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205 results on '"Bettella, F."'

1. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci

Author

Andreassen, OA, Harbo, HF, Wang, Y, Thompson, WK, Schork, AJ, Mattingsdal, M, Zuber, V, Bettella, F, Ripke, S, Kelsoe, JR, Kendler, KS, O'Donovan, MC, Sklar, P, McEvoy, LK, Desikan, RS, Lie, BA, Djurovic, S, and Dale, AM

Subjects
Clinical Research, Neurosciences, Multiple Sclerosis, Prevention, Autoimmune Disease, Genetics, Schizophrenia, Bipolar Disorder, Brain Disorders, Human Genome, Mental Health, Serious Mental Illness, Neurodegenerative, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Female, Follow-Up Studies, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, HLA Antigens, Humans, Male, Polymorphism, Single Nucleotide, false discovery rate, HLA region, multiple sclerosis, polygenic pleiotropy, schizophrenia, Psychiatric Genomics Consortium (PGC) Bipolar Disorder and Schizophrenia Work Groups, International Multiple Sclerosis Genetics Consortium, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21,856) and multiple sclerosis (MS) (n=43,879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16,731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility.

Published
2015

5. In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

Author

Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I

Published
2022

6. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics throughMeta-AnalysisBipolar Disorder Working Group

Author

Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I

Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

7. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

Author

Haukvik, U.K., Gurholt, T.P., Nerland, S., Elvsåshagen, T., Akudjedu, T.N., Alda, M., Alnaes, D., Alonso-Lana, S., Bauer, J., Baune, B.T., Benedetti, F. De, Berk, M., Bettella, F., Bøen, E., Bonnín, C.M., Brambilla, P., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Díaz-Zuluaga, A.M., Erp, T.G. van, Fatjó-Vilas, M., Foley, S.F., Förster, K., Fullerton, J.M., Goikolea, J.M., Grotegerd, D., Gruber, O., Haarman, B.C.M., Haatveit, B., Hajek, T., Hallahan, B., Harris, M., Hawkins, E.L., Howells, F.M., Hülsmann, C., Jahanshad, N., Jørgensen, K.N., Kircher, T., Krämer, B., Krug, A., Kuplicki, R., Lagerberg, T.V., Lancaster, T.M., Lenroot, R.K., Lonning, V., López-Jaramillo, C., Malt, U.F., McDonald, C., McIntosh, A.M., McPhilemy, G., Meer, D. van der, Melle, I., Melloni, E.M.T., Mitchell, P.B., Nabulsi, L., Nenadić, I., Oertel, V., Oldani, L., Opel, N., Otaduy, M.C.G., Overs, B.J., Pineda-Zapata, J.A., Pomarol-Clotet, E., Radua, J., Rauer, L., Redlich, R., Repple, J., Rive, M.M., Roberts, G., Ruhe, H.G., Salminen, L.E., Salvador, R., Sarró, S., Savitz, J., Schene, A.H., Sim, K., Soeiro-de-Souza, M.G., Stäblein, M., Stein, D.J., Stein, F., Tamnes, C.K., Temmingh, H.S., Thomopoulos, S.I., Veltman, D.J., Vieta, E., Waltemate, L., Westlye, L.T., Whalley, H.C., Sämann, P.G., Thompson, P.M., Ching, C.R., Andreassen, O.A., Agartz, I., Haukvik, U.K., Gurholt, T.P., Nerland, S., Elvsåshagen, T., Akudjedu, T.N., Alda, M., Alnaes, D., Alonso-Lana, S., Bauer, J., Baune, B.T., Benedetti, F. De, Berk, M., Bettella, F., Bøen, E., Bonnín, C.M., Brambilla, P., Canales-Rodríguez, E.J., Cannon, D.M., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Díaz-Zuluaga, A.M., Erp, T.G. van, Fatjó-Vilas, M., Foley, S.F., Förster, K., Fullerton, J.M., Goikolea, J.M., Grotegerd, D., Gruber, O., Haarman, B.C.M., Haatveit, B., Hajek, T., Hallahan, B., Harris, M., Hawkins, E.L., Howells, F.M., Hülsmann, C., Jahanshad, N., Jørgensen, K.N., Kircher, T., Krämer, B., Krug, A., Kuplicki, R., Lagerberg, T.V., Lancaster, T.M., Lenroot, R.K., Lonning, V., López-Jaramillo, C., Malt, U.F., McDonald, C., McIntosh, A.M., McPhilemy, G., Meer, D. van der, Melle, I., Melloni, E.M.T., Mitchell, P.B., Nabulsi, L., Nenadić, I., Oertel, V., Oldani, L., Opel, N., Otaduy, M.C.G., Overs, B.J., Pineda-Zapata, J.A., Pomarol-Clotet, E., Radua, J., Rauer, L., Redlich, R., Repple, J., Rive, M.M., Roberts, G., Ruhe, H.G., Salminen, L.E., Salvador, R., Sarró, S., Savitz, J., Schene, A.H., Sim, K., Soeiro-de-Souza, M.G., Stäblein, M., Stein, D.J., Stein, F., Tamnes, C.K., Temmingh, H.S., Thomopoulos, S.I., Veltman, D.J., Vieta, E., Waltemate, L., Westlye, L.T., Whalley, H.C., Sämann, P.G., Thompson, P.M., Ching, C.R., Andreassen, O.A., and Agartz, I.

Abstract

Contains fulltext : 252169.pdf (Publisher’s version ) (Open Access), The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

11. Genetic control of variability in subcortical and intracranial volumes

Author

Córdova-Palomera, A., Meer, D. van der, Kaufmann, T., Bettella, F., Wang, Y., Alnæs, D., Doan, N.T., Agartz, I., Bertolino, A., Buitelaar, J.K., Coynel, D., Djurovic, S., Dørum, E.S., Espeseth, T., Fazio, L., Franke, B., Frei, O., Håberg, A., Hellard, S. Le, Jönsson, E.G., Kolskår, K.K., Lund, M.J., Moberget, T., Nordvik, J.E., Nyberg, L., Papassotiropoulos, A., Pergola, G., Quervain, D. de, Rampino, A., Richard, G., Rokicki, J., Sanders, A.M., Schwarz, E., Smeland, O.B., Steen, V.M., Starrfelt, J., Sønderby, I.E., Ulrichsen, K.M., Andreassen, O.A., Westlye, L.T., Córdova-Palomera, A., Meer, D. van der, Kaufmann, T., Bettella, F., Wang, Y., Alnæs, D., Doan, N.T., Agartz, I., Bertolino, A., Buitelaar, J.K., Coynel, D., Djurovic, S., Dørum, E.S., Espeseth, T., Fazio, L., Franke, B., Frei, O., Håberg, A., Hellard, S. Le, Jönsson, E.G., Kolskår, K.K., Lund, M.J., Moberget, T., Nordvik, J.E., Nyberg, L., Papassotiropoulos, A., Pergola, G., Quervain, D. de, Rampino, A., Richard, G., Rokicki, J., Sanders, A.M., Schwarz, E., Smeland, O.B., Steen, V.M., Starrfelt, J., Sønderby, I.E., Ulrichsen, K.M., Andreassen, O.A., and Westlye, L.T.

Abstract

Item does not contain fulltext, Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.

Published
2021

15. Common brain disorders are associated with heritable patterns of apparent aging of the brain

Author

Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnaes, D., Barch, D. M., Baur-Streubel, R., Bertolino, A., Bettella, F., Beyer, M. K., Boen, E., Borgwardt, S., Brandt, C. L., Buitelaar, J., Celius, E. G., Cervenka, S., Conzelmann, A., Cordova-Palomera, A., Dale, A. M., de Quervain, D. J. F., Carlo, P. D., Djurovic, S., Dorum, E. S., Eisenacher, S., Elvsashagen, T., Espeseth, T., Fatouros-Bergman, H., Flyckt, L., Franke, B., Frei, O., Haatveit, B., Haberg, A. K., Harbo, H. F., Hartman, C. A., Heslenfeld, D., Hoekstra, P. J., Hogestol, E. A., Jernigan, T. L., Jonassen, R., Jonsson, E. G., Farde, L., Engberg, G., Erhardt, S., Schwieler, L., Piehl, F., Collste, K., Victorsson, P., Malmqvist, A., Hedberg, M., Orhan, F., Kirsch, P., Kloszewska, I., Kolskar, K. K., Landro, N. I., Hellard, S. L., Lesch, K. -P., Lovestone, S., Lundervold, A., Lundervold, A. J., Maglanoc, L. A., Malt, U. F., Mecocci, P., Melle, I., Meyer-Lindenberg, A., Moberget, T., Norbom, L. B., Nordvik, J. E., Nyberg, L., Oosterlaan, J., Papalino, M., Papassotiropoulos, A., Pauli, P., Pergola, G., Persson, K., Richard, G., Rokicki, J., Sanders, A. -M., Selbaek, G., Shadrin, A. A., Smeland, O. B., Soininen, H., Sowa, P., Steen, V. M., Tsolaki, M., Ulrichsen, K. M., Vellas, B., Wang, L., Westman, E., Ziegler, G. C., Zink, M., Andreassen, O. A., Westlye, L. T., Le Hellard, S., Di Carlo, P., Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), General Paediatrics, ARD - Amsterdam Reproduction and Development, Pediatric surgery, Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, and RS: MHeNs - R2 - Mental Health

Subjects
0301 basic medicine, Brain age gap, brain disorders, genetic architecture, pleiotropy, Male, Aging, Schizophrenia/genetics, LOCI, Genome-wide association study, Neuropsychological Tests, 0302 clinical medicine, 80 and over, Brain age gap, Aging/genetics, ALZHEIMERS, Child, Aged, 80 and over, Brain Diseases, Sex Characteristics, General Neuroscience, Mental Disorders, Brain, MULTIPLE-SCLEROSIS, brain disorders, Middle Aged, Magnetic Resonance Imaging, 3. Good health, Schizophrenia, Child, Preschool, Female, Alzheimer's disease, Neurovetenskaper, Algorithms, MRI, Adult, Adolescent, Brain Diseases/diagnostic imaging, Brain Structure and Function, Mental Disorders/diagnostic imaging, Biology, Article, 03 medical and health sciences, Young Adult, AGE, pleiotropy, Genetic architecture, Genetic Pleiotropy, medicine, Humans, Preschool, Brain disorders, Aged, Pleiotropy, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Brain age gaps, Multiple sclerosis, Neurosciences, Infant, medicine.disease, Brain/diagnostic imaging, genetic architecture, 030104 developmental biology, Pleiotropy (drugs), Neuroscience, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3–96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders. The author list between I.A. and M.Z. is in alphabetic order. The authors were funded by the Research Council of Norway (276082 LifespanHealth (T.K.), 213837 (O.A.A), 223273 NORMENT (O.A.A.), 204966 (L.T.W.), 229129 (O.A.A.), 249795 (L.T.W.), 273345 (L.T.W.) and 283798 SYNSCHIZ (O.A.A.)), the South-Eastern Norway Regional Health Authority (2013-123 (O.A.A.), 2014-097 (L.T.W.), 2015-073 (L.T.W.) and 2016083 (L.T.W.)), Stiftelsen Kristian Gerhard Jebsen (SKGJ-MED-008), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting Grant, Grant agreement No. 802998 BRAINMINT (L.T.W.)), NVIDIA Corporation GPU Grant (T.K.), and the European Commission 7th Framework Programme (602450, IMAGEMEND (A.M.-L.)).

Published
2019
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16. Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories

Author

Shahram Bahrami, Oleksandr Frei, Bettella F, Alexey A. Shadrin, Uggen Tke, Olav B. Smeland, Andreassen Oa, Srdjan Djurovic, Anders M. Dale, Dominic Holland, Kevin S. O’Connell, and Osman A. B. S. M. Gani

Subjects
0303 health sciences, Computational biology, Biology, Genome, Phenotype, 03 medical and health sciences, Annotation, 0302 clinical medicine, Functional annotation, Genetic etiology, Mixture modeling, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Determining the contribution of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here we present Annotation Informed MiXeR: a likelihood-based method to estimate the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Applying the model to 11 complex phenotypes suggests diverse patterns of functional category-specific genetic architectures across human diseases and traits.

Published
2019
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17. Identification of common genetic risk variants for autism spectrum disorder

Author

Grove, J., Ripke, S., Als, T.D., Mattheisen, M., Walters, R.K., Won, H., Pallesen, J., Agerbo, E., Andreassen, O.A., Anney, R., Awashti, S., Belliveau, R., Bettella, F., Buxbaum, J.D., Bybjerg-Grauholm, J., Baekvad-Hansen, M., Cerrato, F., Chambert, K., Christensen, J.H., Churchhouse, C., Dellenvall, K., Demontis, D., Rubeis, S. de, Devlin, B., Djurovic, S., Dumont, A.L., Goldstein, J.I., Hansen, C.S., Hauberg, M.E., Hollegaard, M.V., Hope, S., Howrigan, D.P., Huang, H., Hultman, C.M., Klei, L., Maller, J., Martin, J., Martin, A.R., Moran, J.L., Nyegaard, M., Naeland, T., Palmer, D.S., Palotie, A., Pedersen, C.B., Pedersen, M.G., dPoterba, T., Poulsen, J.B., St Pourcain, B., Qvist, P., Rehnstrom, K., Reichenberg, A., Reichert, J., Robinson, E.B., Roeder, K., Roussos, P., Saemundsen, E., Sandin, S., Satterstrom, F.K., Smith, G.D., Stefansson, H., Steinberg, S., Stevens, C.R., Sullivan, P.F., Turley, P., Walters, G.B., Xu, X.Y., Stefansson, K., Geschwind, D.H., Nordentoft, M., Hougaard, D.M., Werge, T., Mors, O., Mortensen, P.B., Neale, B.M., Daly, M.J., Borglum, A.D., Wray, N.R., Trzaskowski, M., Byrne, E.M., Abdellaoui, A., Adams, M.J., Air, T.M., Andlauer, T.F.M., Bacanu, S.A., Beekman, A.T.F., Bigdeli, T.B., Binder, E.B., Blackwood, D.H.R., Bryois, J., Buttenschon, H.N., Cai, N., Castelao, E., Clarke, T.K., Coleman, J.R.I., Colodro-Conde, L., Couvy-Duchesne, B., Craddock, N., Crawford, G.E., Davies, G., Deary, I.J., Degenhardt, F., Derks, E.M., Direk, N., Dolan, C.V., Dunn, E.C., Eley, T.C., Escott-Price, V., Kiadeh, F.F.H., Finucane, H.K., Forstner, A.J., Frank, J., Gaspar, H.A., Gill, M., Goes, F.S., Gordon, S.D., Hall, L.S., Hansen, T.F., Herms, S., Hickie, I.B., Hoffmann, P., Homuth, G., Horn, C., Hottenga, J.J., Ising, M., Jansen, R., Jorgenson, E., Knowles, J.A., Kohane, I.S., Kraft, J., Kretzschmar, W.W., Krogh, J., Kutalik, Z., Li, Y., Lind, P.A., MacIntyre, D.J., MacKinnon, D.F., Maier, R.M., Maier, W., Marchini, J., Mbarek, H., McGrath, P., McGuffin, P., Medland, S.E., Mehta, D., Middeldorp, C.M., Mihailov, E., Milaneschi, Y., Milani, L., Mondimore, F.M., Montgomery, G.W., Mostafavi, S., Mullins, N., Nauck, M., Ng, B., Nivard, M.G., Nyholt, D.R., O'Reilly, P.F., Oskarsson, H., Owen, M.J., Painter, J.N., Peterson, R.E., Pettersson, E., Peyrot, W.J., Pistis, G., Posthuma, D., Quiroz, J.A., Rice, J.P., Riley, B.P., Rivera, M., Mirza, S.S., Schoevers, R., Schulte, E.C., Shen, L., Shi, J.X., Shyn, S.I., Sigurdsson, E., Sinnamon, G.C.B., Smit, J.H., Smith, D.J., Streit, F., Strohmaier, J., Tansey, K.E., Teismann, H., Teumer, A., Thompson, W., Thomson, P.A., Thorgeirsson, T.E., Traylor, M., Treutlein, J., Trubetskoy, V., Uitterlinden, A.G., Umbricht, D., Auwera, S. van der, Hemert, A.M. van, Viktorin, A., Visscher, P.M., Wang, Y.P., Webb, B.T., Weinsheimer, S.M., Wellmann, J., Willemsen, G., Witt, S.H., Wu, Y., Xi, H.S., Yang, J., Zhang, F.T., Arolt, V., Baune, B.T., Berger, K., Boomsma, D.I., Cichon, S., Dannlowski, U., Geus, E.J.C. de, DePaulo, J.R., Domenici, E., Domschke, K., Esko, T., Grabe, H.J., Hamilton, S.P., Hayward, C., Heath, A.C., Kendler, K.S., Kloiber, S., Lewis, G., Li, Q.S., Lucae, S., Madden, P.A.F., Magnusson, P.K., Martin, N.G., McIntosh, A.M., Metspalu, A., Muller-Myhsok, B., Nothen, M.M., O'Donovan, M.C., Paciga, S.A., Pedersen, N.L., Penninx, B.W.J.H., Perlis, R.H., Porteous, D.J., Potash, J.B., Preisig, M., Rietschel, M., Schaefer, C., Schulze, T.G., Smoller, J.W., Tiemeier, H., Uher, R., Volzke, H., Weissman, M.M., Lewis, C.M., Levinson, D.F., Breen, G., Agee, M., Alipanahi, B., Auton, A., Bell, R.K., Bryc, K., Elson, S.L., Fontanillas, P., Furlotte, N.A., Hromatka, B.S., Huber, K.E., Kleinman, A., Litterman, N.K., McIntyre, M.H., Mountain, J.L., Noblin, E.S., Northover, C.A.M., Pitts, S.J., Sathirapongsasuti, J.F., Sazonova, O.V., Shelton, J.F., Shringarpure, S., Tung, J.Y., Vacic, V., Wilson, C.H., Psychiat Genomics Consortium, BUPGEN, 23andMe Re, Biological Psychology, APH - Methodology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Adult Psychiatry, Psychiatry, Human genetics, Amsterdam Reproduction & Development (AR&D), VU University medical center, APH - Digital Health, Aarno Palotie / Principal Investigator, Institute for Molecular Medicine Finland, Genomics of Neurological and Neuropsychiatric Disorders, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Me Research Team, Epidemiology, and Child and Adolescent Psychiatry / Psychology

Subjects
Male, Netherlands Twin Register (NTR), Multifactorial Inheritance, Autism Spectrum Disorder, Denmark, LD SCORE REGRESSION, LOCI, Genome-wide association study, DE-NOVO, 0302 clinical medicine, Polymorphism (computer science), Risk Factors, SYNAPTIC PLASTICITY, CELL-SURFACE, Child, Genetics, 0303 health sciences, HERITABILITY, Genetic Predisposition to Disease/genetics, 1184 Genetics, developmental biology, physiology, Polymorphism, Single Nucleotide/genetics, Phenotype, 3. Good health, Schizophrenia, Autism spectrum disorder, Child, Preschool, Genome-Wide Association Study/methods, Female, SIMONS SIMPLEX COLLECTION, Adolescent, Biology, NEURITE OUTGROWTH, Polymorphism, Single Nucleotide, behavioral disciplines and activities, Article, 03 medical and health sciences, mental disorders, medicine, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, SDG 2 - Zero Hunger, Multifactorial Inheritance/genetics, METAANALYSIS, 030304 developmental biology, Case-control study, Heritability, medicine.disease, Autism Spectrum Disorder/genetics, Case-Control Studies, 3111 Biomedicine, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Published in final edited form as: Nat Genet. 2019 March ; 51(3): 431–444. doi:10.1038/s41588-019-0344-8., Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD., The iPSYCH project is funded by the Lundbeck Foundation (grant numbers R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH and PGC samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (SFARI 311789 to MJD), and NIMH (5U01MH094432–02 to MJD). The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by NIMH (1U01MH109514–01 to M O’Donovan and ADB). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to ADB). Drs. S De Rubeis and JD Buxbaum were supported by NIH grants MH097849 (to JDB) and MH111661 (to JDB), and by the Seaver Foundation (to SDR and JDB). Dr J Martin was supported by the Wellcome Trust (grant no: 106047). O. Andreassen received funding from Research Council of Norway (#213694, #223273, #248980, #248778), Stiftelsen KG Jebsen and South-East Norway Health Authority. We thank the research participants and employees of 23andMe for making this work possible.

Published
2019
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19. GBA and APOE epsilon 4 associate with sporadic dementia with Lewy bodies in European genome wide association study (vol 9, 7013, 2019)

Author

Rongve, A, Witoelar, A, Ruiz, A, Athanasiu, L, Abdelnour, C, Clarimon, J, Heilmann-Heimbach, S, Hernandez, I, Moreno-Grau, S, de Rojas, I, Morenas-Rodriguez, E, Fladby, T, Sando, SB, Brathen, G, Blanc, F, Bousiges, O, Lemstra, AW, van Steenoven, I, Londos, E, Almdahl, IS, Palhaugen, L, Eriksen, JA, Djurovic, S, Stordal, E, Saltvedt, I, Ulstein, ID, Bettella, F, Desikan, RS, Idland, AV, Toft, M, Pihlstrom, L, Snaedal, J, Tarraga, L, Boada, M, Lleo, A, Stefansson, H, Stefansson, K, Ramirez, A, Aarsland, D, and Andreassen, OA

Published
2019

20. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder

Author

Schwarz, E., Doan, N. T., Pergola, G., Westlye, L. T., Kaufmann, T., Wolfers, T., Brecheisen, R., Quarto, T., Ing, A. J., Di Carlo, P., Gurholt, T. P., Harms, R. L., Noirhomme, Q., Moberget, T., Agartz, I., Andreassen, O. A., Bellani, M., Bertolino, A., Blasi, G., Brambilla, P., Buitelaar, J. K., Cervenka, S., Flyckt, L., Frangou, S., Franke, B., Hall, J., Heslenfeld, D. J., Kirsch, P., Mcintosh, A. M., Nothen, M. M., Papassotiropoulos, A., de Quervain, D. J. -F., Rietschel, M., Schumann, G., Tost, H., Witt, S. H., Zink, M., Meyer-Lindenberg, A., Bettella, F., Brandt, C. L., Clarke, T. -K., Coynel, D., Degenhardt, F., Djurovic, S., Eisenacher, S., Fastenrath, M., Fatouros-Bergman, H., Forstner, A. J., Frank, J., Gambi, F., Gelao, B., Geschwind, L., Di Giannantonio, M., Di Giorgio, A., Hartman, C. A., Heilmann-Heimbach, S., Herms, S., Hoekstra, P. J., Hoffmann, P., Hoogman, M., Jonsson, E. G., Loos, E., Maggioni, E., Oosterlaan, J., Papalino, M., Rampino, A., Romaniuk, L., Selvaggi, P., Sepede, G., Sonderby, I. E., Spalek, K., Sussmann, J. E., Thompson, P. M., Vasquez, A. A., Vogler, C., Whalley, H., Farde, L., Engberg, G., Erhardt, S., Schwieler, L., Collste, K., Victorsson, P., Malmqvist, A., Hedberg, M., Orhan, F., Cognitive Psychology, IBBA, Behavioural Sciences, Elvira Brattico / Principal Investigator, Department of Psychology and Logopedics, Cognitive Brain Research Unit, Faculty of Medicine, University of Helsinki, General Paediatrics, ARD - Amsterdam Reproduction and Development, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Multiscale Imaging of Brain Connectivity, RS: FPN CN 11, Vision, and RS: FPN CN 1

Subjects
0301 basic medicine, Male, Multivariate statistics, Bipolar Disorder, SEGMENTATION, 3124 Neurology and psychiatry, Machine Learning, 0302 clinical medicine, DEFICITS, Gray Matter, Psychiatry, RISK, medicine.diagnostic_test, 220 Statistical Imaging Neuroscience, LIKELIHOOD ESTIMATION, Middle Aged, MRI SCANS, Magnetic Resonance Imaging, Justice and Strong Institutions, 3. Good health, Psychiatry and Mental health, medicine.anatomical_structure, bipolar disorders, Schizophrenia, Female, brain structural patterns, MRI, Adult, SDG 16 - Peace, Adolescent, Brain Structure and Function, Grey matter, Psykiatri, CLASSIFICATION, Article, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, Young Adult, Text mining, medicine, Humans, Bipolar disorder, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, METAANALYSIS, schizophrenia, grey matter alterations, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, 1ST-EPISODE, SDG 16 - Peace, Justice and Strong Institutions, Magnetic resonance imaging, medicine.disease, 030104 developmental biology, Sample size determination, Attention Deficit Disorder with Hyperactivity, Case-Control Studies, VOLUME, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 202693.pdf (Publisher’s version ) (Open Access) Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

Published
2019
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21. Genetics of brain age suggest an overlap with common brain disorders

Author

Mona K. Beyer, Ramona Baur-Streubel, Asta Håberg, Lundervold A, Barbara Franke, Andreas Meyer-Lindenberg, Mathias Zink, Helena Fatouros-Bergman, Ole A. Andreassen, Anne-Marthe Sanders, Kristine Moe Ulrichsen, Vidar M. Steen, Alessandro Bertolino, Nhat Trung Doan, Bettella F, Jaap Oosterlaan, Iwona Kłoszewska, Eric Westman, Hanne F. Harbo, Patrizia Mecocci, Piotr Sowa, Elisabeth Gulowsen Celius, Erlend Bøen, Luigi Angelo Maglanoc, Marco Papalino, Lu Wang, Le Hellard S, Ingrid Melle, Ingrid Agartz, Oleksandr Frei, Terry L. Jernigan, Olav B. Smeland, Aldo Córdova-Palomera, Jan Egil Nordvik, Torbjørn Elvsåshagen, Andreas Papassotiropoulos, C.A. Hartman, Geir Selbæk, Knut-Kristian Kolskår, Di Carlo P, Geneviève Richard, Karin Persson, Tobias Kaufmann, Peter Kirsch, Anders M. Dale, Thomas Espeseth, Lars Nyberg, Stefan Borgwardt, Martina J. Lund, Pieter J. Hoekstra, Lars T. Westlye, Lena Flyckt, Giulio Pergola, Beathe Haatveit, Annette Conzelmann, Linn B. Norbom, Einar August Høgestøl, Dirk J. Heslenfeld, Klaus-Peter Lesch, Simon Cervenka, Rune Jonassen, Erik G. Jönsson, Srdjan Djurovic, Jan K. Buitelaar, Dag Alnæs, Magda Tsolaki, Sarah Eisenacher, Christine L. Brandt, van der Meer D, Ulrik Fredrik Malt, Jaroslav Rokicki, Simon Lovestone, H. Soininen, Erlend S. Dørum, Arvid Lundervold, Bruno Vellas, Emanuel Schwarz, Torgeir Moberget, Alexey A. Shadrin, de Quervain Dj, Georg C. Ziegler, Paul Pauli, and Nils Inge Landrø

Subjects
0303 health sciences, Multiple sclerosis, Brain Structure and Function, Chronological age, Biology, medicine.disease, Structural magnetic resonance imaging, 03 medical and health sciences, 0302 clinical medicine, Schizophrenia, medicine, Trait, Dementia, Brain aging, Neuroscience, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

Published
2018
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22. Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

Author

Maki-Marttunen, T., Kaufmann, T., Elvsashagen, T., Devor, A., Djurovic, S., Westlye, L.T., Linne, M.L., Rietschel, M., Schubert, D., Borgwardt, S., Efrim-Budisteanu, M., Bettella, F., Halnes, G., Hagen, E., Naess, S., Ness, T.V., Moberget, T., Metzner, C., Edwards, A.G., Fyhn, M., Dale, A.M., Einevoll, G.T., Andreassen, O.A., Maki-Marttunen, T., Kaufmann, T., Elvsashagen, T., Devor, A., Djurovic, S., Westlye, L.T., Linne, M.L., Rietschel, M., Schubert, D., Borgwardt, S., Efrim-Budisteanu, M., Bettella, F., Halnes, G., Hagen, E., Naess, S., Ness, T.V., Moberget, T., Metzner, C., Edwards, A.G., Fyhn, M., Dale, A.M., Einevoll, G.T., and Andreassen, O.A.

Abstract

Contains fulltext : 208684.pdf (publisher's version ) (Open Access), The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field

Published
2019

23. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk

Author

Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, Ida K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., Brækhus, A., Bråthen, G., de Leeuw, C., Desikan, R. S., Djurovic, S., Dumitrescu, L., Fladby, T., Hohman, T. J., Jonsson, P. V., Kiddle, S. J., Rongve, A., Saltvedt, I., Sando, S. B., Selbæk, G., Shoai, M., Skene, N. G., Snaedal, J., Stordal, E., Ulstein, I. D., Wang, Y., White, L. R., Hardy, J., Hjerling-Leffler, J., Sullivan, P. F., van der Flier, W. M., Dobson, R., Davis, L. K., Stefansson, H., Stefansson, K., Pedersen, N. L., Ripke, S., Andreassen, O. A., Posthuma, D., Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, Ida K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., Brækhus, A., Bråthen, G., de Leeuw, C., Desikan, R. S., Djurovic, S., Dumitrescu, L., Fladby, T., Hohman, T. J., Jonsson, P. V., Kiddle, S. J., Rongve, A., Saltvedt, I., Sando, S. B., Selbæk, G., Shoai, M., Skene, N. G., Snaedal, J., Stordal, E., Ulstein, I. D., Wang, Y., White, L. R., Hardy, J., Hjerling-Leffler, J., Sullivan, P. F., van der Flier, W. M., Dobson, R., Davis, L. K., Stefansson, H., Stefansson, K., Pedersen, N. L., Ripke, S., Andreassen, O. A., and Posthuma, D.

Abstract

Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

Published
2019
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24. Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer

Author

Zuber, V., Bettella, F., Witoelar, A.W., Andreassen, O.A., Mills, I.G., Urbanucci, A., Eeles, R.A., Easton, D.F., Kote-Jarai, Z., Al Olama, A.A., Benlloch, S., Muir, K., Giles, G.G., Wiklund, F., Grönberg, H., Haiman, C.A., Schleutker, J., Weischer, M., Travis, R.C., Neal, D., Pharoah, P., Khaw, K.T., Stanford, J.L., Blot, W.J., Thibodeau, S.N., Maier, C., Kibel, A.S., Cybulski, C., Cannon-Albright, L., Brenner, H., Park, J., Kaneva, R., Batra, J., Teixeira, M.R., Pandha, H., Chenevix-Trench, G., Humphreys, M.K., Hung, R.J., Han, Y., Brennan, P., Bickeböller, H., Rosenberger, A., Houlston, R.S., Caporaso, N., Landi, M.T., Heinrich, J., Risch, A., Wu, X., Ye, Y., Christiani, D.C., Amos, C.I., Michailidou, K., Bolla, M.K., Wang, Q., Berchuck, A., Antoniou, A.C., McGuffog, L., Couch, F.J., Offit, K., Dennis, J., Dunning, A.M., Lee, A., Dicks, E., Luccarini, C., Benítez, J., González-Neira, A., Simard, J., Tessier, D.C., Bacot, F., Vincent, D., Laboissiere, S., and Wrightson, R

Subjects
Male, Quantitative Trait Loci, Breast Neoplasms, Cell Cycle Proteins, Brd4, Breast Cancer Risk, Chromatin, Functional Annotation, Genome-wide Association Studies, Prostate Cancer Risk, Risk Loci, Schizophrenia, Snps, Super-enhancer, Genome-wide association studies, Polymorphism, Single Nucleotide, Epigenesis, Genetic, Histones, breast cancer risk, SDG 3 - Good Health and Well-being, super-enhancer, Genetics, Journal Article, Humans, Genetic Predisposition to Disease, Risk loci, Binding Sites, Chromosome Mapping, Computational Biology, Nuclear Proteins, Prostatic Neoplasms, Functional annotation, Prostate cancer risk, schizophrenia, Gene Expression Regulation, Neoplastic, Enhancer Elements, Genetic, Organ Specificity, Receptors, Androgen, BRD4, Female, Research Article, SNPs, Genome-Wide Association Study, Protein Binding, Transcription Factors, Biotechnology
Abstract

Background Epigenetic information can be used to identify clinically relevant genomic variants single nucleotide polymorphisms (SNPs) of functional importance in cancer development. Super-enhancers are cell-specific DNA elements, acting to determine tissue or cell identity and driving tumor progression. Although previous approaches have been tried to explain risk associated with SNPs in regulatory DNA elements, so far epigenetic readers such as bromodomain containing protein 4 (BRD4) and super-enhancers have not been used to annotate SNPs. In prostate cancer (PC), androgen receptor (AR) binding sites to chromatin have been used to inform functional annotations of SNPs. Results Here we establish criteria for enhancer mapping which are applicable to other diseases and traits to achieve the optimal tissue-specific enrichment of PC risk SNPs. We used stratified Q-Q plots and Fisher test to assess the differential enrichment of SNPs mapping to specific categories of enhancers. We find that BRD4 is the key discriminant of tissue-specific enhancers, showing that it is more powerful than AR binding information to capture PC specific risk loci, and can be used with similar effect in breast cancer (BC) and applied to other diseases such as schizophrenia. Conclusions This is the first study to evaluate the enrichment of epigenetic readers in genome-wide associations studies for SNPs within enhancers, and provides a powerful tool for enriching and prioritizing PC and BC genetic risk loci. Our study represents a proof of principle applicable to other diseases and traits that can be used to redefine molecular mechanisms of human phenotypic variation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3620-y) contains supplementary material, which is available to authorized users.

Published
2018
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25. Common risk variants identified in autism spectrum disorder

Author

Julian Maller, Patrick Turley, Benjamin M. Neale, Terje Nærland, Bettella F, Elise B. Robinson, Manuel Mattheisen, A. Palotie, Evald Saemundsen, Jennifer L. Moran, Stacy Steinberg, Hong-Hee Won, E. Agerbo, Daniel P. Howrigan, Mark J. Daly, Srdjan Djurovic, St Pourcain B, Richard Anney, Jacqueline I. Goldstein, Marianne Giørtz Pedersen, Jennifer Reichert, A. Reichenberg, Kimberly Chambert, Bernie Devlin, Felecia Cerrato, Joanna Martin, Satterstrom Fk, Kathryn Roeder, Richard A. Belliveau, Stephan Ripke, De Rubeis S, Robin G. Walters, Hailiang Huang, Mette Nyegaard, Walters Gb, Jakob Grove, Ditte Demontis, Karola Rehnström, David M. Hougaard, Ole Mors, Sigrun Hope, Preben Bo Mortensen, Mads V. Hollegaard, Joseph D. Buxbaum, Sven Sandin, Cathy A. Stevens, Ole A. Andreassen, Duncan Palmer, Jesper Buchhave Poulsen, Carsten Bøcker Pedersen, Patrick F. Sullivan, Thomas Werge, Jane H. Christensen, Christine Søholm Hansen, Timothy Poterba, Jonatan Pallesen, Claire Churchhouse, Thomas Damm Als, Mads E. Hauberg, Anders D. Børglum, Jonas Bybjerg-Grauholm, Lambertus Klei, Kari Stefansson, Alicia R. Martin, Per Qvist, Ashley Dumont, Hreinn Stefansson, Panagiotis Roussos, Karin Dellenvall, George Davey Smith, Daniel H. Geschwind, Marie Bækved-Hansen, Merete Nordentoft, Christina M. Hultman, and Xinyi Xu

Subjects
0303 health sciences, business.industry, medicine.disease, behavioral disciplines and activities, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Text mining, Autism spectrum disorder, mental disorders, Medicine, business, 030217 neurology & neurosurgery, 030304 developmental biology, Clinical psychology
Abstract

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

Published
2017
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26. Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases

Author

Witoelar, A, Jansen, IE, Wang, Y, Desikan, RS, Gibbs, JR, Blauwendraat, C, Thompson, WK, Hernandez, DG, Djurovic, S, Schork, AJ, Bettella, F, Ellinghaus, D, Franke, A, Lie, BA, McEvoy, LK, Karlsen, T, Lesage, S, Morris, HR, Brice, A, Wood, NW, Heutink, P, Hardy, J, Singleton, AB, Dale, AM, Gasser, T, Andreassen, OA, Sharma, M, Conso, IPDG, Amer, BECN, and Co, UKBE

Published
2017
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27. Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer

Author

Zuber, V, Bettella, F, Witoelar, A, Andreassen, OA, Mills, IG, Urbanucci, A, Eeles, R, Easton, D, Kote-Jarai, Z, Al Olama, AA, Benlloch, S, Muir, K, Giles, GG, Wiklund, F, Gronberg, H, Haiman, CA, Schleutker, J, Weischer, M, Travis, RC, Neal, D, Pharoah, P, Khaw, KT, Stanford, JL, Blot, WJ, Thibodeau, S, Maier, C, Kibel, AS, Cybulski, C, Cannon-Albright, L, Brenner, H, Park, J, Kaneva, R, Batra, J, Teixeira, MR, Pandha, H, Chenevix-Trench, G, Humphreys, M, Hung, RJ, Han, Y, Brennan, P, Bickeböller, H, Rosenberger, A, Houlston, RS, Caporaso, N, Landi, MT, Heinrich, J, Risch, A, Wu, X, Ye, Y, Christiani, DC, Amos, CI, Easton, DF, Michailidou, K, Bolla, MK, Wang, Q, Berchuck, A, Antoniou, A, McGuffog, L, Couch, F, Offit, K, Dennis, J, Dunning, AM, Lee, A, Dicks, E, Luccarini, C, Benitez, J, Gonzalez-Neira, A, Simard, J, Tessier, DC, Bacot, F, Vincent, D, LaBoissière, S, Zuber, Verena [0000-0001-9827-1877], Pharoah, Paul [0000-0001-8494-732X], Khaw, Kay-Tee [0000-0002-8802-2903], Easton, Douglas [0000-0003-2444-3247], Wang, Jean [0000-0002-9139-0627], Antoniou, Antonis [0000-0001-9223-3116], Dennis, Joe [0000-0003-4591-1214], Dunning, Alison [0000-0001-6651-7166], Lee, Andrew [0000-0003-0677-0252], Dicks, Ed [0000-0002-0617-0401], and Apollo - University of Cambridge Repository

Subjects
risk loci, schizophrenia, breast cancer risk, super-enhancer, genome-wide association studies, BRD4, chromatin, functional annotation, prostate cancer risk, SNPs
Abstract

$\textbf{Background:}$ Epigenetic information can be used to identify clinically relevant genomic variants single nucleotide polymorphisms (SNPs) of functional importance in cancer development. Super-enhancers are cell-specific DNA elements, acting to determine tissue or cell identity and driving tumor progression. Although previous approaches have been tried to explain risk associated with SNPs in regulatory DNA elements, so far epigenetic readers such as bromodomain containing protein 4 (BRD4) and super-enhancers have not been used to annotate SNPs. In prostate cancer (PC), androgen receptor (AR) binding sites to chromatin have been used to inform functional annotations of SNPs. $\textbf{Results:}$ Here we establish criteria for enhancer mapping which are applicable to other diseases and traits to achieve the optimal tissue-specific enrichment of PC risk SNPs. We used stratified Q-Q plots and Fisher test to assess the differential enrichment of SNPs mapping to specific categories of enhancers. We find that BRD4 is the key discriminant of tissue-specific enhancers, showing that it is more powerful than AR binding information to capture PC specific risk loci, and can be used with similar effect in breast cancer (BC) and applied to other diseases such as schizophrenia. $\textbf{Conclusions:}$ This is the first study to evaluate the enrichment of epigenetic readers in genome-wide associations studies for SNPs within enhancers, and provides a powerful tool for enriching and prioritizing PC and BC genetic risk loci. Our study represents a proof of principle applicable to other diseases and traits that can be used to redefine molecular mechanisms of human phenotypic variation.

Published
2017
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29. Genetic Markers of Human Evolution Are Enriched in Schizophrenia

Author

Srinivasan, S, Bettella, F, Mattingsdal, M, Wang, Y, Witoelar, A, Schork, AJ, Thompson, WK, Zuber, V, Winsvold, BS, Zwart, JA, Collier, DA, Desikan, RS, Melle, I, Werge, T, Dale, AM, Djurovic, S, and Andreassen, OA

Subjects
mental disorders
Abstract

© 2016 Society of Biological Psychiatry Background Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans’ language, creative thinking, and cognitive abilities. Methods We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score. Results Gene loci associated with schizophrenia are significantly (p = 7.30 × 10−9) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (i.e., with a low NSS score). Variants in brain-related genes with a low NSS score confer significantly higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development. Conclusions Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

Published
2016
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30. Psychotic patients who used cannabis frequently before illness onset have higher genetic predisposition to schizophrenia than those who did not

Author

Aas, M., primary, Melle, I., additional, Bettella, F., additional, Djurovic, S., additional, Le Hellard, S., additional, Bjella, T., additional, Ringen, P. A., additional, Lagerberg, T. V., additional, Smeland, O. B., additional, Agartz, I., additional, Andreassen, O. A., additional, and Tesli, M., additional

Published
2017
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31. Genome-wide meta-analysis identifies new susceptibility loci for migraine

Author

Anttila V, Winsvold BS, Gormley P, Kurth T, Bettella F, McMahon G, Kallela M, Malik R, de Vries B, Terwindt G, Medland SE, Todt U, McArdle WL, Quaye L, Koiranen M, Ligthart L, Hottenga JJ, Vink JM, Penninx BW, Boomsma DI, Strachan DP, Kubisch C, Ferrari MD, van den Maagdenberg AM, Dichgans M, Wessman M, Smith GD, Stefansson K, Daly MJ, Nyholt DR, Chasman DI, Palotie A, North American Brain Expression Consortium, UK Brain Expression Consortium, International Headache Genetics Consortium, Anttila V, Winsvold BS, Gormley P, Kurth T, Bettella F, McMahon G, Kallela M, Malik R, de Vries B, Terwindt G, Medland SE, Todt U, McArdle WL, Quaye L, Koiranen M, Ligthart L, Hottenga JJ, Vink JM, Penninx BW, Boomsma DI, Strachan DP, Kubisch C, Ferrari MD, van den Maagdenberg AM, Dichgans M, Wessman M, Smith GD, Stefansson K, Daly MJ, Nyholt DR, Chasman DI, and Palotie A

Published
2013

32. Genetic sharing with cardiovascular disease risk factors and diabetes reveals novel bone mineral density loci

Author

Reppe, S, Wang, Y, Thompson, WK, McEvoy, LK, Schork, AJ, Zuber, V, LeBlanc, M, Bettella, F, Mills, IG, Desikan, RS, Djurovic, S, Gautvik, KM, Dale, AM, Andreassen, OA, Estrada, K, Styrkarsdottir, U, Evangelou, E, Hsu, YH, Duncan, EL, Ntzani, EE, Oei, L, Albagha, OME, Amin, N, Kemp, JP, Koller, DL, Li, G, Liu, CT, Minster, RL, Moayyeri, A, Vandenput, L, Willner, D, Xiao, SM, Yerges-Armstrong, LM, Zheng, HF, Alonso, N, Eriksson, J, Kammerer, CM, Kaptoge, SK, Leo, PJ, Thorleifsson, G, Wilson, SG, Wilson, JF, Aalto, V, Alen, M, Aragaki, AK, Aspelund, T, Center, JR, Dailiana, Z, Duggan, DJ, Garcia, M, Garcia-Giralt, N, Giroux, S, Hallmans, G, Hocking, LJ, Husted, LB, Jameson, KA, Khusainova, R, Kim, GS, Kooperberg, C, Koromila, T, Kruk, M, Laaksonen, M, and Lacroix, AZ

Subjects
musculoskeletal diseases, General Science & Technology, MD Multidisciplinary
Abstract

© 2015 Reppe 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. Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.

Published
2015
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33. Large-scale genomics unveil polygenic architecture of human cortical surface area

Author

Chen, CH, Peng, Q, Schork, AJ, Lo, MT, Fan, CC, Wang, Y, Desikan, RS, Bettella, F, Hagler, DJ, Westlye, LT, Kremen, WS, Jernigan, TL, Le Hellard, S, Steen, VM, Espeseth, T, Huentelman, M, Håberg, AK, Agartz, I, Djurovic, S, Andreassen, OA, Schork, N, Dale, AM, McCabe, C, Chang, L, Akshoomoff, N, Newman, E, Ernst, T, Van Zijl, P, Kuperman, J, Murray, S, Bloss, C, Appelbaum, M, Gamst, A, Thompson, W, Bartsch, H, Weiner, M, Aisen, P, Petersen, R, Jack, CR, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, J, Shaw, LM, Khachaturian, Z, Sorensen, G, Carrillo, M, Kuller, L, Raichle, M, Paul, S, Davies, P, Fillit, H, Hefti, F, Holtzman, D, Mesulman, MM, Potter, W, Snyder, PJ, Schwartz, A, Montine, T, Thomas, RG, Donohue, M, Walter, S, Gessert, D, Sather, T, Jiminez, G, Harvey, D, Bernstein, M, Fox, N, Thompson, P, Schuff, N, DeCarli, C, Borowski, B, Gunter, J, Senjem, M, Vemuri, P, Jones, D, Kantarci, K, Ward, C, Koeppe, RA, Foster, N, and Reiman, EM

Abstract

© 2015 Macmillan Publishers Limited. All rights reserved. Little is known about how genetic variation contributes to neuroanatomical variability, and whether particular genomic regions comprising genes or evolutionarily conserved elements are enriched for effects that influence brain morphology. Here, we examine brain imaging and single-nucleotide polymorphisms (SNPs) data from ∼ 2,700 individuals. We show that a substantial proportion of variation in cortical surface area is explained by additive effects of SNPs dispersed throughout the genome, with a larger heritable effect for visual and auditory sensory and insular cortices (h2 ∼ 0.45). Genome-wide SNPs collectively account for, on average, about half of twin heritability across cortical regions (N = 466 twins). We find enriched genetic effects in or near genes. We also observe that SNPs in evolutionarily more conserved regions contributed significantly to the heritability of cortical surface area, particularly, for medial and temporal cortical regions. SNPs in less conserved regions contributed more to occipital and dorsolateral prefrontal cortices.

Published
2015
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34. Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing

Author

Jansen, I. (Iris), Ye, H. (Hui), Heetveld, S. (Sasja), Lechler, M.C. (Marie C.), Michels, H. (Helen), Seinstra, R.I. (Renée I.), Lubbe, S.J. (Steven J.), Drouet, V. (Valérie), Lesage, S. (Suzanne), Majounie, E. (Elisa), Gibbs, J.R. (J.Raphael), Nalls, M.A. (Michael), Ryten, M. (Mina), Botia, J.A. (Juan A.), Vandrovcova, J. (Jana), Simón-Sánchez, J. (Javier), Castillo-Lizardo, M. (Melissa), Rizzu, P. (Patrizia), Blauwendraat, C. (Cornelis), Chouhan, A.K. (Amit K.), Li, Y. (Yarong), Yogi, P. (Puja), Amin, N. (Najaf), Duijn, C.M. (Cornelia) van, Morris, H.R. (Huw R.), Brice, A. (Alexis), Singleton, A. (Andrew), David, D.C. (Della C.), Nollen, E.A. (Ellen A.), Jain, A. (Ashok), Shulman, J.M., Heutink, P. (Peter), Hernandez, D.G. (Dena), Arepalli, S. (Sampath), Brooks, J. (Janet), Price, R. (Ryan), Nicolas, A. (Aude), Chong, S. (Sean), Cookson, M.R. (Mark), Dillman, A. (Allissa), Moore, M. (Matt), Traynor, B.J. (Bryan), Plagnol, V. (Vincent), Nicholas W Wood, Sheerin, U.-M. (Una-Marie), Jose M Bras, Charlesworth, K. (Kate), Gardner, M. (Mac), Guerreiro, R. (Rita), Trabzuni, D. (Danyah), Hardy, J. (John), Sharma, M., Saad, M. (Mohamad), Javier Simón-Sánchez, Schulte, C. (Claudia), Corvol, J.C. (Jean-Christophe), Dürr, A. (Alexandra), Vidailhet, M. (M.), Sveinbjörnsdóttir, S. (Sigurlaug), Barker, R.A. (Roger), Caroline H Williams-Gray, Ben-Shlomo, Y., Berendse, H.W. (Henk W.), Dijk, K.D. (Karin) van, Berg, D. (Daniela), Brockmann, K., Wurster, K.D. (Kathrin), Mätzler, W. (Walter), Gasser, T. (Thomas), Martinez, M. (Maria), Bie, R.M.A. (Rob) de, Biffi, A. (Alessandro), Velseboer, D. (Daan), Bloem, B.R. (Bastiaan), Post, B. (Bart), Wickremaratchi, M. (Mirdhu), Warrenburg, B. (Bart) van de, Bochdanovits, Z. (Zoltan), Bonin, M. (Malte) von, Pétursson, H. (Hjörvar), Riess, O. (Olaf), Burn, D.J. (David), Lubbe, S. (Steven), Cooper, J.M. (J Mark), McNeill, N.H. (Nathan), Schapira, A. (Anthony), Lungu, C. (Codrin), Chen, H. (Honglei), Dong, J. (Jing), Chinnery, P.F. (Patrick F.), Hudson, G. (Gavin), Clarke, C.E. (Carl E.), Moorby, C. (Catriona), Counsell, C. (Carl), Damier, P. (Philippe), Dartigues, J.-F., Deloukas, P. (Panagiotis), Gray, E. (Emma), Edkins, T. (Ted), Hunt, S.E. (Sarah E.), Potter, S.C. (Simon), Tashakkori-Ghanbaria, A. (Avazeh), Deuschl, G. (Günther), Lorenz, D. (Delia), Dexter, D.T. (David), Durif, F. (Frank), Evans, J. (Jonathan Mark), Langford, C. (Cordelia), Foltynie, T. (Thomas), Goate, A.M. (Alison), Harris, C. (Clare), Hilten, J.J. (Jacobus) van, Hofman, A. (Albert), Hollenbeck, J.R. (John R.), Holton, J.L. (Janice), Hu, M. (Michele), Huang, X. (Xiaohong), Illig, T. (Thomas), Jónsson, P.V. (Pálmi), Lambert, J.-C., O'Sullivan, S.S. (Sean), Revesz, T. (Tamas), Shaw, K. (Karen), Lees, A.J. (Andrew), Lichtner, P. (Peter), Limousin, P. (Patricia), Lopez, G., Escott-Price, V. (Valentina), Pearson, J. (Justin), Williams, N. (Nigel), Mudanohwo, E. (Ese), Perlmutter, J.S. (Joel), Pollak, P. (Pierre), Rivadeneira Ramirez, F. (Fernando), Uitterlinden, A.G. (André), Sawcer, S.J. (Stephen), Scheffer, H. (Hans), Shoulson, I. (Ira), Shulman, L. (Lee), Smith, C. (Colin), Walker, R. (Robert), Spencer, C.C.A. (Chris C.), Strange, A. (Amy), Stefansson, H. (Hreinn), Bettella, F. (Francesco), Zwart, J-A. (John-Anker), Stockton, J.D. (Joanna D.), Talbot, D., Tanner, C.M. (Carlie), Tison, F. (François), Winder-Rhodes, S. (Sophie), Bhatia, K.P. (Kailash), Jansen, I. (Iris), Ye, H. (Hui), Heetveld, S. (Sasja), Lechler, M.C. (Marie C.), Michels, H. (Helen), Seinstra, R.I. (Renée I.), Lubbe, S.J. (Steven J.), Drouet, V. (Valérie), Lesage, S. (Suzanne), Majounie, E. (Elisa), Gibbs, J.R. (J.Raphael), Nalls, M.A. (Michael), Ryten, M. (Mina), Botia, J.A. (Juan A.), Vandrovcova, J. (Jana), Simón-Sánchez, J. (Javier), Castillo-Lizardo, M. (Melissa), Rizzu, P. (Patrizia), Blauwendraat, C. (Cornelis), Chouhan, A.K. (Amit K.), Li, Y. (Yarong), Yogi, P. (Puja), Amin, N. (Najaf), Duijn, C.M. (Cornelia) van, Morris, H.R. (Huw R.), Brice, A. (Alexis), Singleton, A. (Andrew), David, D.C. (Della C.), Nollen, E.A. (Ellen A.), Jain, A. (Ashok), Shulman, J.M., Heutink, P. (Peter), Hernandez, D.G. (Dena), Arepalli, S. (Sampath), Brooks, J. (Janet), Price, R. (Ryan), Nicolas, A. (Aude), Chong, S. (Sean), Cookson, M.R. (Mark), Dillman, A. (Allissa), Moore, M. (Matt), Traynor, B.J. (Bryan), Plagnol, V. (Vincent), Nicholas W Wood, Sheerin, U.-M. (Una-Marie), Jose M Bras, Charlesworth, K. (Kate), Gardner, M. (Mac), Guerreiro, R. (Rita), Trabzuni, D. (Danyah), Hardy, J. (John), Sharma, M., Saad, M. (Mohamad), Javier Simón-Sánchez, Schulte, C. (Claudia), Corvol, J.C. (Jean-Christophe), Dürr, A. (Alexandra), Vidailhet, M. (M.), Sveinbjörnsdóttir, S. (Sigurlaug), Barker, R.A. (Roger), Caroline H Williams-Gray, Ben-Shlomo, Y., Berendse, H.W. (Henk W.), Dijk, K.D. (Karin) van, Berg, D. (Daniela), Brockmann, K., Wurster, K.D. (Kathrin), Mätzler, W. (Walter), Gasser, T. (Thomas), Martinez, M. (Maria), Bie, R.M.A. (Rob) de, Biffi, A. (Alessandro), Velseboer, D. (Daan), Bloem, B.R. (Bastiaan), Post, B. (Bart), Wickremaratchi, M. (Mirdhu), Warrenburg, B. (Bart) van de, Bochdanovits, Z. (Zoltan), Bonin, M. (Malte) von, Pétursson, H. (Hjörvar), Riess, O. (Olaf), Burn, D.J. (David), Lubbe, S. (Steven), Cooper, J.M. (J Mark), McNeill, N.H. (Nathan), Schapira, A. (Anthony), Lungu, C. (Codrin), Chen, H. (Honglei), Dong, J. (Jing), Chinnery, P.F. (Patrick F.), Hudson, G. (Gavin), Clarke, C.E. (Carl E.), Moorby, C. (Catriona), Counsell, C. (Carl), Damier, P. (Philippe), Dartigues, J.-F., Deloukas, P. (Panagiotis), Gray, E. (Emma), Edkins, T. (Ted), Hunt, S.E. (Sarah E.), Potter, S.C. (Simon), Tashakkori-Ghanbaria, A. (Avazeh), Deuschl, G. (Günther), Lorenz, D. (Delia), Dexter, D.T. (David), Durif, F. (Frank), Evans, J. (Jonathan Mark), Langford, C. (Cordelia), Foltynie, T. (Thomas), Goate, A.M. (Alison), Harris, C. (Clare), Hilten, J.J. (Jacobus) van, Hofman, A. (Albert), Hollenbeck, J.R. (John R.), Holton, J.L. (Janice), Hu, M. (Michele), Huang, X. (Xiaohong), Illig, T. (Thomas), Jónsson, P.V. (Pálmi), Lambert, J.-C., O'Sullivan, S.S. (Sean), Revesz, T. (Tamas), Shaw, K. (Karen), Lees, A.J. (Andrew), Lichtner, P. (Peter), Limousin, P. (Patricia), Lopez, G., Escott-Price, V. (Valentina), Pearson, J. (Justin), Williams, N. (Nigel), Mudanohwo, E. (Ese), Perlmutter, J.S. (Joel), Pollak, P. (Pierre), Rivadeneira Ramirez, F. (Fernando), Uitterlinden, A.G. (André), Sawcer, S.J. (Stephen), Scheffer, H. (Hans), Shoulson, I. (Ira), Shulman, L. (Lee), Smith, C. (Colin), Walker, R. (Robert), Spencer, C.C.A. (Chris C.), Strange, A. (Amy), Stefansson, H. (Hreinn), Bettella, F. (Francesco), Zwart, J-A. (John-Anker), Stockton, J.D. (Joanna D.), Talbot, D., Tanner, C.M. (Carlie), Tison, F. (François), Winder-Rhodes, S. (Sophie), and Bhatia, K.P. (Kailash)

Abstract

Background: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. Results: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. Conclusions: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies.

Published
2017
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35. Genetic sharing with cardiovascular disease risk factors and diabetes reveals novel bone mineral density loci

Author

Reppe, S. Wang, Y. Thompson, W.K. McEvoy, L.K. Schork, A.J. Zuber, V. LeBlanc, M. Bettella, F. Mills, I.G. Desikan, R.S. Djurovic, S. Gautvik, K.M. Dale, A.M. Andreassen, O.A. Estrada, K. Styrkarsdottir, U. Evangelou, E. Hsu, Y.-H. Duncan, E.L. Ntzani, E.E. Oei, L. Albagha, O.M.E. Amin, N. Kemp, J.P. Koller, D.L. Li, G. Liu, C.-T. Minster, R.L. Moayyeri, A. Vandenput, L. Willner, D. Xiao, S.-M. Yerges-Armstrong, L.M. Zheng, H.-F. Alonso, N. Eriksson, J. Kammerer, C.M. Kaptoge, S.K. Leo, P.J. Thorleifsson, G. Wilson, S.G. Wilson, J.F. Aalto, V. Alen, M. Aragaki, A.K. Aspelund, T. Center, J.R. Dailiana, Z. Duggan, D.J. Garcia, M. Garcia-Giralt, N. Giroux, S. Hallmans, G. Hocking, L.J. Husted, L.B. Jameson, K.A. Khusainova, R. Kim, G.S. Kooperberg, C. Koromila, T. Kruk, M. Laaksonen, M. Lacroix, A.Z. Lee, S.H. Leung, P.C. Lewis, J.R. Masi, L. Mencej-Bedrac, S. Nguyen, T.V. Nogues, X. Patel, M.S. Prezelj, J. Rose, L.M. Scollen, S. Siggeirsdottir, K. Smith, A.V. Svensson, O. Trompet, S. Trummer, O. Van Schoor, N.M. Woo, J. Zhu, K. Balcells, S. Brandi, M.L. Buckley, B.M. Cheng, S. Christiansen, C. Cooper, C. Dedoussis, G. Ford, I. Frost, M. Goltzman, D. González-Macías, J. Kähönen, M. Karlsson, M. Khusnutdinova, E. Koh, J.-M. Kollia, P. Langdahl, B.L. Leslie, W.D. Lips, P. Ljunggren, Ö. Lorenc, R.S. Marc, J. Mellström, D. Obermayer-Pietsch, B. Olmos, J.M. Pettersson-Kymmer, U. Reid, D.M. Riancho, J.A. Ridker, P.M. Rousseau, F. Slagboom, P.E. Tang, N.L.S. Urreizti, R. Van Hul, W. Viikari, J. Zarrabeitia, M.T. Aulchenko, Y.S. Castano-Betancourt, M. Grundberg, E. Herrera, L. Ingvarsson, T. Johannsdottir, H. Kwan, T. Li, R. Luben, R. Medina-Gómez, C. Palsson, S.Th. Rotter, J.I. Sigurdsson, G. Van Meurs, J.B.J. Verlaan, D. Williams, F.M.K. Wood, A.R. Zhou, Y. Pastinen, T. Raychaudhuri, S. Cauley, J.A. Chasman, D.I. Clark, G.R. Cummings, S.R. Danoy, P. Dennison, E.M. Eastell, R. Eisman, J.A. Gudnason, V. Hofman, A. Jackson, R.D. Jones, G. Jukema, J.W. Khaw, K.-T. Lehtimäki, T. Liu, Y. Lorentzon, M. McCloskey, E. Mitchell, B.D. Nandakumar, K. Nicholson, G.C. Oostra, B.A. Peacock, M. Pols, H.A.P. Prince, R.L. Raitakari, O. Reid, I.R. Robbins, J. Sambrook, P.N. Sham, P.C. Shuldiner, A.R. Tylavsky, F.A. Van Duijn, C.M. Wareham, N.J. Cupples, L.A. Econs, M.J. Evans, D.M. Harris, T.B. Kung, A.W.C. Psaty, B.M. Reeve, J. Spector, T.D. Streeten, E.A. Zillikens, M.C. Thorsteinsdottir, U. Ohlsson, C. Karasik, D. Richards, J.B. Brown, M.A. Stefansson, K. Uitterlinden, A.G. Ralston, S.H. Ioannidis, J.P.A. Kiel, D.P. Rivadeneira, F. GEFOS Consortium

Subjects
musculoskeletal diseases
Abstract

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity. © 2015 Reppe 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
2015

36. Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases

Author

Witoelar, A.W., Jansen, I.E., Wang, Y., Desikan, R.S., Gibbs, J.R., Blauwendraat, C., Thompson, W.K., Hernandez, D.G., Djurovic, S., Schork, A.J., Bettella, F., Ellinghaus, D., Franke, A., Lie, B.A., McEvoy, L.K., Karlsen, T.H., Lesage, S., Morris, H.R., Brice, A., Wood, N.W., Heutink, P., Hardy, J., Singleton, A.B., Dale, A.M., Gasser, T., Andreassen, O.A., Sharma, M., Nalls, M.A., Plagnol, V., Sheerin, U.M., Saad, M., Simon-Sanchez, J., Schulte, C., Sveinbjörnsdóttir, S., Arepalli, S., Barker, R.A., Ben-Shlomo, Y., Berendse, H.W., Berg, D., Bhatia, K.P., de Bie, R.M.A., Biffi, A., Bloem, B., Bochdanovits, Z., Bonin, M., Bras, J.M., Brockmann, K., Brooks, J.M., Burn, D.J., Majounie, E., Illig, T., Lichtner, P., Weale, M.E., Neurology, Amsterdam Neuroscience - Neurodegeneration, Human genetics, Hu, M, ANS - Neurodegeneration, ANS - Amsterdam Neuroscience, Intensive Care Medicine, AII - Inflammatory diseases, ANS - Neuroinfection & -inflammation, Graduate School, ACS - Amsterdam Cardiovascular Sciences, and APH - Aging & Later Life

Subjects
0301 basic medicine, Aging, genetics [Autoimmune Diseases], genetics [Colitis, Ulcerative], Ulcerative, Genome-wide association study, Disease, Neurodegenerative, North American Brain Expression Consortium, Bioinformatics, Arthritis, Rheumatoid, International Parkinson’s Disease Genomics Consortium (IPDGC), 0302 clinical medicine, Crohn Disease, genetics [Parkinson Disease], Risk Factors, Pleiotropy, Rheumatoid, Pleiotropism, 2.1 Biological and endogenous factors, Medicine, genetics [Celiac Disease], Aetiology, Original Investigation, Parkinson's Disease, Genetic Pleiotropy, Parkinson Disease, Colitis, LRRK2, International Parkinson’s Disease Genomics Consortium, Neurological, Cognitive Sciences, Type 1, Biotechnology, genetics [Crohn Disease], Multiple Sclerosis, genetics [Arthritis, Rheumatoid], Clinical Sciences, Human leukocyte antigen, genetics [Psoriasis], Autoimmune Disease, Autoimmune Diseases, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Diabetes Mellitus, Genetics, Humans, Psoriasis, Genetic Predisposition to Disease, ddc:610, Genetic association, Neurology & Neurosurgery, North American Brain Expression Consortium (NABEC), business.industry, Arthritis, Prevention, Inflammatory and immune system, Human Genome, Inflammatory Bowel Disease, genetics [Multiple Sclerosis], Neurosciences, genetics [Diabetes Mellitus, Type 1], Brain Disorders, Celiac Disease, Diabetes Mellitus, Type 1, 030104 developmental biology, Genetic Loci, and United Kingdom Brain Expression Consortium (UKBEC) Investigators, Colitis, Ulcerative, Neurology (clinical), Digestive Diseases, business, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Importance Recent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes. Objectives To test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach. Design, Setting, and Participants Using the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017. Main Outcomes and Measures The primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases. Results Genome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK , HLA-DRB5 , LRRK2 , and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA , LRRK2 , MAPT , TRIM10 , and SE TD1A in PD. Among the novel genes discovered, WNT3 , KANSL1 , CRHR1 , BOLA2 , and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes. Conclusions and Relevance The study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents.

Published
2017
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37. Pleiotropic Analysis of Lung Cancer and Blood Triglycerides

Author

Zuber, V., Marconett, C.N., Shi, J., Hua, X., Wheeler, W., Yang, C., Song, L, Dale, A.M., Laplana, M., Risch, A., Witoelar, A., Thompson, W.K., Schork, A.J., Bettella, F., Wang, Y, Djurovic, S., Zhou, B., Borok, Z., Heijden, H.F.M. van der, Graaf, J. de, Swinkels, D.W., Aben, K.K.H., McKay, J., Hung, R.J., Bikeboller, H., Stevens, V.L., Albanes, D., Caporaso, N.E., Han, Y., Wei, Y., Panadero, M.A., Mayordomo, J.I., Christiani, D.C., Kiemeney, L.A., Andreassen, O.A., Houlston, R., Amos, C.I., Chatterjee, N., Laird-Offringa, I.A., Mills, I.G., Landi, M.T., Zuber, V., Marconett, C.N., Shi, J., Hua, X., Wheeler, W., Yang, C., Song, L, Dale, A.M., Laplana, M., Risch, A., Witoelar, A., Thompson, W.K., Schork, A.J., Bettella, F., Wang, Y, Djurovic, S., Zhou, B., Borok, Z., Heijden, H.F.M. van der, Graaf, J. de, Swinkels, D.W., Aben, K.K.H., McKay, J., Hung, R.J., Bikeboller, H., Stevens, V.L., Albanes, D., Caporaso, N.E., Han, Y., Wei, Y., Panadero, M.A., Mayordomo, J.I., Christiani, D.C., Kiemeney, L.A., Andreassen, O.A., Houlston, R., Amos, C.I., Chatterjee, N., Laird-Offringa, I.A., Mills, I.G., and Landi, M.T.

Abstract

Item does not contain fulltext, Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10(-6)) and blood triglycerides (P = 1.39x10(-5)). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40 x 10(-4)) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21 ,: Pcombined = 5.20x10(-9)). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer.

Published
2016

38. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease

Author

Nalls, M.A. Pankratz, N. Lill, C.M. Do, C.B. Hernandez, D.G. Saad, M. Destefano, A.L. Kara, E. Bras, J. Sharma, M. Schulte, C. Keller, M.F. Arepalli, S. Letson, C. Edsall, C. Stefansson, H. Liu, X. Pliner, H. Lee, J.H. Cheng, R. Ikram, M.A. Ioannidis, J.P.A. Hadjigeorgiou, G.M. Bis, J.C. Martinez, M. Perlmutter, J.S. Goate, A. Marder, K. Fiske, B. Sutherland, M. Xiromerisiou, G. Myers, R.H. Clark, L.N. Stefansson, K. Hardy, J.A. Heutink, P. Chen, H. Wood, N.W. Houlden, H. Payami, H. Brice, A. Scott, W.K. Gasser, T. Bertram, L. Eriksson, N. Foroud, T. Singleton, A.B. Plagnol, V. Sheerin, U.-M. Simón-Sánchez, J. Lesage, S. Sveinbjörnsdóttir, S. Barker, R. Ben-Shlomo, Y. Berendse, H.W. Berg, D. Bhatia, K. de Bie, R.M.A. Biffi, A. Bloem, B. Bochdanovits, Z. Bonin, M. Bras, J.M. Brockmann, K. Brooks, J. Burn, D.J. Charlesworth, G. Chinnery, P.F. Chong, S. Clarke, C.E. Cookson, M.R. Cooper, J.M. Corvol, J.C. Counsell, C. Damier, P. Dartigues, J.-F. Deloukas, P. Deuschl, G. Dexter, D.T. van Dijk, K.D. Dillman, A. Durif, F. Dürr, A. Edkins, S. Evans, J.R. Foltynie, T. Dong, J. Gardner, M. Gibbs, J.R. Gray, E. Guerreiro, R. Harris, C. van Hilten, J.J. Hofman, A. Hollenbeck, A. Holton, J. Hu, M. Huang, X. Wurster, I. Mätzler, W. Hudson, G. Hunt, S.E. Huttenlocher, J. Illig, T. Jónsson, P.V. Lambert, J.-C. Langford, C. Lees, A. Lichtner, P. Limousin, P. Lopez, G. Lorenz, D. McNeill, A. Moorby, C. Moore, M. Morris, H.R. Morrison, K.E. Mudanohwo, E. O’sullivan, S.S. Pearson, J. Pétursson, H. Pollak, P. Post, B. Potter, S. Ravina, B. Revesz, T. Riess, O. Rivadeneira, F. Rizzu, P. Ryten, M. Sawcer, S. Schapira, A. Scheffer, H. Shaw, K. Shoulson, I. Sidransky, E. Smith, C. Spencer, C.C.A. Stefánsson, H. Bettella, F. Stockton, J.D. Strange, A. Talbot, K. Tanner, C.M. Tashakkori-Ghanbaria, A. Tison, F. Trabzuni, D. Traynor, B.J. Uitterlinden, A.G. Velseboer, D. Vidailhet, M. Walker, R. van de Warrenburg, B. Wickremaratchi, M. Williams, N. Williams-Gray, C.H. Winder-Rhodes, S. Stefánsson, K. Hardy, J. Factor, S. Higgins, D. Evans, S. Shill, H. Stacy, M. Danielson, J. Marlor, L. Williamson, K. Jankovic, J. Hunter, C. Simon, D. Ryan, P. Scollins, L. Saunders-Pullman, R. Boyar, K. Costan-Toth, C. Ohmann, E. Sudarsky, L. Joubert, C. Friedman, J. Chou, K. Fernandez, H. Lannon, M. Galvez-Jimenez, N. Podichetty, A. Thompson, K. Lewitt, P. Deangelis, M. O'brien, C. Seeberger, L. Dingmann, C. Judd, D. Marder, K. Fraser, J. Harris, J. Bertoni, J. Peterson, C. Rezak, M. Medalle, G. Chouinard, S. Panisset, M. Hall, J. Poiffaut, H. Calabrese, V. Roberge, P. Wojcieszek, J. Belden, J. Jennings, D. Marek, K. Mendick, S. Reich, S. Dunlop, B. Jog, M. Horn, C. Uitti, R. Turk, M. Ajax, T. Mannetter, J. Sethi, K. Carpenter, J. Dill, B. Hatch, L. Ligon, K. Narayan, S. Blindauer, K. Abou-Samra, K. Petit, J. Elmer, L. Aiken, E. Davis, K. Schell, C. Wilson, S. Velickovic, M. Koller, W. Phipps, S. Feigin, A. Gordon, M. Hamann, J. Licari, E. Marotta-Kollarus, M. Shannon, B. Winnick, R. Simuni, T. Videnovic, A. Kaczmarek, A. Williams, K. Wolff, M. Rao, J. Cook, M. Fernandez, M. Kostyk, S. Hubble, J. Campbell, A. Reider, C. Seward, A. Camicioli, R. Carter, J. Nutt, J. Andrews, P. Morehouse, S. Stone, C. Mendis, T. Grimes, D. Alcorn-Costa, C. Gray, P. Haas, K. Vendette, J. Sutton, J. Hutchinson, B. Young, J. Rajput, A. Klassen, L. Shirley, T. Manyam, B. Simpson, P. Whetteckey, J. Wulbrecht, B. Truong, D. Pathak, M. Frei, K. Luong, N. Tra, T. Tran, A. Vo, J. Lang, A. Kleiner-Fisman, G. Nieves, A. Johnston, L. So, J. Podskalny, G. Giffin, L. Atchison, P. Allen, C. Martin, W. Wieler, M. Suchowersky, O. Furtado, S. Klimek, M. Hermanowicz, N. Niswonger, S. Shults, C. Fontaine, D. Aminoff, M. Christine, C. Diminno, M. Hevezi, J. Dalvi, A. Kang, U. Richman, J. Uy, S. Sahay, A. Gartner, M. Schwieterman, D. Hall, D. Leehey, M. Culver, S. Derian, T. Demarcaida, T. Thurlow, S. Rodnitzky, R. Dobson, J. Lyons, K. Pahwa, R. Gales, T. Thomas, S. Shulman, L. Weiner, W. Dustin, K. Singer, C. Zelaya, L. Tuite, P. Hagen, V. Rolandelli, S. Schacherer, R. Kosowicz, J. Gordon, P. Werner, J. Serrano, C. Roque, S. Kurlan, R. Berry, D. Gardiner, I. Hauser, R. Sanchez-Ramos, J. Zesiewicz, T. Delgado, H. Price, K. Rodriguez, P. Wolfrath, S. Pfeiffer, R. Davis, L. Pfeiffer, B. Dewey, R. Hayward, B. Johnson, A. Meacham, M. Estes, B. Walker, F. Hunt, V. O'neill, C. Racette, B. Swisher, L. Dijamco, C. Conley, E.D. Dorfman, E. Tung, J.Y. Hinds, D.A. Mountain, J.L. Wojcicki, A. Lew, M. Klein, C. Golbe, L. Growdon, J. Wooten, G.F. Watts, R. Guttman, M. Goldwurm, S. Saint-Hilaire, M.H. Baker, K. Litvan, I. Nicholson, G. Nance, M. Drasby, E. Isaacson, S. Burn, D. Pramstaller, P. Al-Hinti, J. Moller, A. Sherman, S. Roxburgh, R. Slevin, J. Perlmutter, J. Mark, M.H. Huggins, N. Pezzoli, G. Massood, T. Itin, I. Corbett, A. Chinnery, P. Ostergaard, K. Snow, B. Cambi, F. Kay, D. Samii, A. Agarwal, P. Roberts, J.W. Higgins, D.S. Molho, E. Rosen, A. Montimurro, J. Martinez, E. Griffith, A. Kusel, V. Yearout, D. Factor, S. Zabetian, C. Clark, L.N. Liu, X. Lee, J.H. Cheng Taub, R. Louis, E.D. Cote, L.J. Waters, C. Ford, B. Fahn, S. Vance, J.M. Beecham, G.W. Martin, E.R. Nuytemans, K. Pericak-Vance, M.A. Haines, J.L. Destefano, A. Seshadri, S. Choi, S.H. Frank, S. Bis, J.C. Psaty, B.M. Rice, K. Longstreth, W.T., Jr. Ton, T.G.N. Jain, S. van Duijn, C.M. Uitterlinden, A.G. Verlinden, V.J. Koudstaal, P.J. Singleton, A. Cookson, M. Gibbs, J.R. Hernandez, D. Nalls, M. Zonderman, A. Ferrucci, L. Johnson, R. Longo, D. O'brien, R. Traynor, B. Troncoso, J. van der Brug, M. Zielke, R. Weale, M. Ramasamy, A. Dardiotis, E. Tsimourtou, V. Spanaki, C. Plaitakis, A. Bozi, M. Stefanis, L. Vassilatis, D. Koutsis, G. Panas, M. Hadjigeorgiou, G.M. Lunnon, K. Lupton, M. Powell, J. Parkkinen, L. Ansorge, O. International Parkinson's Disease Genomics Consortium (IPDGC) Parkinson's Study Group (PSG) Parkinson's Research: The Organized GENetics Initiative (PROGENI) 23andMe GenePD NeuroGenetics Research Consortium (NGRC) Hussman Institute of Human Genomics (HIHG) The Ashkenazi Jewish Dataset Investigator Cohorts for Health Aging Research in Genetic Epidemiology (CHARGE) North American Brain Expression Consortium (NABEC) United Kingdom Brain Expression Consortium (UKBEC) Greek Parkinson's Disease Consortium Alzheimer Genetic Analysis Group

Abstract

We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55-4.30; P = 2 × 10-16). We also show six risk loci associated with proximal gene expression or DNA methylation. © 2014 Nature America, Inc. All rights reserved.

Published
2014

39. Genome-wide association analysis identifies 13 new risk loci for schizophrenia

Author

Ripke, S, O'Dushlaine, C, Chambert, K, Moran, Jl, Kähler, Ak, Akterin, S, Bergen, Se, Collins, Al, Crowley, Jj, Fromer, M, Kim, Y, Lee, Sh, Magnusson, Pk, Sanchez, N, Stahl, Ea, Williams, S, Wray, Nr, Xia, K, Bettella, F, Borglum, Ad, Bulik Sullivan, Bk, Cormican, P, Craddock, N, Leeuw, De, C, Durmishi, N, Gill, M, Golimbet, V, Hamshere, Ml, Holmans, P, Hougaard, Dm, Kendler, Ks, Lin, K, Morris, Dw, Mors, O, Mortensen, Pb, Neale, Bm, O'Neill, Fa, Owen, Mj, Milovancevic, Mp, Posthuma, D, Powell, J, Richards, Al, Riley, Bp, Ruderfer, D, Rujescu, D, Sigurdsson, E, Silagadze, T, Smit, Ab, Stefansson, H, Steinberg, S, Suvisaari, J, Tosato, Sarah, Verhage, M, Walters, Jt, Multicenter Genetic Studies of Schizophrenia Consortium, Levinson, Df, Gejman, Pv, Laurent, C, Mowry, Bj, O'Donovan, Mc, Pulver, Ae, Schwab, Sg, Wildenauer, Db, Dudbridge, F, Shi, J, Albus, M, Alexander, M, Campion, D, Cohen, D, Dikeos, D, Duan, J, Eichhammer, P, Godard, S, Hansen, M, Lerer, Fb, Liang, Ky, Maier, W, Mallet, J, Nertney, Da, Nestadt, G, Norton, N, Papadimitriou, Gn, Ribble, R, Sanders, Ar, Silverman, Jm, Walsh, D, Williams, Nm, Wormley, B, Psychosis Endophenotypes International Consortium, Arranz, Mj, Bakker, S, Bender, S, Bramon, E, Collier, D, Crespo Facorro, B, Hall, J, Iyegbe, C, Jablensky, A, Kahn, Rs, Kalaydjieva, L, Lawrie, S, Lewis, Cm, Linszen, Dh, Mata, I, Mcintosh, A, Murray, Rm, Ophoff, Ra, Van, Os, J, Walshe, M, Weisbrod, M, Wiersma, D, Wellcome Trust Case Control Consortium 2, Donnelly, P, Barroso, I, Blackwell, Jm, Brown, Ma, Casas, Jp, Corvin, Ap, Deloukas, P, Duncanson, A, Jankowski, J, Markus, Hs, Mathew, Cg, Palmer, Cn, Plomin, R, Rautanen, A, Sawcer, Sj, Trembath, Rc, Viswanathan, Ac, Wood, Nw, Spencer, Cc, Band, G, Bellenguez, C, Freeman, C, Hellenthal, G, Giannoulatou, E, Pirinen, M, Pearson, Rd, Strange, A, Su, Z, Vukcevic, D, Langford, C, Hunt, Se, Edkins, S, Gwilliam, R, Blackburn, H, Bumpstead, Sj, Dronov, S, Gillman, M, Gray, E, Hammond, N, Jayakumar, A, Mccann, Ot, Liddle, J, Potter, Sc, Ravindrarajah, R, Ricketts, M, Tashakkori Ghanbaria, A, Waller, Mj, Weston, P, Widaa, S, Whittaker, P, Mccarthy, Mi, Stefansson, K, Scolnick, E, Purcell, S, Mccarroll, Sa, Sklar, P, Hultman, Cm, Sullivan, P. F., Functional Genomics, Molecular and Cellular Neurobiology, AIMMS, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, Neuroscience Campus Amsterdam - Brain Mechanisms in Health & Disease, Adult Psychiatry, Ripke, Stephan, O'Dushlaine, Colm, Chambert, Kimberly, Moran, Jennifer L, Lee, Sang Hong, Sullivan, Patrick F, Multicenter Genetic Studies of Schizophrenia Consortium, Psychosis Endophenotypes International Consortium, Wellcome Trust Case Control Consortium 2, Massachusetts Gen Hosp, Analyt & Translat Genet Unit, Boston, MA 02114 USA Broad Inst MIT & Harvard, Stanley Ctr Psychiat Res, Cambridge, MA USA Univ N Carolina, Dept Genet, Chapel Hill, NC 27515 USA Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden Oslo Univ Hosp, Div Mental Hlth & Addict, Oslo, Norway Icahn Sch Med Mt Sinai, Dept Psychiat, Div Psychiat Genom, New York, NY USA Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia Univ N Carolina, Dept Psychiat, Chapel Hill, NC USA deCODE Genet, Reykjavik, Iceland Aarhus Univ Hosp, Risskov, Denmark Aarhus Univ, Ctr Integrat Sequencing iSEQ, Aarhus, Denmark Lundbeck Fdn Initiat Integrat Psychiat Res iPSYCH, Aarhus, Denmark Lundbeck Fdn Initiat Integrat Psychiat Res iPSYCH, Copenhagen, Denmark Univ Dublin Trinity Coll, Dept Psychiat, Dublin 2, Ireland Cardiff Univ, Sch Med, Ctr Psychiat Genet & Genom, MRC, Cardiff CF10 3AX, S Glam, Wales Vrije Univ Amsterdam, Ctr Neurogen & Cognit Res, Dept Funct Genom, Amsterdam, Netherlands Vrije Univ Amsterdam Med Ctr, Amsterdam, Netherlands Radboud Univ Nijmegen, Inst Comp & Informat Sci, NL-6525 ED Nijmegen, Netherlands Univ Clin Psychiat, Dept Child & Adolescent Psychiat, Skopje, Macedonia Univ Dublin Trinity Coll, Neuropsychiat Genet Res Grp, Dublin 2, Ireland Russian Acad Med Sci, Mental Hlth Res Ctr, Moscow 109801, Russia Statens Serum Inst, DK-2300 Copenhagen, Denmark Virginia Commonwealth Univ, Dept Psychiat, Richmond, VA USA Virginia Commonwealth Univ, Virginia Inst Psychiat & Behav Genet, Richmond, VA USA Kings Coll London, Inst Psychiat, London, England Aarhus Univ Hosp, Ctr Psychiat Res, Risskov, Denmark Aarhus Univ, Natl Ctr Register Based Res, Aarhus, DenmarkQueens Univ Belfast, Ctr Publ Hlth, Belfast, Antrim, North Ireland Univ Belgrade, Fac Med, Belgrade, Serbia Erasmus Univ, Med Ctr, Dept Child & Adolescent Psychiat, Rotterdam, Netherlands Kings Coll London, Dept Neurosci, London, England Virginia Commonwealth Univ, Dept Human & Mol Genet, Richmond, VA USA Univ Halle, Dept Psychiat, Halle, Germany Univ Munich, Dept Psychiat, D-80539 Munich, Germany Univ Iceland, Dept Psychiat, Reykjavik, Iceland Landspitali University Hospital Reykjavik, Iceland Tbilisi State Univ, Dept Psychiat, GE-380086 Tbilisi, Rep of Georgia Vrije Univ Amsterdam, Ctr Neurogen & Cognit Res, Amsterdam, Netherlands Vrije Univ Amsterdam, Dept Mol & Cellular Neurosci, Amsterdam, Netherlands Natl Inst Hlth & Welf, Mental Hlth & Subst Abuse Serv, Helsinki, Finland Univ Verona, Sect Psychiat, I-37100 Verona, Italy UCL, Inst Cognit Neurosci, London, England UCL, Mental Hlth Sci Unit, London, England Massachusetts Gen Hosp, Psychiat & Neurodev Genet Unit, Boston, MA 02114 USA Harvard Univ, Sch Med, Dept Genet, Boston, MA USA, Child and Adolescent Psychiatry / Psychology, Psychiatrie & Neuropsychologie, RS: MHeNs School for Mental Health and Neuroscience, Human genetics, NCA - Brain mechanisms in health and disease, and NCA - Neurobiology of mental health

Subjects
Male, Candidate gene, SNP, Single-nucleotide polymorphism, Genome-wide association study, Disease, Biology, heritability, neuronal calcium signaling, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Genetics, medicine, Humans, GWAS, Genetic Predisposition to Disease, Bipolar disorder, 030304 developmental biology, Genetic association, Sweden, Genetics & Heredity, 0303 health sciences, medicine.disease, 3. Good health, genome sequencing, schizophrenia, Schizophrenia, Meta-analysis, Case-Control Studies, genetic variation, Female, genome-wide scan, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field. Schizophrenia is an idiopathic mental disorder with a heritable component and a substantial public health impact. We conducted a multi-stage genome-wide association study (GWAS) for schizophrenia beginning with a Swedish national sample (5,001 cases and 6,243 controls) followed by meta-analysis with previous schizophrenia GWAS (8,832 cases and 12,067 controls) and finally by replication of SNPs in 168 genomic regions in independent samples (7,413 cases, 19,762 controls and 581 parent-offspring trios). We identified 22 loci associated at genome-wide significance; 13 of these are new, and 1 was previously implicated in bipolar disorder. Examination of candidate genes at these loci suggests the involvement of neuronal calcium signaling. We estimate that 8,300 independent, mostly common SNPs (95% credible interval of 6,300-10,200 SNPs) contribute to risk for schizophrenia and that these collectively account for at least 32% of the variance in liability. Common genetic variation has an important role in the etiology of schizophrenia, and larger studies will allow more detailed understanding of this disorder. NIMH R01 MH077139 R01 MH095034 Stanley Center for Psychiatric Research Sylvan Herman Foundation Friedman Brain Institute at the Mount Sinai School of Medicine Karolinska Institutet, Karolinska University Hospital Swedish Research Council Swedish County Council Soderstrom Konigska Foundation Netherlands Scientific Organization NWO 645-000-003 info:eu-repo/grantAgreement/EC/FP7/223423 Danish Strategic Research Council H. Lundbeck A/S Faculty of Health Sciences at Aarhus University Lundbeck Foundation Stanley Research Foundation Wellcome Trust 085475/B/08/Z 085475/Z/08/Z

Published
2013
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40. Genetic sharing with cardiovascular disease risk factors and diabetes reveals novel bone mineral density loci

Author

Reppe, S. (Sjur), Wang, Y. (Yunpeng), Thompson, W.K. (Wesley K.), McEvoy, L.K. (Linda K.), Schork, N.J. (Nicholas), Zuber, V. (Verena), Leblanc, M. (Marissa), Bettella, F. (Francesco), Mills, I.G. (Ian G.), Desikan, R.S. (Rahul S.), Djurovic, S. (Srdjan), Gautvik, K.M. (Kaare), Dale, A.M. (Anders), Andreassen, O.A. (Ole), Estrada Gil, K. (Karol), Styrkarsdottir, U. (Unnur), Evangelou, E. (Evangelos), Hsu, Y.-H. (Yi-Hsiang), Duncan, E.L. (Emma), Ntzani, E.E. (Evangelia), Oei, L. (Ling), Albagha, O.M.E. (Omar M.), Amin, N. (Najaf), Kemp, J.P. (John), Koller, D.L. (Daniel), Li, G. (Guo), Liu, C.-T. (Ching-Ti), Minster, R.L. (Ryan), Moayyeri, A. (Alireza), Vandenput, L. (Liesbeth), Willner, D. (Dana), Xiao, S.-M. (Su-Mei), Yerges-Armstrong, L.M. (Laura), Zheng, H.-F. (Hou-Feng), Alonso, N. (Nerea), Eriksson, J. (Joel), Kammerer, C.M. (Candace), Kaptoge, S. (Stephen), Leo, P.J. (Paul), Thorleifsson, G. (Gudmar), Wilson, S.G. (Scott), Wilson, J.F. (James F), Aalto, V. (Ville), Alen, M. (Markku), Aragaki, A.K. (Aaron), Aspelund, T. (Thor), Center, J.R. (Jacqueline), Dailiana, Z. (Zoe), Duggan, C., Garcia, M. (Melissa), Garcia-Giralt, N. (Natàlia), Giroux, S. (Sylvie), Hallmans, G. (Göran), Hocking, L.J. (Lynne), Husted, L.B. (Lise Bjerre), Jameson, K. (Karen), Khusainova, R. (Rita), Kim, G.S. (Ghi Su), Kooperberg, C. (Charles), Koromila, T. (Theodora), Kruk, M. (Marcin), Laaksonen, M. (Marika), Lacroix, A.Z. (Andrea Z.), Lee, S.H. (Seung Hun), Leung, P.C. (Ping C.), Lewis, J.R. (Joshua), Masi, L. (Laura), Mencej-Bedrac, S. (Simona), Nguyen, T.V. (Tuan), Nogues, X. (Xavier), Patel, M.S. (Millan), Prezelj, J. (Janez), Rose, L.M. (Lynda), Scollen, S. (Serena), Siggeirsdottir, K. (Kristin), Smith, A.V. (Davey), Svensson, O. (Olle), Trompet, S. (Stella), Trummer, O. (Olivia), Schoor, N.M. (Natasja) van, Woo, J. (Jean), Zhu, K. (Kun), Balcells, S. (Susana), Brandi, M.L., Buckley, B.M. (Brendan M.), Cheng, S. (Sulin), Christiansen, C., Cooper, C. (Charles), Dedoussis, G.V. (George), Ford, I. (Ian), Frost, M. (Morten), Goltzman, D. (David), González-Macías, J. (Jesús), Kähönen, M. (Mika), Karlsson, M. (Magnus), Khusnutdinova, E.K. (Elza), Koh, J.-M. (Jung-Min), Kollia, P. (Panagoula), Langdahl, B.L. (Bente), Leslie, W.D. (William D.), Lips, P. (Paul), Ljunggren, O. (Östen), Lorenc, R. (Roman), Marc, J. (Janja), Mellström, D. (Dan), Obermayer-Pietsch, B. (Barbara), Olmos, D. (David), Pettersson-Kymmer, U. (Ulrika), Reid, D.M. (David), Riancho, J.A. (José), Ridker, P.M. (Paul), Rousseau, M.F. (Francois), Slagboom, P.E. (Eline), Tang, N.L.S. (Nelson L.S.), Urreizti, R. (Roser), Van Hul, W. (Wim), Viikari, J. (Jorma), Zarrabeitia, M.T. (María), Aulchenko, Y.S. (Yurii), Castaño Betancourt, M.C. (Martha), Grundberg, E. (Elin), Herrera, L. (Lizbeth), Ingvarsson, T. (Torvaldur), Johannsdottir, H. (Hrefna), Kwan, T. (Tony), Li, R. (Rui), Luben, R.N. (Robert), Medina-Gomez, M.C. (Carolina), Palsson, S.T. (Stefan Th), Rotter, J.I. (Jerome I.), Sigurdsson, G. (Gunnar), Meurs, J.B.J. (Joyce) van, Verlaan, D.J. (Dominique), Williams, F.M. (Frances), Wood, A.R. (Andrew), Zhou, Y. (Yanhua), Pastinen, T. (Tomi), Raychaudhuri, S. (Soumya), Cauley, J.A. (Jane), Chasman, D.I. (Daniel), Clark, G.R. (Graeme), Cummings, S.R. (Steven R.), Danoy, P. (Patrick), Dennison, E.M. (Elaine), Eastell, R. (Richard), Eisman, J.A. (John), Gudnason, V. (Vilmundur), Hofman, A. (Albert), Jackson, R.D. (Rebecca), Jones, G. (Graeme), Jukema, J.W. (Jan Wouter), Khaw, K.T., Lehtimäki, T. (Terho), Liu, Y. (YongMei), Lorentzon, M. (Mattias), McCloskey, E.V. (Eugene), Mitchell, B.D. (Braxton), Nandakumar, K. (Kannabiran), Nicholson, G.C. (Geoffrey), Oostra, B.A. (Ben), Peacock, M. (Munro), Pols, H.A.P. (Huib), Prince, R.L. (Richard), Raitakari, O. (Olli), Reid, I.R. (Ian), Robbins, J. (John), Sambrook, P.N. (Philip), Sham, P.C. (Pak Chung), Shuldiner, A.R. (Alan), Tylavsky, F.A. (Frances), Duijn, C.M. (Cornelia) van, Wareham, N.J. (Nicholas J.), Cupples, L.A. (Adrienne), Econs, M.J. (Michael), Evans, D.M. (David), Harris, T.B. (Tamara B.), Kung, A.W.C. (Annie Wai Chee), Psaty, B.M. (Bruce), Reeve, J. (Jonathan), Spector, T.D. (Timothy), Streeten, E.A. (Elizabeth), Zillikens, M.C. (Carola), Thorsteinsdottir, U. (Unnur), Ohlsson, C. (Claes), Karasik, D. (David), Richards, J.B. (Brent), Brown, M.A. (Matthew), Zwart, J-A. (John-Anker), Uitterlinden, A.G. (André), Ralston, S.H. (Stuart), Ioannidis, J.P.A. (John P.A.), Kiel, D.P. (Douglas P.), Rivadeneira Ramirez, F. (Fernando), Reppe, S. (Sjur), Wang, Y. (Yunpeng), Thompson, W.K. (Wesley K.), McEvoy, L.K. (Linda K.), Schork, N.J. (Nicholas), Zuber, V. (Verena), Leblanc, M. (Marissa), Bettella, F. (Francesco), Mills, I.G. (Ian G.), Desikan, R.S. (Rahul S.), Djurovic, S. (Srdjan), Gautvik, K.M. (Kaare), Dale, A.M. (Anders), Andreassen, O.A. (Ole), Estrada Gil, K. (Karol), Styrkarsdottir, U. (Unnur), Evangelou, E. (Evangelos), Hsu, Y.-H. (Yi-Hsiang), Duncan, E.L. (Emma), Ntzani, E.E. (Evangelia), Oei, L. (Ling), Albagha, O.M.E. (Omar M.), Amin, N. (Najaf), Kemp, J.P. (John), Koller, D.L. (Daniel), Li, G. (Guo), Liu, C.-T. (Ching-Ti), Minster, R.L. (Ryan), Moayyeri, A. (Alireza), Vandenput, L. (Liesbeth), Willner, D. (Dana), Xiao, S.-M. (Su-Mei), Yerges-Armstrong, L.M. (Laura), Zheng, H.-F. (Hou-Feng), Alonso, N. (Nerea), Eriksson, J. (Joel), Kammerer, C.M. (Candace), Kaptoge, S. (Stephen), Leo, P.J. (Paul), Thorleifsson, G. (Gudmar), Wilson, S.G. (Scott), Wilson, J.F. (James F), Aalto, V. (Ville), Alen, M. (Markku), Aragaki, A.K. (Aaron), Aspelund, T. (Thor), Center, J.R. (Jacqueline), Dailiana, Z. (Zoe), Duggan, C., Garcia, M. (Melissa), Garcia-Giralt, N. (Natàlia), Giroux, S. (Sylvie), Hallmans, G. (Göran), Hocking, L.J. (Lynne), Husted, L.B. (Lise Bjerre), Jameson, K. (Karen), Khusainova, R. (Rita), Kim, G.S. (Ghi Su), Kooperberg, C. (Charles), Koromila, T. (Theodora), Kruk, M. (Marcin), Laaksonen, M. (Marika), Lacroix, A.Z. (Andrea Z.), Lee, S.H. (Seung Hun), Leung, P.C. (Ping C.), Lewis, J.R. (Joshua), Masi, L. (Laura), Mencej-Bedrac, S. (Simona), Nguyen, T.V. (Tuan), Nogues, X. (Xavier), Patel, M.S. (Millan), Prezelj, J. (Janez), Rose, L.M. (Lynda), Scollen, S. (Serena), Siggeirsdottir, K. (Kristin), Smith, A.V. (Davey), Svensson, O. (Olle), Trompet, S. (Stella), Trummer, O. (Olivia), Schoor, N.M. (Natasja) van, Woo, J. (Jean), Zhu, K. (Kun), Balcells, S. (Susana), Brandi, M.L., Buckley, B.M. (Brendan M.), Cheng, S. (Sulin), Christiansen, C., Cooper, C. (Charles), Dedoussis, G.V. (George), Ford, I. (Ian), Frost, M. (Morten), Goltzman, D. (David), González-Macías, J. (Jesús), Kähönen, M. (Mika), Karlsson, M. (Magnus), Khusnutdinova, E.K. (Elza), Koh, J.-M. (Jung-Min), Kollia, P. (Panagoula), Langdahl, B.L. (Bente), Leslie, W.D. (William D.), Lips, P. (Paul), Ljunggren, O. (Östen), Lorenc, R. (Roman), Marc, J. (Janja), Mellström, D. (Dan), Obermayer-Pietsch, B. (Barbara), Olmos, D. (David), Pettersson-Kymmer, U. (Ulrika), Reid, D.M. (David), Riancho, J.A. (José), Ridker, P.M. (Paul), Rousseau, M.F. (Francois), Slagboom, P.E. (Eline), Tang, N.L.S. (Nelson L.S.), Urreizti, R. (Roser), Van Hul, W. (Wim), Viikari, J. (Jorma), Zarrabeitia, M.T. (María), Aulchenko, Y.S. (Yurii), Castaño Betancourt, M.C. (Martha), Grundberg, E. (Elin), Herrera, L. (Lizbeth), Ingvarsson, T. (Torvaldur), Johannsdottir, H. (Hrefna), Kwan, T. (Tony), Li, R. (Rui), Luben, R.N. (Robert), Medina-Gomez, M.C. (Carolina), Palsson, S.T. (Stefan Th), Rotter, J.I. (Jerome I.), Sigurdsson, G. (Gunnar), Meurs, J.B.J. (Joyce) van, Verlaan, D.J. (Dominique), Williams, F.M. (Frances), Wood, A.R. (Andrew), Zhou, Y. (Yanhua), Pastinen, T. (Tomi), Raychaudhuri, S. (Soumya), Cauley, J.A. (Jane), Chasman, D.I. (Daniel), Clark, G.R. (Graeme), Cummings, S.R. (Steven R.), Danoy, P. (Patrick), Dennison, E.M. (Elaine), Eastell, R. (Richard), Eisman, J.A. (John), Gudnason, V. (Vilmundur), Hofman, A. (Albert), Jackson, R.D. (Rebecca), Jones, G. (Graeme), Jukema, J.W. (Jan Wouter), Khaw, K.T., Lehtimäki, T. (Terho), Liu, Y. (YongMei), Lorentzon, M. (Mattias), McCloskey, E.V. (Eugene), Mitchell, B.D. (Braxton), Nandakumar, K. (Kannabiran), Nicholson, G.C. (Geoffrey), Oostra, B.A. (Ben), Peacock, M. (Munro), Pols, H.A.P. (Huib), Prince, R.L. (Richard), Raitakari, O. (Olli), Reid, I.R. (Ian), Robbins, J. (John), Sambrook, P.N. (Philip), Sham, P.C. (Pak Chung), Shuldiner, A.R. (Alan), Tylavsky, F.A. (Frances), Duijn, C.M. (Cornelia) van, Wareham, N.J. (Nicholas J.), Cupples, L.A. (Adrienne), Econs, M.J. (Michael), Evans, D.M. (David), Harris, T.B. (Tamara B.), Kung, A.W.C. (Annie Wai Chee), Psaty, B.M. (Bruce), Reeve, J. (Jonathan), Spector, T.D. (Timothy), Streeten, E.A. (Elizabeth), Zillikens, M.C. (Carola), Thorsteinsdottir, U. (Unnur), Ohlsson, C. (Claes), Karasik, D. (David), Richards, J.B. (Brent), Brown, M.A. (Matthew), Zwart, J-A. (John-Anker), Uitterlinden, A.G. (André), Ralston, S.H. (Stuart), Ioannidis, J.P.A. (John P.A.), Kiel, D.P. (Douglas P.), and Rivadeneira Ramirez, F. (Fernando)

Abstract

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.

Published
2015
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42. A pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson's disease

Author

Holmans, P., Moskvina, V., Jones, L., Sharma, M., Vedernikov, A., Buchel, F., Sadd, M., Bras, J.M., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Gibbs, J.R., Schulte, C., Durr, A., Guerreiro, R., Hernandez, D., Brice, A., Stefansson, H., Majamaa, K., Gasser, T., Heutink, P., Wood, N.W., Martinez, M., Singleton, A.B., Nalls, M.A., Hardy, J., Morris, H.R., Williams, N.M., Bloem, B., Post, B., Warrenburg, B.P.C. van de, Ravina, B., Shoulson, I., et al., Holmans, P., Moskvina, V., Jones, L., Sharma, M., Vedernikov, A., Buchel, F., Sadd, M., Bras, J.M., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Gibbs, J.R., Schulte, C., Durr, A., Guerreiro, R., Hernandez, D., Brice, A., Stefansson, H., Majamaa, K., Gasser, T., Heutink, P., Wood, N.W., Martinez, M., Singleton, A.B., Nalls, M.A., Hardy, J., Morris, H.R., Williams, N.M., Bloem, B., Post, B., Warrenburg, B.P.C. van de, Ravina, B., Shoulson, I., and et al.

Abstract

Item does not contain fulltext, Parkinson's disease (PD) is the second most common neurodegenerative disease affecting 1-2% in people >60 and 3-4% in people >80. Genome-wide association (GWA) studies have now implicated significant evidence for association in at least 18 genomic regions. We have studied a large PD-meta analysis and identified a significant excess of SNPs (P < 1 x 10(-16)) that are associated with PD but fall short of the genome-wide significance threshold. This result was independent of variants at the 18 previously implicated regions and implies the presence of additional polygenic risk alleles. To understand how these loci increase risk of PD, we applied a pathway-based analysis, testing for biological functions that were significantly enriched for genes containing variants associated with PD. Analysing two independent GWA studies, we identified that both had a significant excess in the number of functional categories enriched for PD-associated genes (minimum P = 0.014 and P = 0.006, respectively). Moreover, 58 categories were significantly enriched for associated genes in both GWA studies (P < 0.001), implicating genes involved in the 'regulation of leucocyte/lymphocyte activity' and also 'cytokine-mediated signalling' as conferring an increased susceptibility to PD. These results were unaltered by the exclusion of all 178 genes that were present at the 18 genomic regions previously reported to be strongly associated with PD (including the HLA locus). Our findings, therefore, provide independent support to the strong association signal at the HLA locus and imply that the immune-related genetic susceptibility to PD is likely to be more widespread in the genome than previously appreciated.

Published
2013

43. Genome-wide meta-analysis identifies new susceptibility loci for migraine

Author

Anttila, V., Winsvold, B.S., Gormley, P., Kurth, T., Bettella, F., McMahon, G., Kallela, M., Malik, R., de Vries, B., Terwindt, G., Medland, S.E., Todt, U., McArdle, W.L., Quaye, L., Koiranen, M., Ikram, M.A., Lehtimäki, T., Stam, A.H., Ligthart, L., Wedenoja, J., Dunham, I., Neale, B. M., Palta, P., Hamalainen, E., Schürks, M., Rose, L.M., Buring, J.E., Ridker, P.M., Steinberg, S., Stefansson, H., Jakobsson, F., Lawlor, D.A., Evans, D.M., Ring, S.M., Färkkilä, M., Artto, V., Kaunisto, M.A., Freilinger, T., Schoenen, J., Frants, R.R., Pelzer, N., Weller, C.M., Zielman, R., Heath, A.C., Madden, P.A.F., Montgomery, G.W., Martin, N.G., Borck, G., Göbel, H., Heinze, A., Heinze-Kuhn, K., Williams, F.M., Hartikainen, A.-L., Pouta, A., van den Ende, J.., Uitterlinden, A.G., Hofman, A., Amin, N., Hottenga, J.J., Vink, J.M., Heikkilä, K., Alexander, M., Muller-Myhsok, B., Schreiber, S, Meitinger, T., Wichmann, H. E., Aromaa, A., Eriksson, J.G., Traynor, B.J., Trabzuni, D., Rossin, E., Lage, K., Jacobs, S.B., Gibbs, J.R., Birney, E., Kaprio, J., Penninx, B.W.J.H., Boomsma, D.I., van Duijn, C.M., Raitakari, O., Jarvelin, M.-R., Zwart, J.A., Cherkas, L., Strachan, D.P., Kubisch, C., Ferrari, M.D., van den Maagdenberg, A.M.J.M., Dichgans, M., Wessman, M., Smith, G.D., Stefansson, K., Daly, M.J., Nyholt, DR, Chasman, D.I., Palotie, A., Anttila, V., Winsvold, B.S., Gormley, P., Kurth, T., Bettella, F., McMahon, G., Kallela, M., Malik, R., de Vries, B., Terwindt, G., Medland, S.E., Todt, U., McArdle, W.L., Quaye, L., Koiranen, M., Ikram, M.A., Lehtimäki, T., Stam, A.H., Ligthart, L., Wedenoja, J., Dunham, I., Neale, B. M., Palta, P., Hamalainen, E., Schürks, M., Rose, L.M., Buring, J.E., Ridker, P.M., Steinberg, S., Stefansson, H., Jakobsson, F., Lawlor, D.A., Evans, D.M., Ring, S.M., Färkkilä, M., Artto, V., Kaunisto, M.A., Freilinger, T., Schoenen, J., Frants, R.R., Pelzer, N., Weller, C.M., Zielman, R., Heath, A.C., Madden, P.A.F., Montgomery, G.W., Martin, N.G., Borck, G., Göbel, H., Heinze, A., Heinze-Kuhn, K., Williams, F.M., Hartikainen, A.-L., Pouta, A., van den Ende, J.., Uitterlinden, A.G., Hofman, A., Amin, N., Hottenga, J.J., Vink, J.M., Heikkilä, K., Alexander, M., Muller-Myhsok, B., Schreiber, S, Meitinger, T., Wichmann, H. E., Aromaa, A., Eriksson, J.G., Traynor, B.J., Trabzuni, D., Rossin, E., Lage, K., Jacobs, S.B., Gibbs, J.R., Birney, E., Kaprio, J., Penninx, B.W.J.H., Boomsma, D.I., van Duijn, C.M., Raitakari, O., Jarvelin, M.-R., Zwart, J.A., Cherkas, L., Strachan, D.P., Kubisch, C., Ferrari, M.D., van den Maagdenberg, A.M.J.M., Dichgans, M., Wessman, M., Smith, G.D., Stefansson, K., Daly, M.J., Nyholt, DR, Chasman, D.I., and Palotie, A.

Abstract

Migraine is the most common brain disorder, affecting approximately 14% of the adult population, but its molecular mechanisms are poorly understood. We report the results of a meta-analysis across 29 genome-wide association studies, including a total of 23,285 individuals with migraine (cases) and 95,425 population-matched controls. We identified 12 loci associated with migraine susceptibility (P < 5 × 10

Published
2013
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44. Replication and meta-analysis of common variants identifies a genome-wide significant locus in migraine

Author

Esserlind, A-L, Christensen, A F, Le, H, Kirchmann, M, Hauge, A W, Toyserkani, N M, Hansen, Torben, Grarup, Niels, Werge, T, Steinberg, S, Bettella, F, Stefansson, H, Olesen, J, Esserlind, A-L, Christensen, A F, Le, H, Kirchmann, M, Hauge, A W, Toyserkani, N M, Hansen, Torben, Grarup, Niels, Werge, T, Steinberg, S, Bettella, F, Stefansson, H, and Olesen, J

Abstract

Genetic factors contribute to the aetiology of the prevalent form of migraine without aura (MO) and migraine with typical aura (MTA). Due to the complex inheritance of MO and MTA, the genetic background is still not fully established. In a population-based genome-wide association study by Chasman et al. (Nat Genet 2011: 43: 695-698), three common variants were found to confer risk of migraine at a genome-wide significant level (P <5 × 10(-8) ). We aimed to evaluate the top association single nucleotide polymorphisms (SNPs) from the discovery set by Chasman et al. in a primarily clinic-based Danish and Icelandic cohort.

Published
2013

45. Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Author

Keller, M.F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Buchel, F., Sharma, M., Gibbs, J.R., Schulte, C., Moskvina, V., Durr, A., Holmans, P., Kilarski, L.L., Guerreiro, R., Hernandez, D.G., Brice, A., Ylikotila, P., Stefansson, H., Majamaa, K., Morris, H.R., Williams, N., Gasser, T., Heutink, P., Wood, N.W., Hardy, J., Martinez, M., Singleton, A.B., Nalls, M.A., Donnelly, P., Bloem, B., et al., Keller, M.F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Buchel, F., Sharma, M., Gibbs, J.R., Schulte, C., Moskvina, V., Durr, A., Holmans, P., Kilarski, L.L., Guerreiro, R., Hernandez, D.G., Brice, A., Ylikotila, P., Stefansson, H., Majamaa, K., Morris, H.R., Williams, N., Gasser, T., Heutink, P., Wood, N.W., Hardy, J., Martinez, M., Singleton, A.B., Nalls, M.A., Donnelly, P., Bloem, B., and et al.

Abstract

Item does not contain fulltext, Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

Published
2012

46. Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Author

Keller, M. F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Buchel, F., Sharma, M., Gibbs, J. R., Schulte, C., Moskvina, V., Durr, A., Holmans, P., Kilarski, L. L., Guerreiro, R., Hernandez, D. G., Brice, A., Ylikotila, P., Stefansson, H., Majamaa, K., Morris, H. R., Williams, N., Gasser, T., Heutink, P., Wood, N. W., Hardy, J., Martinez, M., Jankowski, Janusz, Nalls, M. A., Singleton, A. B., Keller, M. F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Buchel, F., Sharma, M., Gibbs, J. R., Schulte, C., Moskvina, V., Durr, A., Holmans, P., Kilarski, L. L., Guerreiro, R., Hernandez, D. G., Brice, A., Ylikotila, P., Stefansson, H., Majamaa, K., Morris, H. R., Williams, N., Gasser, T., Heutink, P., Wood, N. W., Hardy, J., Martinez, M., Jankowski, Janusz, Nalls, M. A., and Singleton, A. B.

Abstract

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17–38, P = 8.08E − 08) phenotypic variance associated with all types of PD, 15% (95% CI −0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17–44, P = 1.34E − 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

Published
2012

47. Pharmacokinetic profile of Oncofid-S after intraperitoneal and intravenous administration in the rat.

Author

Tringali, Giuseppe, Bettella, F, Greco, Maria Cristina, Campisi, M, Renier, D, Navarra, Pierluigi, Tringali, Giuseppe (ORCID:0000-0002-1030-9429), Navarra, Pierluigi (ORCID:0000-0002-4424-650X), Tringali, Giuseppe, Bettella, F, Greco, Maria Cristina, Campisi, M, Renier, D, Navarra, Pierluigi, Tringali, Giuseppe (ORCID:0000-0002-1030-9429), and Navarra, Pierluigi (ORCID:0000-0002-4424-650X)

Abstract

OBJECTIVES: Oncofid-S is a bio-conjugate molecule obtained from the binding of campthotecin, SN-38, to hyaluronic acid. In view of a possible clinical development for loco-regional treatment of peritoneal carcinomatosis, this study aimed to establish the pharmacokinetic profile of Oncofid-S after single intraperitoneal or intravenous administration in the rat. METHODS: Single-dose intraperitoneal or intravenous administrations of Oncofid-S were performed. Groups of six rats were sacrificed at various times (up to 24 and 72 h in i.p. and i.v. study, respectively) after drug injection. Trunk blood, livers and spleens were collected for subsequent analysis. Total SN-38 was assayed by HPLC. KEY FINDINGS: We found that Oncofid-S was poorly absorbed after intraperitoneal injection, the estimated AUC0–72 being less than 2%. The drug was distributed in liver,but not spleen, and was eliminated with a terminal half-life of 16 h. After intravenous dosing, Oncofid-S was found in liver as well as in spleen. CONCLUSIONS: Here we have demonstrated that Oncofid-S administered intraperitoneally in the rat was poorly absorbed into the systemic circulation, even after the administration of an extremely high dose. This finding reinforces the rationale for developing Oncofid-S in the loco-regional intraperitoneal treatment of peritoneal carcinomatosis in man.

Published
2012

50. Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci

Author

Andreassen, O A, Harbo, H F, Wang, Y, Thompson, W K, Schork, A J, Mattingsdal, M, Zuber, V, Bettella, F, Ripke, S, Kelsoe, J R, Kendler, K S, O'Donovan, M C, Sklar, P, McEvoy, L K, Desikan, R S, Lie, B A, Djurovic, S, and Dale, A M

Subjects
false discovery rate, HLA region, multiple sclerosis, polygenic pleiotropy, schizophrenia
Abstract

Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21 856) and multiple sclerosis (MS) (n=43 879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16 731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility.

Published
2015
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