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30 results on '"Lopera F."'

1. Using global team science to identify genetic parkinson's disease worldwide

Author: Vollstedt, E, Kasten, M, Klein, C, Aasly, J, Adler, C, Ahmad-Annuar, A, Albanese, A, Alcalay, R, Al-Mubarak, B, Alvarez, V, Andree-Munoz, B, Annesi, G, Appel-Cresswell, S, Arkadir, D, Armasu, S, Barber, T, Bardien, S, Barkhuizen, M, Barrett, M, Basak, A, Beach, T, Benitez, B, Berg, D, Bhatia, K, Binkofski, F, Blauwendraat, C, Bonifati, V, Borges, V, Bozi, M, Brice, A, Brighina, L, Brockmann, K, Brucke, T, Bruggemann, N, Camacho, M, Cardoso, F, Belin, A, Carr, J, Chan, P, Chang-Castello, J, Chase, B, Chen-Plotkin, A, Ju Chung, S, Cilia, R, Clarimon, J, Clark, L, Cornejo-Olivas, M, Corvol, J, Cosentino, C, Cras, P, Crosiers, D, Damasio, J, Das, P, de Carvalho Aguiar, P, De Michele, G, De Rosa, A, Dieguez, E, Dorszewska, J, Erer, S, Ertan, S, Farrer, M, Fedotova, E, Ferese, R, Ferrarese, C, Ferraz, H, Fiala, O, Foroud, T, Friedman, A, Frigerio, R, Funayama, M, Gambardella, S, Garraux, G, Gatto, E, Genc, G, Giladi, N, Goldwurm, S, Gomez-Esteban, J, Gomez-Garre, P, Gorostidi, A, Grosset, D, Hanagasi, H, Hardy, J, Hassan, A, Hattori, N, Hauser, R, Hedera, P, Hentati, F, Hertz, J, Holton, J, Houlden, H, Hutz, M, Ikeuchi, T, Illarioshkin, S, Inca-Martinez, M, Infante, J, Jankovic, J, Jeon, B, Jesus, S, Jimenez-Del-Rio, M, Kaasinen, V, Kataoka, H, Kawakami, H, Kim, Y, Klivenyi, P, Koks, S, Konig, I, Kostic, V, Koziorowski, D, Kruger, R, Krygowska-Wajs, A, Kulisevsky, J, Lai, D, Lang, A, Ledoux, M, Lesage, S, Lim, S, Lin, C, Lohmann, K, Lopera, F, Lopez, G, Lu, C, Lynch, T, Machaczka, M, Madoev, H, Magalhaes, M, Majamaa, K, Maraganore, D, Marder, K, Markopoulou, K, Martikainen, M, Mata, I, Mazzetti, P, Mellick, G, Menendez-Gonzalez, M, Micheli, F, Mirelman, A, Mir, P, Morino, H, Morris, H, Munhoz, R, Naito, A, Olszewska, D, Ozelius, L, Padmanabhan, S, Paisan-Ruiz, C, Payami, H, Peluso, S, Petkovic, S, Petrucci, S, Pezzoli, G, Pimentel, M, Pirker, W, Pramstaller, P, Pulkes, T, Puschmann, A, Quattrone, A, Raggio, V, Ransmayr, G, Rieder, C, Riess, O, Rodriguez-Porcel, F, Rogaeva, E, Ross, O, Ruiz-Martinez, J, Sammler, E, San Luciano, M, Satake, W, Saunders-Pullman, R, Sazci, A, Scherzer, C, Schrag, A, Schumacher-Schuh, A, Sharma, M, Sidransky, E, Singleton, A, Petersen, M, Smolders, S, Spitz, M, Stefanis, L, Struhal, W, Sue, C, Swan, M, Swanberg, M, Taba, P, Taipa, R, Tan, M, Tan, A, Tan, E, Tang, B, Tayebi, N, Thaler, A, Thomas, A, Toda, T, Toft, M, Torres, L, Tumas, V, Valente, E, Van Broeckhoven, C, Vecsei, L, Velez-Pardo, C, Vidailhet, M, Warner, T, Williams-Gray, C, Winkelmann, J, Woitalla, D, Wood, N, Wszolek, Z, Wu, R, Wu, Y, Xie, T, Yoshino, H, Zhang, B, Zimprich, A, Vollstedt E. -J., Kasten M., Klein C., Aasly J., Adler C., Ahmad-Annuar A., Albanese A., Alcalay R. N., Al-Mubarak B., Alvarez V., Andree-Munoz B., Annesi G., Appel-Cresswell S., Arkadir D., Armasu S., Barber T. R., Bardien S., Barkhuizen M., Barrett M. J., Basak A. N., Beach T., Benitez B. A., Berg D., Bhatia K., Binkofski F., Blauwendraat C., Bonifati V., Borges V., Bozi M., Brice A., Brighina L., Brockmann K., Brucke T., Bruggemann N., Camacho M., Cardoso F., Belin A. C., Carr J., Chan P., Chang-Castello J., Chase B., Chen-Plotkin A., Ju Chung S., Cilia R., Clarimon J., Clark L., Cornejo-Olivas M., Corvol J. -C., Cosentino C., Cras P., Crosiers D., Damasio J., Das P., de Carvalho Aguiar P., De Michele G., De Rosa A., Dieguez E., Dorszewska J., Erer S., Ertan S., Farrer M., Fedotova E., Ferese R., Ferrarese C., Ferraz H., Fiala O., Foroud T., Friedman A., Frigerio R., Funayama M., Gambardella S., Garraux G., Gatto E. M., Genc G., Giladi N., Goldwurm S., Gomez-Esteban J. C., Gomez-Garre P., Gorostidi A., Grosset D., Hanagasi H., Hardy J., Hassan A., Hattori N., Hauser R. A., Hedera P., Hentati F., Hertz J. M., Holton J. L., Houlden H., Hutz M. H., Ikeuchi T., Illarioshkin S., Inca-Martinez M., Infante J., Jankovic J., Jeon B. S., Jesus S., Jimenez-Del-Rio M., Kaasinen V., Kataoka H., Kawakami H., Kim Y. J., Klivenyi P., Koks S., Konig I. R., Kostic V., Koziorowski D., Kruger R., Krygowska-Wajs A., Kulisevsky J., Lai D., Lang A., LeDoux M., Lesage S., Lim S. -Y., Lin C. -H., Lohmann K., Lopera F., Lopez G., Lu C. -S., Lynch T., Machaczka M., Madoev H., Magalhaes M., Majamaa K., Maraganore D., Marder K., Markopoulou K., Martikainen M. H., Mata I., Mazzetti P., Mellick G., Menendez-Gonzalez M., Micheli F., Mirelman A., Mir P., Morino H., Morris H., Munhoz R. P., Naito A., Olszewska D. A., Ozelius L. J., Padmanabhan S., Paisan-Ruiz C., Payami H., Peluso S., Petkovic S., Petrucci S., Pezzoli G., Pimentel M., Pirker W., Pramstaller P. P., Pulkes T., Puschmann A., Quattrone A., Raggio V., Ransmayr G., Rieder C., Riess O., Rodriguez-Porcel F., Rogaeva E., Ross O. A., Ruiz-Martinez J., Sammler E., San Luciano M., Satake W., Saunders-Pullman R., Sazci A., Scherzer C., Schrag A., Schumacher-Schuh A., Sharma M., Sidransky E., Singleton A. B., Petersen M. S., Smolders S., Spitz M., Stefanis L., Struhal W., Sue C. M., Swan M., Swanberg M., Taba P., Taipa R., Tan M., Tan A. H., Tan E. -K., Tang B., Tayebi N., Thaler A., Thomas A., Toda T., Toft M., Torres L., Tumas V., Valente E. M., Van Broeckhoven C., Vecsei L., Velez-Pardo C., Vidailhet M., Warner T. T., Williams-Gray C. H., Winkelmann J., Woitalla D., Wood N. W., Wszolek Z. K., Wu R. -M., Wu Y. -R., Xie T., Yoshino H., Zhang B., Zimprich A., Vollstedt, E, Kasten, M, Klein, C, Aasly, J, Adler, C, Ahmad-Annuar, A, Albanese, A, Alcalay, R, Al-Mubarak, B, Alvarez, V, Andree-Munoz, B, Annesi, G, Appel-Cresswell, S, Arkadir, D, Armasu, S, Barber, T, Bardien, S, Barkhuizen, M, Barrett, M, Basak, A, Beach, T, Benitez, B, Berg, D, Bhatia, K, Binkofski, F, Blauwendraat, C, Bonifati, V, Borges, V, Bozi, M, Brice, A, Brighina, L, Brockmann, K, Brucke, T, Bruggemann, N, Camacho, M, Cardoso, F, Belin, A, Carr, J, Chan, P, Chang-Castello, J, Chase, B, Chen-Plotkin, A, Ju Chung, S, Cilia, R, Clarimon, J, Clark, L, Cornejo-Olivas, M, Corvol, J, Cosentino, C, Cras, P, Crosiers, D, Damasio, J, Das, P, de Carvalho Aguiar, P, De Michele, G, De Rosa, A, Dieguez, E, Dorszewska, J, Erer, S, Ertan, S, Farrer, M, Fedotova, E, Ferese, R, Ferrarese, C, Ferraz, H, Fiala, O, Foroud, T, Friedman, A, Frigerio, R, Funayama, M, Gambardella, S, Garraux, G, Gatto, E, Genc, G, Giladi, N, Goldwurm, S, Gomez-Esteban, J, Gomez-Garre, P, Gorostidi, A, Grosset, D, Hanagasi, H, Hardy, J, Hassan, A, Hattori, N, Hauser, R, Hedera, P, Hentati, F, Hertz, J, Holton, J, Houlden, H, Hutz, M, Ikeuchi, T, Illarioshkin, S, Inca-Martinez, M, Infante, J, Jankovic, J, Jeon, B, Jesus, S, Jimenez-Del-Rio, M, Kaasinen, V, Kataoka, H, Kawakami, H, Kim, Y, Klivenyi, P, Koks, S, Konig, I, Kostic, V, Koziorowski, D, Kruger, R, Krygowska-Wajs, A, Kulisevsky, J, Lai, D, Lang, A, Ledoux, M, Lesage, S, Lim, S, Lin, C, Lohmann, K, Lopera, F, Lopez, G, Lu, C, Lynch, T, Machaczka, M, Madoev, H, Magalhaes, M, Majamaa, K, Maraganore, D, Marder, K, Markopoulou, K, Martikainen, M, Mata, I, Mazzetti, P, Mellick, G, Menendez-Gonzalez, M, Micheli, F, Mirelman, A, Mir, P, Morino, H, Morris, H, Munhoz, R, Naito, A, Olszewska, D, Ozelius, L, Padmanabhan, S, Paisan-Ruiz, C, Payami, H, Peluso, S, Petkovic, S, Petrucci, S, Pezzoli, G, Pimentel, M, Pirker, W, Pramstaller, P, Pulkes, T, Puschmann, A, Quattrone, A, Raggio, V, Ransmayr, G, Rieder, C, Riess, O, Rodriguez-Porcel, F, Rogaeva, E, Ross, O, Ruiz-Martinez, J, Sammler, E, San Luciano, M, Satake, W, Saunders-Pullman, R, Sazci, A, Scherzer, C, Schrag, A, Schumacher-Schuh, A, Sharma, M, Sidransky, E, Singleton, A, Petersen, M, Smolders, S, Spitz, M, Stefanis, L, Struhal, W, Sue, C, Swan, M, Swanberg, M, Taba, P, Taipa, R, Tan, M, Tan, A, Tan, E, Tang, B, Tayebi, N, Thaler, A, Thomas, A, Toda, T, Toft, M, Torres, L, Tumas, V, Valente, E, Van Broeckhoven, C, Vecsei, L, Velez-Pardo, C, Vidailhet, M, Warner, T, Williams-Gray, C, Winkelmann, J, Woitalla, D, Wood, N, Wszolek, Z, Wu, R, Wu, Y, Xie, T, Yoshino, H, Zhang, B, Zimprich, A, Vollstedt E. -J., Kasten M., Klein C., Aasly J., Adler C., Ahmad-Annuar A., Albanese A., Alcalay R. N., Al-Mubarak B., Alvarez V., Andree-Munoz B., Annesi G., Appel-Cresswell S., Arkadir D., Armasu S., Barber T. R., Bardien S., Barkhuizen M., Barrett M. J., Basak A. N., Beach T., Benitez B. A., Berg D., Bhatia K., Binkofski F., Blauwendraat C., Bonifati V., Borges V., Bozi M., Brice A., Brighina L., Brockmann K., Brucke T., Bruggemann N., Camacho M., Cardoso F., Belin A. C., Carr J., Chan P., Chang-Castello J., Chase B., Chen-Plotkin A., Ju Chung S., Cilia R., Clarimon J., Clark L., Cornejo-Olivas M., Corvol J. -C., Cosentino C., Cras P., Crosiers D., Damasio J., Das P., de Carvalho Aguiar P., De Michele G., De Rosa A., Dieguez E., Dorszewska J., Erer S., Ertan S., Farrer M., Fedotova E., Ferese R., Ferrarese C., Ferraz H., Fiala O., Foroud T., Friedman A., Frigerio R., Funayama M., Gambardella S., Garraux G., Gatto E. M., Genc G., Giladi N., Goldwurm S., Gomez-Esteban J. C., Gomez-Garre P., Gorostidi A., Grosset D., Hanagasi H., Hardy J., Hassan A., Hattori N., Hauser R. A., Hedera P., Hentati F., Hertz J. M., Holton J. L., Houlden H., Hutz M. H., Ikeuchi T., Illarioshkin S., Inca-Martinez M., Infante J., Jankovic J., Jeon B. S., Jesus S., Jimenez-Del-Rio M., Kaasinen V., Kataoka H., Kawakami H., Kim Y. J., Klivenyi P., Koks S., Konig I. R., Kostic V., Koziorowski D., Kruger R., Krygowska-Wajs A., Kulisevsky J., Lai D., Lang A., LeDoux M., Lesage S., Lim S. -Y., Lin C. -H., Lohmann K., Lopera F., Lopez G., Lu C. -S., Lynch T., Machaczka M., Madoev H., Magalhaes M., Majamaa K., Maraganore D., Marder K., Markopoulou K., Martikainen M. H., Mata I., Mazzetti P., Mellick G., Menendez-Gonzalez M., Micheli F., Mirelman A., Mir P., Morino H., Morris H., Munhoz R. P., Naito A., Olszewska D. A., Ozelius L. J., Padmanabhan S., Paisan-Ruiz C., Payami H., Peluso S., Petkovic S., Petrucci S., Pezzoli G., Pimentel M., Pirker W., Pramstaller P. P., Pulkes T., Puschmann A., Quattrone A., Raggio V., Ransmayr G., Rieder C., Riess O., Rodriguez-Porcel F., Rogaeva E., Ross O. A., Ruiz-Martinez J., Sammler E., San Luciano M., Satake W., Saunders-Pullman R., Sazci A., Scherzer C., Schrag A., Schumacher-Schuh A., Sharma M., Sidransky E., Singleton A. B., Petersen M. S., Smolders S., Spitz M., Stefanis L., Struhal W., Sue C. M., Swan M., Swanberg M., Taba P., Taipa R., Tan M., Tan A. H., Tan E. -K., Tang B., Tayebi N., Thaler A., Thomas A., Toda T., Toft M., Torres L., Tumas V., Valente E. M., Van Broeckhoven C., Vecsei L., Velez-Pardo C., Vidailhet M., Warner T. T., Williams-Gray C. H., Winkelmann J., Woitalla D., Wood N. W., Wszolek Z. K., Wu R. -M., Wu Y. -R., Xie T., Yoshino H., Zhang B., and Zimprich A.
Published: 2019

2. Analysis and Evaluation of Handwriting in Patients with Parkinson's Disease Using kinematic, Geometrical, and Non-linear Features

Author: Rios-Urrego, C. D., Vásquez-Correa, J. C., Vargas-Bonilla, J. F., Nöth, E., Lopera, F., Orozco-Arroyave, J. R., Rios-Urrego, C. D., Vásquez-Correa, J. C., Vargas-Bonilla, J. F., Nöth, E., Lopera, F., and Orozco-Arroyave, J. R.
Abstract: Background and objectives: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting analysis can help in supporting the diagnosis and in monitoring the progress of the disease. This paper aims to evaluate the importance of different groups of features to model handwriting deficits that appear due to Parkinson's disease; and how those features are able to discriminate between Parkinson's disease patients and healthy subjects. Methods: Features based on kinematic, geometrical and non-linear dynamics analyses were evaluated to classify Parkinson's disease and healthy subjects. Classifiers based on K-nearest neighbors, support vector machines, and random forest were considered. Results: Accuracies of up to $93.1\%$ were obtained in the classification of patients and healthy control subjects. A relevance analysis of the features indicated that those related to speed, acceleration, and pressure are the most discriminant. The automatic classification of patients in different stages of the disease shows $\kappa$ indexes between $0.36$ and $0.44$. Accuracies of up to $83.3\%$ were obtained in a different dataset used only for validation purposes. Conclusions: The results confirmed the negative impact of aging in the classification process when we considered different groups of healthy subjects. In addition, the results reported with the separate validation set comprise a step towards the development of automated tools to support the diagnosis process in clinical practice.
Published: 2020
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3. Using global team science to identify genetic Parkinson's disease worldwide

Author: Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., Zimprich, A., Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., and Zimprich, A.
Abstract: Talks on rare diseases in the field of neurology often start with a statement like this: “About 80% of all rare diseases have a neurologic manifestation and about 80% of those are genetic in origin.” Although these numbers probably represent more of an estimate than well‐documented evidence, rapidly advancing and cost‐effective sequencing technologies have led to the quickly growing identification of patients with hereditary neurological diseases...
Published: 2019

4. Using global team science to identify genetic Parkinson's disease worldwide

Author: Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., Zimprich, A., Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., and Zimprich, A.
Abstract: Talks on rare diseases in the field of neurology often start with a statement like this: “About 80% of all rare diseases have a neurologic manifestation and about 80% of those are genetic in origin.” Although these numbers probably represent more of an estimate than well‐documented evidence, rapidly advancing and cost‐effective sequencing technologies have led to the quickly growing identification of patients with hereditary neurological diseases...
Published: 2019

5. Biological and Cognitive Markers of Presenilin1 E280A Autosomal Dominant Alzheimer's Disease: A Comprehensive Review of the Colombian Kindred.

Author: UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service de neurologie, Fuller, J T, Cronin-Golomb, A, Gatchel, J R, Norton, D J, Guzmán-Vélez, E, Jacobs, H I L, Hanseeuw, Bernard, Pardilla-Delgado, E, Artola, A, Baena, A, Bocanegra, Y, Kosik, K S, Chen, K, Tariot, P N, Johnson, K, Sperling, R A, Reiman, E M, Lopera, F, Quiroz, Y T, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service de neurologie, Fuller, J T, Cronin-Golomb, A, Gatchel, J R, Norton, D J, Guzmán-Vélez, E, Jacobs, H I L, Hanseeuw, Bernard, Pardilla-Delgado, E, Artola, A, Baena, A, Bocanegra, Y, Kosik, K S, Chen, K, Tariot, P N, Johnson, K, Sperling, R A, Reiman, E M, Lopera, F, and Quiroz, Y T
Abstract: The study of individuals with autosomal dominant Alzheimer's disease affords one of the best opportunities to characterize the biological and cognitive changes of Alzheimer's disease that occur over the course of the preclinical and symptomatic stages. Unifying the knowledge gained from the past three decades of research in the world's largest single-mutation autosomal dominant Alzheimer's disease kindred - a family in Antioquia, Colombia with the E280A mutation in the Presenilin1 gene - will provide new directions for Alzheimer's research and a framework for generalizing the findings from this cohort to the more common sporadic form of Alzheimer's disease. As this specific mutation is virtually 100% penetrant for the development of the disease by midlife, we use a previously defined median age of onset for mild cognitive impairment for this cohort to examine the trajectory of the biological and cognitive markers of the disease as a function of the carriers' estimated years to clinical onset. Studies from this cohort suggest that structural and functional brain abnormalities - such as cortical thinning and hyperactivation in memory networks - as well as differences in biofluid and in vivo measurements of Alzheimer's-related pathological proteins distinguish Presenilin1 E280A mutation carriers from non-carriers as early as childhood, or approximately three decades before the median age of onset of clinical symptoms. We conclude our review with discussion on future directions for Alzheimer's disease research, with specific emphasis on ways to design studies that compare the generalizability of research in autosomal dominant Alzheimer's disease to the larger sporadic Alzheimer's disease population.
Published: 2019

6. Using global team science to identify genetic Parkinson's disease worldwide

Author: Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., Zimprich, A., Vollstedt, E‐J, Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad‐Annuar, A., Albanese, A., Alcalay, R., Al‐Mubarak, B., Alvarez, V., Andree‐Muñoz, B., Annesi, G., Appel‐Cresswell, S., Arkadir, D., Armasu, S., Barber, T.R., Bardien, S., Barkhuizen, M., Barrett, M.J., BaŞak, A.N., Beach, T., Benitez, B.A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brüggemann, N., Camacho, M., Cardoso, F., Belin, A.C., Carr, J., Chan, P., Chang‐Castello, J., Chase, B., Chen‐Plotkin, A., Chung, S.J., Cilia, R., Clarimon, J., Clark, L., Cornejo‐Olivas, M., Corvol, J‐C, Cosentino, C., Cras, P., Crosiers, D., Damásio, J., Das, P., Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E.M., Genç, G., Goldwurm, S., Gomez‐Esteban, J.C., Gómez‐Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R.A., Hedera, P., Hentati, F., Hertz, J.M., Holton, J.L., Houlden, H., Hutz, M.H., Ikeuchi, T., Illarioshkin, S., Inca‐Martinez, M., Infante, J., Jankovic, J., Jeon, B.S., Jesús, S., Jimenez‐Del‐Rio, M., Kataoka, H., Kawakami, H., Kim, Y.J., Klivényi, P., Kõks, S., König, I.R., KostiĆ, V., Koziorowski, D., Krüger, R., Krygowska‐Wajs, A., Kulisevsky, J., Lang, A., LeDoux, M., Lesage, S., Lim, S‐Y, Lin, C‐H, Lohmann, K., Lopera, F., Lopez, G., Lu, C‐S, Lynch, T., Machaczka, M., Madoev, H., Magalhães, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M.H., Mata, I., Mazzetti, P., Mellick, G., Menéndez‐González, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R.P., Naito, A., Olszewska, D.A., Ozelius, L.J., Padmanabhan, S., Paisán‐Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P.P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez‐Porcel, F., Rogaeva, E., Ross, O.A., Ruiz‐Martinez, J., Sammler, E., Luciano, M.S., Satake, W., Saunders‐Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher‐Schuh, A., Sharma, M., Sidransky, E., Singleton, A.B., Petersen, M.S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A.H., Tan, E‐K, Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E.M., Van Broeckhoven, C., Vecsei, L., Velez‐Pardo, C., Vidailhet, M., Warner, T.T., Williams‐Gray, C.H., Winkelmann, J., Woitalla, D., Wood, N.W., Wszolek, Z.K., Wu, R‐M, Wu, Y‐R, Xie, T., Yoshino, H., Zhang, B., and Zimprich, A.
Abstract: Talks on rare diseases in the field of neurology often start with a statement like this: “About 80% of all rare diseases have a neurologic manifestation and about 80% of those are genetic in origin.” Although these numbers probably represent more of an estimate than well‐documented evidence, rapidly advancing and cost‐effective sequencing technologies have led to the quickly growing identification of patients with hereditary neurological diseases...
Published: 2019

7. Biological and Cognitive Markers of Presenilin1 E280A Autosomal Dominant Alzheimer's Disease: A Comprehensive Review of the Colombian Kindred.

Author: Fuller, JT, Fuller, JT, Cronin-Golomb, A, Gatchel, JR, Norton, DJ, Guzmán-Vélez, E, Jacobs, HIL, Hanseeuw, B, Pardilla-Delgado, E, Artola, A, Baena, A, Bocanegra, Y, Kosik, KS, Chen, K, Tariot, PN, Johnson, K, Sperling, RA, Reiman, EM, Lopera, F, Quiroz, YT, Fuller, JT, Fuller, JT, Cronin-Golomb, A, Gatchel, JR, Norton, DJ, Guzmán-Vélez, E, Jacobs, HIL, Hanseeuw, B, Pardilla-Delgado, E, Artola, A, Baena, A, Bocanegra, Y, Kosik, KS, Chen, K, Tariot, PN, Johnson, K, Sperling, RA, Reiman, EM, Lopera, F, and Quiroz, YT
Abstract: The study of individuals with autosomal dominant Alzheimer's disease affords one of the best opportunities to characterize the biological and cognitive changes of Alzheimer's disease that occur over the course of the preclinical and symptomatic stages. Unifying the knowledge gained from the past three decades of research in the world's largest single-mutation autosomal dominant Alzheimer's disease kindred - a family in Antioquia, Colombia with the E280A mutation in the Presenilin1 gene - will provide new directions for Alzheimer's research and a framework for generalizing the findings from this cohort to the more common sporadic form of Alzheimer's disease. As this specific mutation is virtually 100% penetrant for the development of the disease by midlife, we use a previously defined median age of onset for mild cognitive impairment for this cohort to examine the trajectory of the biological and cognitive markers of the disease as a function of the carriers' estimated years to clinical onset. Studies from this cohort suggest that structural and functional brain abnormalities - such as cortical thinning and hyperactivation in memory networks - as well as differences in biofluid and in vivo measurements of Alzheimer's-related pathological proteins distinguish Presenilin1 E280A mutation carriers from non-carriers as early as childhood, or approximately three decades before the median age of onset of clinical symptoms. We conclude our review with discussion on future directions for Alzheimer's disease research, with specific emphasis on ways to design studies that compare the generalizability of research in autosomal dominant Alzheimer's disease to the larger sporadic Alzheimer's disease population.
Published: 2019

8. Using global team science to identify genetic parkinson's disease worldwide

Author: Vollstedt, E. -J., Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad-Annuar, A., Albanese, Alberto, Alcalay, R. N., Al-Mubarak, B., Alvarez, V., Andree-Munoz, B., Annesi, G., Appel-Cresswell, S., Arkadir, D., Armasu, S., Barber, T. R., Bardien, S., Barkhuizen, M., Barrett, M. J., Basak, A. N., Beach, T., Benitez, B. A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brucke, T., Bruggemann, N., Camacho, M., Cardoso, F., Belin, A. C., Carr, J., Chan, P., Chang-Castello, J., Chase, B., Chen-Plotkin, A., Ju Chung, S., Cilia, R., Clarimon, J., Clark, L., Cornejo-Olivas, M., Corvol, J. -C., Cosentino, C., Cras, P., Crosiers, D., Damasio, J., Das, P., de Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E. M., Genc, G., Giladi, N., Goldwurm, S., Gomez-Esteban, J. C., Gomez-Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R. A., Hedera, P., Hentati, F., Hertz, J. M., Holton, J. L., Houlden, H., Hutz, M. H., Ikeuchi, T., Illarioshkin, S., Inca-Martinez, M., Infante, J., Jankovic, J., Jeon, B. S., Jesus, S., Jimenez-Del-Rio, M., Kaasinen, V., Kataoka, H., Kawakami, H., Kim, Y. J., Klivenyi, P., Koks, S., Konig, I. R., Kostic, V., Koziorowski, D., Kruger, R., Krygowska-Wajs, A., Kulisevsky, J., Lai, D., Lang, A., Ledoux, M., Lesage, S., Lim, S. -Y., Lin, C. -H., Lohmann, K., Lopera, F., Lopez, G., Lu, C. -S., Lynch, T., Machaczka, M., Madoev, H., Magalhaes, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M. H., Mata, I., Mazzetti, P., Mellick, G., Menendez-Gonzalez, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R. P., Naito, A., Olszewska, D. A., Ozelius, L. J., Padmanabhan, S., Paisan-Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P. P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez-Porcel, F., Rogaeva, E., Ross, O. A., Ruiz-Martinez, J., Sammler, E., San Luciano, M., Satake, W., Saunders-Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher-Schuh, A., Sharma, M., Sidransky, E., Singleton, A. B., Petersen, M. S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C. M., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A. H., Tan, E. -K., Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E. M., Van Broeckhoven, C., Vecsei, L., Velez-Pardo, C., Vidailhet, M., Warner, T. T., Williams-Gray, C. H., Winkelmann, J., Woitalla, D., Wood, N. W., Wszolek, Z. K., Wu, R. -M., Wu, Y. -R., Xie, T., Yoshino, H., Zhang, B., Zimprich, A., Albanese A. (ORCID:0000-0002-5864-0006), Vollstedt, E. -J., Kasten, M., Klein, C., Aasly, J., Adler, C., Ahmad-Annuar, A., Albanese, Alberto, Alcalay, R. N., Al-Mubarak, B., Alvarez, V., Andree-Munoz, B., Annesi, G., Appel-Cresswell, S., Arkadir, D., Armasu, S., Barber, T. R., Bardien, S., Barkhuizen, M., Barrett, M. J., Basak, A. N., Beach, T., Benitez, B. A., Berg, D., Bhatia, K., Binkofski, F., Blauwendraat, C., Bonifati, V., Borges, V., Bozi, M., Brice, A., Brighina, L., Brockmann, K., Brucke, T., Bruggemann, N., Camacho, M., Cardoso, F., Belin, A. C., Carr, J., Chan, P., Chang-Castello, J., Chase, B., Chen-Plotkin, A., Ju Chung, S., Cilia, R., Clarimon, J., Clark, L., Cornejo-Olivas, M., Corvol, J. -C., Cosentino, C., Cras, P., Crosiers, D., Damasio, J., Das, P., de Carvalho Aguiar, P., De Michele, G., De Rosa, A., Dieguez, E., Dorszewska, J., Erer, S., Ertan, S., Farrer, M., Fedotova, E., Ferese, R., Ferrarese, C., Ferraz, H., Fiala, O., Foroud, T., Friedman, A., Frigerio, R., Funayama, M., Gambardella, S., Garraux, G., Gatto, E. M., Genc, G., Giladi, N., Goldwurm, S., Gomez-Esteban, J. C., Gomez-Garre, P., Gorostidi, A., Grosset, D., Hanagasi, H., Hardy, J., Hassan, A., Hattori, N., Hauser, R. A., Hedera, P., Hentati, F., Hertz, J. M., Holton, J. L., Houlden, H., Hutz, M. H., Ikeuchi, T., Illarioshkin, S., Inca-Martinez, M., Infante, J., Jankovic, J., Jeon, B. S., Jesus, S., Jimenez-Del-Rio, M., Kaasinen, V., Kataoka, H., Kawakami, H., Kim, Y. J., Klivenyi, P., Koks, S., Konig, I. R., Kostic, V., Koziorowski, D., Kruger, R., Krygowska-Wajs, A., Kulisevsky, J., Lai, D., Lang, A., Ledoux, M., Lesage, S., Lim, S. -Y., Lin, C. -H., Lohmann, K., Lopera, F., Lopez, G., Lu, C. -S., Lynch, T., Machaczka, M., Madoev, H., Magalhaes, M., Majamaa, K., Maraganore, D., Marder, K., Markopoulou, K., Martikainen, M. H., Mata, I., Mazzetti, P., Mellick, G., Menendez-Gonzalez, M., Micheli, F., Mirelman, A., Mir, P., Morino, H., Morris, H., Munhoz, R. P., Naito, A., Olszewska, D. A., Ozelius, L. J., Padmanabhan, S., Paisan-Ruiz, C., Payami, H., Peluso, S., Petkovic, S., Petrucci, S., Pezzoli, G., Pimentel, M., Pirker, W., Pramstaller, P. P., Pulkes, T., Puschmann, A., Quattrone, A., Raggio, V., Ransmayr, G., Rieder, C., Riess, O., Rodriguez-Porcel, F., Rogaeva, E., Ross, O. A., Ruiz-Martinez, J., Sammler, E., San Luciano, M., Satake, W., Saunders-Pullman, R., Sazci, A., Scherzer, C., Schrag, A., Schumacher-Schuh, A., Sharma, M., Sidransky, E., Singleton, A. B., Petersen, M. S., Smolders, S., Spitz, M., Stefanis, L., Struhal, W., Sue, C. M., Swan, M., Swanberg, M., Taba, P., Taipa, R., Tan, M., Tan, A. H., Tan, E. -K., Tang, B., Tayebi, N., Thaler, A., Thomas, A., Toda, T., Toft, M., Torres, L., Tumas, V., Valente, E. M., Van Broeckhoven, C., Vecsei, L., Velez-Pardo, C., Vidailhet, M., Warner, T. T., Williams-Gray, C. H., Winkelmann, J., Woitalla, D., Wood, N. W., Wszolek, Z. K., Wu, R. -M., Wu, Y. -R., Xie, T., Yoshino, H., Zhang, B., Zimprich, A., and Albanese A. (ORCID:0000-0002-5864-0006)
Abstract: Talks on rare diseases in the field of neurology often start with a statement like this: “About 80% of all rare diseases have a neurologic manifestation and about 80% of those are genetic in origin.” Although these numbers probably represent more of an estimate than well-documented evidence, rapidly advancing and cost-effective sequencing technologies have led to the quickly growing identification of patients with hereditary neurological diseases. Although the importance of genetics for diagnosis and genetic counseling is undisputed, the recent development of first genetargeted therapies entering clinical trial1,2 is adding an important new layer to the (re-)consideration of genetic testing in neurology. However, establishing accurate genotype– phenotype and genotype–treatment relationships requires large sample sizes. Systematic reviews can serve as instruments to combine information from several small samples, but unfortunately, this is often complicated by inconsistent and incomplete reporting of clinical and genetic data across studies. Thus, large multicenter approaches are necessary to systematically and uniformly characterize patients with genetic neurologic conditions and to eventually establish sizable clinical trial-ready cohorts.
Published: 2019

9. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

10. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

11. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

12. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

13. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

14. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

15. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

Author: Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J., Valencia, G. M., Anaya, J. A., and Caro-Lopera, F. J.
Abstract: Revista oficial de la Asociación Española de Teledetección, [EN] This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling., [ES] En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesa-das con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos
Published: 2016

16. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease

Author: Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, De Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, Kosik, KS, Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, De Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, and Kosik, KS
Abstract: We have sequenced the complete genomes of 72 individuals affected with early-onset familial Alzheimer's disease caused by an autosomal dominant, highly penetrant mutation in the presenilin-1 (PSEN1) gene, and performed genome-wide association testing to identify variants that modify age at onset (AAO) of Alzheimer's disease. Our analysis identified a haplotype of single-nucleotide polymorphisms (SNPs) on chromosome 17 within a chemokine gene cluster associated with delayed onset of mild-cognitive impairment and dementia. Individuals carrying this haplotype had a mean AAO of mild-cognitive impairment at 51.0±5.2 years compared with 41.1±7.4 years for those without these SNPs. This haplotype thus appears to modify Alzheimer's AAO, conferring a large (∼10 years) protective effect. The associated locus harbors several chemokines including eotaxin-1 encoded by CCL11, and the haplotype includes a missense polymorphism in this gene. Validating this association, we found plasma eotaxin-1 levels were correlated with disease AAO in an independent cohort from the University of California San Francisco Memory and Aging Center. In this second cohort, the associated haplotype disrupted the typical age-associated increase of eotaxin-1 levels, suggesting a complex regulatory role for this haplotype in the general population. Altogether, these results suggest eotaxin-1 as a novel modifier of Alzheimer's disease AAO and open potential avenues for therapy.
Published: 2015

17. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease.

Author: Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, de Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, Kosik, KS, Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, de Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, and Kosik, KS
Abstract: We have sequenced the complete genomes of 72 individuals affected with early-onset familial Alzheimer's disease caused by an autosomal dominant, highly penetrant mutation in the presenilin-1 (PSEN1) gene, and performed genome-wide association testing to identify variants that modify age at onset (AAO) of Alzheimer's disease. Our analysis identified a haplotype of single-nucleotide polymorphisms (SNPs) on chromosome 17 within a chemokine gene cluster associated with delayed onset of mild-cognitive impairment and dementia. Individuals carrying this haplotype had a mean AAO of mild-cognitive impairment at 51.0 ± 5.2 years compared with 41.1 ± 7.4 years for those without these SNPs. This haplotype thus appears to modify Alzheimer's AAO, conferring a large (~10 years) protective effect. The associated locus harbors several chemokines including eotaxin-1 encoded by CCL11, and the haplotype includes a missense polymorphism in this gene. Validating this association, we found plasma eotaxin-1 levels were correlated with disease AAO in an independent cohort from the University of California San Francisco Memory and Aging Center. In this second cohort, the associated haplotype disrupted the typical age-associated increase of eotaxin-1 levels, suggesting a complex regulatory role for this haplotype in the general population. Altogether, these results suggest eotaxin-1 as a novel modifier of Alzheimer's disease AAO and open potential avenues for therapy.
Published: 2015

18. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease

Author: Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, De Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, Kosik, KS, Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, De Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, and Kosik, KS
Abstract: We have sequenced the complete genomes of 72 individuals affected with early-onset familial Alzheimer's disease caused by an autosomal dominant, highly penetrant mutation in the presenilin-1 (PSEN1) gene, and performed genome-wide association testing to identify variants that modify age at onset (AAO) of Alzheimer's disease. Our analysis identified a haplotype of single-nucleotide polymorphisms (SNPs) on chromosome 17 within a chemokine gene cluster associated with delayed onset of mild-cognitive impairment and dementia. Individuals carrying this haplotype had a mean AAO of mild-cognitive impairment at 51.0±5.2 years compared with 41.1±7.4 years for those without these SNPs. This haplotype thus appears to modify Alzheimer's AAO, conferring a large (∼10 years) protective effect. The associated locus harbors several chemokines including eotaxin-1 encoded by CCL11, and the haplotype includes a missense polymorphism in this gene. Validating this association, we found plasma eotaxin-1 levels were correlated with disease AAO in an independent cohort from the University of California San Francisco Memory and Aging Center. In this second cohort, the associated haplotype disrupted the typical age-associated increase of eotaxin-1 levels, suggesting a complex regulatory role for this haplotype in the general population. Altogether, these results suggest eotaxin-1 as a novel modifier of Alzheimer's disease AAO and open potential avenues for therapy.
Published: 2015

19. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease.

Author: Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, de Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, Kosik, KS, Lalli, MA, Lalli, MA, Bettcher, BM, Arcila, ML, Garcia, G, Guzman, C, Madrigal, L, Ramirez, L, Acosta-Uribe, J, Baena, A, Wojta, KJ, Coppola, G, Fitch, R, de Both, MD, Huentelman, MJ, Reiman, EM, Brunkow, ME, Glusman, G, Roach, JC, Kao, AW, Lopera, F, and Kosik, KS
Abstract: We have sequenced the complete genomes of 72 individuals affected with early-onset familial Alzheimer's disease caused by an autosomal dominant, highly penetrant mutation in the presenilin-1 (PSEN1) gene, and performed genome-wide association testing to identify variants that modify age at onset (AAO) of Alzheimer's disease. Our analysis identified a haplotype of single-nucleotide polymorphisms (SNPs) on chromosome 17 within a chemokine gene cluster associated with delayed onset of mild-cognitive impairment and dementia. Individuals carrying this haplotype had a mean AAO of mild-cognitive impairment at 51.0 ± 5.2 years compared with 41.1 ± 7.4 years for those without these SNPs. This haplotype thus appears to modify Alzheimer's AAO, conferring a large (~10 years) protective effect. The associated locus harbors several chemokines including eotaxin-1 encoded by CCL11, and the haplotype includes a missense polymorphism in this gene. Validating this association, we found plasma eotaxin-1 levels were correlated with disease AAO in an independent cohort from the University of California San Francisco Memory and Aging Center. In this second cohort, the associated haplotype disrupted the typical age-associated increase of eotaxin-1 levels, suggesting a complex regulatory role for this haplotype in the general population. Altogether, these results suggest eotaxin-1 as a novel modifier of Alzheimer's disease AAO and open potential avenues for therapy.
Published: 2015

20. Neuropathological differences between familial Alzheimer's disease (FAD) caused by Presenilin mutation E280A and sporadic Alzheimer's disease (SAD)

Author: Sepulveda-Falla, D, Dinkel, F, Dohler, F, Hagel, C, Lopera, F, Glatzel, M, Sepulveda-Falla, D, Dinkel, F, Dohler, F, Hagel, C, Lopera, F, and Glatzel, M
Published: 2015

21. Neuropathological differences between familial Alzheimer's disease (FAD) caused by Presenilin mutation E280A and sporadic Alzheimer's disease (SAD)

Author: Sepulveda-Falla, D, Dinkel, F, Dohler, F, Hagel, C, Lopera, F, Glatzel, M, Sepulveda-Falla, D, Dinkel, F, Dohler, F, Hagel, C, Lopera, F, and Glatzel, M
Published: 2015

22. A cooperative interaction between LPHN3 and 11q doubles the risk for ADHD.

Author: Jain, M, Jain, M, Vélez, JI, Acosta, MT, Palacio, LG, Balog, J, Roessler, E, Pineda, D, Londoño, AC, Palacio, JD, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Seitz, C, Freitag, CM, Palmason, H, Meyer, J, Romanos, M, Walitza, S, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K-P, Swanson, J, Castellanos, FX, Bailey-Wilson, JE, Arcos-Burgos, M, Muenke, M, Jain, M, Jain, M, Vélez, JI, Acosta, MT, Palacio, LG, Balog, J, Roessler, E, Pineda, D, Londoño, AC, Palacio, JD, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Seitz, C, Freitag, CM, Palmason, H, Meyer, J, Romanos, M, Walitza, S, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K-P, Swanson, J, Castellanos, FX, Bailey-Wilson, JE, Arcos-Burgos, M, and Muenke, M
Abstract: In previous studies of a genetic isolate, we identified significant linkage of attention deficit hyperactivity disorder (ADHD) to 4q, 5q, 8q, 11q and 17p. The existence of unique large size families linked to multiple regions, and the fact that these families came from an isolated population, we hypothesized that two-locus interaction contributions to ADHD were plausible. Several analytical models converged to show significant interaction between 4q and 11q (P<1 × 10(-8)) and 11q and 17p (P<1 × 10(-6)). As we have identified that common variants of the LPHN3 gene were responsible for the 4q linkage signal, we focused on 4q-11q interaction to determine that single-nucleotide polymorphisms (SNPs) harbored in the LPHN3 gene interact with SNPs spanning the 11q region that contains DRD2 and NCAM1 genes, to double the risk of developing ADHD. This interaction not only explains genetic effects much better than taking each of these loci effects by separated but also differences in brain metabolism as depicted by proton magnetic resonance spectroscopy data and pharmacogenetic response to stimulant medication. These findings not only add information about how high order genetic interactions might be implicated in conferring susceptibility to develop ADHD but also show that future studies of the effects of genetic interactions on ADHD clinical information will help to shape predictive models of individual outcome.
Published: 2012

23. Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

Author: Velez, J I, Velez, J I, Chandrasekharappa, S C, Henao, E, Martinez, A F, Harper, U, Jones, M, Solomon, B D, Lopez, L, Garcia, G, Aguirre-Acevedo, D C, Acosta-Baena, N, Correa, J C, Lopera-Gomez, C M, Jaramillo-Elorza, M C, Rivera, D, Kosik, K S, Schork, N J, Swanson, J M, Lopera, F, Arcos-Burgos, M, Velez, J I, Velez, J I, Chandrasekharappa, S C, Henao, E, Martinez, A F, Harper, U, Jones, M, Solomon, B D, Lopez, L, Garcia, G, Aguirre-Acevedo, D C, Acosta-Baena, N, Correa, J C, Lopera-Gomez, C M, Jaramillo-Elorza, M C, Rivera, D, Kosik, K S, Schork, N J, Swanson, J M, Lopera, F, and Arcos-Burgos, M
Abstract: The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10−12; NPHP1, rs10173717, P=1.74 × 10−12; CADPS2, rs3757536, P=1.54 × 10−10; GREM2, rs12129547, P=1.69 × 10−13, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.
Published: 2012

24. Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

Author: Velez, J I, Velez, J I, Chandrasekharappa, S C, Henao, E, Martinez, A F, Harper, U, Jones, M, Solomon, B D, Lopez, L, Garcia, G, Aguirre-Acevedo, D C, Acosta-Baena, N, Correa, J C, Lopera-Gomez, C M, Jaramillo-Elorza, M C, Rivera, D, Kosik, K S, Schork, N J, Swanson, J M, Lopera, F, Arcos-Burgos, M, Velez, J I, Velez, J I, Chandrasekharappa, S C, Henao, E, Martinez, A F, Harper, U, Jones, M, Solomon, B D, Lopez, L, Garcia, G, Aguirre-Acevedo, D C, Acosta-Baena, N, Correa, J C, Lopera-Gomez, C M, Jaramillo-Elorza, M C, Rivera, D, Kosik, K S, Schork, N J, Swanson, J M, Lopera, F, and Arcos-Burgos, M
Abstract: The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10−12; NPHP1, rs10173717, P=1.74 × 10−12; CADPS2, rs3757536, P=1.54 × 10−10; GREM2, rs12129547, P=1.69 × 10−13, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.
Published: 2012

25. A cooperative interaction between LPHN3 and 11q doubles the risk for ADHD.

Author: Jain, M, Jain, M, Vélez, JI, Acosta, MT, Palacio, LG, Balog, J, Roessler, E, Pineda, D, Londoño, AC, Palacio, JD, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Seitz, C, Freitag, CM, Palmason, H, Meyer, J, Romanos, M, Walitza, S, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K-P, Swanson, J, Castellanos, FX, Bailey-Wilson, JE, Arcos-Burgos, M, Muenke, M, Jain, M, Jain, M, Vélez, JI, Acosta, MT, Palacio, LG, Balog, J, Roessler, E, Pineda, D, Londoño, AC, Palacio, JD, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Seitz, C, Freitag, CM, Palmason, H, Meyer, J, Romanos, M, Walitza, S, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K-P, Swanson, J, Castellanos, FX, Bailey-Wilson, JE, Arcos-Burgos, M, and Muenke, M
Abstract: In previous studies of a genetic isolate, we identified significant linkage of attention deficit hyperactivity disorder (ADHD) to 4q, 5q, 8q, 11q and 17p. The existence of unique large size families linked to multiple regions, and the fact that these families came from an isolated population, we hypothesized that two-locus interaction contributions to ADHD were plausible. Several analytical models converged to show significant interaction between 4q and 11q (P<1 × 10(-8)) and 11q and 17p (P<1 × 10(-6)). As we have identified that common variants of the LPHN3 gene were responsible for the 4q linkage signal, we focused on 4q-11q interaction to determine that single-nucleotide polymorphisms (SNPs) harbored in the LPHN3 gene interact with SNPs spanning the 11q region that contains DRD2 and NCAM1 genes, to double the risk of developing ADHD. This interaction not only explains genetic effects much better than taking each of these loci effects by separated but also differences in brain metabolism as depicted by proton magnetic resonance spectroscopy data and pharmacogenetic response to stimulant medication. These findings not only add information about how high order genetic interactions might be implicated in conferring susceptibility to develop ADHD but also show that future studies of the effects of genetic interactions on ADHD clinical information will help to shape predictive models of individual outcome.
Published: 2012

26. A common variant of the latrophilin 3 gene, LPHN3, confers susceptibility to ADHD and predicts effectiveness of stimulant medication

Author: Arcos-Burgos, M, Jain, M, Acosta, M T, Shively, S, Stanescu, H, Wallis, D, Domené, S, Vélez, J I, Karkera, J D, Balog, J, Berg, K, Kleta, R, Gahl, W A, Roessler, E, Long, R, Lie, J, Pineda, D, Londoño, A C, Palacio, J D, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Johansson, S, Knappskog, P M, Haavik, J, Ribases, M, Cormand, B, Bayes, M, Casas, M, Ramos-Quiroga, J A, Hervas, A, Maher, B S, Faraone, S V, Seitz, C, Freitag, C M, Palmason, H, Meyer, J, Romanos, M, Walitza, S; https://orcid.org/0000-0002-8161-8683, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K P, Swanson, J, Vortmeyer, A, Bailey-Wilson, J E, Castellanos, F X, Muenke, M, Arcos-Burgos, M, Jain, M, Acosta, M T, Shively, S, Stanescu, H, Wallis, D, Domené, S, Vélez, J I, Karkera, J D, Balog, J, Berg, K, Kleta, R, Gahl, W A, Roessler, E, Long, R, Lie, J, Pineda, D, Londoño, A C, Palacio, J D, Arbelaez, A, Lopera, F, Elia, J, Hakonarson, H, Johansson, S, Knappskog, P M, Haavik, J, Ribases, M, Cormand, B, Bayes, M, Casas, M, Ramos-Quiroga, J A, Hervas, A, Maher, B S, Faraone, S V, Seitz, C, Freitag, C M, Palmason, H, Meyer, J, Romanos, M, Walitza, S; https://orcid.org/0000-0002-8161-8683, Hemminger, U, Warnke, A, Romanos, J, Renner, T, Jacob, C, Lesch, K P, Swanson, J, Vortmeyer, A, Bailey-Wilson, J E, Castellanos, F X, and Muenke, M
Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) has a very high heritability (0.8), suggesting that about 80% of phenotypic variance is due to genetic factors. We used the integration of statistical and functional approaches to discover a novel gene that contributes to ADHD. For our statistical approach, we started with a linkage study based on large multigenerational families in a population isolate, followed by fine mapping of targeted regions using a family-based design. Family- and population-based association studies in five samples from disparate regions of the world were used for replication. Brain imaging studies were performed to evaluate gene function. The linkage study discovered a genome region harbored in the Latrophilin 3 gene (LPHN3). In the world-wide samples (total n=6360, with 2627 ADHD cases and 2531 controls) statistical association of LPHN3 and ADHD was confirmed. Functional studies revealed that LPHN3 variants are expressed in key brain regions related to attention and activity, affect metabolism in neural circuits implicated in ADHD, and are associated with response to stimulant medication. Linkage and replicated association of ADHD with a novel non-candidate gene (LPHN3) provide new insights into the genetics, neurobiology, and treatment of ADHD.
Published: 2010

27. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder.

Author: Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J., Buitelaar, J.K., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schafer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E.J.S., Steinhausen, H.C., Meulen, E. van der, Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., Asherson, P., Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J., Buitelaar, J.K., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schafer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E.J.S., Steinhausen, H.C., Meulen, E. van der, Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., and Asherson, P.
Abstract: Contains fulltext : 69243.pdf (publisher's version ) (Closed access), Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.
Published: 2008

28. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder

Author: Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J, Buitelaar, J., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schäfer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E., Steinhausen, H.C., van der Meulen, E., Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., Asherson, P., Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J, Buitelaar, J., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schäfer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E., Steinhausen, H.C., van der Meulen, E., Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., and Asherson, P.
Abstract: Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P
Published: 2008
Full Text: View/download PDF

29. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder.

Author: Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J., Buitelaar, J.K., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schafer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E.J.S., Steinhausen, H.C., Meulen, E. van der, Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., Asherson, P., Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J., Buitelaar, J.K., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schafer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E.J.S., Steinhausen, H.C., Meulen, E. van der, Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., and Asherson, P.
Abstract: Contains fulltext : 69243.pdf (publisher's version ) (Closed access), Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.
Published: 2008

30. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder

Author: Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J, Buitelaar, J., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schäfer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E., Steinhausen, H.C., van der Meulen, E., Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., Asherson, P., Zhou, K., Dempfle, A., Arcos-Burgos, M., Bakker, S.C., Banaschewski, T., Biederman, J, Buitelaar, J., Castellanos, F.X., Doyle, A., Ebstein, R.P., Ekholm, J., Forabosco, P., Franke, B., Freitag, C., Friedel, S., Gill, M., Hebebrand, J., Hinney, A., Jacob, C., Lesch, K.P., Loo, S.K., Lopera, F., McCracken, J.T., McGough, J.J., Meyer, J., Mick, E., Miranda, A., Muenke, M., Mulas, F., Nelson, S.F., Nguyen, T.T., Oades, R.D., Ogdie, M.N., Palacio, J.D., Pineda, D., Reif, A., Renner, T.J., Roeyers, H., Romanos, M., Rothenberger, A., Schäfer, H., Sergeant, J.A., Sinke, R.J., Smalley, S.L., Sonuga-Barke, E., Steinhausen, H.C., van der Meulen, E., Walitza, S., Warnke, A., Lewis, C.M., Faraone, S.V., and Asherson, P.
Abstract: Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P
Published: 2008
Full Text: View/download PDF

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30 results on '"Lopera F."'

1. Using global team science to identify genetic parkinson's disease worldwide

2. Analysis and Evaluation of Handwriting in Patients with Parkinson's Disease Using kinematic, Geometrical, and Non-linear Features

3. Using global team science to identify genetic Parkinson's disease worldwide

4. Using global team science to identify genetic Parkinson's disease worldwide

5. Biological and Cognitive Markers of Presenilin1 E280A Autosomal Dominant Alzheimer's Disease: A Comprehensive Review of the Colombian Kindred.

6. Using global team science to identify genetic Parkinson's disease worldwide

7. Biological and Cognitive Markers of Presenilin1 E280A Autosomal Dominant Alzheimer's Disease: A Comprehensive Review of the Colombian Kindred.

8. Using global team science to identify genetic parkinson's disease worldwide

9. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

10. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

11. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

12. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

13. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

14. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

15. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes

16. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease

17. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease.

18. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease

19. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer's disease.

20. Neuropathological differences between familial Alzheimer's disease (FAD) caused by Presenilin mutation E280A and sporadic Alzheimer's disease (SAD)

21. Neuropathological differences between familial Alzheimer's disease (FAD) caused by Presenilin mutation E280A and sporadic Alzheimer's disease (SAD)

22. A cooperative interaction between LPHN3 and 11q doubles the risk for ADHD.

23. Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

24. Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

25. A cooperative interaction between LPHN3 and 11q doubles the risk for ADHD.

26. A common variant of the latrophilin 3 gene, LPHN3, confers susceptibility to ADHD and predicts effectiveness of stimulant medication

27. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder.

28. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder

29. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder.

30. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder

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