Browsing by Subject "Genetic association"
Now showing 1 - 8 of 8
Results Per Page
Sort Options
Item Open Access Analysis of skewed X-chromosome inactivation in females with rheumatoid arthritis and autoimmune thyroid diseases(BioMed Central, 2009) Chabchoub, G.; Uz, E.; Maalej, A.; Mustafa, C. A.; Rebai, A.; Mnif, M.; Bahloul, Z.; Farid, N. R.; Ozcelik, T.; Ayadi, H.Introduction The majority of autoimmune diseases such as rheumatoid arthritis (RA) and autoimmune thyroid diseases (AITDs) are characterized by a striking female predominance superimposed on a predisposing genetic background. The role of extremely skewed X-chromosome inactivation (XCI) has been questioned in the pathogenesis of several autoimmune diseases.Item Open Access Characterization of a novel zebrafish (Danio rerio) gene, wdr81, associated with cerebellar ataxia, mental retardation and dysequilibrium syndrome (CAMRQ)(BioMed Central Ltd., 2015) Doldur-Balli, F.; Ozel, M. N.; Gulsuner, S.; Tekinay, A. B.; Ozcelik, T.; Konu, O.; Adams, M. M.Background: WDR81 (WD repeat-containing protein 81) is associated with cerebellar ataxia, mental retardation and disequilibrium syndrome (CAMRQ2, [MIM 610185]). Human and mouse studies suggest that it might be a gene of importance during neurodevelopment. This study aimed at fully characterizing the structure of the wdr81 transcript, detecting the possible transcript variants and revealing its expression profile in zebrafish, a powerful model organism for studying development and disease. Results: As expected in human and mouse orthologous proteins, zebrafish wdr81 is predicted to possess a BEACH (Beige and Chediak-Higashi) domain, a major facilitator superfamily domain and WD40-repeats, which indicates a conserved function in these species. We observed that zebrafish wdr81 encodes one open reading frame while the transcript has one 5' untranslated region (UTR) and the prediction of the 3' UTR was mainly confirmed along with a detected insertion site in the embryo and adult brain. This insertion site was also found in testis, heart, liver, eye, tail and muscle, however, there was no amplicon in kidney, intestine and gills, which might be the result of possible alternative polyadenylation processes among tissues. The 5 and 18 hpf were critical timepoints of development regarding wdr81 expression. Furthermore, the signal of the RNA probe was stronger in the eye and brain at 18 and 48 hpf, then decreased at 72 hpf. Finally, expression of wdr81 was detected in the adult brain and eye tissues, including but not restricted to photoreceptors of the retina, presumptive Purkinje cells and some neurogenic brains regions. Conclusions: Taken together these data emphasize the importance of this gene during neurodevelopment and a possible role for neuronal proliferation. Our data provide a basis for further studies to fully understand the function of wdr81.Item Open Access Genetics and epigenetics of liver cancer(Elsevier, 2013) Özen, Çiğdem; Yıldız, Gökhan; Dağcan, Alper Tunga; Çevik, Dilek; Örs, Ayşegül; Keleş, Umut; Topel, Hande; Öztürk, MehmetHepatocellular carcinoma (HCC) represents a major form of primary liver cancer in adults. Chronic infections with hepatitis B (HBV) and C (HCV) viruses and alcohol abuse are the major factors leading to HCC. This deadly cancer affects more than 500,000 people worldwide and it is quite resistant to conventional chemo- and radiotherapy. Genetic and epigenetic studies on HCC may help to understand better its mechanisms and provide new tools for early diagnosis and therapy. Recent literature on whole genome analysis of HCC indicated a high number of mutated genes in addition to well-known genes such as TP53, CTNNB1, AXIN1 and CDKN2A, but their frequencies are much lower. Apart from CTNNB1 mutations, most of the other mutations appear to result in loss-of-function. Thus, HCC-associated mutations cannot be easily targeted for therapy. Epigenetic aberrations that appear to occur quite frequently may serve as new targets. Global DNA hypomethylation, promoter methylation, aberrant expression of non-coding RNAs and dysregulated expression of other epigenetic regulatory genes such as EZH2 are the best-known epigenetic abnormalities. Future research in this direction may help to identify novel biomarkers and therapeutic targets for HCC.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 Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci(Cell Press, 2015) Sanders, S. J.; He, X.; Willsey, A. J.; Ercan-Sencicek, A. G.; Samocha, K. E.; Cicek, A. E.; Murtha, M. T.; Bal, V. H.; Bishop, S. L.; Dong, S.; Goldberg, A. P.; Jinlu, C.; Keaney, J. F.; Keaney III, J. F.; Mandell, J. D.; Moreno-De-Luca, D.; Poultney, C. S.; Robinson, E. B.; Smith L.; Solli-Nowlan, T.; Su, M. Y.; Teran, N. A.; Walker, M. F.; Werling, D. M.; Beaudet, A. L.; Cantor, R. M.; Fombonne, E.; Geschwind, D. H.; Grice, D. E.; Lord, C.; Lowe, J. K.; Mane, S. M.; Martin, D.M.; Morrow, E. M.; Talkowski, M. E.; Sutcliffe, J. S.; Walsh, C. A.; Yu, T. W.; Ledbetter, D. H.; Martin, C. L.; Cook, E. H.; Buxbaum, J. D.; Daly, M. J.; Devlin, B.; Roeder, K.; State, M. W.Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1). Through analysis of de novo mutations in autism spectrum disorder (ASD), Sanders et al. find that small deletions, but not large deletions/duplications, contain one critical gene. Combining CNV and sequencing data, they identify 6 loci and 65 genes associated with ASD.Item Open Access An integrated map of structural variation in 2,504 human genomes(Nature Publishing Group, 2015) Sudmant, P. H.; Rausch, T.; Gardner, E. J.; Handsaker, R. E.; Abyzov, A.; Huddleston, J.; Zhang, Y.; Ye, K.; Jun, G.; Fritz, M. Hsi-Yang; Konkel, M. K.; Malhotra, A.; Stütz, A. M.; Shi, X.; Casale, F. P.; Chen, J.; Hormozdiari, F.; Dayama, G.; Chen, K.; Malig, M.; Chaisson, M. J. P.; Walter, K.; Meiers, S.; Kashin, S.; Garrison, E.; Auton, A.; Lam, H. Y. K.; Mu, X. J.; Alkan, C.; Antaki, D.; Bae, T.; Cerveira, E.; Chines, P.; Chong, Z.; Clarke, L.; Dal, E.; Ding, L.; Emery, S.; Fan, X.; Gujral, M.; Kahveci, F.; Kidd, J. M.; Kong, Y.; Lameijer, Eric-Wubbo; McCarthy, S.; Flicek, P.; Gibbs, R. A.; Marth, G.; Mason, C. E.; Menelaou, A.; Muzny, D. M.; Nelson, B. J.; Noor, A.; Parrish, N. F.; Pendleton, M.; Quitadamo, A.; Raeder, B.; Schadt, E. E.; Romanovitch, M.; Schlattl, A.; Sebra, R.; Shabalin, A. A.; Untergasser, A.; Walker J. A.; Wang, M.; Yu, F.; Zhang, C.; Zhang, J.; Zheng-Bradley, X.; Zhou, W.; Zichner, T.; Sebat, J.; Batzer, M. A.; McCarroll, S. A.; Mills, R. E.; Gerstein, M. B.; Bashir, A.; Stegle, O.; Devine, S. E.; Lee, C.; Eichler, E. E.; Korbel, J. O.Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.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 mESAdb: microRNA expression and sequence analysis database(Oxford University Press, 2011) Kaya, Koray D.; Karakülah, G.; Yakıcıer, Cengiz M.; Acar, Aybar C.; Konu, ÖzlenMicroRNA expression and sequence analysis database (http://konulab.fen. bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.