, Depressive Disorder, Major genetics, Fatty Acids, Omega-3 therapeutic use, Vitamin D therapeutic use
Abstract: Trials testing the effect of vitamin D or omega-3 polyunsaturated fatty acid (n3-PUFA) supplementation on major depressive disorder (MDD) reported conflicting findings. These trials were inspired by epidemiological evidence suggesting an inverse association of circulating 25-hydroxyvitamin D (25-OH-D) and n3-PUFA levels with MDD. Observational associations may emerge from unresolved confounding, shared genetic risk, or direct causal relationships. We explored the nature of these associations exploiting data and statistical tools from genomics. Results from genome-wide association studies on 25-OH-D (N = 79 366), n3-PUFA (N = 24 925), and MDD (135 458 cases, 344 901 controls) were applied to individual-level data (>2000 subjects with measures of genotype, DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) lifetime MDD diagnoses and circulating 25-OH-D and n3-PUFA) and summary-level data analyses. Shared genetic risk between traits was tested by polygenic risk scores (PRS). Two-sample Mendelian Randomization (2SMR) analyses tested the potential bidirectional causality between traits. In individual-level data analyses, PRS were associated with the phenotype of the same trait (PRS 25-OH-D p = 1.4e - 20, PRS n3-PUFA p = 9.3e - 6, PRS MDD p = 1.4e - 4), but not with the other phenotypes, suggesting a lack of shared genetic effects. In summary-level data analyses, 2SMR analyses provided no evidence of a causal role on MDD of 25-OH-D (p = 0.50) or n3-PUFA (p = 0.16), or for a causal role of MDD on 25-OH-D (p = 0.25) or n3-PUFA (p = 0.66). Applying genomics tools indicated that shared genetic risk or direct causality between 25-OH-D, n3-PUFA, and MDD is unlikely: unresolved confounding may explain the associations reported in observational studies. These findings represent a cautionary tale for testing supplementation of these compounds in preventing or treating MDD.
Published: 2019
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28. Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder.

Author: Trzaskowski M, Mehta D, Peyrot WJ, Hawkes D, Davies D, Howard DM, Kemper KE, Sidorenko J, Maier R, Ripke S, Mattheisen M, Baune BT, Grabe HJ, Heath AC, Jones L, Jones I, Madden PAF, McIntosh AM, Breen G, Lewis CM, Børglum AD, Sullivan PF, Martin NG, Kendler KS, Levinson DF, and Wray NR
Subjects: Adult, Case-Control Studies, Cohort Effect, Cohort Studies, Databases, Genetic, Depressive Disorder, Major physiopathology, Female, Genetic Predisposition to Disease genetics, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide genetics, Prevalence, Risk Factors, Sex Factors, Depressive Disorder, Major epidemiology, Depressive Disorder, Major genetics
Abstract: Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between-cohort and between-sex heterogeneity. First, we used genome-wide association study (GWAS) summary statistics to investigate between-cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; N cases = 16,823, N controls = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between-sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD., (© 2019 Wiley Periodicals, Inc.)
Published: 2019
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29. Does Childhood Trauma Moderate Polygenic Risk for Depression? A Meta-analysis of 5765 Subjects From the Psychiatric Genomics Consortium.

Author: Peyrot WJ, Van der Auwera S, Milaneschi Y, Dolan CV, Madden PAF, Sullivan PF, Strohmaier J, Ripke S, Rietschel M, Nivard MG, Mullins N, Montgomery GW, Henders AK, Heat AC, Fisher HL, Dunn EC, Byrne EM, Air TA, Baune BT, Breen G, Levinson DF, Lewis CM, Martin NG, Nelson EN, Boomsma DI, Grabe HJ, Wray NR, and Penninx BWJH
Subjects: Adult, Adult Survivors of Child Abuse psychology, Case-Control Studies, Child, Depressive Disorder, Major epidemiology, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Humans, Logistic Models, Male, Multifactorial Inheritance, Psychiatric Status Rating Scales, Risk Assessment, Risk Factors, Child Abuse psychology, Depressive Disorder, Major genetics
Abstract: Background: The heterogeneity of genetic effects on major depressive disorder (MDD) may be partly attributable to moderation of genetic effects by environment, such as exposure to childhood trauma (CT). Indeed, previous findings in two independent cohorts showed evidence for interaction between polygenic risk scores (PRSs) and CT, albeit in opposing directions. This study aims to meta-analyze MDD-PRS × CT interaction results across these two and other cohorts, while applying more accurate PRSs based on a larger discovery sample., Methods: Data were combined from 3024 MDD cases and 2741 control subjects from nine cohorts contributing to the MDD Working Group of the Psychiatric Genomics Consortium. MDD-PRS were based on a discovery sample of ∼110,000 independent individuals. CT was assessed as exposure to sexual or physical abuse during childhood. In a subset of 1957 cases and 2002 control subjects, a more detailed five-domain measure additionally included emotional abuse, physical neglect, and emotional neglect., Results: MDD was associated with the MDD-PRS (odds ratio [OR] = 1.24, p = 3.6 × 10 -5 , R 2 = 1.18%) and with CT (OR = 2.63, p = 3.5 × 10 -18 and OR = 2.62, p = 1.4 ×10 -5 for the two- and five-domain measures, respectively). No interaction was found between MDD-PRS and the two-domain and five-domain CT measure (OR = 1.00, p = .89 and OR = 1.05, p = .66)., Conclusions: No meta-analytic evidence for interaction between MDD-PRS and CT was found. This suggests that the previously reported interaction effects, although both statistically significant, can best be interpreted as chance findings. Further research is required, but this study suggests that the genetic heterogeneity of MDD is not attributable to genome-wide moderation of genetic effects by CT., (Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2018
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30. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression.

Author: Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TMF, Bacanu SA, Bækvad-Hansen M, Beekman AFT, Bigdeli TB, Binder EB, Blackwood DRH, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JIR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Crowley CA, Dashti HS, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Eriksson N, Escott-Price V, Kiadeh FHF, Finucane HK, Forstner AJ, Frank J, Gaspar HA, Gill M, Giusti-Rodríguez P, Goes FS, Gordon SD, Grove J, Hall LS, Hannon E, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Hu M, Hyde CL, Ising M, Jansen R, Jin F, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Krogh J, Kutalik Z, Lane JM, Li Y, Li Y, Lind PA, Liu X, Lu L, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mill J, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O'Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Pettersson E, Peyrot WJ, Pistis G, Posthuma D, Purcell SM, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Saeed Mirza S, Saxena R, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GBC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Stockmeier CA, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Tian C, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus ECJ, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Hinds DA, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PFA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O'Donovan MC, Paciga SA, Pedersen NL, Penninx BWJH, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Winslow AR, Lewis CM, Levinson DF, Breen G, Børglum AD, and Sullivan PF
Subjects: Case-Control Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Humans, Male, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Schizophrenia genetics, Depressive Disorder, Major genetics, Multifactorial Inheritance
Abstract: Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.
Published: 2018
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31. Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression.

Author: Culverhouse RC, Saccone NL, Horton AC, Ma Y, Anstey KJ, Banaschewski T, Burmeister M, Cohen-Woods S, Etain B, Fisher HL, Goldman N, Guillaume S, Horwood J, Juhasz G, Lester KJ, Mandelli L, Middeldorp CM, Olié E, Villafuerte S, Air TM, Araya R, Bowes L, Burns R, Byrne EM, Coffey C, Coventry WL, Gawronski KAB, Glei D, Hatzimanolis A, Hottenga JJ, Jaussent I, Jawahar C, Jennen-Steinmetz C, Kramer JR, Lajnef M, Little K, Zu Schwabedissen HM, Nauck M, Nederhof E, Petschner P, Peyrot WJ, Schwahn C, Sinnamon G, Stacey D, Tian Y, Toben C, Van der Auwera S, Wainwright N, Wang JC, Willemsen G, Anderson IM, Arolt V, Åslund C, Bagdy G, Baune BT, Bellivier F, Boomsma DI, Courtet P, Dannlowski U, de Geus EJC, Deakin JFW, Easteal S, Eley T, Fergusson DM, Goate AM, Gonda X, Grabe HJ, Holzman C, Johnson EO, Kennedy M, Laucht M, Martin NG, Munafò MR, Nilsson KW, Oldehinkel AJ, Olsson CA, Ormel J, Otte C, Patton GC, Penninx BWJH, Ritchie K, Sarchiapone M, Scheid JM, Serretti A, Smit JH, Stefanis NC, Surtees PG, Völzke H, Weinstein M, Whooley M, Nurnberger JI Jr, Breslau N, and Bierut LJ
Subjects: Cooperative Behavior, Gene-Environment Interaction, Genetic Predisposition to Disease, Genotype, Humans, Life Change Events, Stress, Psychological genetics, Depression genetics, Depression psychology, Serotonin Plasma Membrane Transport Proteins genetics, Stress, Psychological complications
Abstract: The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 data sets containing 38 802 European ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analysed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis) with qualifying unpublished data, were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalisable, but must be of modest effect size and only observable in limited situations.
Published: 2018
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32. Genome-wide gene-environment interaction in depression: A systematic evaluation of candidate genes: The childhood trauma working-group of PGC-MDD.

Author: Van der Auwera S, Peyrot WJ, Milaneschi Y, Hertel J, Baune B, Breen G, Byrne E, Dunn EC, Fisher H, Homuth G, Levinson D, Lewis C, Mills N, Mullins N, Nauck M, Pistis G, Preisig M, Rietschel M, Ripke S, Sullivan P, Teumer A, Völzke H, Boomsma DI, Wray NR, Penninx B, and Grabe H
Subjects: Case-Control Studies, Depression etiology, Depressive Disorder, Major etiology, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Humans, Life Change Events, Male, Polymorphism, Single Nucleotide genetics, Risk Factors, White People genetics, Depression genetics, Depressive Disorder, Major genetics
Abstract: Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD., (© 2017 Wiley Periodicals, Inc.)
Published: 2018
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33. Genetic Association of Major Depression With Atypical Features and Obesity-Related Immunometabolic Dysregulations.

Author: Milaneschi Y, Lamers F, Peyrot WJ, Baune BT, Breen G, Dehghan A, Forstner AJ, Grabe HJ, Homuth G, Kan C, Lewis C, Mullins N, Nauck M, Pistis G, Preisig M, Rivera M, Rietschel M, Streit F, Strohmaier J, Teumer A, Van der Auwera S, Wray NR, Boomsma DI, and Penninx BWJH
Subjects: Adult, Body Mass Index, C-Reactive Protein analysis, Diagnostic and Statistical Manual of Mental Disorders, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, International Cooperation, Male, Psychiatric Status Rating Scales, Craving, Depressive Disorder, Major diagnosis, Depressive Disorder, Major genetics, Depressive Disorder, Major immunology, Depressive Disorder, Major metabolism, Obesity diagnosis, Obesity genetics, Obesity psychology, Weight Gain
Abstract: Importance: The association between major depressive disorder (MDD) and obesity may stem from shared immunometabolic mechanisms particularly evident in MDD with atypical features, characterized by increased appetite and/or weight (A/W) during an active episode., Objective: To determine whether subgroups of patients with MDD stratified according to the A/W criterion had a different degree of genetic overlap with obesity-related traits (body mass index [BMI] and levels of C-reactive protein [CRP] and leptin)., Design, Setting, and Patients: This multicenter study assembled genome-wide genotypic and phenotypic measures from 14 data sets of the Psychiatric Genomics Consortium. Data sets were drawn from case-control, cohort, and population-based studies, including 26 628 participants with established psychiatric diagnoses and genome-wide genotype data. Data on BMI were available for 15 237 participants. Data were retrieved and analyzed from September 28, 2015, through May 20, 2017., Main Outcomes and Measures: Lifetime DSM-IV MDD was diagnosed using structured diagnostic instruments. Patients with MDD were stratified into subgroups according to change in the DSM-IV A/W symptoms as decreased or increased., Results: Data included 11 837 participants with MDD and 14 791 control individuals, for a total of 26 628 participants (59.1% female and 40.9% male). Among participants with MDD, 5347 (45.2%) were classified in the decreased A/W and 1871 (15.8%) in the increased A/W subgroups. Common genetic variants explained approximately 10% of the heritability in the 2 subgroups. The increased A/W subgroup showed a strong and positive genetic correlation (SE) with BMI (0.53 [0.15]; P = 6.3 × 10-4), whereas the decreased A/W subgroup showed an inverse correlation (-0.28 [0.14]; P = .06). Furthermore, the decreased A/W subgroup had a higher polygenic risk for increased BMI (odds ratio [OR], 1.18; 95% CI, 1.12-1.25; P = 1.6 × 10-10) and levels of CRP (OR, 1.08; 95% CI, 1.02-1.13; P = 7.3 × 10-3) and leptin (OR, 1.09; 95% CI, 1.06-1.12; P = 1.7 × 10-3)., Conclusions and Relevance: The phenotypic associations between atypical depressive symptoms and obesity-related traits may arise from shared pathophysiologic mechanisms in patients with MDD. Development of treatments effectively targeting immunometabolic dysregulations may benefit patients with depression and obesity, both syndromes with important disability.
Published: 2017
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34. Correction: Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

Author: Graff M, Scott RA, Justice AE, Young KL, Feitosa MF, Barata L, Winkler TW, Chu AY, Mahajan A, Hadley D, Xue L, Workalemahu T, Heard-Costa NL, den Hoed M, Ahluwalia TS, Qi Q, Ngwa JS, Renström F, Quaye L, Eicher JD, Hayes JE, Cornelis M, Kutalik Z, Lim E, Luan J, Huffman JE, Zhang W, Zhao W, Griffin PJ, Haller T, Ahmad S, Marques-Vidal PM, Bien S, Yengo L, Teumer A, Smith AV, Kumari M, Harder MN, Justesen JM, Kleber ME, Hollensted M, Lohman K, Rivera NV, Whitfield JB, Zhao JH, Stringham HM, Lyytikäinen LP, Huppertz C, Willemsen G, Peyrot WJ, Wu Y, Kristiansson K, Demirkan A, Fornage M, Hassinen M, Bielak LF, Cadby G, Tanaka T, Mägi R, van der Most PJ, Jackson AU, Bragg-Gresham JL, Vitart V, Marten J, Navarro P, Bellis C, Pasko D, Johansson Å, Snitker S, Cheng YC, Eriksson J, Lim U, Aadahl M, Adair LS, Amin N, Balkau B, Auvinen J, Beilby J, Bergman RN, Bergmann S, Bertoni AG, Blangero J, Bonnefond A, Bonnycastle LL, Borja JB, Brage S, Busonero F, Buyske S, Campbell H, Chines PS, Collins FS, Corre T, Smith GD, Delgado GE, Dueker N, Dörr M, Ebeling T, Eiriksdottir G, Esko T, Faul JD, Fu M, Færch K, Gieger C, Gläser S, Gong J, Gordon-Larsen P, Grallert H, Grammer TB, Grarup N, van Grootheest G, Harald K, Hastie ND, Havulinna AS, Hernandez D, Hindorff L, Hocking LJ, Holmens OL, Holzapfel C, Hottenga JJ, Huang J, Huang T, Hui J, Huth C, Hutri-Kähönen N, James AL, Jansson JO, Jhun MA, Juonala M, Kinnunen L, Koistinen HA, Kolcic I, Komulainen P, Kuusisto J, Kvaløy K, Kähönen M, Lakka TA, Launer LJ, Lehne B, Lindgren CM, Lorentzon M, Luben R, Marre M, Milaneschi Y, Monda KL, Montgomery GW, De Moor MHM, Mulas A, Müller-Nurasyid M, Musk AW, Männikkö R, Männistö S, Narisu N, Nauck M, Nettleton JA, Nolte IM, Oldehinkel AJ, Olden M, Ong KK, Padmanabhan S, Paternoster L, Perez J, Perola M, Peters A, Peters U, Peyser PA, Prokopenko I, Puolijoki H, Raitakari OT, Rankinen T, Rasmussen-Torvik LJ, Rawal R, Ridker PM, Rose LM, Rudan I, Sarti C, Sarzynski MA, Savonen K, Scott WR, Sanna S, Shuldiner AR, Sidney S, Silbernagel G, Smith BH, Smith JA, Snieder H, Stančáková A, Sternfeld B, Swift AJ, Tammelin T, Tan ST, Thorand B, Thuillier D, Vandenput L, Vestergaard H, van Vliet-Ostaptchouk JV, Vohl MC, Völker U, Waeber G, Walker M, Wild S, Wong A, Wright AF, Zillikens MC, Zubair N, Haiman CA, Lemarchand L, Gyllensten U, Ohlsson C, Hofman A, Rivadeneira F, Uitterlinden AG, Pérusse L, Wilson JF, Hayward C, Polasek O, Cucca F, Hveem K, Hartman CA, Tönjes A, Bandinelli S, Palmer LJ, Kardia SLR, Rauramaa R, Sørensen TIA, Tuomilehto J, Salomaa V, Penninx BWJH, de Geus EJC, Boomsma DI, Lehtimäki T, Mangino M, Laakso M, Bouchard C, Martin NG, Kuh D, Liu Y, Linneberg A, März W, Strauch K, Kivimäki M, Harris TB, Gudnason V, Völzke H, Qi L, Järvelin MR, Chambers JC, Kooner JS, Froguel P, Kooperberg C, Vollenweider P, Hallmans G, Hansen T, Pedersen O, Metspalu A, Wareham NJ, Langenberg C, Weir DR, Porteous DJ, Boerwinkle E, Chasman DI, Abecasis GR, Barroso I, McCarthy MI, Frayling TM, O'Connell JR, van Duijn CM, Boehnke M, Heid IM, Mohlke KL, Strachan DP, Fox CS, Liu CT, Hirschhorn JN, Klein RJ, Johnson AD, Borecki IB, Franks PW, North KE, Cupples LA, Loos RJF, and Kilpeläinen TO
Abstract: [This corrects the article DOI: 10.1371/journal.pgen.1006528.].
Published: 2017
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35. Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

Author: Graff M, Scott RA, Justice AE, Young KL, Feitosa MF, Barata L, Winkler TW, Chu AY, Mahajan A, Hadley D, Xue L, Workalemahu T, Heard-Costa NL, den Hoed M, Ahluwalia TS, Qi Q, Ngwa JS, Renström F, Quaye L, Eicher JD, Hayes JE, Cornelis M, Kutalik Z, Lim E, Luan J, Huffman JE, Zhang W, Zhao W, Griffin PJ, Haller T, Ahmad S, Marques-Vidal PM, Bien S, Yengo L, Teumer A, Smith AV, Kumari M, Harder MN, Justesen JM, Kleber ME, Hollensted M, Lohman K, Rivera NV, Whitfield JB, Zhao JH, Stringham HM, Lyytikäinen LP, Huppertz C, Willemsen G, Peyrot WJ, Wu Y, Kristiansson K, Demirkan A, Fornage M, Hassinen M, Bielak LF, Cadby G, Tanaka T, Mägi R, van der Most PJ, Jackson AU, Bragg-Gresham JL, Vitart V, Marten J, Navarro P, Bellis C, Pasko D, Johansson Å, Snitker S, Cheng YC, Eriksson J, Lim U, Aadahl M, Adair LS, Amin N, Balkau B, Auvinen J, Beilby J, Bergman RN, Bergmann S, Bertoni AG, Blangero J, Bonnefond A, Bonnycastle LL, Borja JB, Brage S, Busonero F, Buyske S, Campbell H, Chines PS, Collins FS, Corre T, Smith GD, Delgado GE, Dueker N, Dörr M, Ebeling T, Eiriksdottir G, Esko T, Faul JD, Fu M, Færch K, Gieger C, Gläser S, Gong J, Gordon-Larsen P, Grallert H, Grammer TB, Grarup N, van Grootheest G, Harald K, Hastie ND, Havulinna AS, Hernandez D, Hindorff L, Hocking LJ, Holmens OL, Holzapfel C, Hottenga JJ, Huang J, Huang T, Hui J, Huth C, Hutri-Kähönen N, James AL, Jansson JO, Jhun MA, Juonala M, Kinnunen L, Koistinen HA, Kolcic I, Komulainen P, Kuusisto J, Kvaløy K, Kähönen M, Lakka TA, Launer LJ, Lehne B, Lindgren CM, Lorentzon M, Luben R, Marre M, Milaneschi Y, Monda KL, Montgomery GW, De Moor MHM, Mulas A, Müller-Nurasyid M, Musk AW, Männikkö R, Männistö S, Narisu N, Nauck M, Nettleton JA, Nolte IM, Oldehinkel AJ, Olden M, Ong KK, Padmanabhan S, Paternoster L, Perez J, Perola M, Peters A, Peters U, Peyser PA, Prokopenko I, Puolijoki H, Raitakari OT, Rankinen T, Rasmussen-Torvik LJ, Rawal R, Ridker PM, Rose LM, Rudan I, Sarti C, Sarzynski MA, Savonen K, Scott WR, Sanna S, Shuldiner AR, Sidney S, Silbernagel G, Smith BH, Smith JA, Snieder H, Stančáková A, Sternfeld B, Swift AJ, Tammelin T, Tan ST, Thorand B, Thuillier D, Vandenput L, Vestergaard H, van Vliet-Ostaptchouk JV, Vohl MC, Völker U, Waeber G, Walker M, Wild S, Wong A, Wright AF, Zillikens MC, Zubair N, Haiman CA, Lemarchand L, Gyllensten U, Ohlsson C, Hofman A, Rivadeneira F, Uitterlinden AG, Pérusse L, Wilson JF, Hayward C, Polasek O, Cucca F, Hveem K, Hartman CA, Tönjes A, Bandinelli S, Palmer LJ, Kardia SLR, Rauramaa R, Sørensen TIA, Tuomilehto J, Salomaa V, Penninx BWJH, de Geus EJC, Boomsma DI, Lehtimäki T, Mangino M, Laakso M, Bouchard C, Martin NG, Kuh D, Liu Y, Linneberg A, März W, Strauch K, Kivimäki M, Harris TB, Gudnason V, Völzke H, Qi L, Järvelin MR, Chambers JC, Kooner JS, Froguel P, Kooperberg C, Vollenweider P, Hallmans G, Hansen T, Pedersen O, Metspalu A, Wareham NJ, Langenberg C, Weir DR, Porteous DJ, Boerwinkle E, Chasman DI, Abecasis GR, Barroso I, McCarthy MI, Frayling TM, O'Connell JR, van Duijn CM, Boehnke M, Heid IM, Mohlke KL, Strachan DP, Fox CS, Liu CT, Hirschhorn JN, Klein RJ, Johnson AD, Borecki IB, Franks PW, North KE, Cupples LA, Loos RJF, and Kilpeläinen TO
Subjects: Adiposity physiology, Body Mass Index, Epigenomics, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Obesity physiopathology, Waist Circumference, Waist-Hip Ratio, Adiposity genetics, Alpha-Ketoglutarate-Dependent Dioxygenase FTO genetics, Exercise, Obesity genetics
Abstract: Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.
Published: 2017
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36. Genetic effects influencing risk for major depressive disorder in China and Europe.

Author: Bigdeli TB, Ripke S, Peterson RE, Trzaskowski M, Bacanu SA, Abdellaoui A, Andlauer TF, Beekman AT, Berger K, Blackwood DH, Boomsma DI, Breen G, Buttenschøn HN, Byrne EM, Cichon S, Clarke TK, Couvy-Duchesne B, Craddock N, de Geus EJ, Degenhardt F, Dunn EC, Edwards AC, Fanous AH, Forstner AJ, Frank J, Gill M, Gordon SD, Grabe HJ, Hamilton SP, Hardiman O, Hayward C, Heath AC, Henders AK, Herms S, Hickie IB, Hoffmann P, Homuth G, Hottenga JJ, Ising M, Jansen R, Kloiber S, Knowles JA, Lang M, Li QS, Lucae S, MacIntyre DJ, Madden PA, Martin NG, McGrath PJ, McGuffin P, McIntosh AM, Medland SE, Mehta D, Middeldorp CM, Milaneschi Y, Montgomery GW, Mors O, Müller-Myhsok B, Nauck M, Nyholt DR, Nöthen MM, Owen MJ, Penninx BW, Pergadia ML, Perlis RH, Peyrot WJ, Porteous DJ, Potash JB, Rice JP, Rietschel M, Riley BP, Rivera M, Schoevers R, Schulze TG, Shi J, Shyn SI, Smit JH, Smoller JW, Streit F, Strohmaier J, Teumer A, Treutlein J, Van der Auwera S, van Grootheest G, van Hemert AM, Völzke H, Webb BT, Weissman MM, Wellmann J, Willemsen G, Witt SH, Levinson DF, Lewis CM, Wray NR, Flint J, Sullivan PF, and Kendler KS
Subjects: Bayes Theorem, Case-Control Studies, China, Europe, Female, Genetic Predisposition to Disease, Humans, Male, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Asian People genetics, Depressive Disorder, Major genetics, White People genetics
Abstract: Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log 10 Bayes Factor=8.08) but failed to replicate in an independent European sample (P=0.911). Gene-set enrichment analyses indicate enrichment of genes involved in neuronal development and axonal trafficking. We successfully demonstrate a partially shared polygenic basis of MDD in East Asian and European populations. Taken together, these findings support a complex etiology for MDD and possible population differences in predisposing genetic factors, with important implications for future genetic studies.
Published: 2017
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37. Using Clinical Characteristics to Identify Which Patients With Major Depressive Disorder Have a Higher Genetic Load for Three Psychiatric Disorders.

Author: Verduijn J, Milaneschi Y, Peyrot WJ, Hottenga JJ, Abdellaoui A, de Geus EJC, Smit JH, Breen G, Lewis CM, Boomsma DI, Beekman ATF, and Penninx BWJH
Subjects: Adult, Age of Onset, Bipolar Disorder diagnosis, Depressive Disorder, Major diagnosis, Female, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Psychiatric Status Rating Scales, Risk Factors, Schizophrenia diagnosis, Bipolar Disorder genetics, Depressive Disorder, Major genetics, Genetic Load, Genetic Predisposition to Disease, Schizophrenia genetics
Abstract: Background: Limited successes of gene finding for major depressive disorder (MDD) may be partly due to phenotypic heterogeneity. We tested whether the genetic load for MDD, bipolar disorder, and schizophrenia (SCZ) is increased in phenotypically more homogenous MDD patients identified by specific clinical characteristics., Methods: Patients (n = 1539) with a DSM-IV MDD diagnosis and control subjects (n = 1792) were from two large cohort studies (Netherlands Study of Depression and Anxiety and Netherlands Twin Register). Genomic profile risk scores (GPRSs) for MDD, bipolar disorder, and SCZ were based on meta-analysis results of the Psychiatric Genomics Consortium. Regression analyses (adjusted for year of birth, sex, three principal components) examined the association between GPRSs with characteristics and GPRSs with MDD subgroups stratified according to the most relevant characteristics. The proportion of liability variance explained by GPRSs for each MDD subgroup was estimated., Results: GPRS-MDD explained 1.0% (p = 4.19e -09 ) of MDD variance, and 1.5% (p = 4.23e -09 ) for MDD endorsing nine DSM symptoms. GPRS-bipolar disorder explained 0.6% (p = 2.97e -05 ) of MDD variance and 1.1% (p = 1.30e -05 ) for MDD with age at onset <18 years. GPRS-SCZ explained 2.0% (p = 6.15e -16 ) of MDD variance, 2.6% (p = 2.88e -10 ) for MDD with higher symptom severity, and 2.3% (p = 2.26e -13 ) for MDD endorsing nine DSM symptoms. An independent sample replicated the same pattern of stronger associations between cases with more DSM symptoms, as compared to overall MDD, and GPRS-SCZ., Conclusions: MDD patients with early age at onset and higher symptom severity have an increased genetic risk for three major psychiatric disorders, suggesting that it is useful to create phenotypically more homogenous groups when searching for genes associated with MDD., (Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2017
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38. Exploring Boundaries for the Genetic Consequences of Assortative Mating for Psychiatric Traits.

Author: Peyrot WJ, Robinson MR, Penninx BW, and Wray NR
Subjects: Cross-Sectional Studies, Female, Genetics, Population, Genotype, Humans, Male, Mental Disorders epidemiology, Risk, Statistics as Topic, Marriage psychology, Mental Disorders genetics, Models, Genetic, Phenotype, Reproductive Behavior psychology, Selection, Genetic genetics
Abstract: Importance: Considerable partner resemblances have been found for a wide range of psychiatric disorders, meaning that partners of affected individuals have an increased risk of being affected compared with partners of unaffected individuals. If this resemblance is reflected in genetic similarity between partners, genetic risk is anticipated to accumulate in offspring, but these potential consequences have not been quantified and have been left implicit., Observations: The anticipated consequences of partner resemblance on prevalence and heritability of psychiatric traits in the offspring generation were modeled for disorders with varying heritabilities, population prevalence (lifetime risk), and magnitudes of partner resemblance. These models facilitate interpretation for a wide range of psychiatric disorders, such as autism, schizophrenia, and depression. The genetic consequences of partner resemblance are most pronounced when attributable to phenotypic assortment (driven by the psychiatric trait). Phenotypic assortment results in increased genetic variance in the offspring generation, which may result in increased heritability and population prevalence. These consequences add generation after generation to a limit, but assortative mating is unlikely to balance the impact of reduced fecundity of patients with psychiatric disorders in the long term. This modeling suggests that the heritabilities of psychiatric disorders are unlikely to increase by more than 5% from 1 generation of assortative mating (maximally 13% across multiple generations). The population prevalence will increase most for less common disorders with high heritability; for example, the prevalence of autism might increase by 1.5-fold after 1 generation of assortative mating (≥2.4-fold in the long term) depending on several assumptions., Conclusions and Relevance: The considerable partner resemblances found for psychiatric disorders deserve more detailed interpretation than has been provided thus far. Although the limitations of modeling are emphasized, the anticipated consequences are at most modest for the heritability but may be considerable for the population prevalence of rare disorders with a high heritability.
Published: 2016
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39. Risk of psychiatric illness from advanced paternal age is not predominantly from de novo mutations.

Author: Gratten J, Wray NR, Peyrot WJ, McGrath JJ, Visscher PM, and Goddard ME
Subjects: Age Factors, Child, Humans, Male, Risk Factors, Autistic Disorder etiology, Genetic Markers genetics, Mutation genetics, Paternal Age, Schizophrenia etiology
Abstract: The offspring of older fathers have higher risk of psychiatric disorders such as schizophrenia and autism. Paternal-age-related de novo mutations are widely assumed to be the underlying causal mechanism, and, although such mutations must logically make some contribution, there are alternative explanations (for example, elevated liability to psychiatric illness may delay fatherhood). We used population genetic models based on empirical observations of key parameters (for example, mutation rate, prevalence, and heritability) to assess the genetic relationship between paternal age and risk of psychiatric illness. These models suggest that age-related mutations are unlikely to explain much of the increased risk of psychiatric disorders in children of older fathers. Conversely, a model incorporating a weak correlation between age at first child and liability to psychiatric illness matched epidemiological observations. Our results suggest that genetic risk factors shared by older fathers and their offspring are a credible alternative explanation to de novo mutations for risk to children of older fathers.
Published: 2016
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40. Genome-wide association study identifies 74 loci associated with educational attainment.

Author: Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, Turley P, Chen GB, Emilsson V, Meddens SF, Oskarsson S, Pickrell JK, Thom K, Timshel P, de Vlaming R, Abdellaoui A, Ahluwalia TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Pina Concas M, Derringer J, Furlotte NA, Galesloot TE, Girotto G, Gupta R, Hall LM, Harris SE, Hofer E, Horikoshi M, Huffman JE, Kaasik K, Kalafati IP, Karlsson R, Kong A, Lahti J, van der Lee SJ, deLeeuw C, Lind PA, Lindgren KO, Liu T, Mangino M, Marten J, Mihailov E, Miller MB, van der Most PJ, Oldmeadow C, Payton A, Pervjakova N, Peyrot WJ, Qian Y, Raitakari O, Rueedi R, Salvi E, Schmidt B, Schraut KE, Shi J, Smith AV, Poot RA, St Pourcain B, Teumer A, Thorleifsson G, Verweij N, Vuckovic D, Wellmann J, Westra HJ, Yang J, Zhao W, Zhu Z, Alizadeh BZ, Amin N, Bakshi A, Baumeister SE, Biino G, Bønnelykke K, Boyle PA, Campbell H, Cappuccio FP, Davies G, De Neve JE, Deloukas P, Demuth I, Ding J, Eibich P, Eisele L, Eklund N, Evans DM, Faul JD, Feitosa MF, Forstner AJ, Gandin I, Gunnarsson B, Halldórsson BV, Harris TB, Heath AC, Hocking LJ, Holliday EG, Homuth G, Horan MA, Hottenga JJ, de Jager PL, Joshi PK, Jugessur A, Kaakinen MA, Kähönen M, Kanoni S, Keltigangas-Järvinen L, Kiemeney LA, Kolcic I, Koskinen S, Kraja AT, Kroh M, Kutalik Z, Latvala A, Launer LJ, Lebreton MP, Levinson DF, Lichtenstein P, Lichtner P, Liewald DC, Loukola A, Madden PA, Mägi R, Mäki-Opas T, Marioni RE, Marques-Vidal P, Meddens GA, McMahon G, Meisinger C, Meitinger T, Milaneschi Y, Milani L, Montgomery GW, Myhre R, Nelson CP, Nyholt DR, Ollier WE, Palotie A, Paternoster L, Pedersen NL, Petrovic KE, Porteous DJ, Räikkönen K, Ring SM, Robino A, Rostapshova O, Rudan I, Rustichini A, Salomaa V, Sanders AR, Sarin AP, Schmidt H, Scott RJ, Smith BH, Smith JA, Staessen JA, Steinhagen-Thiessen E, Strauch K, Terracciano A, Tobin MD, Ulivi S, Vaccargiu S, Quaye L, van Rooij FJ, Venturini C, Vinkhuyzen AA, Völker U, Völzke H, Vonk JM, Vozzi D, Waage J, Ware EB, Willemsen G, Attia JR, Bennett DA, Berger K, Bertram L, Bisgaard H, Boomsma DI, Borecki IB, Bültmann U, Chabris CF, Cucca F, Cusi D, Deary IJ, Dedoussis GV, van Duijn CM, Eriksson JG, Franke B, Franke L, Gasparini P, Gejman PV, Gieger C, Grabe HJ, Gratten J, Groenen PJ, Gudnason V, van der Harst P, Hayward C, Hinds DA, Hoffmann W, Hyppönen E, Iacono WG, Jacobsson B, Järvelin MR, Jöckel KH, Kaprio J, Kardia SL, Lehtimäki T, Lehrer SF, Magnusson PK, Martin NG, McGue M, Metspalu A, Pendleton N, Penninx BW, Perola M, Pirastu N, Pirastu M, Polasek O, Posthuma D, Power C, Province MA, Samani NJ, Schlessinger D, Schmidt R, Sørensen TI, Spector TD, Stefansson K, Thorsteinsdottir U, Thurik AR, Timpson NJ, Tiemeier H, Tung JY, Uitterlinden AG, Vitart V, Vollenweider P, Weir DR, Wilson JF, Wright AF, Conley DC, Krueger RF, Davey Smith G, Hofman A, Laibson DI, Medland SE, Meyer MN, Yang J, Johannesson M, Visscher PM, Esko T, Koellinger PD, Cesarini D, and Benjamin DJ
Subjects: Alzheimer Disease genetics, Bipolar Disorder genetics, Cognition, Computational Biology, Gene-Environment Interaction, Humans, Molecular Sequence Annotation, Schizophrenia genetics, United Kingdom, Brain metabolism, Educational Status, Fetus metabolism, Gene Expression Regulation genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide genetics
Abstract: Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.
Published: 2016
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41. Polygenic dissection of major depression clinical heterogeneity.

Author: Milaneschi Y, Lamers F, Peyrot WJ, Abdellaoui A, Willemsen G, Hottenga JJ, Jansen R, Mbarek H, Dehghan A, Lu C, Boomsma DI, and Penninx BW
Subjects: Adult, Bipolar Disorder genetics, Bipolar Disorder metabolism, Body Mass Index, C-Reactive Protein metabolism, Depressive Disorder, Major metabolism, Female, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Male, Meta-Analysis as Topic, Middle Aged, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide, Risk Factors, Triglycerides metabolism, Depressive Disorder, Major genetics
Abstract: The molecular mechanisms underlying major depressive disorder (MDD) are largely unknown. Limited success of previous genetics studies may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the polygenic features of MDD and two common clinical subtypes (typical and atypical) defined by symptom profiles in a large sample of adults with established diagnoses. Data were from 1530 patients of the Netherlands Study of Depression and Anxiety (NESDA) and 1700 controls mainly from the Netherlands Twin Register (NTR). Diagnoses of MDD and its subtypes were based on DSM-IV symptoms. Genetic overlap of MDD and subtypes with psychiatric (MDD, bipolar disorder, schizophrenia) and metabolic (body mass index (BMI), C-reactive protein, triglycerides) traits was evaluated via genomic profile risk scores (GPRS) generated from meta-analysis results of large international consortia. Single nucleotide polymorphism (SNP)-heritability of MDD and subtypes was also estimated. MDD was associated with psychiatric GPRS, while no association was found for GPRS of metabolic traits. MDD subtypes had differential polygenic signatures: typical was strongly associated with schizophrenia GPRS (odds ratio (OR)=1.54, P=7.8e-9), while atypical was additionally associated with BMI (OR=1.29, P=2.7e-4) and triglycerides (OR=1.21, P=0.006) GPRS. Similar results were found when only the highly discriminatory symptoms of appetite/weight were used to define subtypes. SNP-heritability was 32% for MDD, 38% and 43% for subtypes with, respectively, decreased (typical) and increased (atypical) appetite/weight. In conclusion, MDD subtypes are characterized by partially distinct polygenic liabilities and may represent more homogeneous phenotypes. Disentangling MDD heterogeneity may help the psychiatric field moving forward in the search for molecular roots of depression.
Published: 2016
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42. Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease.

Author: Peyrot WJ, Boomsma DI, Penninx BW, and Wray NR
Subjects: Alleles, Depressive Disorder, Major diagnosis, Depressive Disorder, Major genetics, Gene Frequency, Genetic Loci, Genetic Markers, Genetic Predisposition to Disease, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Genetic Association Studies methods
Abstract: Genome-wide association studies (GWASs) are an optimal design for discovery of disease risk loci for diseases whose underlying genetic architecture includes many common causal loci of small effect (a polygenic architecture). We consider two designs that deserve careful consideration if the true underlying genetic architecture of the trait is polygenic: parent-offspring trios and unscreened control subjects. We assess these designs in terms of quantification of the total contribution of genome-wide genetic markers to disease risk (SNP heritability) and power to detect an associated risk allele. First, we show that trio designs should be avoided when: (1) the disease has a lifetime risk > 1%; (2) trio probands are ascertained from families with more than one affected sibling under which scenario the SNP heritability can drop by more than 50% and power can drop as much as from 0.9 to 0.15 for a sample of 20,000 subjects; or (3) assortative mating occurs (spouse correlation of the underlying liability to the disorder), which decreases the SNP heritability but not the power to detect a single locus in the trio design. Some studies use unscreened rather than screened control subjects because these can be easier to collect; we show that the estimated SNP heritability should then be scaled by dividing by (1 - K × u)(2) for disorders with population prevalence K and proportion of unscreened control subjects u. When omitting to scale appropriately, the SNP heritability of, for example, major depressive disorder (K = 0.15) would be underestimated by 28% when none of the control subjects are screened., (Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Published: 2016
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43. The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects.

Author: Peyrot WJ, Lee SH, Milaneschi Y, Abdellaoui A, Byrne EM, Esko T, de Geus EJ, Hemani G, Hottenga JJ, Kloiber S, Levinson DF, Lucae S, Martin NG, Medland SE, Metspalu A, Milani L, Noethen MM, Potash JB, Rietschel M, Rietveld CA, Ripke S, Shi J, Willemsen G, Zhu Z, Boomsma DI, Wray NR, and Penninx BW
Subjects: Adult, Aged, Cohort Studies, Estonia epidemiology, Female, Gene-Environment Interaction, Genetic Association Studies, Genotype, Humans, Likelihood Functions, Male, Middle Aged, Netherlands epidemiology, Odds Ratio, Psychiatric Status Rating Scales, Regression Analysis, Depressive Disorder, Major epidemiology, Depressive Disorder, Major genetics, Depressive Disorder, Major psychology, Educational Status, Polymorphism, Single Nucleotide genetics
Abstract: An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.
Published: 2015
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44. Glucocorticoid and mineralocorticoid receptor polymorphisms and recurrence of major depressive disorder.

Author: Hardeveld F, Spijker J, Peyrot WJ, de Graaf R, Hendriks SM, Nolen WA, Penninx BW, and Beekman AT
Subjects: Adolescent, Adult, Adult Survivors of Child Adverse Events psychology, Aged, Cohort Studies, Depressive Disorder, Major psychology, Female, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Prospective Studies, Recurrence, Stress, Psychological genetics, Stress, Psychological psychology, Young Adult, Depressive Disorder, Major genetics, Receptors, Glucocorticoid genetics, Receptors, Mineralocorticoid genetics
Abstract: Objective: Previous research found that variants of the glucocorticoid receptor (GR) (9β, ER22/23EK, BclI, TthIIIl, NR3C1-1 and N363S) and mineralocorticoid receptor (MR) gene polymorphism (-2 C/G and I180V) are associated with both glucocorticoid (GC) sensitivity and major depressive disorder (MDD). There are no data which investigated prospectively whether these variants are associated with recurrence of MDD., Methods: Data were derived from the Netherlands Study of Depression and Anxiety (NESDA) which used the Composite International Diagnostic Interview (CIDI) to determine MDD. Polymorphisms in the GR and MR gene were determined and haplotypes were characterized. We analyzed in retrospect whether recurrent MDD (n=951) in comparison with first onset MDD (n=919) was associated with polymorphisms in the GR and MR gene. Furthermore, we analyzed prospectively for 4 years the time to recurrence among 683 subjects with a remitted MDD diagnosis. Time to recurrence of MDD was assessed using the CIDI and a life chart interview. Additionally, we analyzed interactions of the investigated polymorphisms with childhood trauma and recent negative life events., Results: GR and MR gene polymorphisms and derived haplotypes were not associated with recurrence of depression in both retrospective and prospective analyses. In addition, no consistent interactions between GR and MR polymorphisms and childhood trauma or life events were found., Conclusion: This study did not find consistent associations between GR and MR gene polymorphisms, interactions between GR and MR haplotypes and stressful conditions and recurrence of MDD., (Copyright © 2015 Elsevier Ltd. All rights reserved.)
Published: 2015
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45. Effect of polygenic risk scores on depression in childhood trauma.

Author: Peyrot WJ, Milaneschi Y, Abdellaoui A, Sullivan PF, Hottenga JJ, Boomsma DI, and Penninx BW
Subjects: Adult, Case-Control Studies, Child, Depressive Disorder, Major genetics, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Humans, Longitudinal Studies, Male, Middle Aged, Netherlands epidemiology, Risk Assessment, Risk Factors, Adult Survivors of Child Abuse statistics & numerical data, Child Abuse statistics & numerical data, Depressive Disorder, Major epidemiology
Abstract: Background: Research on gene × environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic., Aims: To test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma., Method: The study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium., Results: The polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves., Conclusions: The interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD., (Royal College of Psychiatrists.)
Published: 2014
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46. Collaborative stepped care v. care as usual for common mental disorders: 8-month, cluster randomised controlled trial.

Author: Oosterbaan DB, Verbraak MJ, Terluin B, Hoogendoorn AW, Peyrot WJ, Muntingh A, and van Balkom AJ
Subjects: Adult, Anxiety Disorders psychology, Depressive Disorder psychology, Female, Follow-Up Studies, Humans, Male, Middle Aged, Primary Health Care, Treatment Outcome, Anxiety Disorders therapy, Cognitive Behavioral Therapy methods, Depressive Disorder therapy, Patient Care Team, Self Care
Abstract: Background: Thus far collaborative stepped care (CSC) studies have not incorporated self-help as a first step., Aims: To evaluate the effectiveness of CSC in the treatment of common mental disorders., Method: An 8-month cluster randomised controlled trial comparing CSC to care as usual (CAU) (Dutch Trial Register identifier NTR1224). The CSC consisted of a stepped care approach guided by a psychiatric nurse in primary care with the addition of antidepressants dependent on the severity of the disorder, followed by cognitive-behavioural therapy in mental healthcare., Results: Twenty general practitioners (GPs) and 8 psychiatric nurses were randomised to provide CSC or CAU. The GPs recruited 163 patients of whom 85% completed the post-test measurements. At 4-month mid-test CSC was superior to CAU: 74.7% (n = 68) v. 50.8% (n = 31) responders (P = 0.003). At 8-month post-test and 12-month follow-up no significant differences were found as the patients in the CAU group improved as well., Conclusions: Treatment within a CSC model resulted in an earlier treatment response compared with CAU.
Published: 2013
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47. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.

Author: Rietveld CA, Medland SE, Derringer J, Yang J, Esko T, Martin NW, Westra HJ, Shakhbazov K, Abdellaoui A, Agrawal A, Albrecht E, Alizadeh BZ, Amin N, Barnard J, Baumeister SE, Benke KS, Bielak LF, Boatman JA, Boyle PA, Davies G, de Leeuw C, Eklund N, Evans DS, Ferhmann R, Fischer K, Gieger C, Gjessing HK, Hägg S, Harris JR, Hayward C, Holzapfel C, Ibrahim-Verbaas CA, Ingelsson E, Jacobsson B, Joshi PK, Jugessur A, Kaakinen M, Kanoni S, Karjalainen J, Kolcic I, Kristiansson K, Kutalik Z, Lahti J, Lee SH, Lin P, Lind PA, Liu Y, Lohman K, Loitfelder M, McMahon G, Vidal PM, Meirelles O, Milani L, Myhre R, Nuotio ML, Oldmeadow CJ, Petrovic KE, Peyrot WJ, Polasek O, Quaye L, Reinmaa E, Rice JP, Rizzi TS, Schmidt H, Schmidt R, Smith AV, Smith JA, Tanaka T, Terracciano A, van der Loos MJ, Vitart V, Völzke H, Wellmann J, Yu L, Zhao W, Allik J, Attia JR, Bandinelli S, Bastardot F, Beauchamp J, Bennett DA, Berger K, Bierut LJ, Boomsma DI, Bültmann U, Campbell H, Chabris CF, Cherkas L, Chung MK, Cucca F, de Andrade M, De Jager PL, De Neve JE, Deary IJ, Dedoussis GV, Deloukas P, Dimitriou M, Eiríksdóttir G, Elderson MF, Eriksson JG, Evans DM, Faul JD, Ferrucci L, Garcia ME, Grönberg H, Guðnason V, Hall P, Harris JM, Harris TB, Hastie ND, Heath AC, Hernandez DG, Hoffmann W, Hofman A, Holle R, Holliday EG, Hottenga JJ, Iacono WG, Illig T, Järvelin MR, Kähönen M, Kaprio J, Kirkpatrick RM, Kowgier M, Latvala A, Launer LJ, Lawlor DA, Lehtimäki T, Li J, Lichtenstein P, Lichtner P, Liewald DC, Madden PA, Magnusson PK, Mäkinen TE, Masala M, McGue M, Metspalu A, Mielck A, Miller MB, Montgomery GW, Mukherjee S, Nyholt DR, Oostra BA, Palmer LJ, Palotie A, Penninx BW, Perola M, Peyser PA, Preisig M, Räikkönen K, Raitakari OT, Realo A, Ring SM, Ripatti S, Rivadeneira F, Rudan I, Rustichini A, Salomaa V, Sarin AP, Schlessinger D, Scott RJ, Snieder H, St Pourcain B, Starr JM, Sul JH, Surakka I, Svento R, Teumer A, Tiemeier H, van Rooij FJ, Van Wagoner DR, Vartiainen E, Viikari J, Vollenweider P, Vonk JM, Waeber G, Weir DR, Wichmann HE, Widen E, Willemsen G, Wilson JF, Wright AF, Conley D, Davey-Smith G, Franke L, Groenen PJ, Hofman A, Johannesson M, Kardia SL, Krueger RF, Laibson D, Martin NG, Meyer MN, Posthuma D, Thurik AR, Timpson NJ, Uitterlinden AG, van Duijn CM, Visscher PM, Benjamin DJ, Cesarini D, and Koellinger PD
Subjects: Cognition, Endophenotypes, Female, Genetic Loci, Humans, Male, Multifactorial Inheritance, Educational Status, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract: A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.
Published: 2013
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48. Strong effects of environmental factors on prevalence and course of major depressive disorder are not moderated by 5-HTTLPR polymorphisms in a large Dutch sample.

Author: Peyrot WJ, Middeldorp CM, Jansen R, Smit JH, de Geus EJ, Hottenga JJ, Willemsen G, Vink JM, Virding S, Barragan I, Ingelman-Sundberg M, Sim SC, Boomsma DI, and Penninx BW
Subjects: Adult, Adult Survivors of Child Abuse psychology, Case-Control Studies, Educational Status, Female, Haplotypes, Humans, Longitudinal Studies, Male, Middle Aged, Netherlands epidemiology, Polymorphism, Single Nucleotide, Prevalence, Risk Factors, Sex Offenses psychology, Depressive Disorder, Major epidemiology, Depressive Disorder, Major genetics, Gene-Environment Interaction, Life Change Events, Serotonin Plasma Membrane Transport Proteins genetics
Abstract: Background: There is ongoing interest in the possible interaction of the serotonin-transporter-linked polymorphic region (5-HTTLPR) with environmental factors in determining Major Depressive Disorder (MDD). The current study contributes to this research area by comprehensively examining the interaction-effects and direct-effects of 5-HTTLPR and five environmental factors on MDD prevalence and course in a well-characterized longitudinal sample., Methods: The sample consisted of 1625 patients with a CIDI-confirmed diagnosis of MDD and 1698 screened controls from the Netherlands. Four MDD outcomes were studied as dependent variables: one main MDD prevalence-outcome (all MDD), two more severe MDD prevalence-outcomes (suicidal and chronic MDD), and one MDD course outcome (chronic versus non-chronic MDD). Because SNP rs25531 modifies the effect of 5-HTTLPR, haplotypes of 5-HTTLPR and rs25531 were measured. For the four MDD outcome measures, we examined the direct effects of 5-HTTLPR/rs25531-haplotypes, five environmental factors (lifetime and recent stressful life-events, sexual abuse, low educational attainment, and childhood trauma) and their interaction in logistic regression models., Results: The environmental factors had large and consistent effects on all four MDD outcomes, including course of MDD. The 5-HTTLPR/rs25531-haplotype had a suggestive effect on course of MDD, but not on presence of MDD. Gene-by-environment interaction was significant (<0.05) for one of the 20 tests performed, which is not more than expected by chance., Limitations: Environmental factors were not assessed before the onset of MDD., Conclusions: Environmental factors had a strong impact on the presence and course of MDD, but no evidence for gene-by-environment interaction was found., (Copyright © 2012 Elsevier B.V. All rights reserved.)
Published: 2013
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49. Genomic aberrations relate early and advanced stage ovarian cancer.

Author: Zaal A, Peyrot WJ, Berns PM, van der Burg ME, Veerbeek JH, Trimbos JB, Cadron I, van Diest PJ, van Wieringen WN, Krijgsman O, Meijer GA, Piek JM, Timmers PJ, Vergote I, Verheijen RH, Ylstra B, and Zweemer RP
Subjects: Adult, Aged, Aged, 80 and over, Cluster Analysis, Comparative Genomic Hybridization, Disease-Free Survival, Female, Gene Dosage genetics, Humans, Kaplan-Meier Estimate, Middle Aged, Neoplasm Staging, Chromosome Aberrations, Genome, Human genetics, Ovarian Neoplasms genetics, Ovarian Neoplasms pathology
Abstract: Background: Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events., Methods: Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations., Results: The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p = 0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p = 0.001), and that did differ significantly in survival (p = 0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade., Conclusion: This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients.
Published: 2012
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