- Menu
2017
Authors
Tukiainen, T; Villani, A; Yen, A; Rivas, MA; Marshall, JL; Satija, R; Aguirre, M; Gauthier, L; Fleharty, M; Kirby, A; Cummings, BB; Castel, SE; Karczewski, KJ; Aguet, F; Byrnes, A; Aguet, F; Ardlie, KG; Cummings, BB; Gelfand, ET; Getz, G; Hadley, K; Handsaker, RE; Huang, KH; Kashin, S; Karczewski, KJ; Lek, M; Li, X; MacArthur, DG; Nedzel, JL; Nguyen, DT; Noble, MS; Segrè, AV; Trowbridge, CA; Tukiainen, T; Abell, NS; Balliu, B; Barshir, R; Basha, O; Battle, A; Bogu, GK; Brown, A; Brown, CD; Castel, SE; Chen, LS; Chiang, C; Conrad, DF; Cox, NJ; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Frésard, L; Gamazon, ER; Garrido-Martín, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Kyung Im, H; Jo, B; Yong Kang, E; Kim, Y; Kim-Hellmuth, S; Lappalainen, T; Li, G; Li, X; Liu, B; Mangul, S; McCarthy, MI; McDowell, IC; Mohammadi, P; Monlong, J; Montgomery, SB; Muñoz-Aguirre, M; Ndungu, AW; Nicolae, DL; Nobel, AB; Oliva, M; Ongen, H; Palowitch, JJ; Panousis, N; Papasaikas, P; Park, Y; Parsana, P; Payne, AJ; Peterson, CB; Quan, J; Reverter, F; Sabatti, C; Saha, A; Sammeth, M; Scott, AJ; Shabalin, AA; Sodaei, R; Stephens, M; Stranger, BE; Strober, BJ; Sul, JH; Tsang, EK; Urbut, S; van de Bunt, M; Wang, G; Wen, X; Wright, FA; Xi, HS; Yeger-Lotem, E; Zappala, Z; Zaugg, JB; Zhou, Y; Akey, JM; Bates, D; Chan, J; Chen, LS; Claussnitzer, M; Demanelis, K; Diegel, M; Doherty, JA; Feinberg, AP; Fernando, MS; Halow, J; Hansen, KD; Haugen, E; Hickey, PF; Hou, L; Jasmine, F; Jian, R; Jiang, L; Johnson, A; Kaul, R; Kellis, M; Kibriya, MG; Lee, K; Li, JB; Li, Q; Li, X; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Montgomery, SB; Nelson, J; Neri, FJ; Oliva, M; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Stranger, BE; Tang, H; Tsang, EK; Wang, L; Wang, M; Van Wittenberghe, N; Wu, F; Zhang, R; Nierras, CR; Branton, PA; Carithers, LJ; Guan, P; Moore, HM; Rao, A; Vaught, JB; Gould, SE; Lockart, NC; Martin, C; Struewing, JP; Volpi, S; Addington, AM; Koester, SE; Little, AR; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; Mestichelli, B; Myer, K; Roe, B; Salvatore, M; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Bridge, J; Foster, BA; Gillard, BM; Karasik, E; Kumar, R; Miklos, M; Moser, MT; Jewell, SD; Montroy, RG; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Undale, AH; Smith, AM; Tabor, DE; Roche, NV; McLean, JA; Vatanian, N; Robinson, KL; Sobin, L; Barcus, ME; Valentino, KM; Qi, L; Hunter, S; Hariharan, P; Singh, S; Um, KS; Matose, T; Tomaszewski, MM; Barker, LK; Mosavel, M; Siminoff, LA; Traino, HM; Flicek, P; Juettemann, T; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Craft, B; Goldman, M; Haeussler, M; Kent, WJ; Lee, CM; Paten, B; Rosenbloom, KR; Vivian, J; Zhu, J; Lappalainen, T; Regev, A; Ardlie, KG; Hacohen, N; MacArthur, DG;
Publication
Nature
Abstract
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals1,2. The extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
2017
Authors
Tan, MH; Li, Q; Shanmugam, R; Piskol, R; Kohler, J; Young, AN; Liu, KI; Zhang, R; Ramaswami, G; Ariyoshi, K; Gupte, A; Keegan, LP; George, CX; Ramu, A; Huang, N; Pollina, EA; Leeman, DS; Rustighi, A; Goh, YPS; Aguet, F; Ardlie, KG; Cummings, BB; Gelfand, ET; Getz, G; Hadley, K; Handsaker, RE; Huang, KH; Kashin, S; Karczewski, KJ; Lek, M; Li, X; MacArthur, DG; Nedzel, JL; Nguyen, DT; Noble, MS; Segrè, AV; Trowbridge, CA; Tukiainen, T; Abell, NS; Balliu, B; Barshir, R; Basha, O; Battle, A; Bogu, GK; Brown, A; Brown, CD; Castel, SE; Chen, LS; Chiang, C; Conrad, DF; Cox, NJ; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Frésard, L; Gamazon, ER; Garrido-Martín, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Kyung Im, H; Jo, B; Yong Kang, E; Kim, Y; Kim-Hellmuth, S; Lappalainen, T; Li, G; Li, X; Liu, B; Mangul, S; McCarthy, MI; McDowell, IC; Mohammadi, P; Monlong, J; Montgomery, SB; Muñoz-Aguirre, M; Ndungu, AW; Nicolae, DL; Nobel, AB; Oliva, M; Ongen, H; Palowitch, JJ; Panousis, N; Papasaikas, P; Park, Y; Parsana, P; Payne, AJ; Peterson, CB; Quan, J; Reverter, F; Sabatti, C; Saha, A; Sammeth, M; Scott, AJ; Shabalin, AA; Sodaei, R; Stephens, M; Stranger, BE; Strober, BJ; Sul, JH; Tsang, EK; Urbut, S; van de Bunt, M; Wang, G; Wen, X; Wright, FA; Xi, HS; Yeger-Lotem, E; Zappala, Z; Zaugg, JB; Zhou, Y; Akey, JM; Bates, D; Chan, J; Chen, LS; Claussnitzer, M; Demanelis, K; Diegel, M; Doherty, JA; Feinberg, AP; Fernando, MS; Halow, J; Hansen, KD; Haugen, E; Hickey, PF; Hou, L; Jasmine, F; Jian, R; Jiang, L; Johnson, A; Kaul, R; Kellis, M; Kibriya, MG; Lee, K; Li, JB; Li, Q; Li, X; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Montgomery, SB; Nelson, J; Neri, FJ; Oliva, M; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Stranger, BE; Tang, H; Tsang, EK; Wang, L; Wang, M; Van Wittenberghe, N; Wu, F; Zhang, R; Nierras, CR; Branton, PA; Carithers, LJ; Guan, P; Moore, HM; Rao, A; Vaught, JB; Gould, SE; Lockart, NC; Martin, C; Struewing, JP; Volpi, S; Addington, AM; Koester, SE; Little, AR; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; Mestichelli, B; Myer, K; Roe, B; Salvatore, M; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Bridge, J; Foster, BA; Gillard, BM; Karasik, E; Kumar, R; Miklos, M; Moser, MT; Jewell, SD; Montroy, RG; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Undale, AH; Smith, AM; Tabor, DE; Roche, NV; McLean, JA; Vatanian, N; Robinson, KL; Sobin, L; Barcus, ME; Valentino, KM; Qi, L; Hunter, S; Hariharan, P; Singh, S; Um, KS; Matose, T; Tomaszewski, MM; Barker, LK; Mosavel, M; Siminoff, LA; Traino, HM; Flicek, P; Juettemann, T; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Craft, B; Goldman, M; Haeussler, M; Kent, WJ; Lee, CM; Paten, B; Rosenbloom, KR; Vivian, J; Zhu, J; Chawla, A; Del Sal, G; Peltz, G; Brunet, A; Conrad, DF; Samuel, CE; O’Connell, MA; Walkley, CR; Nishikura, K; Li, JB;
Publication
Nature
Abstract
Adenosine-to-inosine (A-to-I) RNA editing is a conserved posttranscriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules1. Although many editing sites have recently been discovered2-7, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood8-10. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of nonrepetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis-and trans-regulation of A-to-I editing.
2017
Authors
Cummings, BB; Marshall, JL; Tukiainen, T; Lek, M; Donkervoort, S; Foley, AR; Bolduc, V; Waddell, LB; Sandaradura, SA; O'Grady, GL; Estrella, E; Reddy, HM; Zhao, F; Weisburd, B; Karczewski, KJ; O'Donnell Luria, AH; Birnbaum, D; Sarkozy, A; Hu, Y; Gonorazky, H; Claeys, K; Joshi, H; Bournazos, A; Oates, EC; Ghaoui, R; Davis, MR; Laing, NG; Topf, A; Kang, PB; Beggs, AH; North, KN; Straub, V; Dowling, JJ; Muntoni, F; Clarke, NF; Cooper, ST; Bönnemann, CG; MacArthur, DG; Ardlie, KG; Getz, G; Gelfand, ET; Segrè, AV; Aguet, F; Sullivan, TJ; Li, X; Nedzel, JL; Trowbridge, CA; Hadley, K; Huang, KH; Noble, MS; Nguyen, DT; Nobel, AB; Wright, FA; Shabalin, AA; Palowitch, JJ; Zhou, YH; Dermitzakis, ET; McCarthy, MI; Payne, AJ; Lappalainen, T; Castel, S; Kim Hellmuth, S; Mohammadi, P; Battle, A; Parsana, P; Mostafavi, S; Brown, A; Ongen, H; Delaneau, O; Panousis, N; Howald, C; Van De Bunt, M; Guigo, R; Monlong, J; Reverter, F; Garrido, D; Munoz, M; Bogu, G; Sodaei, R; Papasaikas, P; Ndungu, AW; Montgomery, SB; Li, X; Fresard, L; Davis, JR; Tsang, EK; Zappala, Z; Abell, NS; Gloudemans, MJ; Liu, B; Damani, FN; Saha, A; Kim, Y; Strober, BJ; He, Y; Stephens, M; Pritchard, JK; Wen, X; Urbut, S; Cox, NJ; Nicolae, DL; Gamazon, ER; Im, HK; Brown, CD; Engelhardt, BE; Park, Y; Jo, B; McDowell, IC; Gewirtz, A; Gliner, G; Conrad, D; Hall, I; Chiang, C; Scott, A; Sabatti, C; Eskin, E; Peterson, C; Hormozdiari, F; Kang, EY; Mangul, S; Han, B; Sul, JH; Feinberg, AP; Rizzardi, LF; Hansen, KD; Hickey, P; Akey, J; Kellis, M; Li, JB; Snyder, M; Tang, H; Jiang, L; Lin, S; Stranger, BE; Fernando, M; Oliva, M; Stamatoyannopoulos, J; Kaul, R; Halow, J; Sandstrom, R; Haugen, E; Johnson, A; Lee, K; Bates, D; Diegel, M; Pierce, BL; Chen, L; Kibriya, MG; Jasmine, F; Doherty, J; Demanelis, K; Smith, KS; Li, Q; Zhang, R; Nierras, CR; Moore, HM; Rao, A; Guan, P; Vaught, JB; Branton, PA; Carithers, LJ; Volpi, S; Struewing, JP; Martin, CG; Nicole, LC; Koester, SE; Addington, AM; Little, AR; Leinweber, WF; Thomas, JA; Kopen, G; McDonald, A; Mestichelli, B; Shad, S; Lonsdale, JT; Salvatore, M; Hasz, R; Walters, G; Johnson, M; Washington, M; Brigham, LE; Johns, C; Wheeler, J; Roe, B; Hunter, M; Myer, K; Foster, BA; Moser, MT; Karasik, E; Gillard, BM; Kumar, R; Bridge, J; Miklos, M; Jewell, SD; Rohrer, DC; Valley, D; Montroy, RG; Mash, DC; Davis, DA; Undale, AH; Smith, AM; Tabor, DE; Roche, NV; McLean, JA; Vatanian, N; Robinson, KL; Sobin, L; Barcus, ME; Valentino, KM; Qi, L; Hunter, S; Hariharan, P; Singh, S; Um, KS; Matose, T; Tomadzewski, MM; Siminoff, LA; Traino, HM; Mosavel, M; Barker, LK; Zerbino, DR; Juettmann, T; Taylor, K; Ruffier, M; Sheppard, D; Trevanion, S; Flicek, P; Kent, WJ; Rosenbloom, KR; Haeussler, M; Lee, CM; Paten, B; Vivan, J; Zhu, J; Goldman, M; Craft, B; Li, G; Ferreira, PG; Yeger Lotem, E; Maurano, MT; Barshir, R; Basha, O; Xi, HS; Quan, J; Sammeth, M; Zaugg, JB;
Publication
Science Translational Medicine
Abstract
Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches. 2017 © The Authors.
2017
Authors
Bryois, J; Buil, A; Ferreira, PG; Panousis, NI; Brown, AA; Viñuela, A; Planchon, A; Bielser, D; Small, K; Spector, T; Dermitzakis, ET;
Publication
Genome Research
Abstract
Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature. ©2017 Bryois et al.
2017
Authors
Almeida, R; Dias, C; Silva, ME; Rocha, AP;
Publication
Complexity and Nonlinearity in Cardiovascular Signals
Abstract
In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The ARFIMA-GARCH modeling considered here allows the quantification of long range correlations and time-varying volatility. ARFIMA-GARCH HRV analysis is integrated with multimodal brain monitoring in several acute cerebral phenomena such as intracranial hypertension, decompressive craniectomy and brain death. The results indicate that ARFIMA-GARCH modeling appears to reflect changes in Heart Rate Variability (HRV) dynamics related both with the Acute Brain Injury (ABI) and the medical treatments effects. © 2017, Springer International Publishing AG.
2017
Authors
Monteiro, A; Menezes, R; Silva, ME;
Publication
SPATIAL STATISTICS
Abstract
This study aims at characterizing the spatial and temporal dynamics of spatio-temporal data sets, characterized by high resolution in the temporal dimension which are becoming the norm rather than the exception in many application areas, namely environmental modelling. In particular, air pollution data, such as NO2 concentration levels, often incorporate also multiple recurring patterns in time imposed by social habits, anthropogenic activities and meteorological conditions. A two-stage modelling approach is proposed which combined with a block bootstrap procedure correctly assesses uncertainty in parameters estimates and produces reliable confidence regions for the space-time phenomenon under study. The methodology provides a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction and forecasting capability and computational costs. The proposed framework is potentially useful for scenario drawing in many areas, including assessment of environmental impact and environmental policies, and in a myriad applications to other research fields.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.