Browsing by Subject "Genetic variation"
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Item Open Access Genome sequencing highlights the dynamic early history of dogs(Public Library of Science, 2014) Freedman, A. H.; Gronau I.; Schweizer, R. M.; Ortega-Del Vecchyo, D.; Han, E.; Silva, P. M.; Galaverni, M.; Fan, Z.; Marx P.; Lorente-Galdos, B.; Beale, H.; Ramirez, O.; Hormozdiari, F.; Alkan C.; Vilà, C.; Squire K.; Geffen, E.; Kusak, J.; Boyko, A. R.; Parker, H. G.; Lee C.; Tadigotla, V.; Siepel, A.; Bustamante, C. D.; Harkins, T. T.; Nelson, S. F.; Ostrander, E. A.; Marques Bonet, T.; Wayne, R. K.; Novembre, J.To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history, we generated high-quality genome sequences from three gray wolves, one from each of the three putative centers of dog domestication, two basal dog lineages (Basenji and Dingo) and a golden jackal as an outgroup. Analysis of these sequences supports a demographic model in which dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow. In dogs, the domestication bottleneck involved at least a 16-fold reduction in population size, a much more severe bottleneck than estimated previously. A sharp bottleneck in wolves occurred soon after their divergence from dogs, implying that the pool of diversity from which dogs arose was substantially larger than represented by modern wolf populations. We narrow the plausible range for the date of initial dog domestication to an interval spanning 11-16 thousand years ago, predating the rise of agriculture. In light of this finding, we expand upon previous work regarding the increase in copy number of the amylase gene (AMY2B) in dogs, which is believed to have aided digestion of starch in agricultural refuse. We find standing variation for amylase copy number variation in wolves and little or no copy number increase in the Dingo and Husky lineages. In conjunction with the estimated timing of dog origins, these results provide additional support to archaeological finds, suggesting the earliest dogs arose alongside hunter-gathers rather than agriculturists. Regarding the geographic origin of dogs, we find that, surprisingly, none of the extant wolf lineages from putative domestication centers is more closely related to dogs, and, instead, the sampled wolves form a sister monophyletic clade. This result, in combination with dog-wolf admixture during the process of domestication, suggests that a re-evaluation of past hypotheses regarding dog origins is necessary. © 2014.Item Open Access Genomic landscape of the Greater Middle East(Nature Publishing Group, 2016-09) Özçelik, T.; Onat, O. E.Study of the Greater Middle East (GME), home to approximately 10% of the world's population, has made invaluable contributions to the characterization of rare genetic disease, especially recessive conditions arising from the tradition of consanguinity and large families with multiple children. A new study now reports 1,111 unrelated exomes from the GME and provides a comprehensive view of genetic variation for enhanced discovery of disease-associated genes.Item Open Access A global reference for human genetic variation(Nature Publishing Group, 2015) Auton, A.; Abecasis, G. R.; Altshuler, D. M.; Durbin, R. M.; Bentley, D. R.; Chakravarti, A.; Clark, A. G.; Donnelly, P.; Eichler, E. E.; Flicek, P.; Gabriel, S. B.; Gibbs, R. A.; Green, E. D.; Hurles, M. E.; Knoppers, B. M.; Korbel, J. O.; Lander, E. S.; Lee, C.; Lehrach, H.; Mardis, E. R.; Marth, G. T.; McVean, G. A.; Nickerson, D. A.; Schmidt, J. P.; Sherry, S. T.; Wang, J.; Wilson, R. K.; Boerwinkle, E.; Doddapaneni, H.; Han, Y.; Korchina, V.; Kovar, C.; Lee, S.; Muzny, D.; Reid, J. G.; Zhu, Y.; Chang, Y.; Feng, Q.; Fang, X.; Guo, X.; Jian, M.; Jiang, H.; Jin, X.; Lan, T.; Li, G.; Li, J.; Li, Y.; Liu, S.; Liu, X.; Lu, Y.; Ma, X.; Tang, M.; Wang, B.; Wang, G.; Wu, H.; Wu, R.; Xu, X.; Yin, Y.; Zhang, D.; Zhang, W.; Zhao, J.; Zhao, M.; Zheng, X.; Gupta, N.; Gharani, N.; Toji, L. H.; Gerry, N. P.; Resch, A. M.; Barker, J.; Clarke, L.; Gil, L.; Hunt, S. E.; Kelman, G.; Kulesha, E.; Leinonen, R.; McLaren, W. M.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Smith, R. E.; Streeter, I.; Thormann, A.; Toneva, I.; Vaughan, B.; Zheng-Bradley, X.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Sudbrak, R.; Albrecht, M. W.; Amstislavskiy, V. S.; Borodina, T. A.; Lienhard, M.; Mertes, F.; Sultan, M.; Timmermann, B.; Yaspo, Marie-Laure; Fulton, L.; Ananiev, V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Church, D.; Cohen, R.; Cook, C.; Garner, J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez, J.; Meric, P.; O'Sullivan, C.; Ostapchuk, Y.; Phan, L.; Ponomarov, S.; Schneider, V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang, H.; Balasubramaniam, S.; Burton, J.; Danecek, P.; Keane, T. M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker, J.; Quail, M.; Davies, C. J.; Gollub, J.; Webster, T.; Wong, B.; Zhan, Y.; Campbell, C. L.; Kong, Y.; Marcketta, A.; Yu, F.; Antunes, L.; Bainbridge, M.; Sabo, A.; Huang, Z.; Coin, L. J. M.; Fang, L.; Li, Q.; Li, Z.; Lin, H.; Liu, B.; Luo, R.; Shao, H.; Xie, Y.; Ye, C.; Yu, C.; Zhang, F.; Zheng, H.; Zhu, H.; Alkan, C.; Dal, E.; Kahveci, F.; Garrison, E. P.; Kural, D.; Lee, W. P.; Leong, W. F.; Stromberg, M.; Ward, A. N.; Wu, J.; Zhang, M.; Daly, M. J.; DePristo, M. A.; Handsaker, R. E.; Banks, E.; Bhatia, G.; Del Angel, G.; Genovese, G.; Li, H.; Kashin, S.; McCarroll, S. A.; Nemesh, J. C.; Poplin, R. E.; Yoon, S. C.; Lihm, J.; Makarov, V.; Gottipati, S.; Keinan, A.; Rodriguez-Flores, J. L.; Rausch, T.; Fritz, M. H.; Stütz, A. M.; Beal, K.; Datta, A.; Herrero, J.; Ritchie, G. R. S.; Zerbino, D.; Sabeti, P. C.; Shlyakhter, I.; Schaffner, S. F.; Vitti, J.; Cooper, D. N.; Ball, E. V.; Stenson, P. D.; Barnes, B.; Bauer, M.; Cheetham, R. K.; Cox, A.; Eberle, M.; Kahn, S.; Murray, L.; Peden, J.; Shaw, R.; Kenny, E. E.; Batzer, M. A.; Konkel, M. K.; Walker, J. A.; MacArthur, D. G.; Lek, M.; Herwig, R.; Ding, L.; Koboldt, D. C.; Larson, D.; Ye, K.; Gravel, S.; Swaroop, A.; Chew, E.; Lappalainen, T.; Erlich, Y.; Gymrek, M.; Willems, T. F.; Simpson, J. T.; Shriver, M. D.; Rosenfeld, J. A.; Bustamante, C. D.; Montgomery, S. B.; De La Vega, F. M.; Byrnes, J. K.; Carroll, A. W.; DeGorter, M. K.; Lacroute, P.; Maples, B. K.; Martin, A. R.; Moreno-Estrada, A.; Shringarpure, S. S.; Zakharia, F.; Halperin, E.; Baran, Y.; Cerveira, E.; Hwang, J.; Malhotra, A.; Plewczynski, D.; Radew, K.; Romanovitch, M.; Zhang, C.; Hyland, F. C. L.; Craig, D. W.; Christoforides, A.; Homer, N.; Izatt, T.; Kurdoglu, A. A.; Sinari, S. A.; Squire, K.; Xiao, C.; Sebat, J.; Antaki, D.; Gujral, M.; Noor, A.; 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.; Devine, S. E.; Kang, H. M.; Kidd, J. M.; Blackwell, T.; Caron, S.; Chen, W.; Emery, S.; Fritsche, L.; Fuchsberger, C.; Jun, G.; Li, B.; Lyons, R.; Scheller, C.; Sidore, C.; Song, S.; Sliwerska, E.; Taliun, D.; Tan, A.; Welch, R.; Wing, M. K.; Zhan, X.; Awadalla, P.; Hodgkinson, A.; Li, Y.; Shi, X.; Quitadamo, A.; Lunter, G.; Marchini, J. L.; Myers, S.; Churchhouse, C.; Delaneau, O.; Gupta-Hinch, A.; Kretzschmar, W.; Iqbal, Z.; Mathieson, I.; Menelaou, A.; Rimmer, A.; Xifara, D. K.; Oleksyk, T. K.; Fu, Y.; Liu, X.; Xiong, M.; Jorde, L.; Witherspoon, D.; Xing, J.; Browning, B. L.; Browning, S. R.; Hormozdiari, F.; Sudmant, P. H.; Khurana, E.; Tyler-Smith, C.; Albers, C. A.; Ayub, Q.; Chen, Y.; Colonna, V.; Jostins, L.; Walter, K.; Xue, Y.; Gerstein, M. B.; Abyzov, A.; Balasubramanian, S.; Chen, J.; Clarke, D.; Fu, Y.; Harmanci, A. O.; Jin, M.; Lee, D.; Liu, J.; Mu, X. J.; Zhang, J.; Zhang, Y.; Hartl, C.; Shakir, K.; Degenhardt, J.; Meiers, S.; Raeder, B.; Casale, F. P.; Stegle, O.; Lameijer, E. W.; Hall, I.; Bafna, V.; Michaelson, J.; Gardner, E. J.; Mills, R. E.; Dayama, G.; Chen, K.; Fan, X.; Chong, Z.; Chen, T.; Chaisson, M. J.; Huddleston, J.; Malig, M.; Nelson, B. J.; Parrish, N. F.; Blackburne, B.; Lindsay, S. J.; Ning, Z.; Zhang, Y.; Lam, H.; Sisu, C.; Challis, D.; Evani, U. S.; Lu, J.; Nagaswamy, U.; Yu, J.; Li, W.; Habegger, L.; Yu, H.; Cunningham, F.; Dunham, I.; Lage, K.; Jespersen, J. B.; Horn, H.; Kim, D.; Desalle, R.; Narechania, A.; Sayres, M. A. W.; Mendez, F. L.; Poznik, G. D.; Underhill, P. A.; Mittelman, D.; Banerjee, R.; Cerezo, M.; Fitzgerald, T. W.; Louzada, S.; Massaia, A.; Yang, F.; Kalra, D.; Hale, W.; Dan, X.; Barnes, K. C.; Beiswanger, C.; Cai, H.; Cao, H.; Henn, B.; Jones, D.; Kaye, J. S.; Kent, A.; Kerasidou, A.; Mathias, R.; Ossorio, P. N.; Parker, M.; Rotimi, C. N.; Royal, C. D.; Sandoval, K.; Su, Y.; Tian, Z.; Tishkoff, S.; Via, M.; Wang, Y.; Yang, H.; Yang, L.; Zhu, J.; Bodmer, W.; Bedoya, G.; Cai, Z.; Gao, Y.; Chu, J.; Peltonen, L.; Garcia-Montero, A.; Orfao, A.; Dutil, J.; Martinez-Cruzado, J. C.; Mathias, R. A.; Hennis, A.; Watson, H.; McKenzie, C.; Qadri, F.; LaRocque, R.; Deng, X.; Asogun, D.; Folarin, O.; Happi, C.; Omoniwa, O.; Stremlau, M.; Tariyal, R.; Jallow, M.; Joof, F. S.; Corrah, T.; Rockett, K.; Kwiatkowski, D.; Kooner, J.; Hien, T. T.; Dunstan, S. J.; ThuyHang, N.; Fonnie, R.; Garry, R.; Kanneh, L.; Moses, L.; Schieffelin, J.; Grant, D. S.; Gallo, C.; Poletti, G.; Saleheen, D.; Rasheed, A.; Brooks, L. D.; Felsenfeld, A. L.; McEwen, J. E.; Vaydylevich, Y.; Duncanson, A.; Dunn, M.; Schloss, J. A.The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. © 2015 Macmillan Publishers Limited. All rights reserved.Item Open Access Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes(Lippincott Williams & Wilkins, 2015) Yao, C.; Chen, B. H.; Joehanes, R.; Otlu, B.; Zhang X.; Liu, C.; Huan, T.; Tastan, O.; Cupples, L. A.; Meigs, J. B.; Fox, C. S.; Freedman, J. E.; Courchesne, P.; O'Donnell, C. J.; Munson, P. J.; Keles, S.; Levy, D.BACKGROUND - : Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown. METHODS AND RESULTS - : We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes. CONCLUSIONS - : Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.Item Open Access Oligonucleotide-based label-free detection with optical microresonators: strategies and challenges(Royal Society of Chemistry, 2016) Toren, P.; Ozgur E.; Bayındır, MehmetThis review targets diversified oligonucleotide-based biodetection techniques, focusing on the use of microresonators of whispering gallery mode (WGM) type as optical biosensors mostly integrated with lab-on-a-chip systems. On-chip and microfluidics combined devices along with optical microresonators provide rapid, robust, reproducible and multiplexed biodetection abilities in considerably small volumes. We present a detailed overview of the studies conducted so far, including biodetection of various oligonucleotide biomarkers as well as deoxyribonucleic acids (DNAs), ribonucleic acids (RNAs) and proteins. We particularly advert to chemical surface modifications for specific and selective biosensing.Item Open Access Privacy-preserving genomic testing in the clinic: a model using HIV treatment(Nature Publishing Group, 2016) Mclaren, P. J.; Raisaro, J. L.; Aouri, M.; Rotger, M.; Ayday, E.; Bartha, I.; Delgado, M. B.; Vallet, Y.; Günthard, H. F.; Cavassini, M.; Furrer, H.; Doco-Lecompte, T.; Marzolini, C.; Schmid, P.; Di Benedetto, C.; Decosterd, L. A.; Fellay, J.; Hubaux, Jean-Pierre; Telenti A.Purpose:The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.Methods:We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers.Results:A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865 ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%.Conclusions:The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine.