Human genome meeting 2016

Srivastava, A. K.; Wang, Y.; Huang, R.; Skinner, C.; Thompson, T.; Pollard, L.; Wood, T.; Luo, F.; Stevenson, R.; Polimanti, R.; Gelernter, J.; Lin, X.; Lim, I. Y.; Wu, Y.; Teh, A. L.; Chen, L.; Aris, I. M.; Soh, S. E.; Tint, M. T.; MacIsaac, J. L.; Yap, F.; Kwek, K.; Saw, S. M.; Kobor, M. S.; Meaney, M. J.; Godfrey, K. M.; Chong, Y. S.; Holbrook, J. D.; Lee, Y. S.; Gluckman, P. D.; Karnani, N.; Kapoor, A.; Lee, D.; Chakravarti, A.; Maercker, C.; Graf, F.; Boutros, M.; Stamoulis, G.; Santoni, F.; Makrythanasis, P.; Letourneau, A.; Guipponi, M.; Panousis, N.; Garieri, M.; Ribaux, P.; Falconnet, E.; Borel, C.; Antonarakis, S. E.; Kumar, S.; Curran, J.; Blangero, J.; Chatterjee, S.; Kapoor, A.; Akiyama, J.; Auer, D.; Berrios, C.; Pennacchio, L.; Chakravarti, A.; Donti, T. R.; Cappuccio, G.; Miller, M.; Atwal, P.; Kennedy, A.; Cardon, A.; Bacino, C.; Emrick, L.; Hertecant, J.; Baumer, F.; Porter, B.; Bainbridge, M.; Bonnen, P.; Graham, B.; Sutton, R.; Sun, Q.; Elsea, S.; Hu, Z.; Wang, P.; Zhu, Y.; Zhao, J.; Xiong, M.; Bennett, David A.; Hidalgo-Miranda, A.; Romero-Cordoba, S.; Rodriguez-Cuevas, S.; Rebollar-Vega, R.; Tagliabue, E.; Iorio, M.; D’Ippolito, E.; Baroni, S.; Kaczkowski, B.; Tanaka, Y.; Kawaji, H.; Sandelin, A.; Andersson, R.; Itoh, M.; Lassmann, T.; Hayashizaki, Y.; Carninci, P.; Forrest, A. R. R.; Semple, C. A.; Rosenthal, E. A.; Shirts, B.; Amendola, L.; Gallego, C.; Horike-Pyne, M.; Burt, A.; Robertson, P.; Beyers, P.; Nefcy, C.; Veenstra, D.; Hisama, F.; Bennett, R.; Dorschner, M.; Nickerson, D.; Smith, J.; Patterson, K.; Crosslin, D.; Nassir, R.; Zubair, N.; Harrison, T.; Peters, U.; Jarvik, G.; Menghi, F.; Inaki, K.; Woo, X.; Kumar, P.; Grzeda, K.; Malhotra, A.; Kim, H.; Ucar, D.; Shreckengast, P.; Karuturi, K.; Keck, J.; Chuang, J.; Liu, E. T.; Ji, B.; Tyler, A.; Ananda, G.; Carter, G.; Nikbakht, H.; Montagne, M.; Zeinieh, M.; Harutyunyan, A.; Mcconechy, M.; Jabado, N.; Lavigne, P.; Majewski, J.; Goldstein, J. B.; Overman, M.; Varadhachary, G.; Shroff, R.; Wolff, R.; Javle, M.; Futreal, A.; Fogelman, D.; Bravo, L.; Fajardo, W.; Gomez, H.; Castaneda, C.; Rolfo, C.; Pinto, J. A.; Akdemir, K. C.; Chin, L.; Futreal, A.; Patterson, S.; Statz, C.; Mockus, S.; Nikolaev, S. N.; Bonilla, X. I.; Parmentier, L.; King, B.; Bezrukov, F.; Kaya, G.; Zoete, V.; Seplyarskiy, V.; Sharpe, H.; McKee, T.; Letourneau, A.; Ribaux, P.; Popadin, K.; Basset-Seguin, N.; Chaabene, R. Ben; Santoni, F.; Andrianova, M.; Guipponi, M.; Garieri, M.; Verdan, C.; Grosdemange, K.; Sumara, O.; Eilers, M.; Aifantis, I.; Michielin, O.; de Sauvage, F.; Antonarakis, S.; Likhitrattanapisal, S.; Lincoln, S.; Kurian, A.; Desmond, A.; Yang, S.; Kobayashi, Y.; Ford, J.; Ellisen, L.; Peters, T. L.; Alvarez, K. R.; Hollingsworth, E. F.; Lopez-Terrada, D. H.; Hastie, A.; Dzakula, Z.; Pang, A. W.; Lam, E. T.; Anantharaman, T.; Saghbini, M.; Cao, H.; Gonzaga-Jauregui, C.; Ma, L.; King, A.; Rosenzweig, E. Berman; Krishnan, U.; Reid, J. G.; Overton, J. D.; Dewey, F.; Chung, W. K.; Small, K.; DeLuca, A.; Cremers, F.; Lewis, R. A.; Puech, V.; Bakall, B.; Silva-Garcia, R.; Rohrschneider, K.; Leys, M.; Shaya, F. S.; Stone, E.; Sobreira, N. L.; Schiettecatte, F.; Ling, H.; Pugh, E.; Witmer, D.; Hetrick, K.; Zhang, P.; Doheny, K.; Valle, D.; Hamosh, A.; Jhangiani, S. N.; Akdemir, Z. Coban; Bainbridge, M. N.; Charng, W.; Wiszniewski, W.; Gambin, T.; Karaca, E.; Bayram, Y.; Eldomery, M. K.; Posey, J.; Doddapaneni, H.; Hu, J.; Sutton, V. R.; Muzny, D. M.; Boerwinkle, E. A.; Valle, D.; Lupski, J. R.; Gibbs, R. A.; Shekar, S.; Salerno, W.; English, A.; Mangubat, A.; Bruestle, J.; Thorogood, A.; Knoppers, B. M.; Takahashi, H.; Nitta, K. R.; Kozhuharova, A.; Suzuki, A. M.; Sharma, H.; Cotella, D.; Santoro, C.; Zucchelli, S.; Gustincich, S.; Carninci, P.; Mulvihill, J. J.; Baynam, G.; Gahl, W.; Groft, S. C.; Kosaki, K.; Lasko, P.; Melegh, B.; Taruscio, D.; Ghosh, R.; Plon, S.; Scherer, S.; Qin, X.; Sanghvi, R.; Walker, K.; Chiang, T.; Muzny, D.; Wang, L.; Black, J.; Boerwinkle, E.; Weinshilboum, R.; Gibbs, R.; Karpinets, T.; Calderone, T.; Wani, K.; Yu, X.; Creasy, C.; Haymaker, C.; Forget, M.; Nanda, V.; Roszik, J.; Wargo, J.; Haydu, L.; Song, X.; Lazar, A.; Gershenwald, J.; Davies, M.; Bernatchez, C.; Zhang, J.; Futreal, A.; Woodman, S.; Chesler, E. J.; Reynolds, T.; Bubier, J. A.; Phillips, C.; Langston, M. A.; Baker, E. J.; Xiong, M.; Ma, L.; Lin, N.; Amos, C.; Lin, N.; Wang, P.; Zhu, Y.; Zhao, J.; Calhoun, V.; Xiong, M.; Dobretsberger, O.; Egger, M.; Leimgruber, F.; Sadedin, S.; Oshlack, A.; Antonio, V. A. A.; Ono, N.; Ahmed, Z.; Bolisetty, M.; Zeeshan, S.; Anguiano, E.; Ucar, D.; Sarkar, A.; Nandineni, M. R.; Zeng, C.; Shao, J.; Cao, H.; Hastie, A.; Pang, A. W.; Lam, E. T.; Liang, T.; Pham, K.; Saghbini, M.; Dzakula, Z.; Chee-Wei, Y.; Dongsheng, L.; Lai-Ping, W.; Lian, D.; Hee, R. O. Twee; Yunus, Y.; Aghakhanian, F.; Mokhtar, S. S.; Lok-Yung, C. V.; Bhak, J.; Phipps, M.; Shuhua, X.; Yik-Ying, T.; Kumar, V.; Boon-Peng, H.; Campbell, I.; Young, M. -A.; James, P.; Rain, M.; Mohammad, G.; Kukreti, R.; Pasha, Q.; Akilzhanova, A. R.; Guelly, C.; Abilova, Z.; Rakhimova, S.; Akhmetova, A.; Kairov, U.; Trajanoski, S.; Zhumadilov, Z.; Bekbossynova, M.; Schumacher, C.; Sandhu, S.; Harkins, T.; Makarov, V.; Doddapaneni, H.; Glenn, R.; Momin, Z.; Dilrukshi, B.; Chao, H.; Meng, Q.; Gudenkauf, B.; Kshitij, R.; Jayaseelan, J.; Nessner, C.; Lee, S.; Blankenberg, K.; Lewis, L.; Hu, J.; Han, Y.; Dinh, H.; Jireh, S.; Walker, K.; Boerwinkle, E.; Muzny, D.; Gibbs, R.; Hu, J.; Walker, K.; Buhay, C.; Liu, X.; Wang, Q.; Sanghvi, R.; Doddapaneni, H.; Ding, Y.; Veeraraghavan, N.; Yang, Y.; Boerwinkle, E.; Beaudet, A. L.; Eng, C. M.; Muzny, D. M.; Gibbs, R. A.; Worley, K. C. C.; Liu, Y.; Hughes, D. S. T.; Murali, S. C.; Harris, R. A.; English, A. C.; Qin, X.; Hampton, O. A.; Larsen, P.; Beck, C.; Han, Y.; Wang, M.; Doddapaneni, H.; Kovar, C. L.; Salerno, W. J.; Yoder, A.; Richards, S.; Rogers, J.; Lupski, J. R.; Muzny, D. M.; Gibbs, R. A.; Meng, Q.; Bainbridge, M.; Wang, M.; Doddapaneni, H.; Han, Y.; Muzny, D.; Gibbs, R.; Harris, R. A.; Raveenedran, M.; Xue, C.; Dahdouli, M.; Cox, L.; Fan, G.; Ferguson, B.; Hovarth, J.; Johnson, Z.; Kanthaswamy, S.; Kubisch, M.; Platt, M.; Smith, D.; Vallender, E.; Wiseman, R.; Liu, X.; Below, J.; Muzny, D.; Gibbs, R.; Yu, F.; Rogers, J.; Lin, J.; Zhang, Y.; Ouyang, Z.; Moore, A.; Wang, Z.; Hofmann, J.; Purdue, M.; Stolzenberg-Solomon, R.; Weinstein, S.; Albanes, D.; Liu, C. S.; Cheng, W. L.; Lin, T. T.; Lan, Q.; Rothman, N.; Berndt, S.; Chen, E. S.; Bahrami, H.; Khoshzaban, A.; Keshal, S. Heidari; Bahrami, H.; Khoshzaban, A.; Keshal, S. Heidari; Alharbi, K. K. R.; Zhalbinova, M.; Akilzhanova, A.; Rakhimova, S.; Bekbosynova, M.; Myrzakhmetova, S.; Matar, M.; Mili, N.; Molinari, R.; Ma, Y.; Guerrier, S.; Elhawary, N.; Tayeb, M.; Bogari, N.; Qotb, N.; McClymont, S. A.; Hook, P. W.; Goff, L. A.; McCallion, A.; Kong, Y.; Charette, J. R.; Hicks, W. L.; Naggert, J. K.; Zhao, L.; Nishina, P. M.; Edrees, B. M.; Athar, M.; Al-Allaf, F. A.; Taher, M. M.; Khan, W.; Bouazzaoui, A.; Harbi, N. A.; Safar, R.; Al-Edressi, H.; Anazi, A.; Altayeb, N.; Ahmed, M. A.; Alansary, K.; Abduljaleel, Z.; Kratz, A.; Beguin, P.; Poulain, S.; Kaneko, M.; Takahiko, C.; Matsunaga, A.; Kato, S.; Suzuki, A. M.; Bertin, N.; Lassmann, T.; Vigot, R.; Carninci, P.; Plessy, C.; Launey, T.; Graur, D.; Lee, D.; Kapoor, A.; Chakravarti, A.; Friis-Nielsen, J.; Izarzugaza, J. M.; Brunak, S.; Chakraborty, A.; Basak, J.; Mukhopadhyay, A.; Soibam, B. S.; Das, D.; Biswas, N.; Das, S.; Sarkar, S.; Maitra, A.; Panda, C.; Majumder, P.; Morsy, H.; Gaballah, A.; Samir, M.; Shamseya, M.; Mahrous, H.; Ghazal, A.; Arafat, W.; Hashish, M.; Gruber, J. J.; Jaeger, N.; Snyder, M.; Patel, K.; Bowman, S.; Davis, T.; Kraushaar, D.; Emerman, A.; Russello, S.; Henig, N.; Hendrickson, C.; Zhang, K.; Rodriguez-Dorantes, M.; Cruz-Hernandez, C. D.; Garcia-Tobilla, C. D. P.; Solorzano-Rosales, S.; Jäger, N.; Chen, J.; Haile, R.; Hitchins, M.; Brooks, J. D.; Snyder, M.; Jiménez-Morales, S.; Ramírez, M.; Nuñez, J.; Bekker, V.; Leal, Y.; Jiménez, E.; Medina, A.; Hidalgo, A.; Mejía, J.; Halytskiy, V.; Naggert, J.; Collin, G. B.; DeMauro, K.; Hanusek, R.; Nishina, P. M.; Belhassa, K.; Belhassan, K.; Bouguenouch, L.; Samri, I.; Sayel, H.; moufid, FZ.; El Bouchikhi, I.; Trhanint, S.; Hamdaoui, H.; Elotmani, I.; Khtiri, I.; Kettani, O.; Quibibo, L.; Ahagoud, M.; Abbassi, M.; Ouldim, K.; Marusin, A. V.; Kornetov, A. N.; Swarovskaya, M.; Vagaiceva, K.; Stepanov, V.; De La Paz, E. M. Cutiongco; Sy, R.; Nevado, J.; Reganit, P.; Santos, L.; Magno, J. D.; Punzalan, F. E.; Ona, D.; Llanes, E.; Santos-Cortes, R. L.; Tiongco, R.; Aherrera, J.; Abrahan, L.; Pagauitan-Alan, P.; Morelli, K. H.; Domire, J. S.; Pyne, N.; Harper, S.; Burgess, R.; Zhalbinova, M.; Akilzhanova, A.; Rakhimova, S.; Bekbosynova, M.; Myrzakhmetova, S.; Gari, M. A.; Dallol, A.; Alsehli, H.; Gari, A.; Gari, M.; Abuzenadah, A.; Thomas, M.; Sukhai, M.; Garg, S.; Misyura, M.; Zhang, T.; Schuh, A.; Stockley, T.; Kamel-Reid, S.; Sherry, S.; Xiao, C.; Slotta, D.; Rodarmer, K.; Feolo, M.; Kimelman, M.; Godynskiy, G.; O’Sullivan, C.; Yaschenko, E.; Xiao, C.; Yaschenko, E.; Sherry, S.; Rangel-Escareño, C.; Rueda-Zarate, H.; Tayubi, I. A.; Mohammed, R.; Ahmed, I.; Ahmed, T.; Seth, S.; Amin, S.; Song, X.; Mao, X.; Sun, H.; Verhaak, R. G.; Futreal, A.; Zhang, J.; Whiite, S. J.; Chiang, T.; English, A.; Farek, J.; Kahn, Z.; Salerno, W.; Veeraraghavan, N.; Boerwinkle, E.; Gibbs, R.; Kasukawa, T.; Lizio, M.; Harshbarger, J.; Hisashi, S.; Severin, J.; Imad, A.; Sahin, S.; Freeman, T. C.; Baillie, K.; Sandelin, A.; Carninci, P.; Forrest, A. R. R.; Kawaji, H.; Salerno, W.; English, A.; Shekar, S. N.; Mangubat, A.; Bruestle, J.; Boerwinkle, E.; Gibbs, R. A.; Salem, A. H.; Ali, M.; Ibrahim, A.; Ibrahim, M.; Barrera, H. A.; Garza, L.; Torres, J. A.; Barajas, V.; Ulloa-Aguirre, A.; Kershenobich, D.; Mortaji, Shahroj; Guizar, Pedro; Loera, Eliezer; Moreno, Karen; De León, Adriana; Monsiváis, Daniela; Gómez, Jackeline; Cardiel, Raquel; Fernandez-Lopez, J. C.; Bonifaz-Peña, V.; Rangel-Escareño, C.; Hidalgo-Miranda, A.; Contreras, A. V.; Polfus, L.; Wang, X.; Philip, V.; Carter, G.; Abuzenadah, A. A.; Gari, M.; Turki, R.; Dallol, A.; Uyar, A.; Kaygun, A.; Zaman, S.; Marquez, E.; George, J.; Ucar, D.; Hendrickson, C. L.; Emerman, A.; Kraushaar, D.; Bowman, S.; Henig, N.; Davis, T.; Russello, S.; Patel, K.; Starr, D. B.; Baird, M.; Kirkpatrick, B.; Sheets, K.; Nitsche, R.; Prieto-Lafuente, L.; Landrum, M.; Lee, J.; Rubinstein, W.; Maglott, D.; Thavanati, P. K. R.; de Dios, A. Escoto; Hernandez, R. E. Navarro; Aldrate, M. E. Aguilar; Mejia, M. R. Ruiz; Kanala, K. R. R.; Abduljaleel, Z.; Khan, W.; Al-Allaf, F. A.; Athar, M.; Taher, M. M.; Shahzad, N.; Bouazzaoui, A.; Huber, E.; Dan, A.; Al-Allaf, F. A.; Herr, W.; Sprotte, G.; Köstler, J.; Hiergeist, A.; Gessner, A.; Andreesen, R.; Holler, E.; Al-Allaf, F.; Alashwal, A.; Abduljaleel, Z.; Taher, M.; Bouazzaoui, A.; Abalkhail, H.; Al-Allaf, A.; Bamardadh, R.; Athar, M.; Filiptsova, O.; Kobets, M.; Kobets, Y.; Burlaka, I.; Timoshyna, I.; Filiptsova, O.; Kobets, M. N.; Kobets, Y.; Burlaka, I.; Timoshyna, I.; Filiptsova, O.; Kobets, M. N.; Kobets, Y.; Burlaka, I.; Timoshyna, I.; Al-allaf, F. A.; Mohiuddin, M. T.; Zainularifeen, A.; Mohammed, A.; Abalkhail, H.; Owaidah, T.; Bouazzaoui, A.

doi:10.1186/s40246-016-0063-5

Volume 10 Supplement 1

Human Genome Meeting 2016

Meeting Abstracts
Open Access
Published: 26 May 2016

Human genome meeting 2016

Houston, TX, USA. 28 February - 2 March 2016

A. K. Srivastava¹,
Y. Wang²,
R. Huang³,
C. Skinner¹,
T. Thompson³,
L. Pollard³,
T. Wood³,
F. Luo²,
R. Stevenson¹,
R. Polimanti⁴,
J. Gelernter^4,5,6,
X. Lin⁷,
I. Y. Lim⁷,
Y. Wu⁷,
A. L. Teh⁷,
L. Chen⁷,
I. M. Aris⁷,
S. E. Soh⁷,
M. T. Tint⁸,
J. L. MacIsaac⁹,
F. Yap¹⁰,
K. Kwek¹⁰,
S. M. Saw⁸,
M. S. Kobor⁹,
M. J. Meaney⁷,
K. M. Godfrey¹¹,
Y. S. Chong⁷,
J. D. Holbrook⁷,
Y. S. Lee⁷,
P. D. Gluckman^7,12,
N. Karnani⁷,
GUSTO study group,
A. Kapoor¹³,
D. Lee¹³,
A. Chakravarti¹³,
C. Maercker¹⁴,
F. Graf¹⁵,
M. Boutros¹⁵,
G. Stamoulis¹⁶,
F. Santoni¹⁷,
P. Makrythanasis¹⁷,
A. Letourneau¹⁶,
M. Guipponi¹⁷,
N. Panousis¹⁶,
M. Garieri¹⁶,
P. Ribaux¹⁶,
E. Falconnet¹⁶,
C. Borel¹⁶,
S. E. Antonarakis^16,17,18,
S. Kumar¹⁹,
J. Curran²⁰,
J. Blangero²⁰,
S. Chatterjee²¹,
A. Kapoor²¹,
J. Akiyama²²,
D. Auer²¹,
C. Berrios²¹,
L. Pennacchio²²,
A. Chakravarti²¹,
T. R. Donti²³,
G. Cappuccio²⁴,
M. Miller²³,
P. Atwal²³,
A. Kennedy²⁵,
A. Cardon²⁶,
C. Bacino²³,
L. Emrick²⁶,
J. Hertecant²⁷,
F. Baumer²⁸,
B. Porter²⁸,
M. Bainbridge²³,
P. Bonnen²³,
B. Graham²³,
R. Sutton²³,
Q. Sun²³,
S. Elsea²³,
Z. Hu²⁹,
P. Wang³⁰,
Y. Zhu³¹,
J. Zhao³¹,
M. Xiong³⁰,
David A. Bennett³²,
A. Hidalgo-Miranda³³,
S. Romero-Cordoba³³,
S. Rodriguez-Cuevas³⁴,
R. Rebollar-Vega³³,
E. Tagliabue³⁵,
M. Iorio³⁵,
E. D’Ippolito³⁵,
S. Baroni³⁵,
B. Kaczkowski³⁶,
Y. Tanaka³⁷,
H. Kawaji³⁷,
A. Sandelin³⁸,
R. Andersson³⁸,
M. Itoh³⁶,
T. Lassmann³⁹,
The FANTOM5 Consortium,
Y. Hayashizaki⁴⁰,
P. Carninci³⁶,
A. R. R. Forrest⁴¹,
C. A. Semple⁴²,
E. A. Rosenthal⁴³,
B. Shirts⁴³,
L. Amendola⁴³,
C. Gallego⁴⁴,
M. Horike-Pyne⁴³,
A. Burt⁴³,
P. Robertson⁴³,
P. Beyers⁴³,
C. Nefcy⁴³,
D. Veenstra⁴³,
F. Hisama⁴³,
R. Bennett⁴³,
M. Dorschner⁴³,
D. Nickerson⁴³,
J. Smith⁴³,
K. Patterson⁴³,
D. Crosslin⁴³,
R. Nassir⁴⁵,
N. Zubair⁴⁶,
T. Harrison⁴⁶,
U. Peters^43,46,
G. Jarvik⁴³,
NHLBI GO Exome Sequencing Project,
F. Menghi⁴⁷,
K. Inaki⁴⁷,
X. Woo⁴⁷,
P. Kumar⁴⁷,
K. Grzeda⁴⁷,
A. Malhotra⁴⁷,
H. Kim⁴⁷,
D. Ucar⁴⁷,
P. Shreckengast⁴⁷,
K. Karuturi⁴⁷,
J. Keck⁴⁸,
J. Chuang⁴⁷,
E. T. Liu⁴⁷,
B. Ji⁴⁹,
A. Tyler⁴⁹,
G. Ananda⁴⁹,
G. Carter⁴⁹,
H. Nikbakht⁵⁰,
M. Montagne⁵¹,
M. Zeinieh⁵⁰,
A. Harutyunyan⁵⁰,
M. Mcconechy⁵⁰,
N. Jabado⁵²,
P. Lavigne⁵¹,
J. Majewski⁵⁰,
J. B. Goldstein⁵³,
M. Overman⁵⁴,
G. Varadhachary⁵⁴,
R. Shroff⁵⁴,
R. Wolff⁵⁴,
M. Javle⁵⁴,
A. Futreal⁵³,
D. Fogelman⁵⁴,
L. Bravo⁵⁵,
W. Fajardo⁵⁵,
H. Gomez⁵⁶,
C. Castaneda⁵⁶,
C. Rolfo⁵⁷,
J. A. Pinto⁵⁶,
K. C. Akdemir⁵⁸,
L. Chin⁵⁹,
A. Futreal⁵⁸,
ICGC PCAWG Structural Alterations Group,
S. Patterson⁶⁰,
C. Statz⁶⁰,
S. Mockus⁶⁰,
S. N. Nikolaev⁶¹,
X. I. Bonilla⁶¹,
L. Parmentier⁶²,
B. King⁶³,
F. Bezrukov⁶⁴,
G. Kaya⁶⁵,
V. Zoete⁶⁶,
V. Seplyarskiy⁶⁷,
H. Sharpe⁶⁸,
T. McKee⁶⁹,
A. Letourneau⁷⁰,
P. Ribaux⁷⁰,
K. Popadin⁷⁰,
N. Basset-Seguin⁷¹,
R. Ben Chaabene⁷⁰,
F. Santoni⁷⁰,
M. Andrianova⁶⁷,
M. Guipponi⁷²,
M. Garieri⁷⁰,
C. Verdan⁶⁹,
K. Grosdemange⁶⁵,
O. Sumara⁷³,
M. Eilers⁷³,
I. Aifantis⁶³,
O. Michielin⁶⁶,
F. de Sauvage⁶⁸,
S. Antonarakis⁶¹,
S. Likhitrattanapisal⁷⁴,
S. Lincoln⁷⁵,
A. Kurian⁷⁶,
A. Desmond⁷⁷,
S. Yang⁷⁵,
Y. Kobayashi⁷⁵,
J. Ford⁷⁸,
L. Ellisen⁷⁷,
T. L. Peters⁷⁹,
K. R. Alvarez⁸⁰,
E. F. Hollingsworth⁷⁹,
D. H. Lopez-Terrada^79,80,
A. Hastie⁸¹,
Z. Dzakula⁸¹,
A. W. Pang⁸¹,
E. T. Lam⁸¹,
T. Anantharaman⁸¹,
M. Saghbini⁸¹,
H. Cao⁸¹,
BioNano Genomics,
C. Gonzaga-Jauregui⁸²,
L. Ma⁸³,
A. King⁸²,
E. Berman Rosenzweig^83,84,
U. Krishnan⁸³,
J. G. Reid⁸²,
J. D. Overton⁸²,
F. Dewey⁸²,
W. K. Chung^83,84,
K. Small⁸⁵,
A. DeLuca⁸⁶,
F. Cremers⁸⁷,
R. A. Lewis⁸⁸,
V. Puech⁸⁹,
B. Bakall⁹⁰,
R. Silva-Garcia⁸⁵,
K. Rohrschneider⁹¹,
M. Leys⁹²,
F. S. Shaya⁸⁵,
E. Stone⁹³,
N. L. Sobreira⁹⁴,
F. Schiettecatte⁹⁵,
H. Ling⁹⁶,
E. Pugh⁹⁶,
D. Witmer⁹⁶,
K. Hetrick⁹⁶,
P. Zhang⁹⁶,
K. Doheny⁹⁶,
D. Valle⁹⁴,
A. Hamosh⁹⁴,
S. N. Jhangiani⁹⁷,
Z. Coban Akdemir⁹⁸,
M. N. Bainbridge⁹⁷,
W. Charng⁹⁸,
W. Wiszniewski⁹⁸,
T. Gambin⁹⁸,
E. Karaca⁹⁸,
Y. Bayram⁹⁸,
M. K. Eldomery⁹⁸,
J. Posey⁹⁸,
H. Doddapaneni⁹⁷,
J. Hu⁹⁷,
V. R. Sutton⁹⁸,
D. M. Muzny⁹⁷,
E. A. Boerwinkle⁹⁷,
D. Valle⁹⁹,
J. R. Lupski⁹⁸,
R. A. Gibbs⁹⁷,
S. Shekar¹⁰⁰,
W. Salerno¹⁰¹,
A. English¹⁰¹,
A. Mangubat¹⁰⁰,
J. Bruestle¹⁰⁰,
A. Thorogood¹⁰²,
B. M. Knoppers¹⁰²,
Global Alliance for Genomics and Health - Regulatory and Ethics Working Group,
H. Takahashi¹⁰³,
K. R. Nitta¹⁰³,
A. Kozhuharova¹⁰³,
A. M. Suzuki¹⁰³,
H. Sharma¹⁰³,
D. Cotella¹⁰⁴,
C. Santoro¹⁰⁴,
S. Zucchelli¹⁰⁵,
S. Gustincich¹⁰⁵,
P. Carninci¹⁰³,
J. J. Mulvihill¹⁰⁶,
G. Baynam¹⁰⁷,
W. Gahl¹⁰⁸,
S. C. Groft¹⁰⁹,
K. Kosaki¹¹⁰,
P. Lasko¹¹¹,
B. Melegh¹¹²,
D. Taruscio¹¹³,
R. Ghosh¹¹⁴,
S. Plon¹¹⁴,
S. Scherer¹¹⁵,
X. Qin¹¹⁵,
R. Sanghvi¹¹⁵,
K. Walker¹¹⁵,
T. Chiang¹¹⁵,
D. Muzny¹¹⁵,
L. Wang¹¹⁶,
J. Black¹¹⁷,
E. Boerwinkle¹¹⁵,
R. Weinshilboum¹¹⁸,
R. Gibbs¹¹⁵,
T. Karpinets¹¹⁹,
T. Calderone¹¹⁹,
K. Wani¹¹⁹,
X. Yu¹¹⁹,
C. Creasy¹¹⁹,
C. Haymaker¹¹⁹,
M. Forget¹¹⁹,
V. Nanda¹¹⁹,
J. Roszik¹¹⁹,
J. Wargo¹¹⁹,
L. Haydu¹¹⁹,
X. Song¹¹⁹,
A. Lazar¹¹⁹,
J. Gershenwald¹¹⁹,
M. Davies¹¹⁹,
C. Bernatchez¹¹⁹,
J. Zhang¹¹⁹,
A. Futreal¹¹⁹,
S. Woodman¹¹⁹,
E. J. Chesler¹²⁰,
T. Reynolds¹²¹,
J. A. Bubier¹²⁰,
C. Phillips¹²²,
M. A. Langston¹²²,
E. J. Baker¹²¹,
M. Xiong¹²³,
L. Ma¹²³,
N. Lin¹²³,
C. Amos¹²⁴,
N. Lin¹²⁵,
P. Wang¹²⁵,
Y. Zhu¹²⁶,
J. Zhao¹²⁶,
V. Calhoun¹²⁷,
M. Xiong¹²⁸,
O. Dobretsberger¹²⁹,
M. Egger¹²⁹,
F. Leimgruber¹²⁹,
S. Sadedin¹³⁰,
A. Oshlack¹³⁰,
Melbourne Genomics Health Alliance,
V. A. A. Antonio¹³¹,
N. Ono¹³¹,
Clark Kendrick C. Go,
Z. Ahmed¹³²,
M. Bolisetty¹³²,
S. Zeeshan¹³²,
E. Anguiano¹³²,
D. Ucar¹³²,
A. Sarkar¹³³,
M. R. Nandineni¹³³,
C. Zeng¹³⁴,
J. Shao¹³⁴,
H. Cao¹³⁵,
A. Hastie¹³⁵,
A. W. Pang¹³⁵,
E. T. Lam¹³⁵,
T. Liang¹³⁵,
K. Pham¹³⁵,
M. Saghbini¹³⁵,
Z. Dzakula¹³⁵,
Y. Chee-Wei¹³⁶,
L. Dongsheng¹³⁷,
W. Lai-Ping¹³⁸,
D. Lian¹³⁷,
R. O. Twee Hee¹³⁸,
Y. Yunus¹³⁹,
F. Aghakhanian¹⁴⁰,
S. S. Mokhtar¹³⁹,
C. V. Lok-Yung¹³⁶,
J. Bhak¹⁴¹,
M. Phipps¹⁴²,
X. Shuhua¹⁴³,
T. Yik-Ying¹³⁸,
V. Kumar¹³⁶,
H. Boon-Peng¹⁴⁴,
I. Campbell¹⁴⁵,
M. -A. Young¹⁴⁶,
P. James¹⁴⁶,
Lifepool,
M. Rain¹⁴⁷,
G. Mohammad¹⁴⁸,
R. Kukreti¹⁴⁷,
Q. Pasha¹⁴⁷,
A. R. Akilzhanova¹⁴⁹,
C. Guelly¹⁵⁰,
Z. Abilova¹⁴⁹,
S. Rakhimova¹⁴⁹,
A. Akhmetova¹⁴⁹,
U. Kairov¹⁴⁹,
S. Trajanoski¹⁵⁰,
Z. Zhumadilov¹⁴⁹,
M. Bekbossynova¹⁵¹,
C. Schumacher¹⁵²,
S. Sandhu¹⁵²,
T. Harkins¹⁵²,
V. Makarov¹⁵²,
H. Doddapaneni¹⁵³,
R. Glenn¹⁵³,
Z. Momin¹⁵³,
B. Dilrukshi¹⁵³,
H. Chao¹⁵³,
Q. Meng¹⁵³,
B. Gudenkauf¹⁵³,
R. Kshitij¹⁵³,
J. Jayaseelan¹⁵³,
C. Nessner¹⁵³,
S. Lee¹⁵³,
K. Blankenberg¹⁵³,
L. Lewis¹⁵³,
J. Hu¹⁵³,
Y. Han¹⁵³,
H. Dinh¹⁵³,
S. Jireh¹⁵³,
K. Walker¹⁵³,
E. Boerwinkle¹⁵³,
D. Muzny¹⁵³,
R. Gibbs¹⁵³,
J. Hu¹⁵⁴,
K. Walker¹⁵⁴,
C. Buhay¹⁵⁴,
X. Liu¹⁵⁴,
Q. Wang¹⁵⁴,
R. Sanghvi¹⁵⁴,
H. Doddapaneni¹⁵⁴,
Y. Ding^154,155,
N. Veeraraghavan¹⁵⁴,
Y. Yang¹⁵⁵,
E. Boerwinkle^154,156,
A. L. Beaudet¹⁵⁵,
C. M. Eng¹⁵⁵,
D. M. Muzny¹⁵⁴,
R. A. Gibbs¹⁵⁴,
K. C. C. Worley¹⁵⁷,
Y. Liu¹⁵⁷,
D. S. T. Hughes¹⁵⁷,
S. C. Murali¹⁵⁷,
R. A. Harris¹⁵⁷,
A. C. English¹⁵⁷,
X. Qin¹⁵⁷,
O. A. Hampton¹⁵⁷,
P. Larsen¹⁵⁸,
C. Beck¹⁵⁹,
Y. Han¹⁵⁷,
M. Wang¹⁵⁷,
H. Doddapaneni¹⁵⁷,
C. L. Kovar¹⁵⁷,
W. J. Salerno¹⁵⁷,
A. Yoder¹⁵⁸,
S. Richards¹⁵⁷,
J. Rogers¹⁵⁷,
J. R. Lupski¹⁵⁹,
D. M. Muzny¹⁵⁷,
R. A. Gibbs¹⁵⁷,
Q. Meng¹⁶⁰,
M. Bainbridge¹⁶⁰,
M. Wang¹⁶⁰,
H. Doddapaneni¹⁶⁰,
Y. Han¹⁶⁰,
D. Muzny¹⁶⁰,
R. Gibbs¹⁶⁰,
R. A. Harris^161,162,
M. Raveenedran^161,162,
C. Xue^161,162,
M. Dahdouli^161,162,
L. Cox¹⁶³,
G. Fan¹⁶⁴,
B. Ferguson¹⁶⁵,
J. Hovarth¹⁶⁶,
Z. Johnson¹⁶⁷,
S. Kanthaswamy¹⁶⁸,
M. Kubisch¹⁶⁹,
M. Platt¹⁷⁰,
D. Smith¹⁷¹,
E. Vallender¹⁷²,
R. Wiseman¹⁷³,
X. Liu¹⁷⁴,
J. Below¹⁷⁵,
D. Muzny^161,162,
R. Gibbs^161,162,
F. Yu^161,162,
J. Rogers^161,162,
J. Lin¹⁷⁶,
Y. Zhang¹⁷⁷,
Z. Ouyang¹⁷⁶,
A. Moore¹⁷⁸,
Z. Wang¹⁷⁹,
J. Hofmann¹⁸⁰,
M. Purdue¹⁷⁸,
R. Stolzenberg-Solomon¹⁷⁸,
S. Weinstein¹⁷⁸,
D. Albanes¹⁷⁸,
C. S. Liu¹⁸¹,
W. L. Cheng¹⁸¹,
T. T. Lin¹⁸¹,
Q. Lan¹⁷⁸,
N. Rothman¹⁷⁸,
S. Berndt¹⁷⁸,
E. S. Chen¹⁸²,
H. Bahrami^183,184,
A. Khoshzaban¹⁸⁴,
S. Heidari Keshal¹⁸³,
H. Bahrami^185,186,
A. Khoshzaban¹⁸⁶,
S. Heidari Keshal¹⁸⁵,
K. K. R. Alharbi¹⁸⁷,
M. Zhalbinova¹⁸⁸,
A. Akilzhanova¹⁸⁸,
S. Rakhimova¹⁸⁸,
M. Bekbosynova¹⁸⁹,
S. Myrzakhmetova¹⁸⁹,
M. Matar¹⁹⁰,
N. Mili¹⁹¹,
R. Molinari¹⁹¹,
Y. Ma¹⁹²,
S. Guerrier¹⁹³,
N. Elhawary^194,195,
M. Tayeb¹⁹⁵,
N. Bogari¹⁹⁵,
N. Qotb¹⁹⁶,
S. A. McClymont¹⁹⁷,
P. W. Hook¹⁹⁷,
L. A. Goff¹⁹⁷,
A. McCallion¹⁹⁷,
Y. Kong^198,199,
J. R. Charette¹⁹⁸,
W. L. Hicks¹⁹⁸,
J. K. Naggert¹⁹⁸,
L. Zhao¹⁹⁸,
P. M. Nishina^198,199,
B. M. Edrees²⁰⁰,
M. Athar²⁰⁰,
F. A. Al-Allaf²⁰⁰,
M. M. Taher²⁰⁰,
W. Khan²⁰¹,
A. Bouazzaoui²⁰⁰,
N. A. Harbi²⁰²,
R. Safar²⁰³,
H. Al-Edressi²⁰³,
A. Anazi²⁰⁴,
N. Altayeb²⁰⁵,
M. A. Ahmed²⁰⁶,
K. Alansary²⁰⁷,
Z. Abduljaleel²⁰⁰,
A. Kratz²⁰⁸,
P. Beguin²⁰⁹,
S. Poulain²⁰⁸,
M. Kaneko²⁰⁹,
C. Takahiko²⁰⁹,
A. Matsunaga²⁰⁹,
S. Kato²⁰⁸,
A. M. Suzuki²⁰⁸,
N. Bertin²⁰⁸,
T. Lassmann²⁰⁸,
R. Vigot²⁰⁸,
P. Carninci²⁰⁸,
C. Plessy²⁰⁸,
T. Launey²⁰⁹,
D. Graur²¹⁰,
D. Lee²¹¹,
A. Kapoor²¹¹,
A. Chakravarti²¹¹,
J. Friis-Nielsen²¹²,
J. M. Izarzugaza²¹²,
S. Brunak²¹²,
A. Chakraborty²¹³,
J. Basak²¹³,
A. Mukhopadhyay²¹⁴,
B. S. Soibam²¹⁵,
D. Das²¹⁶,
N. Biswas²¹⁶,
S. Das²¹⁶,
S. Sarkar²¹⁷,
A. Maitra²¹⁶,
C. Panda²¹⁷,
P. Majumder²¹⁶,
H. Morsy²¹⁸,
A. Gaballah²¹⁹,
M. Samir²²⁰,
M. Shamseya²²¹,
H. Mahrous²¹⁸,
A. Ghazal²¹⁹,
W. Arafat²²²,
M. Hashish²¹⁸,
J. J. Gruber²²³,
N. Jaeger²²³,
M. Snyder²²³,
K. Patel²²⁴,
S. Bowman²²⁴,
T. Davis²²⁵,
D. Kraushaar²²⁴,
A. Emerman²²⁴,
S. Russello²²⁵,
N. Henig²²⁴,
C. Hendrickson²²⁴,
K. Zhang²²⁶,
M. Rodriguez-Dorantes²²⁷,
C. D. Cruz-Hernandez²²⁷,
C. D. P. Garcia-Tobilla²²⁷,
S. Solorzano-Rosales²²⁷,
N. Jäger²²⁸,
J. Chen²²⁸,
R. Haile²²⁹,
M. Hitchins²²⁹,
J. D. Brooks²³⁰,
M. Snyder²²⁸,
S. Jiménez-Morales²³¹,
M. Ramírez²³²,
J. Nuñez²³³,
V. Bekker²³⁴,
Y. Leal²³⁵,
E. Jiménez²³⁶,
A. Medina²³⁷,
A. Hidalgo²³⁸,
J. Mejía²³⁹,
V. Halytskiy²⁴⁰,
J. Naggert²⁴¹,
G. B. Collin²⁴¹,
K. DeMauro²⁴¹,
R. Hanusek²⁴¹,
P. M. Nishina²⁴¹,
K. Belhassa²⁴²,
K. Belhassan²⁴²,
L. Bouguenouch²⁴²,
I. Samri²⁴²,
H. Sayel²⁴²,
FZ. moufid²⁴²,
I. El Bouchikhi²⁴²,
S. Trhanint²⁴²,
H. Hamdaoui²⁴²,
I. Elotmani²⁴²,
I. Khtiri²⁴²,
O. Kettani²⁴²,
L. Quibibo²⁴²,
M. Ahagoud²⁴²,
M. Abbassi²⁴²,
K. Ouldim²⁴²,
A. V. Marusin²⁴³,
A. N. Kornetov²⁴⁴,
M. Swarovskaya²⁴³,
K. Vagaiceva²⁴³,
V. Stepanov²⁴³,
E. M. Cutiongco De La Paz^245,246,
R. Sy²⁴⁷,
J. Nevado²⁴⁵,
P. Reganit²⁴⁷,
L. Santos²⁴⁷,
J. D. Magno²⁴⁷,
F. E. Punzalan²⁴⁷,
D. Ona²⁴⁷,
E. Llanes²⁴⁷,
R. L. Santos-Cortes²⁴⁸,
R. Tiongco²⁴⁷,
J. Aherrera²⁴⁹,
L. Abrahan²⁴⁹,
P. Pagauitan-Alan²⁴⁹,
The Philippine Cardiogenomics Study Group,
K. H. Morelli^250,251,
J. S. Domire²⁵²,
N. Pyne²⁵²,
S. Harper²⁵²,
R. Burgess²⁵¹,
M. Zhalbinova²⁵³,
A. Akilzhanova²⁵³,
S. Rakhimova²⁵³,
M. Bekbosynova²⁵⁴,
S. Myrzakhmetova²⁵⁴,
M. A. Gari²⁵⁵,
A. Dallol²⁵⁶,
H. Alsehli²⁵⁶,
A. Gari²⁵⁶,
M. Gari²⁵⁶,
A. Abuzenadah²⁵⁶,
M. Thomas²⁵⁷,
M. Sukhai²⁵⁷,
S. Garg²⁵⁷,
M. Misyura²⁵⁷,
T. Zhang²⁵⁷,
A. Schuh²⁵⁸,
T. Stockley²⁵⁷,
S. Kamel-Reid²⁵⁷,
S. Sherry²⁵⁹,
C. Xiao²⁵⁹,
D. Slotta²⁵⁹,
K. Rodarmer²⁵⁹,
M. Feolo²⁵⁹,
M. Kimelman²⁵⁹,
G. Godynskiy²⁵⁹,
C. O’Sullivan²⁵⁹,
E. Yaschenko²⁵⁹,
C. Xiao²⁶⁰,
E. Yaschenko²⁶⁰,
S. Sherry²⁶⁰,
C. Rangel-Escareño²⁶¹,
H. Rueda-Zarate²⁶¹,
I. A. Tayubi²⁶² &
R. Mohammed²⁶²,
on behalf of 1,
I. Ahmed²⁶²,
T. Ahmed²⁶²,
S. Seth²⁶³,
S. Amin²⁶⁴,
X. Song²⁶³,
X. Mao²⁶⁴,
H. Sun²⁶⁵,
R. G. Verhaak²⁶⁴,
A. Futreal²⁶⁴,
J. Zhang²⁶³,
S. J. Whiite²⁶⁶,
T. Chiang²⁶⁶,
A. English²⁶⁶,
J. Farek²⁶⁶,
Z. Kahn²⁶⁶,
W. Salerno²⁶⁶,
N. Veeraraghavan²⁶⁶,
E. Boerwinkle²⁶⁷,
R. Gibbs²⁶⁶,
T. Kasukawa²⁶⁸,
M. Lizio²⁶⁸,
J. Harshbarger²⁶⁸,
S. Hisashi^268,269,
J. Severin²⁶⁸,
A. Imad²⁶⁸,
S. Sahin²⁶⁸,
T. C. Freeman²⁷⁰,
K. Baillie²⁷⁰,
A. Sandelin²⁷¹,
P. Carninci²⁶⁸,
A. R. R. Forrest²⁶⁸,
H. Kawaji^268,269,272,
The FANTOM Consortium,
W. Salerno²⁷³,
A. English²⁷³,
S. N. Shekar²⁷⁴,
A. Mangubat²⁷⁴,
J. Bruestle²⁷⁴,
E. Boerwinkle^273,275,
R. A. Gibbs²⁷³,
A. H. Salem²⁷⁶,
M. Ali²⁷⁷,
A. Ibrahim²⁷⁸,
M. Ibrahim²⁷⁹,
H. A. Barrera²⁸⁰,
L. Garza²⁸⁰,
J. A. Torres²⁸⁰,
V. Barajas²⁸⁰,
A. Ulloa-Aguirre²⁸¹,
D. Kershenobich²⁸²,
Shahroj Mortaji²⁸²,
Pedro Guizar²⁸²,
Eliezer Loera²⁸²,
Karen Moreno²⁸²,
Adriana De León²⁸²,
Daniela Monsiváis²⁸²,
Jackeline Gómez²⁸²,
Raquel Cardiel²⁸²,
J. C. Fernandez-Lopez²⁸³,
V. Bonifaz-Peña²⁸³,
C. Rangel-Escareño²⁸³,
A. Hidalgo-Miranda²⁸⁴,
A. V. Contreras²⁸⁵,
L. Polfus²⁸⁶,
CHARGE and NHLBI Exome Sequence Project Working Groups,
X. Wang²⁸⁷,
V. Philip²⁸⁷,
G. Carter²⁸⁷,
A. A. Abuzenadah²⁸⁸,
M. Gari²⁸⁸,
R. Turki²⁸⁹,
A. Dallol²⁸⁸,
A. Uyar²⁹⁰,
A. Kaygun²⁹¹,
S. Zaman²⁹²,
E. Marquez²⁹⁰,
J. George²⁹⁰,
D. Ucar²⁹⁰,
C. L. Hendrickson²⁹³,
A. Emerman²⁹³,
D. Kraushaar²⁹³,
S. Bowman²⁹³,
N. Henig²⁹³,
T. Davis²⁹⁴,
S. Russello²⁹⁴,
K. Patel²⁹³,
D. B. Starr²⁹⁵,
M. Baird²⁹⁶,
B. Kirkpatrick²⁹⁷,
K. Sheets²⁹⁸,
R. Nitsche²⁹⁹,
L. Prieto-Lafuente³⁰⁰,
M. Landrum³⁰¹,
J. Lee³⁰¹,
W. Rubinstein³⁰¹,
D. Maglott³⁰¹,
P. K. R. Thavanati³⁰²,
A. Escoto de Dios³⁰²,
R. E. Navarro Hernandez³⁰³,
M. E. Aguilar Aldrate³⁰⁴,
M. R. Ruiz Mejia³⁰⁵,
K. R. R. Kanala³⁰⁶,
Z. Abduljaleel³⁰⁷,
W. Khan³⁰⁸,
F. A. Al-Allaf³⁰⁷,
M. Athar³⁰⁷,
M. M. Taher³⁰⁷,
N. Shahzad³⁰⁷,
A. Bouazzaoui^309,310,311,
E. Huber³¹²,
A. Dan³¹³,
F. A. Al-Allaf^309,314,
W. Herr³¹¹,
G. Sprotte³¹⁵,
J. Köstler³¹⁶,
A. Hiergeist³¹⁶,
A. Gessner³¹⁶,
R. Andreesen³¹¹,
E. Holler³¹¹,
F. Al-Allaf^317,318,319,
A. Alashwal³²⁰,
Z. Abduljaleel^317,318,
M. Taher^317,318,
A. Bouazzaoui^317,318,
H. Abalkhail³²⁰,
A. Al-Allaf³²¹,
R. Bamardadh³¹⁸,
M. Athar^317,318,
O. Filiptsova³²²,
M. Kobets³²³,
Y. Kobets³²³,
I. Burlaka³²²,
I. Timoshyna³²⁴,
O. Filiptsova³²⁵,
M. N. Kobets³²⁶,
Y. Kobets³²⁶,
I. Burlaka³²⁵,
I. Timoshyna³²⁷,
O. Filiptsova³²⁸,
M. N. Kobets³²⁹,
Y. Kobets³²⁹,
I. Burlaka³²⁸,
I. Timoshyna³³⁰,
F. A. Al-allaf^331,332,333,
M. T. Mohiuddin^331,332,
A. Zainularifeen^331,332,
A. Mohammed^331,332,
H. Abalkhail³³⁴,
T. Owaidah³³⁴ &
…
A. Bouazzaoui^331,332

Human Genomics volume 10, Article number: 12 (2016) Cite this article

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E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project

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O16 Modeling genetic interactions associated with molecular subtypes of breast cancer

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O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors

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O18 Predictive biomarkers to metastatic pancreatic cancer treatment

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O19 DDIT4 gene expression as a prognostic marker in several malignant tumors

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O20 Spatial organization of the genome and genomic alterations in human cancers

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O21 Landscape of targeted therapies in solid tumors

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O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma

S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis

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O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array

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O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation

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C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung

O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13

K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone

O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data

N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh

O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review

S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs

O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio

S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle

O32 Legal interoperability: a sine qua non for international data sharing

A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group

O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target

H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci

O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs

J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio

O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes

R. Ghosh, S. Plon

O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing

S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs

O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma

T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman

O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver

E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker

O40 A general statistic framework for genome-based disease risk prediction

M. Xiong, L. Ma, N. Lin, C. Amos

O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies

N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong

O42 Big data and NGS data analysis: the cloud to the rescue

O. Dobretsberger, M. Egger, F. Leimgruber

O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing

S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance

O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data

V. A. A. Antonio, N. Ono, Clark Kendrick C. Go

O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data

Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar

O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans

C. Zeng, J. Shao

O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations

H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula

O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing

Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng

O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes?

I. Campbell, M.-A. Young, P. James, Lifepool

O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS

C. Schumacher, S. Sandhu, T. Harkins, V. Makarov

O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform

H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs

O55 Rapid capture methods for clinical sequencing

J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs

O56 A diploid personal human genome model for better genomes from diverse sequence data

K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs

O57 Development of PacBio long range capture for detection of pathogenic structural variants

Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs

O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans

R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers

O59 Assessing RNA structure disruption induced by single-nucleotide variation

J. Lin, Y. Zhang, Z. Ouyang

P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number

A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt

P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility

E. S. Chen

P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients

H. Bahrami, A. Khoshzaban, S. Heidari Keshal

P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population

K. K. R. Alharbi

P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding

M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova

P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population

M. Matar

P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization

N. Mili, R. Molinari, Y. Ma, S. Guerrier

P9 Vulnerability of genetic variants to the risk of autism among Saudi children

N. Elhawary, M. Tayeb, N. Bogari, N. Qotb

P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction

S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion

P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3^rd7 mice

Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina

P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients

B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel

P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments

A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey

P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome

D. Graur

P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients

J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak

P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns

B. S. Soibam

P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer

D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder

P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes

J. J. Gruber, N. Jaeger, M. Snyder

P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors

K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson

P23 RNA sequencing identifies gene mutations for neuroblastoma

K. Zhang

P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines

M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales

P25 Targeted Methylation Sequencing of Prostate Cancer

N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder

P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico

S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía

P28 Genetic modifiers of Alström syndrome

J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina

P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos

E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group

P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D

K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess

P34 Molecular regulation of chondrogenic human induced pluripotent stem cells

M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah

P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting

M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid

P36 Accessing genomic evidence for clinical variants at NCBI

S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko

P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA

C. Xiao, E. Yaschenko, S. Sherry

P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling

C. Rangel-Escareño, H. Rueda-Zarate

P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr

S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang

P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data

S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs

P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells

T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium

P43 Rapid and scalable typing of structural variants for disease cohorts

W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs

P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population

A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim

P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations

J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras

P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits

L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups

P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study

S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu

P49 Common variants in casr gene are associated with serum calcium levels in koreans

S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung

P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions

Y. Zhou, S. Xu

P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies

X. Wang, V. Philip, G. Carter

P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment

A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol

P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling

A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar

P54 Direct enrichment for the rapid preparation of targeted NGS libraries

C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel

P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System

R. Nitsche, L. Prieto-Lafuente

P57 ClinVar: a multi-source archive for variant interpretation

M. Landrum, J. Lee, W. Rubinstein, D. Maglott

P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome

Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad

P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation

A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler

P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia

F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder

A. K. Srivastava¹, Y. Wang², R. Huang³, C. Skinner¹, T. Thompson³, L. Pollard³, T. Wood³, F. Luo², R. Stevenson¹

¹JCSRI, Greenwood Genetic Center, Greenwood, SC, USA; ²School of Computing, Clemson University, Clemson, SC, USA; ³Biochemical Genetics Laboratory, Greenwood Genetic Center, Greenwood, SC, USA

Correspondence: A. K. Srivastava – JCSRI, Greenwood Genetic Center, Greenwood, SC, USA

Objectives

From the first description by Leo Kanner [1], autism has been an enigmatic neurobehavioral phenomenon. The new genetic/genomic technologies of the past decade have not been as productive as originally anticipated in unveiling the mysteries of autism. The specific etiology of the majority of cases of autism spectrum disorder (ASD) is unknown, although numerous genetic/genomic variants and alterations of diverse cellular functions have been reported. Prompted by this failure, we have investigated whether the metabolomics approach might yield results which could simultaneously lead to a blood-based screening/diagnostic test and to treatment options.

Methods

Plasma samples from a clinically well-defined cohort of 100 male individuals, ages 2-16+ years, with ASD and 32 age-matched typically developing (TD) controls were subjected to global metabolomic analysis.

Results

We have identified more than 25 plasma metabolites among the approximately 650 metabolites analyzed, representing over 70 biochemical pathways, that can discriminate children with ASD as young as 2 years from children that are developing typically. The discriminating power was greatest in the 2–10 year age group and weaker in older age groups. The initial findings were validated in a second cohort of 83 children, males and females, ages 2–10 years, with ASD and 76 age and gender-matched TD children. The discriminant metabolites were associated with several key biochemical pathways suggestive of potential contributions of increased oxidative stress, mitochondrial dysfunction, inflammation and immune dysregulation in ASD. Further, targeted quantitative analysis of a subset of discriminating metabolites using tandem mass spectrometry provided a reliable laboratory method to detect children with ASD.

Conclusion

Metabolic profiling appears to be a robust technique to identify children with ASD ages 2–10 years and provides insights into the altered metabolic pathways in ASD, which could lead to treatment strategies.

References

1. Kanner, L. Autistic disturbances of affective contact. Nervous Child. 1943; 2: 217–250.

Disclosure of interest

None declared.

O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents

R. Polimanti¹, J. Gelernter^1,2,3

¹Department Psychiatry, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; ²Department Genetics, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; ³Department Neurobiology, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA

Correspondence: R. Polimanti – Dept Psychiatry, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA

Objectives

To uncover novel traits associated with nicotine and alcohol use genetics, we performed a phenome-wide association study in a large multi-ethnic cohort.

Methods

We investigated 7,688 African-Americans (AFR), 1,133 Asian-Americans (ASN), 14,081 European-Americans (EUR), and 3,492 Hispanic-Americans (HISP) from the Women’s Health Initiative, analyzing risk alleles located in the CHRNA5–CHRNA3 locus (rs8034191, rs1051730, rs12914385, rs2036527, and rs16969968) for nicotine-related traits and ADH1B (rs1229984 and rs2066702) and ALDH2 (rs671) for alcohol-related traits with respect to anthropometric characteristics, dietary habits, social status, psychological circumstances, reproductive history, health conditions, and nicotine- and alcohol-related traits.

Results

The investigated loci resulted associated with novel traits: rs1229984 were associated with family income (p=4.1*10⁻¹²), having a pet (p=6.5*10⁻¹¹), partner education (p=1.8*10⁻¹⁰), “usually expect the best” (p=2.4*10⁻⁷), “felt calm and peaceful” (p=2.6*10⁻⁷), education (p=3.7*10⁻⁶), and number of term pregnancies (p=1.12*10⁻⁵) in EUR; rs1051730 and rs16969968 showed a suggestive association with “High cholesterol requiring pills ever” (p=3.8*10⁻⁴ and p=1.8*10⁻⁴) in trans-ethnic meta-analysis. We also replicated the known associations: rs80341911 was associated with cigarettes per day (CIGSDAY, p=3.4*10⁻⁸), smoking status (p=6.7*10⁻³), and “smoked at least 100 cigarettes” (p=7.3*10⁻³) in EUR; rs1051730 and rs16969968 were associated with CIGSDAY (p=9.1*10⁻⁸ and p=1.1*10⁻⁷) and lung cancer (p=7.3*10⁻³ and p=9.9*10⁻³) in EUR; rs2036527 was associated with CIGSDAY (p=3.5*10⁻³) in AFR; rs1229984 showed associations for alcohol servings (ALC, p=2.9*10⁻⁶), beer servings (p=3*10⁻⁶), wine servings (WINE, p=3.9*10⁻⁶), liquor servings (p=5.5*10⁻⁶), dietary alcohol (DIETALC, p=6.1*10⁻⁶), “Drinks alcohol (age 50)” (p=9.3*10⁻⁶) in EUR and for ALC (p=5.2*10⁻⁵) and DIETALC (p=9.6*10⁻⁵) in HISP; rs671 resulted associated with alcohol intake (p=3.8*10⁻⁸), DIETALC (p=1.9*10⁻⁷), ALC (p=1.3*10⁻⁶), WINE (p=1.1*10⁻⁵) and “Drank 12 alcoholic beverages ever” (p=1.2*10⁻⁵) in ASN.

Conclusion

We provided novel genetic data regarding the consequences of smoking and drinking behaviors and confirmed ethnic differences in their genetic predisposition.

Disclosure of interest

None declared.

O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort

X. Lin¹, I. Y. Lim¹, Y. Wu¹, A. L. Teh¹, L. Chen¹, I. M. Aris¹, S. E. Soh¹, M. T. Tint², J. L. MacIsaac³, F. Yap⁴, K. Kwek⁴, S. M. Saw², M. S. Kobor³, M. J. Meaney¹, K. M. Godfrey⁵, Y. S. Chong¹, J. D. Holbrook¹, Y. S. Lee¹, P. D. Gluckman^1,6, N. Karnani¹, GUSTO study group

¹Singapore Institute for Clinical Sciences, Singapore, Singapore; ²National University of Singapore, Singapore, Singapore; ³University of British Columbia, Vancouver, British Columbia, Canada; ⁴KK Women’s and Children’s Hospital, Singapore, Singapore; ⁵University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK; ⁶University of Auckland, Auckland, New Zealand

Correspondence: X. Lin – Singapore Institute for Clinical Sciences, Singapore, Singapore

Objectives

Prenatal environment and genetic polymorphism can have a lasting impact on offspring’s metabolic function by perturbing its epigenome. Birth weight is often used as a surrogate for the overall quality of the intrauterine environment. We present the first neonate epigenome-wide association study in an Asian mother-offspring cohort, that interrogates the effects of prenatal environment variables, umbilical cord DNA methylation and SNPs, on birth weight.

Methods

In GUSTO, a prospective mother-offspring cohort study (N=987), we examined the associations between DNA methylation, SNPs, birth weight and 11 prenatal environment variables. First, we investigated the association between perinatal methylome and birth weight to identify sites of variability in methylation. Second, we interrogated the contribution of genetic and prenatal environmental factors on this variability in the epigenome. Finally, we examined whether these methylation marks at birth were associated with offspring size and adiposity in early childhood.

Results

Methylation levels at 50 CpGs were significantly associated with birth weight, and a subset of these CpGs was located in genes and miRNA known to be involved in metabolic pathways/disorders. We further examined the influence of environmental and genetic factors on methylation at these 50 CpG sites. Sixteen CpGs were associated with both, an additional 24 CpGs were associated with only environmental factors, while only 3 CpGs were associated with genetic factors alone. Environmental factors associated with methylation were predominantly maternal-adiposity-related (pre-pregnancy body mass index, pregnancy weight gain and maternal glucose levels). Methylation levels at half of these CpGs were also associated with offspring size and adiposity in early childhood.

Conclusion

Developmental pathways to obesity begin before birth and involve genetic, epigenetic and environmental factors.

Disclosure of interest

X. Lin: None declared., I. Y. Lim: None declared., Y. Wu: None declared., A. L. Teh: None declared., L. Chen: None declared., I. M. Aris: None declared., S. E. Soh: None declared., M. T. Tint: None declared., J. L. MacIsaac: None declared., F. Yap: None declared., K. Kwek: None declared., S. M. Saw: None declared., M. S. Kobor: None declared., M. J. Meaney: None declared., K. M. Godfrey Conflict with: KMG has received reimbursement for speaking at conferences sponsored by companies selling nutritional products. He is part of an academic consortium that has received research funding from Abbott Nutrition, Nestec and Danone, Y. S. Chong Conflict with: YSC has received reimbursement for speaking at conferences sponsored by companies selling nutritional products. He is part of an academic consortium that has received research funding from Abbott Nutrition, Nestec and Danone, J. D. Holbrook: None declared., Y. S. Lee: None declared., P. D. Gluckman: None declared., N. Karnani: None declared.

O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus

A. Kapoor, D. Lee, A. Chakravarti

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Correspondence: A. Kapoor – McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Objectives

Genome-wide association studies (GWAS) have indicated that sequence variation in cis-regulatory elements (CRE) plays important roles in common disease risk/trait variation, but identification of these causal variants has remained a major challenge in complex trait genetics. Here, we performed reporter assays for all common variants at the QT interval associated SCN5A GWAS locus, with the goal of identifying the underlying causal variants.

Methods

A target region of ~500kb at SCN5A was defined based on recombination hotspots (rate>10cM/Mb; HapMap) flanking the 5 independent QT interval GWAS hits. Within the target region, all common variants (minor allele frequency >5%) from the 1000 Genomes European ancestry populations in moderate linkage disequilibrium (r²>0.3) with any of the 5 GWAS hits were selected. Both alleles of these variants were amplified with flanking sequences and cloned upstream of a minimal promoter driven firefly luciferase gene in pGL4.23. Human cardiomyocyte cells, AC16, were transfected with test constructs and Renilla luciferase vector (for transfection normalization) in triplicate and luciferase assays were performed 24h later. Reporter assays on a subset of variants were repeated for assessing allelic difference in regulatory activity. All cloning and reporter assays were performed in 96- and 24-well plates.

Results

Of a total 121 variants selected, 112 variants in 104 amplicons passed primer design (amplicon size 256-617bp; median 397bp), and we successfully cloned both alleles for 106 variants in 98 amplicons. In reporter assays, compared to empty vector, 24 and 40 amplicons showed enhancer (>2-fold) and suppressor (<0.5-fold) activities in AC16 cells, respectively. Of these only 4 were observed as open chromatin regions in heart tissue in NIH Epigenomics data. Overall, 12 variants showed nominally significant allelic difference (P<0.05) in reporter activity and were repeated with 18 replicates and 7 variants were identified to have repeated significant allelic difference in regulatory activity.

Conclusion

Independent of the available epigenomic data, which are of limited relevance, an unbiased in vitro reporter screen for CREs overlapping all common variants associated with QT interval at the SCN5A GWAS locus identified 7 common cis-regulatory variants. Our immediate next goals are to a) evaluate the effect of deleting these 7 CREs on SCN5A expression in AC16 cells and b) identify the trans-acting factors regulating their functions.

Disclosure of interest

None declared.

O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells

C. Maercker¹, F. Graf², M. Boutros²

¹Esslingen University of Applied Sciences, Esslingen, Germany; ²German Cancer Research Center, Heidelberg, Germany

Correspondence: C. Maercker – Esslingen University of Applied Sciences, Esslingen, Germany

Objectives

Some cancers show a strong tendency to metastasize to bone, a tissue of mesenchymal origin and a prominent site of mesenchymal stem cells (MSC) residing in the stem cell niche. With bone metastasis formation being one of the most detrimental steps in cancer progression, a better understanding of how bone metastases are initially formed is key to successfully targeting bone metastasis of, for example, prostate cancer. Recent reports have suggested that bone-metastasizing cancers may mimic the process of homing of hematopoietic stem cells to their bone niche.

Methods

In order to understand the role of MSC in metastasis formation, we investigated the interaction of primary human bone marrow MSC with established cancer cell lines able to metastasize to bone. With a trans-well migration assay we could show that MSC induced a rapid migration response of prostate and breast cancer cell lines already within two hours after start of the experiment. In order to identify factors stimulating cancer cell migration, MSC cell culture supernatant was separated by size exclusion and ion exchange chromatography. Migratory fractions then were further analyzed by mass spectrometry and antibody array analysis.

Results

With this approach we identified the extracellular matrix proteins type I and type III collagen, fibronectin and laminin 421 as potential drivers of cancer cell migration, which was confirmed by using recombinant proteins. RNAi experiments showed that the cancer cell extracellular matrix receptor beta 1 integrin obviously plays a pivotal role for cell migration.

Conclusion

From our results we conclude that the extracellular matrix as it is produced by MSC obviously plays a crucial role for cancer metastasis and therefore might be a promising anti-cancer drug target.

Disclosure of interest

None declared.

O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies

G. Stamoulis¹, F. Santoni², P. Makrythanasis², A. Letourneau¹, M. Guipponi², N. Panousis¹, M. Garieri¹, P. Ribaux¹, E. Falconnet¹, C. Borel¹, S. E. Antonarakis^1,2,3

¹Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland; ²Geneva University Hospitals-HUG, Service of Genetic Medicine, Geneva, Switzerland; ³iGE3 Institute of Genetics and Genomics of Geneva, University of Geneva Medical School, Geneva, Switzerland

Correspondence: G. Stamoulis – Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

Objectives

Trisomy 21 is a model disorder of altered gene expression. We have previously used a pair of monozygotic twins discordant for trisomy 21 to study the global dysregulation of gene expression, without the noise due to genetic variation among individuals (Nature:508; 345–350;2014). The majority of previous studies focused on aneuploidies were conducted οn cell populations or tissues. Our study focusing on gene and allelic expression behaviour of single cells (SC), aims to reveal biological insights regarding the cellular impact of aneuploidy and uncover the mechanisms of gene dosage.

Methods

We estimated the allele specific expression (ASE) from RNAseq of ~1000 single cells in different aneuploidies. We used 352 SC fibroblasts (173 Normal and 179 T21 cells) from the pair of monozygotic twins discordant for T21, 166 SC from a mosaic T21, 176 SC from a mosaic T18, 151 SC from a mosaic T8, and 146 SC from a mosaic T13.

Results

In the monozygotic twins, a considerable number of heterozygous sites at the non-chr21 genome showed monoallelic expression (MAE);(Normal: 73.5 % monoallelic in 564,668 observations, and T21: 78.7 % monoallelic in 549,799 observations). There was also considerable MAE for chr21 sites in Normal and, surprisingly, in T21 cells as well (Normal: 63,3 % monoallelic in 5,009 observations, and T21: 72.8 % monoallelic in 6,456 observations). We classified the genes on chr21 in 3 classes according to the level of the aggregate MAE of their corresponding sites (9 monoallelic, 29 intermediate, 2 biallelic). Similar results, i.e. extensive MAE on the supernumerary chromosome genes, were also observed in the other aneuploidies.

Conclusion

We hypothesize that each class of genes contributes in a specific way to the phenotypic variability of Down Syndrome. Our analysis showed that, for genes with monoallelic expression, the abnormal gene dosage induced by the aneuploid chromosome is maybe due to the number of cells expressing the gene. This difference in the fraction of expressing cells could contribute to the development and the variability of phenotypes in aneuploidies. This study provides a new fundamental understanding of the allele specific expression in T21 and other aneuploidies.

Disclosure of interest

None declared.

O7 Role of microRNA in LCL to IPSC reprogramming

S. Kumar¹, J. Curran², J. Blangero²

¹South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Edinburg, TX, USA; ²South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Brownsville, TX, USA

Correspondence: S. Kumar – South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Edinburg, TX, USA

Objectives

A large number of EBV immortalized lymphoblastoid cell lines (LCLs) have been generated and maintained in genetic/epidemiological studies as a perpetual source of DNA and as a surrogate in-vitro cell model. Recent successes in reprograming LCLs into induced pluripotent stem cells (iPSCs) have paved the way to generate more relevant in-vitro disease models using this existing bio-resource. However the effects of EBV encoded oncoproteins on cellular transcription and function make LCLs a unique biomaterial to reprogramme. Accumulating evidence now provides support that miRNAs play a critical role in transcription factor-induced reprogramming of iPSCs.

Methods

To investigate the role of miRNAs in regulating gene expression and cellular functions during LCL to iPSC reprogramming, we performed a parallel genome-wide miRNA and mRNA expression analysis in six LCLs and their reprogrammed iPSCs.

Results

A total of 77 miRNAs and 5,228 mRNAs were significantly (FC-abs ≥ 2.0 and FDR ≤ 0.05) differentially expressed (DE) during LCL to iPSC reprogramming out of which 29 miRNAs and 2,317 mRNAs were significantly down-regulated and 48 miRNAs and 2,911mRNAs were significantly up-regulated. The down-regulated miRNAs were highly enriched for LCL specific miRNAs (miR-155, let-7a-i, miR-21, miR-142, miR103, miR-320, miR-146a-b) and the up-regulated miRNAs were highly enriched for iPSC specific miRNAs (miR-302a, miR-302c, miR-371a, miR-302b, miR-302d, miR-372, miR-373miR-92a-1, miR-92a-2, miR-92b, miR-17, miR-20a, miR-18a). Further we performed target prediction analysis for all the significantly DE miRNAs using the miRNA target prediction data bases. The 3,456 genes were predicted to be the targets of the 29 miRNAs that were significantly down-regulated during LCL to iPSC reprogramming. Out of these 3,456 predicted target genes 1,023 were significantly DE during LCL to iPSC reprogramming. For the 48 miRNAs that were significantly up-regulated during LCL to iPSC reprogramming 5,063 target genes were predicted out of which 1,462 were significantly DE during LCL to iPSC reprogramming. The significantly DE genes that were also the predicted targets of the significantly down or up regulated miRNAs were further analyzed for functional annotations and pathway analysis using Ingenuity Pathway Analysis Platform.

Conclusion

In summary, our analysis identifies DE miRNAs and their DE target genes and a global role of miRNAs in broad resetting of cellular transcriptome and function during LCL to iPSC reprogramming.

Disclosure of interest

None declared.

O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease

S. Chatterjee¹, A. Kapoor¹, J. Akiyama², D. Auer¹, C. Berrios¹, L. Pennacchio², A. Chakravarti¹

¹Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; ²Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Correspondence: S. Chatterjee – Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

Objectives

Common sequence variation in cis-regulatory elements (CREs) are the suspected etiological causes of complex disorders. We examined all common (>10% minor allele frequency) non-coding variants within a ~153kb locus surrounding the gene for receptor tyrosine kinase RET, which is most commonly mutated in Hirschsprung disease (HSCR or congenital aganglionosis), a form of functional intestinal obstruction in neonates (1 in 5,000 live births). We hoped to find all causal non-coding polymorphisms disrupting enhancer function leading to the disease.

Methods

We used human and mouse fetal gut at relevant developmental time points for transcriptional profiling, ChIP assays, transgenic enhancer assays and siRNA mediate knockdowns of relevant transcription factors.

Results

We demonstrate that: (i) the three polymorphisms residing in 3 distinct enhancers that increase risk of the disease by 4-, 2- and 1.7-fold. Haplotypes for these three independent variants display wide variation in risk. (ii) the three CREs are Ret enhancers with distinct temporal activities during mouse gut development; (iii) the CREs are bound by the transcription factors Rarb, Gata2/3 and Sox10, respectively, each developmentally expressed concordant with its cognate enhancer activity; (iv) variants in these CREs lead to their loss of activity and reduce Ret expression; (v) Ret is a positive feedback regulator of Sox10 and Gata2/3 transcription; and, (vi) additional feedback interactions affect its ligand Gdnf, co-receptor Gfra1 and signal terminator Cbl.

Conclusion

These results explain how individually common, small effect non-coding polymorphisms can lead to large genetic effects in HSCR, since transcription attenuation of Ret from enhancer mutations are amplified through its auto-regulation. These results implicate RET as a key rate limiting step in early enteric nervous system (ENS) development and explains why >95% of HSCR cases have at least one RET loss-of-function allele. More generally, the phenotypic impact of a complex disorder can only be understood by assessing gene effects in the context of their gene regulatory networks.

Disclosure of interest

None declared.

O9 Metabolomic profiling for the diagnosis of neurometabolic disorders

T. R. Donti¹, G. Cappuccio², M. Miller¹, P. Atwal¹, A. Kennedy³, A. Cardon⁴, C. Bacino¹, L. Emrick⁴, J. Hertecant⁵, F. Baumer⁶, B. Porter⁶, M. Bainbridge¹, P. Bonnen¹, B. Graham¹, R. Sutton¹, Q. Sun¹, S. Elsea¹

¹Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; ²Department of Translational Medical Sciences, Federico II University, Naples, Italy; ³Metabolon Inc, Durham, NC, USA; ⁴Section of Pediatric Neurology and Neuroscience, Baylor College of Medicine, Houston, TX, USA; ⁵Tawam Hospital, Abu Dhabi, United Arab Emirates; ⁶Stanford Medical School, Stanford, CA, USA

Correspondence: T. R. Donti – Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

Objectives

In individuals presenting with undifferentiated phenotypes such as developmental delay, hypotonia, and seizures, the list of differential diagnoses is often very long and includes metabolic/neurometabolic, genomic, and other Mendelian disorders. Here we want to demonstrate the utility of untargeted metabolomic profiling to screen for a wide range of neurometabolic disorders.

Methods

Untargeted small molecule metabolomic profiling was performed as described previously [1] on plasma samples from 12 patients suspected to have a neurometabolic disorder with a presentation of seizures, developmental delay and hypotonia.

Results

We identified 5 different neurometabolic disorders in these 12 patients. We observed elevations of 3-methoxytyrosine and decreased levels of dopamine and vanillylmandelate in AADC deficiency, elevations of 2-pyrrolidinone in ABAT deficiency, elevations of succinyladenosine in ADSL deficiency, increased citrate in citrate transporter deficiency, and elevations of imidazole propionic acid, cis and trans-urocanate in urocanase deficiency. The perturbations in the metabolomic profiles of plasma from these patients are unique, specific and not previously seen in over 300 other samples analyzed as normal controls or for other indications.

Conclusion

The standard diagnostic test for AADC, ABAT, and ADSL deficiency is CSF neurotransmitter analysis, while testing for urocanase deficiency requires an enzyme activity assay from a liver biopsy. These cases demonstrate the ability of untargeted metabolomic profiling for the functional confirmation of pathogenicity of VUS found via WES; moreover, disorders for which there is no biochemical testing or where testing is only available on CSF are able to be diagnosed in a plasma sample. This also demonstrates the utility of metabolomic profiling alone to screen for a wide range of neurometabolic disorders.

References

1. Miller M, Kennedy A, Eckhart A, Burrage L, Wulff J, Miller LD, Milburn M, Ryals J, Beaudet A, Sun Qet al: Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J Inherit Metab Dis 2015:1–11.

Disclosure of interest

None declared.

O10 A novel causal methylation network approach to Alzheimer’s disease

Z. Hu¹, P. Wang², Y. Zhu³, J. Zhao³, M. Xiong² and David A Bennett⁴

¹School of Public Health, Houston Health Science Center, Houston, TX, USA; ²University of Texas, Houston Health Science Center, Houston, TX, USA; ³Tulane University, New Orleans, LO, USA; ⁴ Rush Alzheimer’s Disease Center, Rush University, Chicago, IL, USA

Correspondence: Z. Hu – School of Public Health, Houston Health Science Center, Houston, TX, USA

Objectives

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease and represents a major cause of disability for elderly patients. DNA methylation–are increasingly seen as playing an important role in AD development. However its causal mechanisms remain unclear. Recent studies indicate that AD develops essentially as a result of dysfunction of molecular networks. Our purpose is develop large-scale causal methylation networks to uncover the mechanism of AD development.

Methods

We propose to use causal graphs as a major concept and a general framework for causal methylation network analysis and develop “score and search”-based methods for exact learning causal graphs of methylation networks to find the best-scoring structures for a given methylation dataset. Specifically, we develop novel functional structural equations for modeling methylation networks and use integer programming to search the network with optimal score.

Results

The proposed methods were applied to AD data with 460045 CpG sites from 748 samples. At the first stage, the methylation data of 168 gene from the pathway ‘Alzheimer’s disease were used to create a causal network describing the connection among the methylation sites between these genes. According to the current result, 148 gene was matched and tested in the model. We identified a largest connected causal methylation network with 47 nodes and 96 edges. Most genes were confirmed to play an important role in the AD development from the literature.

Conclusion

The proposed methods provide a highly flexible general framework for causal methylation network analysis and provide more rich information than co-methylation network. The exact learning algorithms will guarantee to find optimal solutions and hence provide accurate estimations of causal graphs of methylation networks. The causal methylation networks are able to uncover the mechanism of AD development.

Disclosure of interest

None declared.

O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway

A. Hidalgo-Miranda¹, S. Romero-Cordoba¹, S. Rodriguez-Cuevas², R. Rebollar-Vega¹, E. Tagliabue³, M. Iorio³, E. D’Ippolito³, S. Baroni³

¹Cancer Genomics Laboratory, National Institute Of Genomic Medicine (INMEGEN), Mexico City, Mexico; ²FUCAM , Mexico City, Mexico; ³National Tumor Institute, Milan, Italy

Correspondence: A. Hidalgo-Miranda – Cancer Genomics Laboratory, National Institute Of Genomic Medicine (INMEGEN), Mexico City, Mexico

Objectives

Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform.

Methods

A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments. Our agglomerative approach was cross-validated by using two other clustering algorithms. 3 cell line models were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs to define interesting miRNAs for further functional analysis.

Results

We found low expression levels of miR-342-3p in TNBC tumors compared with other breast cancer phenotypes, and this down-regulation characterizes one of our miRNA subgroups with high risk to relapse. To characterize its functional role, miR-342-3p was transiently transfected in the cell line MDA-MB-468, showing a decrease in cell proliferation, viability and migration rates. A gene expression profile revealed 140 altered mRNAs, from which 35 are potential direct targets of miR-342-3p defined by an in-silico analysis. The monocarboxylate transporter 1(MCT1), was confirmed as one target of miR-342-3p by a luciferase assay and western blot analysis. MCT1 repression by the miRNA promotes lactate efflux changes in the tumor cells, reflected in the accumulation of exogenous lactate and the increase in levels of extracellular endogenous lactate together with a decrease level of intra and inter cellular glucose concentration.

Conclusion

These data suggest a metabolic change that favors a more glycolytic environment, which lead to a glucose deprivation context that may contribute to the reduction in proliferation, viability and migration capabilities already described.

Disclosure of interest

None declared.

O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types

B. Kaczkowski¹, Y. Tanaka², H. Kawaji², A. Sandelin³, R. Andersson³, M. Itoh¹, T. Lassmann⁴, the FANTOM5 consortium¹, Y. Hayashizaki⁵, P. Carninci¹, A. R. R. Forrest⁶

¹Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan; ²Preventive Medicine and Applied Genomics unit, RIKEN Advanced Center for Computing and Communication, Yokohama, Japan; ³Department of Biology, University of Copenhagen, Copenhagen, Denmark; ⁴Telethon Kids Institute, the University of Western Australia, Perth, Australia; ⁵RIKEN Preventive Medicine & Diagnosis Innovation Program, Wako, Japan; ⁶Harry Perkins Institute of Medical Research, the University of Western Australia, Nedlands, Australia

Correspondence: B. Kaczkowski – Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan

Objectives

We aim to find genes that are frequently deregulated in cancer and thus can be useful as diagnostic markers for early detection, and potentially as therapeutic targets. We focus on biomarkers with pan-cancer potential that can be applicable to multiple cancer types.

Methods

We used the Cap Analysis of Gene Expression (CAGE) profiles of 225 cancer cell lines and 339 normal primary cells from FANTOM5 project. CAGE is a 5′ sequence tag technology that enables promoter-level expression analysis and can be used to estimate the activity of enhancers from bidirectional transcription of enhancer RNAs. As a complementary data set, we used RNA-seq data from 14 tumor types profiled by The Cancer Genome Atlas (TCGA). In both data sets (FANTOM5 and TCGA), we performed cancer vs. normal differential expression analysis in all cancer types.

Results

We identified a set of pan-cancer markers (of both coding and non-coding transcripts) that are recurrently perturbed in both the cancer cell lines (FANTOM5) and clinical tumors (TCGA). The FANTOM5 CAGE data provided novel insights into cancer transcriptome. We used the genomic location of the CAGE TSSs to show that promoters that overlap repetitive elements (especially SINE/Alu and LTR/ERV1 elements) are often upregulated in cancer. Specifically, a little known repeat family, REP522 (~1.8Kb in size, largely palindromic, unclassified interspersed repeat), was strongly enriched for the most cancer-activated promoters. Here we present previously un-published, follow-up results that detail the REP522 activation in cancer. Finally, we present 90 enhancers that are activated in cancer cell lines. With ENCODE ChIA-PET data, we linked 16 of those enhancers to promoters of known cancer genes.

Conclusion

Our transcriptome analysis identified candidate biomarkers with pan-cancer potential and provided new insights into enhancers and repetitive elements that are recurrently activated in cancer.

References

1. Kaczkowski B, et al. Transcriptome analysis of recurrently deregulated genes across multiple cancers identifies new pan-cancer biomarkers. Cancer Research. 2015; 76(2): 216–226.

Disclosure of interest

None declared.

O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types

C. A. Semple

MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, Edinburgh, UK

Objectives

Disruption of gene regulation is thought to play major roles in carcinogenesis and tumour progression. Here, we characterize the mutational profiles of diverse transcription factor binding sites (TFBSs) across 1,574 completely sequenced cancer genomes encompassing 11 tumour types. We assess the relative rates and impact of mutation at the binding sites of 87 different transcription factors (TFs) by comparing the abundance and patterns of single base substitutions within putatively functional binding sites to matched control sites.

Methods

To detect putatively regulatory binding sites in the genome, we used a combination of computational prediction and experimental data. Position weight matrices for 118 transcription factor binding motifs were used to find TFBS motif matches in the genome. We intersected these motif matches with experimentally defined open chromatin regions to define putatively functional TFBSs. Motif matches not occurring within open chromatin were used as control, putatively non-functional sites. Comparisons between these functional and control sites underlie our methods, and we develop novel metrics to assess the relative rates and functional impact of cancer mutations at putatively funcitonal regulatory sites.

Results

We observe a strong and significant excess of mutations at functional binding sites across TFs, and show that the substitutions that accumulate in cancers are often more disruptive than those that are tolerated as germline variants. Putatively functional CTCF binding sites suffer an exceptionally high mutational load in cancer relative to control sites, and those involved in the architecture of higher order chromatin structures are the most highly mutated. The mutational load at CTCF-binding sites appears to be dominantly determined by replication timing and the mutational signature of the tumor sample in question, suggesting that selectively neutral processes underlie the unusual mutation patterns seen at CTCF sites across tumor types.

Conclusion

We show that mutations at active TFBSs are common in tumours, they appear to accumulate largely unchecked by selective processes and are independent of mutations in coding sequences, exhibiting distinct rates among tumor types. Our study thus underlines the functional importance and fragility of the regulatory genome in cancer.

Disclosure of interest

None declared.

O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer

E. A. Rosenthal¹, B. Shirts¹, L. Amendola¹, C. Gallego², M. Horike-Pyne¹, A. Burt¹, P. Robertson¹, P. Beyers¹, C. Nefcy¹, D. Veenstra¹, F. Hisama¹, R. Bennett¹, M. Dorschner¹, D. Nickerson¹, J. Smith¹, K. Patterson¹, D. Crosslin¹, R. Nassir³, N. Zubair⁴, T. Harrison⁴, U. Peters^1,4, G. Jarvik¹, NHLBI GO Exome Sequencing Project

¹Univ of Washington, Seattle, WA, USA; ²Univ of Michigan, Ann Arbor, MI, USA; ³Univ California, Davis, CA, USA; ⁴Fred Hutch, Seattle, WA, USA

Correspondence: E. A. Rosenthal – Univ of Washington, Seattle, WA, USA

Objectives

In the U.S., the lifetime risk of developing colorectal cancer (CRC) is 4.5%. ~5% of cases carry an identified pathogenic variant in known causal genes. In another 20%, CRC appears inherited but no known pathogenic variant has been detected. This is partly due to lack of evidence to classify variants as pathogenic when they occur in genes that are implicated (GWAS, linkage, or biological pathway information), but not proven, to be associated with CRC.

Methods

To find evidence of association for 1128 suspected CRC associated genes, we compared the number of rare (MAF < 0.005), potentially disruptive variants (PDV) (stop gain (SG), splice acceptor/donor change (SA, SD), and frameshift (FS)) found in 169 CRC cases and 3524 controls. Cases included individuals from the Clinical Sequencing Exploratory Research NEXT Medicine study (CSER, N=78), Women’s Health Initiative (WHI, N=76), and Northwest Institute of Genetic Medicine Family Polyposis Study (NWIGM, N=15). Controls were selected randomly with respect to CRC from the Exome Sequencing Project (ESP), and were not known to have any Lynch associated cancers or to carry a known pathogenic CRC variant.

Results

We found a significant association between case status and rare PDV carrier status: 25% of CRC cases carried a rare PDV compared to 4% of controls (p<2e-16, OR 8.1). 96 genes had 174 rare PDVs: 92 SGs, 24 SDs, 15 SAs and 43 FSs. Rare PDVs occurred in cases only for 35 genes, in controls only for 55 genes, and in both for 6 genes. Among cases, there were 11 SGs, 2 SDs, 6 SAs and 26 FSs. Among controls, there were 81 SGs, 22 SDs, 9 SAs and 17 FSs. Case specific genes had ≤2 rare PDVs: those with 2 were MKL2 and PMS1. Interestingly, ATM, ATR and BRCA2 contained PDVs in both cases and controls.

Conclusion

We show the power of aggregate information to find support for disease association in a subset of CRC implicated genes. Additionally, we provide evidence that ATM, ATR and BRCA2 may be associated with CRC in addition to the known association with breast cancer.

Disclosure of interest

None declared.

O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer

F. Menghi¹, K. Inaki¹, X. Woo¹, P. Kumar¹, K. Grzeda¹, A. Malhotra¹, H. Kim¹, D. Ucar¹, P. Shreckengast¹, K. Karuturi¹, J. Keck², J. Chuang¹, E. T. Liu¹

¹The Jackson Laboratory, Farmington, CT, USA; ²The Jackson Laboratory, Sacramento, CA, USA

Correspondence: F. Menghi – The Jackson Laboratory, Farmington, CT, USA

Objectives

Next generation sequencing studies have revealed genome-wide structural variation patterns in cancer, such as chromothripsis and chromoplexy that do not engage a single discernable driver mutation, and that currently have no clinical relevance. We aimed at a detailed molecular characterization of one of these genomic configurations, the tandem duplicator phenotype (TDP).

Methods

We combined whole genome sequencing (WGS) data from 277 human genomes representing 11 cancer types and devised a robust genomic metric able to identify cancers with a chromotype called tandem duplicator phenotype (TDP) characterized by frequent and distributed tandem duplications (TDs).

Results

Enriched only in triple negative breast, ovarian, endometrial, and liver cancers, TDP tumors conjointly exhibit TP53-mutations, low expression of BRCA1, and increased expression of DNA replication genes pointing at re-replication in a defective checkpoint environment as a plausible causal mechanism. The resultant TDs in TDP augment global oncogene expression and disrupt tumor suppressor genes. Importantly the TDP strongly correlates with cisplatin sensitivity in both triple negative breast cancer cell lines and primary patient-derived xenografts.

Conclusion

We conclude that the TDP is a common cancer chromotype that coordinately alters oncogene/tumor suppressor expression with potential as a marker for chemotherapeutic response.

Disclosure of interest

None declared.

O16 Modeling genetic interactions associated with molecular subtypes of breast cancer

B. Ji, A. Tyler, G. Ananda, G. Carter

The Jackson Laboratory, Bar Harbor, ME, USA

Correspondence: G. Carter – The Jackson Laboratory, Bar Harbor, ME, USA

Objectives

The characterization of mRNA-expression subtypes in breast cancer facilitates genomic and genetic studies to identify biological processes that drive distinct molecular subtypes and elucidates the potential feasibility of subtype-specific drug targets. However, such therapies tend to have limited efficacy, often due to unpredicted compensation in the network of mutations. Polygenic models that account for multiple somatic mutations and their interactions can potentially improve target selection and provide a more detailed view of tumor genetic architecture.

Methods

We addressed this problem with a multi-trait genetic interaction analysis of copy-number variation and gene expression data from breast cancer samples in The Cancer Genome Atlas. Modules of co-expressed genes were derived and assessed for biological function and genetic association with mutations in oncogenes and tumor suppressors. Summary module phenotypes with pleiotropic associated loci were simultaneously analyzed to infer direct genetic effects as well as effects mediated by genetic interactions for each module.

Results

We observed widespread evidence of genetic redundancy, in which two mutations combine to yield a less than additive effect that is similar to either mutation in isolation. In addition, we also identified interacting mutations that combinatorially associate with distinct modules and subtypes in a non-additive manner. These somatic mutant combinations were often predictive of molecular subtypes when single mutations were not.

Conclusion

Accounting for interactions among somatic mutations in tumor samples reveals high genetic redundancy and complex regulatory hypotheses for breast cancer subtypes. Our work demonstrates how integrative genetic and genomic analysis can be used to generate more precise hypotheses for tumor genetics, which may be used to prioritize therapeutic targets for robust tumor suppression.

Disclosure of interest

None declared.

O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors

H. Nikbakht¹, M. Montagne², M. Zeinieh¹, A. Harutyunyan¹, M. Mcconechy¹, N. Jabado³, P. Lavigne², J. Majewski¹

¹Human Genetics, McGill University, Montreal, Quebec, Canada; ²Biochemistry, Sherbrooke University, Sherbrooke, Quebec, Canada; ³Pediatrics, McGill University, Montreal, Quebec, Canada

Correspondence: H. Nikbakht – Human Genetics, McGill University, Montreal, Quebec, Canada

Objectives

Activation of MYC pathway has been shown in diverse cancers. MYC Associated Factor X (MAX) plays a key role in the MYC-MAX-MAD gene regulatory network; however, its direct involvement in cancer has not yet been reported. Here we report discovery of a novel recurrent somatic mutation in MAX in brain tumors and study its effects on the development and progression of tumors.

Methods

We found a mutation on Arg 51 residue to Glu in MAX gene in a patient with bilateral thalamic pediatric astrocytoma in which the primary tumors (left and right thalamus) had nearly identical mutation profiles except for the presence of the MAX R51Q only in one.

We used this unique opportunity to study the effects of this mutation on the progression and development of brain tumors.

We performed differential expression on these samples to find the pathways affected by this mutation. Using ChipSeq we studied changes in the chromatin conformation in genes regulated by this network. We used CD experiments, to study how this mutation affects the affinity between Max and other proteins in this family and with DNA.

Results

We screened our dataset and found 7 cases in 180 HGAs exome sequenced by our group (3.8%) with this mutation. We also identified 14 cases in published datasets.

We found that this mutation always appears later in tumor development in subclonal fashion and is accompanied by at least one driver such as H3 K27M.

Our differential expression and ChipSeq experiments revealed lack of a global effect of this mutation but specific effects on groups of genes involved in some pathways such as apoptosis.

We also demonstrate that this mutation has no effect on the binding efficacy between proteins in its regulatory network, but a Max R51Q homodimer binds less efficiently to nonspecific DNA than its wild type. It however, only affects the binding between Myc/Max heterodimer to DNA in E-boxes.

Conclusion

We identify MAX as a new cancer gene, particularly relevant to brain cancer.

Our results show the possible effects of MAX mutation in promoting tumor progression and development. It also suggests the effect of this mutation on the spread of the tumor.

These findings shed new light on the mechanisms underlying cancer progression and the involvement of MYC signalling in development of brain tumors which, in turn, can point us towards new targets for therapeutic approaches.

Disclosure of interest

None declared.

O18 Predictive biomarkers to metastatic pancreatic cancer treatment

J. B. Goldstein¹, M. Overman², G. Varadhachary², R. Shroff², R. Wolff², M. Javle², A. Futreal¹, D. Fogelman²

¹Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; ²Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA

Correspondence: J. B. Goldstein – Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA

Objectives

In 2015, we demonstrated that a strong family history of BRCA related tumors portends a better prognosis in metastatic pancreatic cancer patients. We now investigate if this holds true for more recent patients treated with standard-of-care FOLFIRINOX (FNX) or Gemcitabine/nab-paclitaxel (GA). We hypothesize that targeted sequencing of these tumors for DNA repair pathway aberrations will better predict outcomes than the surrogate marker of family history, which may be subject to patient bias.

Methods

We identified patients with de novo stage 4 pancreatic cancer initially treated at MD Anderson Cancer Center with first-line FNX or GA. We excluded patients with prior surgical resection (bypass was allowed) or radiation as initial therapy, and patients with unknown family history. Survival analysis was performed using the Kaplan-Meier method.

Results

We identified 153 patients initially treated with FNX and 80 patients treated with GA. Median age of the entire cohort was 62 years (36–84), 58% were male. Median OS was 286 and 295 days, respectively. Approximately 5% of patients had a family history of 3 or more BRCA tumors (breast, ovarian, prostate, pancreas). Median OS for these patients was 469 days, as compared to 285, 268, and 296 days for patients with 0, 1, and 2 affected family members. Median survival in patients with 3+ family members affected was 463 days and 511 days for patients on FNX and GA respectively (95% CI 240-636d, 0–1092 d). For patients with 0–2 such family members, median OS was 283 and 268 days, respectively. As expected, ECOG 0–1 and the absence of liver metastases were associated with longer survival. We identified 126 of 153 FNX patients and 51 of 80 GA patients with pathology specimens available for targeted sequencing.

Conclusion

As in our earlier report, we see a trend towards increased survival in patients with 3 or more family members with BRCA related tumors. However, the small number of these patients precludes a definitive assessment. We believe that targeted sequencing of DNA repair pathway and associated genes will better elucidate the mechanism of survival benefit over biased family history.

References

1. Fogelman D, Sugar EA, Oliver G, et al. Family history as a marker of platinum sensitivity in pancreatic adenocarcinoma. Cancer chemotherapy and pharmacology. 2015. 76(3): 489–498.

Human Genome Meeting 2016

Human genome meeting 2016

Table of contents

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder

A. K. Srivastava1, Y. Wang2, R. Huang3, C. Skinner1, T. Thompson3, L. Pollard3, T. Wood3, F. Luo2, R. Stevenson1

1JCSRI, Greenwood Genetic Center, Greenwood, SC, USA; 2School of Computing, Clemson University, Clemson, SC, USA; 3Biochemical Genetics Laboratory, Greenwood Genetic Center, Greenwood, SC, USA

Correspondence: A. K. Srivastava – JCSRI, Greenwood Genetic Center, Greenwood, SC, USA

O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents

R. Polimanti1, J. Gelernter1,2,3

1Department Psychiatry, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; 2Department Genetics, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; 3Department Neurobiology, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA

Correspondence: R. Polimanti – Dept Psychiatry, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA

O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort

X. Lin1, I. Y. Lim1, Y. Wu1, A. L. Teh1, L. Chen1, I. M. Aris1, S. E. Soh1, M. T. Tint2, J. L. MacIsaac3, F. Yap4, K. Kwek4, S. M. Saw2, M. S. Kobor3, M. J. Meaney1, K. M. Godfrey5, Y. S. Chong1, J. D. Holbrook1, Y. S. Lee1, P. D. Gluckman1,6, N. Karnani1, GUSTO study group

Correspondence: X. Lin – Singapore Institute for Clinical Sciences, Singapore, Singapore

O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus

A. Kapoor, D. Lee, A. Chakravarti

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Correspondence: A. Kapoor – McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells

C. Maercker1, F. Graf2, M. Boutros2

1Esslingen University of Applied Sciences, Esslingen, Germany; 2German Cancer Research Center, Heidelberg, Germany

Correspondence: C. Maercker – Esslingen University of Applied Sciences, Esslingen, Germany

O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies

G. Stamoulis1, F. Santoni2, P. Makrythanasis2, A. Letourneau1, M. Guipponi2, N. Panousis1, M. Garieri1, P. Ribaux1, E. Falconnet1, C. Borel1, S. E. Antonarakis1,2,3

1Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland; 2Geneva University Hospitals-HUG, Service of Genetic Medicine, Geneva, Switzerland; 3iGE3 Institute of Genetics and Genomics of Geneva, University of Geneva Medical School, Geneva, Switzerland

Correspondence: G. Stamoulis – Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

O7 Role of microRNA in LCL to IPSC reprogramming

S. Kumar1, J. Curran2, J. Blangero2

1South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Edinburg, TX, USA; 2South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Brownsville, TX, USA

Correspondence: S. Kumar – South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Edinburg, TX, USA

O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease

S. Chatterjee1, A. Kapoor1, J. Akiyama2, D. Auer1, C. Berrios1, L. Pennacchio2, A. Chakravarti1

1Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; 2Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Correspondence: S. Chatterjee – Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

O9 Metabolomic profiling for the diagnosis of neurometabolic disorders

T. R. Donti1, G. Cappuccio2, M. Miller1, P. Atwal1, A. Kennedy3, A. Cardon4, C. Bacino1, L. Emrick4, J. Hertecant5, F. Baumer6, B. Porter6, M. Bainbridge1, P. Bonnen1, B. Graham1, R. Sutton1, Q. Sun1, S. Elsea1

Correspondence: T. R. Donti – Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

O10 A novel causal methylation network approach to Alzheimer’s disease

Z. Hu1, P. Wang2, Y. Zhu3, J. Zhao3, M. Xiong2 and David A Bennett4

1School of Public Health, Houston Health Science Center, Houston, TX, USA; 2University of Texas, Houston Health Science Center, Houston, TX, USA; 3Tulane University, New Orleans, LO, USA; 4 Rush Alzheimer’s Disease Center, Rush University, Chicago, IL, USA

Correspondence: Z. Hu – School of Public Health, Houston Health Science Center, Houston, TX, USA

O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway

A. Hidalgo-Miranda1, S. Romero-Cordoba1, S. Rodriguez-Cuevas2, R. Rebollar-Vega1, E. Tagliabue3, M. Iorio3, E. D’Ippolito3, S. Baroni3

1Cancer Genomics Laboratory, National Institute Of Genomic Medicine (INMEGEN), Mexico City, Mexico; 2FUCAM , Mexico City, Mexico; 3National Tumor Institute, Milan, Italy

Correspondence: A. Hidalgo-Miranda – Cancer Genomics Laboratory, National Institute Of Genomic Medicine (INMEGEN), Mexico City, Mexico

O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types

B. Kaczkowski1, Y. Tanaka2, H. Kawaji2, A. Sandelin3, R. Andersson3, M. Itoh1, T. Lassmann4, the FANTOM5 consortium1, Y. Hayashizaki5, P. Carninci1, A. R. R. Forrest6

Correspondence: B. Kaczkowski – Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan

O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types

C. A. Semple

MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, Edinburgh, UK

O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer

1Univ of Washington, Seattle, WA, USA; 2Univ of Michigan, Ann Arbor, MI, USA; 3Univ California, Davis, CA, USA; 4Fred Hutch, Seattle, WA, USA

Correspondence: E. A. Rosenthal – Univ of Washington, Seattle, WA, USA

O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer

F. Menghi1, K. Inaki1, X. Woo1, P. Kumar1, K. Grzeda1, A. Malhotra1, H. Kim1, D. Ucar1, P. Shreckengast1, K. Karuturi1, J. Keck2, J. Chuang1, E. T. Liu1

1The Jackson Laboratory, Farmington, CT, USA; 2The Jackson Laboratory, Sacramento, CA, USA

Correspondence: F. Menghi – The Jackson Laboratory, Farmington, CT, USA

O16 Modeling genetic interactions associated with molecular subtypes of breast cancer

B. Ji, A. Tyler, G. Ananda, G. Carter

The Jackson Laboratory, Bar Harbor, ME, USA

Correspondence: G. Carter – The Jackson Laboratory, Bar Harbor, ME, USA

O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors

H. Nikbakht1, M. Montagne2, M. Zeinieh1, A. Harutyunyan1, M. Mcconechy1, N. Jabado3, P. Lavigne2, J. Majewski1

1Human Genetics, McGill University, Montreal, Quebec, Canada; 2Biochemistry, Sherbrooke University, Sherbrooke, Quebec, Canada; 3Pediatrics, McGill University, Montreal, Quebec, Canada

Correspondence: H. Nikbakht – Human Genetics, McGill University, Montreal, Quebec, Canada

O18 Predictive biomarkers to metastatic pancreatic cancer treatment

J. B. Goldstein1, M. Overman2, G. Varadhachary2, R. Shroff2, R. Wolff2, M. Javle2, A. Futreal1, D. Fogelman2

1Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; 2Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA

Correspondence: J. B. Goldstein – Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA

A. K. Srivastava¹, Y. Wang², R. Huang³, C. Skinner¹, T. Thompson³, L. Pollard³, T. Wood³, F. Luo², R. Stevenson¹

¹JCSRI, Greenwood Genetic Center, Greenwood, SC, USA; ²School of Computing, Clemson University, Clemson, SC, USA; ³Biochemical Genetics Laboratory, Greenwood Genetic Center, Greenwood, SC, USA

R. Polimanti¹, J. Gelernter^1,2,3

¹Department Psychiatry, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; ²Department Genetics, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA; ³Department Neurobiology, Yale Sch Med and VA CT Healthcare Center, West Haven, CT, USA

X. Lin¹, I. Y. Lim¹, Y. Wu¹, A. L. Teh¹, L. Chen¹, I. M. Aris¹, S. E. Soh¹, M. T. Tint², J. L. MacIsaac³, F. Yap⁴, K. Kwek⁴, S. M. Saw², M. S. Kobor³, M. J. Meaney¹, K. M. Godfrey⁵, Y. S. Chong¹, J. D. Holbrook¹, Y. S. Lee¹, P. D. Gluckman^1,6, N. Karnani¹, GUSTO study group

C. Maercker¹, F. Graf², M. Boutros²

¹Esslingen University of Applied Sciences, Esslingen, Germany; ²German Cancer Research Center, Heidelberg, Germany

G. Stamoulis¹, F. Santoni², P. Makrythanasis², A. Letourneau¹, M. Guipponi², N. Panousis¹, M. Garieri¹, P. Ribaux¹, E. Falconnet¹, C. Borel¹, S. E. Antonarakis^1,2,3

S. Kumar¹, J. Curran², J. Blangero²

¹South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Edinburg, TX, USA; ²South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio-Grande Valley, Brownsville, TX, USA

S. Chatterjee¹, A. Kapoor¹, J. Akiyama², D. Auer¹, C. Berrios¹, L. Pennacchio², A. Chakravarti¹

¹Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; ²Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

T. R. Donti¹, G. Cappuccio², M. Miller¹, P. Atwal¹, A. Kennedy³, A. Cardon⁴, C. Bacino¹, L. Emrick⁴, J. Hertecant⁵, F. Baumer⁶, B. Porter⁶, M. Bainbridge¹, P. Bonnen¹, B. Graham¹, R. Sutton¹, Q. Sun¹, S. Elsea¹

Z. Hu¹, P. Wang², Y. Zhu³, J. Zhao³, M. Xiong² and David A Bennett⁴

¹School of Public Health, Houston Health Science Center, Houston, TX, USA; ²University of Texas, Houston Health Science Center, Houston, TX, USA; ³Tulane University, New Orleans, LO, USA; ⁴ Rush Alzheimer’s Disease Center, Rush University, Chicago, IL, USA

A. Hidalgo-Miranda¹, S. Romero-Cordoba¹, S. Rodriguez-Cuevas², R. Rebollar-Vega¹, E. Tagliabue³, M. Iorio³, E. D’Ippolito³, S. Baroni³

¹Cancer Genomics Laboratory, National Institute Of Genomic Medicine (INMEGEN), Mexico City, Mexico; ²FUCAM , Mexico City, Mexico; ³National Tumor Institute, Milan, Italy

B. Kaczkowski¹, Y. Tanaka², H. Kawaji², A. Sandelin³, R. Andersson³, M. Itoh¹, T. Lassmann⁴, the FANTOM5 consortium¹, Y. Hayashizaki⁵, P. Carninci¹, A. R. R. Forrest⁶

¹Univ of Washington, Seattle, WA, USA; ²Univ of Michigan, Ann Arbor, MI, USA; ³Univ California, Davis, CA, USA; ⁴Fred Hutch, Seattle, WA, USA

F. Menghi¹, K. Inaki¹, X. Woo¹, P. Kumar¹, K. Grzeda¹, A. Malhotra¹, H. Kim¹, D. Ucar¹, P. Shreckengast¹, K. Karuturi¹, J. Keck², J. Chuang¹, E. T. Liu¹

¹The Jackson Laboratory, Farmington, CT, USA; ²The Jackson Laboratory, Sacramento, CA, USA

H. Nikbakht¹, M. Montagne², M. Zeinieh¹, A. Harutyunyan¹, M. Mcconechy¹, N. Jabado³, P. Lavigne², J. Majewski¹

¹Human Genetics, McGill University, Montreal, Quebec, Canada; ²Biochemistry, Sherbrooke University, Sherbrooke, Quebec, Canada; ³Pediatrics, McGill University, Montreal, Quebec, Canada

J. B. Goldstein¹, M. Overman², G. Varadhachary², R. Shroff², R. Wolff², M. Javle², A. Futreal¹, D. Fogelman²

¹Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA; ²Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA