Browsing by Subject "allele"

Now showing 1 - 4 of 4

Open Access
The bonobo genome compared with the chimpanzee and human genomes
(2012) Prüfer, K.; Munch, K.; Hellmann I.; Akagi, K.; Miller J.R.; Walenz, B.; Koren, S.; Sutton G.; Kodira, C.; Winer, R.; Knight J.R.; Mullikin J.C.; Meader, S.J.; Ponting, C.P.; Lunter G.; Higashino, S.; Hobolth, A.; Dutheil J.; Karakoç, E.; Alkan, C.; Sajjadian, S.; Catacchio, C.R.; Ventura, M.; Marques-Bonet, T.; Eichler, E.E.; André, C.; Atencia, R.; Mugisha L.; Junhold J.; Patterson, N.; Siebauer, M.; Good J.M.; Fischer, A.; Ptak, S.E.; Lachmann, M.; Symer, D.E.; Mailund, T.; Schierup, M.H.; Andrés, A.M.; Kelso J.; Pääbo, S.
Two African apes are the closest living relatives of humans: the chimpanzee (Pan troglodytes) and the bonobo (Pan paniscus). Although they are similar in many respects, bonobos and chimpanzees differ strikingly in key social and sexual behaviours, and for some of these traits they show more similarity with humans than with each other. Here we report the sequencing and assembly of the bonobo genome to study its evolutionary relationship with the chimpanzee and human genomes. We find that more than three per cent of the human genome is more closely related to either the bonobo or the chimpanzee genome than these are to each other. These regions allow various aspects of the ancestry of the two ape species to be reconstructed. In addition, many of the regions that overlap genes may eventually help us understand the genetic basis of phenotypes that humans share with one of the two apes to the exclusion of the other. © 2012 Macmillan Publishers Limited. All rights reserved.
Open Access
Extreme clonality in lymphoblastoid cell lines with implications for allele specific expression analyses
(2008) Plagnol V.; Uz, E.; Wallace, C.; Stevens H.; Clayton, D.; Ozcelik, T.; Todd J.A.
Lymphoblastoid cell lines (LCL) are being actively and extensively used to examine the expression of specific genes and (genome-wide expression profiles, including allele specific expression assays. However, it has recently been shown that approximately 10% of human genes exhibit random patterns of monoallelic expression within single clones of LCLs. Consequently allelic imbalance studies could be significantly compromised if bulk populations of donor cells are clonal, or near clonal. Here, using X chromosome inactivation as a readout, we confirm and quantify widespread near monoclonality in two independent sets of cell lines. Consequently, we recommend where possible the use of bulk, non cell line, ex vivo cells for allele specific expression assays. © 2008 Plagnol et al.
Open Access
Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel
(Nature Publishing Group, 2014) Delaneau O.; Marchini J.; McVeanh G.A.; Donnelly P.; Lunter G.; Marchini J.L.; Myers, S.; Gupta-Hinch, A.; Iqbal, Z.; Mathieson I.; Rimmer, A.; Xifara, D.K.; Kerasidou, A.; Churchhouse, C.; Altshuler, D.M.; Gabriel, S.B.; Lander, E.S.; Gupta, N.; Daly, M.J.; DePristo, M.A.; Banks, E.; Bhatia G.; Carneiro, M.O.; Del Angel G.; Genovese G.; Handsaker, R.E.; Hartl, C.; McCarroll, S.A.; Nemesh J.C.; Poplin, R.E.; Schaffner, S.F.; Shakir, K.; Sabeti P.C.; Grossman, S.R.; Tabrizi, S.; Tariyal, R.; Li H.; Reich, D.; Durbin, R.M.; Hurles, M.E.; Balasubramaniam, S.; Burton J.; Danecek P.; Keane, T.M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker J.; Quail, M.; Ayub Q.; Chen, Y.; Coffey, A.J.; Colonna V.; Huang, N.; Jostins L.; Scally, A.; Walter, K.; Xue, Y.; Zhang, Y.; Blackburne, B.; Lindsay, S.J.; Ning, Z.; Frankish, A.; Harrow J.; Chris, T.-S.; Abecasis G.R.; Kang H.M.; Anderson P.; Blackwell, T.; Busonero F.; Fuchsberger, C.; Jun G.; Maschio, A.; Porcu, E.; Sidore, C.; Tan, A.; Trost, M.K.; Bentley, D.R.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Bauer, M.; Cheetham, R.K.; Cox, T.; Eberle, M.; Murray L.; Shaw, R.; Chakravarti, A.; Clark, A.G.; Keinan, A.; Rodriguez-Flores J.L.; De LaVega F.M.; Degenhardt J.; Eichler, E.E.; Flicek P.; Clarke L.; Leinonen, R.; Smith, R.E.; Zheng-Bradley X.; Beal, K.; Cunningham F.; Herrero J.; McLaren W.M.; Ritchie G.R.S.; Barker J.; Kelman G.; Kulesha, E.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Streeter I.; Toneva I.; Gibbs, R.A.; Dinh H.; Kovar, C.; Lee, S.; Lewis L.; Muzny, D.; Reid J.; Wang, M.; Yu F.; Bainbridge, M.; Challis, D.; Evani, U.S.; Lu J.; Nagaswamy, U.; Sabo, A.; Wang, Y.; Yu J.; Fowler G.; Hale W.; Kalra, D.; Green, E.D.; Knoppers, B.M.; Korbel J.O.; Rausch, T.; Sttz, A.M.; Lee, C.; Griffin L.; Hsieh, C.-H.; Mills, R.E.; Von Grotthuss, M.; Zhang, C.; Shi X.; Lehrach H.; Sudbrak, R.; Amstislavskiy V.S.; Lienhard, M.; Mertes F.; Sultan, M.; Timmermann, B.; Yaspo, M.L.; Herwig, S.R.; Mardis, E.R.; Wilson, R.K.; Fulton L.; Fulton, R.; Weinstock G.M.; Chinwalla, A.; Ding L.; Dooling, D.; Koboldt, D.C.; McLellan, M.D.; Wallis J.W.; Wendl, M.C.; Zhang Q.; Marth G.T.; Garrison, E.P.; Kural, D.; Lee W.-P.; Leong W.F.; Ward, A.N.; Wu J.; Zhang, M.; Nickerson, D.A.; Alkan, C.; Hormozdiari F.; Ko, A.; Sudmant P.H.; Schmidt J.P.; Davies, C.J.; Gollub J.; Webster, T.; Wong, B.; Zhan, Y.; Sherry, S.T.; Xiao, C.; Church, D.; Ananiev V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Cohen, R.; Cook, C.; Garner J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez J.; Meric P.; Ostapchuk, Y.; Phan L.; Ponomarov, S.; Schneider V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang H.; Wang J.; Fang X.; Guo X.; Jian, M.; Jiang H.; Jin X.; Li G.; Li J.; Li, Y.; Liu X.; Lu, Y.; Ma X.; Tai, S.; Tang, M.; Wang, B.; Wang G.; Wu H.; Wu, R.; Yin, Y.; Zhang W.; Zhao J.; Zhao, M.; Zheng X.; Lachlan H.; Fang L.; Li Q.; Li, Z.; Lin H.; Liu, B.; Luo, R.; Shao H.; Wang, B.; Xie, Y.; Ye, C.; Yu, C.; Zheng H.; Zhu H.; Cai H.; Cao H.; Su, Y.; Tian, Z.; Yang H.; Yang L.; Zhu J.; Cai, Z.; Wang J.; Albrecht, M.W.; Borodina, T.A.; Auton, A.; Yoon, S.C.; Lihm J.; Makarov V.; Jin H.; Kim W.; Kim, K.C.; Gottipati, S.; Jones, D.; Cooper, D.N.; Ball, E.V.; Stenson P.D.; Barnes, B.; Kahn, S.; Ye, K.; Batzer, M.A.; Konkel, M.K.; Walker J.A.; MacArthur, D.G.; Lek, M.; Shriver, M.D.; Bustamante, C.D.; Gravel, S.; Kenny, E.E.; Kidd J.M.; Lacroute P.; Maples, B.K.; Moreno-Estrada, A.; Zakharia F.; Henn, B.; Sandoval, K.; Byrnes J.K.; Halperin, E.; Baran, Y.; Craig, D.W.; Christoforides, A.; Izatt, T.; Kurdoglu, A.A.; Sinari, S.A.; Homer, N.; Squire, K.; Sebat J.; Bafna V.; Ye, K.; Burchard, E.G.; Hernandez, R.D.; Gignoux, C.R.; Haussler, D.; Katzman, S.J.; Kent W.J.; Howie, B.; Ruiz-Linares, A.; Dermitzakis, E.T.; Lappalainen, T.; Devine, S.E.; Liu X.; Maroo, A.; Tallon L.J.; Rosenfeld J.A.; Michelson L.P.; Angius, A.; Cucca F.; Sanna, S.; Bigham, A.; Jones, C.; Reinier F.; Li, Y.; Lyons, R.; Schlessinger, D.; Awadalla P.; Hodgkinson, A.; Oleksyk, T.K.; Martinez-Cruzado J.C.; Fu, Y.; Liu X.; Xiong, M.; Jorde L.; Witherspoon, D.; Xing J.; Browning, B.L.; Hajirasouliha I.; Chen, K.; Albers, C.A.; Gerstein, M.B.; Abyzov, A.; Chen J.; Fu, Y.; Habegger L.; Harmanci, A.O.; Mu X.J.; Sisu, C.; Balasubramanian, S.; Jin, M.; Khurana, E.; Clarke, D.; Michaelson J.J.; OSullivan, C.; Barnes, K.C.; Gharani, N.; Toji L.H.; Gerry, N.; Kaye J.S.; Kent, A.; Mathias, R.; Ossorio P.N.; Parker, M.; Rotimi, C.N.; Royal, C.D.; Tishkoff, S.; Via, M.; Bodmer W.; Bedoya G.; Yang G.; You, C.J.; Garcia-Montero, A.; Orfao, A.; Dutil J.; Brooks L.D.; Felsenfeld, A.L.; McEwen J.E.; Clemm, N.C.; Guyer, M.S.; Peterson J.L.; Duncanson, A.; Dunn, M.; Peltonen L.
A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved.
Open Access
Mdm2 Snp309 G allele displays high frequency and inverse correlation with somatic P53 mutations in hepatocellular carcinoma
(Elsevier, 2010) Acun T.; Terzioǧlu-Kara, E.; Konu, O.; Ozturk, M.; Yakicier, M. C.
Loss of function of the p53 protein, which may occur through a range of molecular events, is critical in hepatocellular carcinoma (HCC) evolution. MDM2, an oncogene, acts as a major regulator of the p53 protein. A polymorphism in the MDM2 promoter, SNP309 (T/G), has been shown to alter protein expression and may thus play a role in carcinogenesis. MDM2 SNP309 is also associated with HCC. However, the role of SNP309 in hepatocarcinogenesis with respect to TP53 mutations is unknown. In this study, we investigated the distribution of the MDM2 SNP309 genotype and somatic TP53 (the p53 tumor suppressor gene) mutations in 99 human HCC samples from Africa, Europe, China and Japan. Samples exhibited striking geographical differences in their distribution of SNP309 genotypes. The frequency and spectrum of p53 mutations also varied geographically; TP53 mutations were frequent in Africa, where the SNP309 T/T genotype predominated but were rare in Europe and Japan, where the SNP309 G allele was present more frequently. TP53 mutations were detected in 18% (4/22) of SNP309 T/G and G/G and 82% (18/22) of SNP309 T/T genotype holders; this difference was statistically highly significant (P-value = 0.0006). Our results indicated that the presence of the SNP309 G allele is inversely associated with the presence of somatic TP53 mutations because they only coincided in 4% of HCC cases. This finding suggests that the SNP309 G allele may functionally replace p53 mutations, and in addition to known etiological factors, may be partly responsible for differential HCC prevalence. © 2009 Elsevier B.V. All rights reserved.