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1. Statin-induced expression change of INSIG1 in lymphoblastoid cell lines correlates with plasma triglyceride statin response in a sex-specific manner

Author

Theusch, E, Kim, K, Stevens, K, Smith, JD, Chen, Y-DI, Rotter, JI, Nickerson, DA, and Medina, MW

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Clinical Research, Atherosclerosis, Heart Disease, Genetics, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Adult, Aged, Cell Line, Dyslipidemias, Female, Gene Expression Regulation, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Intracellular Signaling Peptides and Proteins, Lymphocytes, Male, Membrane Proteins, Middle Aged, Pharmacogenetics, Pharmacogenomic Variants, Sex Factors, Simvastatin, Treatment Outcome, Triglycerides, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

Statins are widely prescribed to lower plasma low-density lipoprotein (LDL) cholesterol levels. They also modestly reduce plasma triglyceride (TG), an independent cardiovascular disease risk factor, in most people. The mechanism and inter-individual variability of TG statin response is poorly understood. We measured statin-induced gene expression changes in lymphoblastoid cell lines derived from 150 participants of a simvastatin clinical trial and identified 23 genes (false discovery rate, FDR=15%) with expression changes correlated with plasma TG response. The correlation of insulin-induced gene 1 (INSIG1) expression changes with TG response (rho=0.32, q=0.11) was driven by men (interaction P=0.0055). rs73161338 was associated with INSIG1 expression changes (P=5.4 × 10-5) and TG response in two statin clinical trials (P=0.0048), predominantly in men. A combined model including INSIG1 expression level and splicing changes accounted for 29.5% of plasma TG statin response variance in men (P=5.6 × 10-6). Our results suggest that INSIG1 variation may contribute to statin-induced changes in plasma TG in a sex-specific manner.

Published
2017

2. Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines

Author

Chhibber, A, French, CE, Yee, SW, Gamazon, ER, Theusch, E, Qin, X, Webb, A, Papp, AC, Wang, A, Simmons, CQ, Konkashbaev, A, Chaudhry, AS, Mitchel, K, Stryke, D, Ferrin, TE, Weiss, ST, Kroetz, DL, Sadee, W, Nickerson, DA, Krauss, RM, George, AL, Schuetz, EG, Medina, MW, Cox, NJ, Scherer, SE, Giacomini, KM, and Brenner, SE

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Patient Safety, Biotechnology, Generic health relevance, Good Health and Well Being, Adipose Tissue, Alternative Splicing, Cell Line, Computational Biology, Databases, Genetic, Genotype, High-Throughput Nucleotide Sequencing, Humans, Kidney, Liver, Myocardium, Pharmacogenetics, Pharmacogenomic Variants, Phenotype, Sequence Analysis, RNA, Transcriptome, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

Variation in the expression level and activity of genes involved in drug disposition and action ('pharmacogenes') can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20-45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.

Published
2017

3. Characterization of Statin Dose Response in Electronic Medical Records

Author

Wei, W‐Q, Feng, Q, Jiang, L, Waitara, MS, Iwuchukwu, OF, Roden, DM, Jiang, M, Xu, H, Krauss, RM, Rotter, JI, Nickerson, DA, Davis, RL, Berg, RL, Peissig, PL, McCarty, CA, Wilke, RA, and Denny, JC

Subjects
Genetics, Clinical Research, Algorithms, Alleles, Atorvastatin, Cholesterol, LDL, Cohort Studies, Databases, Factual, Dose-Response Relationship, Drug, Electronic Health Records, Genotype, Heptanoic Acids, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Hyperlipidemias, Lipid Metabolism, Lipids, Phenotype, Polymorphism, Single Nucleotide, Pyrroles, Randomized Controlled Trials as Topic, Simvastatin, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy
Abstract

Efforts to define the genetic architecture underlying variable statin response have met with limited success, possibly because previous studies were limited to effect based on a single dose. We leveraged electronic medical records (EMRs) to extract potency (ED50) and efficacy (Emax) of statin dose-response curves and tested them for association with 144 preselected variants. Two large biobanks were used to construct dose-response curves for 2,026 and 2,252 subjects on simvastatin and atorvastatin, respectively. Atorvastatin was more efficacious, was more potent, and demonstrated less interindividual variability than simvastatin. A pharmacodynamic variant emerging from randomized trials (PRDM16) was associated with Emax for both. For atorvastatin, Emax was 51.7 mg/dl in subjects homozygous for the minor allele vs. 75.0 mg/dl for those homozygous for the major allele. We also identified several loci associated with ED50. The extraction of rigorously defined traits from EMRs for pharmacogenetic studies represents a promising approach to further understand the genetic factors contributing to drug response.

Published
2014

5. Functional divergence of the two Elongator subcomplexes during neurodevelopment

Author

Gaik, M, Kojic, M, Stegeman, MR, Oncu-Oner, T, Koscielniak, A, Jones, A, Mohamed, A, Chau, PYS, Sharmin, S, Chramiec-Glabik, A, Indyka, P, Biela, A, Dobosz, D, Millar, A, Chau, V, Unalp, A, Piper, M, Bellingham, MC, Eichler, EE, Nickerson, DA, Guleryuz, H, Abbassi, NEH, Jazgar, K, Davis, MJ, Mercimek-Andrews, S, Cingoz, S, Wainwright, BJ, Glatt, S, Gaik, M, Kojic, M, Stegeman, MR, Oncu-Oner, T, Koscielniak, A, Jones, A, Mohamed, A, Chau, PYS, Sharmin, S, Chramiec-Glabik, A, Indyka, P, Biela, A, Dobosz, D, Millar, A, Chau, V, Unalp, A, Piper, M, Bellingham, MC, Eichler, EE, Nickerson, DA, Guleryuz, H, Abbassi, NEH, Jazgar, K, Davis, MJ, Mercimek-Andrews, S, Cingoz, S, Wainwright, BJ, and Glatt, S

Abstract

The highly conserved Elongator complex is a translational regulator that plays a critical role in neurodevelopment, neurological diseases, and brain tumors. Numerous clinically relevant variants have been reported in the catalytic Elp123 subcomplex, while no missense mutations in the accessory subcomplex Elp456 have been described. Here, we identify ELP4 and ELP6 variants in patients with developmental delay, epilepsy, intellectual disability, and motor dysfunction. We determine the structures of human and murine Elp456 subcomplexes and locate the mutated residues. We show that patient-derived mutations in Elp456 affect the tRNA modification activity of Elongator in vitro as well as in human and murine cells. Modeling the pathogenic variants in mice recapitulates the clinical features of the patients and reveals neuropathology that differs from the one caused by previously characterized Elp123 mutations. Our study demonstrates a direct correlation between Elp4 and Elp6 mutations, reduced Elongator activity, and neurological defects. Foremost, our data indicate previously unrecognized differences of the Elp123 and Elp456 subcomplexes for individual tRNA species, in different cell types and in different key steps during the neurodevelopment of higher organisms.

Published
2022

6. Genetic architectures of proximal and distal colorectal cancer are partly distinct

Author

Huyghe, JR, Harrison, TA, Bien, SA, Hampel, H, Figueiredo, JC, Schmit, SL, Conti, D, Chen, S, Qu, C, Lin, Y, Barfield, R, Baron, JA, Cross, AJ, Diergaarde, B, Duggan, D, Harlid, S, Imaz, L, Kang, HM, Levine, DM, Perduca, V, Perez-Cornago, A, Sakoda, LC, Schumacher, FR, Slattery, ML, Toland, AE, van Duijnhoven, FJB, Van Guelpen, B, Agudo, A, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Banbury, BL, Bassik, MC, Berndt, S, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Caan, BJ, Campbell, PT, Carr, PR, Castells, A, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Curtis, KR, de la Chapelle, A, Easton, DF, English, DR, Feskens, EJM, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Jenab, M, Jenkins, MA, Joshi, AD, Keku, TO, Kooperberg, C, Kuhn, T, Kury, S, Le Marchand, L, Lejbkowicz, F, Li, C, Li, L, Lieb, W, Lindblom, A, Lindor, NM, Mannisto, S, Markowitz, SD, Milne, RL, Moreno, L, Murphy, N, Nassir, R, Offit, K, Ogino, S, Panico, S, Parfrey, PS, Pearlman, R, Pharoah, PDP, Phipps, A, Platz, EA, Potter, JD, Prentice, RL, Qi, L, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Schafmayer, C, Schoen, RE, Seminara, D, Song, M, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Ulrich, CM, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Nickerson, DA, Scacheri, PC, Kundaje, A, Casey, G, Gruber, SB, Hsu, L, Moreno, V, Hayes, RB, Newcomb, PA, Peters, U, Huyghe, JR, Harrison, TA, Bien, SA, Hampel, H, Figueiredo, JC, Schmit, SL, Conti, D, Chen, S, Qu, C, Lin, Y, Barfield, R, Baron, JA, Cross, AJ, Diergaarde, B, Duggan, D, Harlid, S, Imaz, L, Kang, HM, Levine, DM, Perduca, V, Perez-Cornago, A, Sakoda, LC, Schumacher, FR, Slattery, ML, Toland, AE, van Duijnhoven, FJB, Van Guelpen, B, Agudo, A, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Banbury, BL, Bassik, MC, Berndt, S, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Caan, BJ, Campbell, PT, Carr, PR, Castells, A, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Curtis, KR, de la Chapelle, A, Easton, DF, English, DR, Feskens, EJM, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Jenab, M, Jenkins, MA, Joshi, AD, Keku, TO, Kooperberg, C, Kuhn, T, Kury, S, Le Marchand, L, Lejbkowicz, F, Li, C, Li, L, Lieb, W, Lindblom, A, Lindor, NM, Mannisto, S, Markowitz, SD, Milne, RL, Moreno, L, Murphy, N, Nassir, R, Offit, K, Ogino, S, Panico, S, Parfrey, PS, Pearlman, R, Pharoah, PDP, Phipps, A, Platz, EA, Potter, JD, Prentice, RL, Qi, L, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Schafmayer, C, Schoen, RE, Seminara, D, Song, M, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Ulrich, CM, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Nickerson, DA, Scacheri, PC, Kundaje, A, Casey, G, Gruber, SB, Hsu, L, Moreno, V, Hayes, RB, Newcomb, PA, and Peters, U

Abstract

OBJECTIVE: An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined. DESIGN: To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling. RESULTS: We identified 13 loci that reached genome-wide significance (p<5×10-8) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer. CONCLUSION: Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.

Published
2021

7. Predominant and novel de novo variants in 29 individuals withALG13deficiency: Clinical description, biomarker status, biochemical analysis, and treatment suggestions

Author

Ng, BG, Eklund, EA, Shiryaev, SA, Dong, YY, Abbott, M-A, Asteggiano, C, Bamshad, MJ, Barr, E, Bernstein, JA, Chelakkadan, S, Christodoulou, J, Chung, WK, Ciliberto, MA, Cousin, J, Gardiner, F, Ghosh, S, Graf, WD, Grunewald, S, Hammond, K, Hauser, NS, Hoganson, GE, Houck, KM, Kohler, JN, Morava, E, Larson, AA, Liu, P, Madathil, S, McCormack, C, Meeks, NJL, Miller, R, Monaghan, KG, Nickerson, DA, Palculict, TB, Papazoglu, GM, Pletcher, BA, Scheffer, IE, Schenone, AB, Schnur, RE, Si, Y, Rowe, LJ, Russi, AHS, Russo, RS, Thabet, F, Tuite, A, Villanueva, MM, Wang, RY, Webster, R, Wilson, D, Zalan, A, Wolfe, LA, Rosenfeld, JA, Rhodes, L, Freeze, HH, Ng, BG, Eklund, EA, Shiryaev, SA, Dong, YY, Abbott, M-A, Asteggiano, C, Bamshad, MJ, Barr, E, Bernstein, JA, Chelakkadan, S, Christodoulou, J, Chung, WK, Ciliberto, MA, Cousin, J, Gardiner, F, Ghosh, S, Graf, WD, Grunewald, S, Hammond, K, Hauser, NS, Hoganson, GE, Houck, KM, Kohler, JN, Morava, E, Larson, AA, Liu, P, Madathil, S, McCormack, C, Meeks, NJL, Miller, R, Monaghan, KG, Nickerson, DA, Palculict, TB, Papazoglu, GM, Pletcher, BA, Scheffer, IE, Schenone, AB, Schnur, RE, Si, Y, Rowe, LJ, Russi, AHS, Russo, RS, Thabet, F, Tuite, A, Villanueva, MM, Wang, RY, Webster, R, Wilson, D, Zalan, A, Wolfe, LA, Rosenfeld, JA, Rhodes, L, and Freeze, HH

Abstract

Asparagine-linked glycosylation 13 homolog (ALG13) encodes a nonredundant, highly conserved, X-linked uridine diphosphate (UDP)-N-acetylglucosaminyltransferase required for the synthesis of lipid linked oligosaccharide precursor and proper N-linked glycosylation. De novo variants in ALG13 underlie a form of early infantile epileptic encephalopathy known as EIEE36, but given its essential role in glycosylation, it is also considered a congenital disorder of glycosylation (CDG), ALG13-CDG. Twenty-four previously reported ALG13-CDG cases had de novo variants, but surprisingly, unlike most forms of CDG, ALG13-CDG did not show the anticipated glycosylation defects, typically detected by altered transferrin glycosylation. Structural homology modeling of two recurrent de novo variants, p.A81T and p.N107S, suggests both are likely to impact the function of ALG13. Using a corresponding ALG13-deficient yeast strain, we show that expressing yeast ALG13 with either of the highly conserved hotspot variants rescues the observed growth defect, but not its glycosylation abnormality. We present molecular and clinical data on 29 previously unreported individuals with de novo variants in ALG13. This more than doubles the number of known cases. A key finding is that a vast majority of the individuals presents with West syndrome, a feature shared with other CDG types. Among these, the initial epileptic spasms best responded to adrenocorticotropic hormone or prednisolone, while clobazam and felbamate showed promise for continued epilepsy treatment. A ketogenic diet seems to play an important role in the treatment of these individuals.

Published
2020

8. The simulation experiment description markup language (SED-ML): language specification for level 1 version 5

Author

Smith Lucian P., Bergmann Frank T., Garny Alan, Helikar Tomáš, Karr Jonathan, Nickerson David, Sauro Herbert, Waltemath Dagmar, and König Matthias

Subjects
Biotechnology, TP248.13-248.65
Abstract

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Published
2024
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9. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer (vol 138, pg 307, 2019)

Author

Bien, SA, Su, Y-R, Conti, DV, Harrison, TA, Qu, C, Guo, X, Lu, Y, Albanes, D, Auer, PL, Banbury, BL, Berndt, SI, Bezieau, S, Brenner, H, Buchanan, DD, Caan, BJ, Campbell, PT, Carlson, CS, Chan, AT, Chang-Claude, J, Chen, S, Connolly, CM, Easton, DF, Feskens, EJM, Gallinger, S, Giles, GG, Gunter, MJ, Hampe, J, Huyghe, JR, Hoffmeister, M, Hudson, TJ, Jacobs, EJ, Jenkins, MA, Kampman, E, Kang, HM, Kuehn, T, Kury, S, Lejbkowicz, F, Le Marchand, L, Milne, RL, Li, L, Li, CI, Lindblom, A, Lindor, NM, Martin, V, McNeil, CE, Melas, M, Moreno, V, Newcomb, PA, Offit, K, Pharaoh, PDP, Potter, JD, Riboli, E, Rennert, G, Sala, N, Schafmayer, C, Scacheri, PC, Schmit, SL, Severi, G, Slattery, ML, Smith, JD, Trichopoulou, A, Tumino, R, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Casey, G, Nickerson, DA, Gruber, SB, Hsu, L, Zheng, W, Peters, U, Bien, SA, Su, Y-R, Conti, DV, Harrison, TA, Qu, C, Guo, X, Lu, Y, Albanes, D, Auer, PL, Banbury, BL, Berndt, SI, Bezieau, S, Brenner, H, Buchanan, DD, Caan, BJ, Campbell, PT, Carlson, CS, Chan, AT, Chang-Claude, J, Chen, S, Connolly, CM, Easton, DF, Feskens, EJM, Gallinger, S, Giles, GG, Gunter, MJ, Hampe, J, Huyghe, JR, Hoffmeister, M, Hudson, TJ, Jacobs, EJ, Jenkins, MA, Kampman, E, Kang, HM, Kuehn, T, Kury, S, Lejbkowicz, F, Le Marchand, L, Milne, RL, Li, L, Li, CI, Lindblom, A, Lindor, NM, Martin, V, McNeil, CE, Melas, M, Moreno, V, Newcomb, PA, Offit, K, Pharaoh, PDP, Potter, JD, Riboli, E, Rennert, G, Sala, N, Schafmayer, C, Scacheri, PC, Schmit, SL, Severi, G, Slattery, ML, Smith, JD, Trichopoulou, A, Tumino, R, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Casey, G, Nickerson, DA, Gruber, SB, Hsu, L, Zheng, W, and Peters, U

Abstract

Every author has erroneously been assigned to the affiliation "62". The affiliation 62 belongs to the author Graham Casey.

Published
2019

10. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Author

Bien, SA, Su, Y-R, Conti, DV, Harrison, TA, Qu, C, Guo, X, Lu, Y, Albanes, D, Auer, PL, Banbury, BL, Berndt, SI, Bezieau, S, Brenner, H, Buchanan, DD, Caan, BJ, Campbell, PT, Carlson, CS, Chan, AT, Chang-Claude, J, Chen, S, Connolly, CM, Easton, DF, Feskens, EJM, Gallinger, S, Giles, GG, Gunter, MJ, Hampe, J, Huyghe, JR, Hoffmeister, M, Hudson, TJ, Jacobs, EJ, Jenkins, MA, Kampman, E, Kang, HM, Kuehn, T, Kury, S, Lejbkowicz, F, Le Marchand, L, Milne, RL, Li, L, Li, CI, Lindblom, A, Lindor, NM, Martin, V, McNeil, CE, Melas, M, Moreno, V, Newcomb, PA, Offit, K, Pharaoh, PDP, Potter, JD, Riboli, E, Rennert, G, Sala, N, Schafmayer, C, Scacheri, PC, Schmit, SL, Severi, G, Slattery, ML, Smith, JD, Trichopoulou, A, Tumino, R, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Casey, G, Nickerson, DA, Gruber, SB, Hsu, L, Zheng, W, Peters, U, Bien, SA, Su, Y-R, Conti, DV, Harrison, TA, Qu, C, Guo, X, Lu, Y, Albanes, D, Auer, PL, Banbury, BL, Berndt, SI, Bezieau, S, Brenner, H, Buchanan, DD, Caan, BJ, Campbell, PT, Carlson, CS, Chan, AT, Chang-Claude, J, Chen, S, Connolly, CM, Easton, DF, Feskens, EJM, Gallinger, S, Giles, GG, Gunter, MJ, Hampe, J, Huyghe, JR, Hoffmeister, M, Hudson, TJ, Jacobs, EJ, Jenkins, MA, Kampman, E, Kang, HM, Kuehn, T, Kury, S, Lejbkowicz, F, Le Marchand, L, Milne, RL, Li, L, Li, CI, Lindblom, A, Lindor, NM, Martin, V, McNeil, CE, Melas, M, Moreno, V, Newcomb, PA, Offit, K, Pharaoh, PDP, Potter, JD, Riboli, E, Rennert, G, Sala, N, Schafmayer, C, Scacheri, PC, Schmit, SL, Severi, G, Slattery, ML, Smith, JD, Trichopoulou, A, Tumino, R, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Casey, G, Nickerson, DA, Gruber, SB, Hsu, L, Zheng, W, and Peters, U

Abstract

Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10- 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10- 4, replication P = 6.7 × 10- 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight in

Published
2019

11. Disruptive mutations in TANC2 define a neurodevelopmental syndrome associated with psychiatric disorders

Author

Guo, H, Bettella, E, Marcogliese, PC, Zhao, R, Andrews, JC, Nowakowski, TJ, Gillentine, MA, Hoekzema, K, Wang, T, Wu, H, Jangam, S, Liu, C, Ni, H, Willemsen, MH, van Bon, BW, Rinne, T, Stevens, SJC, Kleefstra, T, Brunner, HG, Yntema, HG, Long, M, Zhao, W, Hu, Z, Colson, C, Richard, N, Schwartz, CE, Romano, C, Castiglia, L, Bottitta, M, Dhar, SU, Erwin, DJ, Emrick, L, Keren, B, Afenjar, A, Zhu, B, Bai, B, Stankiewicz, P, Herman, K, Mercimek-Andrews, S, Juusola, J, Wilfert, AB, Abou Jamra, R, Buettner, B, Mefford, HC, Muir, AM, Scheffer, IE, Regan, BM, Malone, S, Gecz, J, Cobben, J, Weiss, MM, Waisfisz, Q, Bijlsma, EK, Hoffer, MJ, Ruivenkamp, CAL, Sartori, S, Xia, F, Rosenfeld, JA, Bernier, RA, Wangler, MF, Yamamoto, S, Xia, K, Stegmann, APA, Bellen, HJ, Murgia, A, Eichler, EE, Nickerson, DA, Bamshad, MJ, Guo, H, Bettella, E, Marcogliese, PC, Zhao, R, Andrews, JC, Nowakowski, TJ, Gillentine, MA, Hoekzema, K, Wang, T, Wu, H, Jangam, S, Liu, C, Ni, H, Willemsen, MH, van Bon, BW, Rinne, T, Stevens, SJC, Kleefstra, T, Brunner, HG, Yntema, HG, Long, M, Zhao, W, Hu, Z, Colson, C, Richard, N, Schwartz, CE, Romano, C, Castiglia, L, Bottitta, M, Dhar, SU, Erwin, DJ, Emrick, L, Keren, B, Afenjar, A, Zhu, B, Bai, B, Stankiewicz, P, Herman, K, Mercimek-Andrews, S, Juusola, J, Wilfert, AB, Abou Jamra, R, Buettner, B, Mefford, HC, Muir, AM, Scheffer, IE, Regan, BM, Malone, S, Gecz, J, Cobben, J, Weiss, MM, Waisfisz, Q, Bijlsma, EK, Hoffer, MJ, Ruivenkamp, CAL, Sartori, S, Xia, F, Rosenfeld, JA, Bernier, RA, Wangler, MF, Yamamoto, S, Xia, K, Stegmann, APA, Bellen, HJ, Murgia, A, Eichler, EE, Nickerson, DA, and Bamshad, MJ

Abstract

Postsynaptic density (PSD) proteins have been implicated in the pathophysiology of neurodevelopmental and psychiatric disorders. Here, we present detailed clinical and genetic data for 20 patients with likely gene-disrupting mutations in TANC2-whose protein product interacts with multiple PSD proteins. Pediatric patients with disruptive mutations present with autism, intellectual disability, and delayed language and motor development. In addition to a variable degree of epilepsy and facial dysmorphism, we observe a pattern of more complex psychiatric dysfunction or behavioral problems in adult probands or carrier parents. Although this observation requires replication to establish statistical significance, it also suggests that mutations in this gene are associated with a variety of neuropsychiatric disorders consistent with its postsynaptic function. We find that TANC2 is expressed broadly in the human developing brain, especially in excitatory neurons and glial cells, but shows a more restricted pattern in Drosophila glial cells where its disruption affects behavioral outcomes.

Published
2019

12. Discovery of common and rare genetic risk variants for colorectal cancer

Author

Huyghe, JR, Bien, SA, Harrison, TA, Kang, HM, Chen, S, Schmit, SL, Conti, DV, Qu, C, Jeon, J, Edlund, CK, Greenside, P, Wainberg, M, Schumacher, FR, Smith, JD, Levine, DM, Nelson, SC, Sinnott-Armstrong, NA, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Bamia, C, Banbury, BL, Baron, JA, Berndt, SI, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Chirlaque, M-D, Cho, SH, Connolly, CM, Cross, AJ, Cuk, K, Curtis, KR, de la Chapelle, A, Doheny, KF, Duggan, D, Easton, DF, Elias, SG, Elliott, F, English, DR, Feskens, EJM, Figueiredo, JC, Fischer, R, FitzGerald, LM, Forman, D, Gala, M, Gallinger, S, Gauderman, WJ, Giles, GG, Gillanders, E, Gong, J, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hampel, H, Harlid, S, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Hunter, DJ, Ibanez-Sanz, G, Idos, GE, Ingersoll, R, Jackson, RD, Jacobs, EJ, Jenkins, MA, Joshi, AD, Joshu, CE, Keku, TO, Key, TJ, Kim, HR, Kobayashi, E, Kolonel, LN, Kooperberg, C, Kuehn, T, Kury, S, Kweon, S-S, Larsson, SC, Laurie, CA, Le Marchand, L, Leal, SM, Lee, SC, Lejbkowicz, F, Lemire, M, Li, CI, Li, L, Lieb, W, Lin, Y, Lindblom, A, Lindor, NM, Ling, H, Louie, TL, Mannisto, S, Markowitz, SD, Martin, V, Masala, G, McNeil, CE, Melas, M, Milne, RL, Moreno, L, Murphy, N, Myte, R, Naccarati, A, Newcomb, PA, Offit, K, Ogino, S, Onland-Moret, NC, Pardini, B, Parfrey, PS, Pearlman, R, Perduca, V, Pharoah, PDP, Pinchev, M, Platz, EA, Prentice, RL, Pugh, E, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Rodriguez-Barranco, M, Romm, J, Sakoda, LC, Schafmayer, C, Schoen, RE, Seminara, D, Shah, M, Shelford, T, Shin, M-H, Shulman, K, Sieri, S, Slattery, ML, Southey, MC, Stadler, ZK, Stegmaier, C, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Thomas, SS, Toland, AE, Trichopoulou, A, Ulrich, CM, Van den Berg, DJ, van Duijnhoven, FJB, Van Guelpen, B, van Kranen, H, Vijai, J, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Win, AK, Wolf, CR, Wolk, A, Woods, MO, Wu, AH, Zaidi, SH, Zanke, BW, Zhang, Q, Zheng, W, Scacheri, PC, Potter, JD, Bassik, MC, Kundaje, A, Casey, G, Moreno, V, Abecasis, GR, Nickerson, DA, Gruber, SB, Hsu, L, Peters, U, Huyghe, JR, Bien, SA, Harrison, TA, Kang, HM, Chen, S, Schmit, SL, Conti, DV, Qu, C, Jeon, J, Edlund, CK, Greenside, P, Wainberg, M, Schumacher, FR, Smith, JD, Levine, DM, Nelson, SC, Sinnott-Armstrong, NA, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Bamia, C, Banbury, BL, Baron, JA, Berndt, SI, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Chirlaque, M-D, Cho, SH, Connolly, CM, Cross, AJ, Cuk, K, Curtis, KR, de la Chapelle, A, Doheny, KF, Duggan, D, Easton, DF, Elias, SG, Elliott, F, English, DR, Feskens, EJM, Figueiredo, JC, Fischer, R, FitzGerald, LM, Forman, D, Gala, M, Gallinger, S, Gauderman, WJ, Giles, GG, Gillanders, E, Gong, J, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hampel, H, Harlid, S, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Hunter, DJ, Ibanez-Sanz, G, Idos, GE, Ingersoll, R, Jackson, RD, Jacobs, EJ, Jenkins, MA, Joshi, AD, Joshu, CE, Keku, TO, Key, TJ, Kim, HR, Kobayashi, E, Kolonel, LN, Kooperberg, C, Kuehn, T, Kury, S, Kweon, S-S, Larsson, SC, Laurie, CA, Le Marchand, L, Leal, SM, Lee, SC, Lejbkowicz, F, Lemire, M, Li, CI, Li, L, Lieb, W, Lin, Y, Lindblom, A, Lindor, NM, Ling, H, Louie, TL, Mannisto, S, Markowitz, SD, Martin, V, Masala, G, McNeil, CE, Melas, M, Milne, RL, Moreno, L, Murphy, N, Myte, R, Naccarati, A, Newcomb, PA, Offit, K, Ogino, S, Onland-Moret, NC, Pardini, B, Parfrey, PS, Pearlman, R, Perduca, V, Pharoah, PDP, Pinchev, M, Platz, EA, Prentice, RL, Pugh, E, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Rodriguez-Barranco, M, Romm, J, Sakoda, LC, Schafmayer, C, Schoen, RE, Seminara, D, Shah, M, Shelford, T, Shin, M-H, Shulman, K, Sieri, S, Slattery, ML, Southey, MC, Stadler, ZK, Stegmaier, C, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Thomas, SS, Toland, AE, Trichopoulou, A, Ulrich, CM, Van den Berg, DJ, van Duijnhoven, FJB, Van Guelpen, B, van Kranen, H, Vijai, J, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Win, AK, Wolf, CR, Wolk, A, Woods, MO, Wu, AH, Zaidi, SH, Zanke, BW, Zhang, Q, Zheng, W, Scacheri, PC, Potter, JD, Bassik, MC, Kundaje, A, Casey, G, Moreno, V, Abecasis, GR, Nickerson, DA, Gruber, SB, Hsu, L, and Peters, U

Abstract

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

Published
2019

13. A 32 kb Critical Region Excluding Y402H in CFH Mediates Risk for Age-Related Macular Degeneration (vol 6, e25598, 2011)

Author

Sivakumaran, TA, Igo, RP, Kidd, JM, Itsara, A, Kopplin, LJ, Chen, W, Hagstrom, SA, Peachey, NS, Francis, PJ, Klein, ML, Chew, EY, Ramprasad, VL, Tay, W-T, Mitchell, P, Seielstad, M, Stambolian, DE, Edwards, AO, Lee, KE, Leontiev, DV, Jun, G, Wang, Y, Tian, L, Qiu, F, Henning, AK, LaFramboise, T, Sen, P, Aarthi, M, George, R, Raman, R, Das, MK, Vijaya, L, Kumaramanickavel, G, Wong, TY, Swaroop, A, Abecasis, GR, Klein, R, Klein, BEK, Nickerson, DA, Eichler, EE, Iyengar, SK, Sivakumaran, TA, Igo, RP, Kidd, JM, Itsara, A, Kopplin, LJ, Chen, W, Hagstrom, SA, Peachey, NS, Francis, PJ, Klein, ML, Chew, EY, Ramprasad, VL, Tay, W-T, Mitchell, P, Seielstad, M, Stambolian, DE, Edwards, AO, Lee, KE, Leontiev, DV, Jun, G, Wang, Y, Tian, L, Qiu, F, Henning, AK, LaFramboise, T, Sen, P, Aarthi, M, George, R, Raman, R, Das, MK, Vijaya, L, Kumaramanickavel, G, Wong, TY, Swaroop, A, Abecasis, GR, Klein, R, Klein, BEK, Nickerson, DA, Eichler, EE, and Iyengar, SK

Abstract

[This corrects the article DOI: 10.1371/journal.pone.0025598.].

Published
2018

14. Monoallelic and biallelic &ITCREB3L1 &ITvariant causes mild and severe osteogenesis imperfecta, respectively

Author

Keller, RB, Tran, TT, Pyott, SM, Pepin, MG, Savarirayan, R, McGillivray, G, Nickerson, DA, Bamshad, MJ, Byers, PH, Keller, RB, Tran, TT, Pyott, SM, Pepin, MG, Savarirayan, R, McGillivray, G, Nickerson, DA, Bamshad, MJ, and Byers, PH

Abstract

PurposeOsteogenesis imperfecta (OI) is a heritable skeletal dysplasia. Dominant pathogenic variants in COL1A1 and COL1A2 explain the majority of OI cases. At least 15 additional genes have been identified, but those still do not account for all OI phenotypes that present. We sought the genetic cause of mild and lethal OI phenotypes in an unsolved family.MethodsWe performed exome sequencing on seven members of the family, both affected and unaffected.ResultsWe identified a variant in cyclic AMP responsive element binding protein 3-like 1 (CREB3L1) in a consanguineous family. The variant caused a prenatal/perinatal lethal OI in homozygotes, similar to that seen in OI type II as a result of mutations in type I collagen genes, and a mild phenotype (fractures, blue sclerae) in multiple heterozygous family members. CREB3L1 encodes old astrocyte specifically induced substance (OASIS), an endoplasmic reticulum stress transducer. The variant disrupts a DNA-binding site and prevents OASIS from acting on its transcriptional targets including SEC24D, which encodes a component of the coat protein II complex.ConclusionThis report confirms that CREB3L1 is an OI-related gene and suggests the pathogenic mechanism of CREB3L1-associated OI involves the altered regulation of proteins involved in cellular secretion.

Published
2018

15. Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data

Author

Zhao, Z, Bien, SA, Auer, PL, Harrison, TA, Qu, C, Connolly, CM, Greenside, PG, Chen, S, Berndt, SI, Bezieau, S, Kang, HM, Huyghe, J, Brenner, H, Casey, G, Chan, AT, Hopper, JL, Banbury, BL, Chang-Claude, J, Chanock, SJ, Haile, RW, Hoffmeister, M, Fuchsberger, C, Jenkins, MA, Leal, SM, Lemire, M, Newcomb, PA, Gallinger, S, Potter, JD, Schoen, RE, Slattery, ML, Smith, JD, Le Marchand, L, White, E, Zanke, BW, Abecasis, GR, Carlson, CS, Peters, U, Nickerson, DA, Kundaje, A, Hsu, L, Zhao, Z, Bien, SA, Auer, PL, Harrison, TA, Qu, C, Connolly, CM, Greenside, PG, Chen, S, Berndt, SI, Bezieau, S, Kang, HM, Huyghe, J, Brenner, H, Casey, G, Chan, AT, Hopper, JL, Banbury, BL, Chang-Claude, J, Chanock, SJ, Haile, RW, Hoffmeister, M, Fuchsberger, C, Jenkins, MA, Leal, SM, Lemire, M, Newcomb, PA, Gallinger, S, Potter, JD, Schoen, RE, Slattery, ML, Smith, JD, Le Marchand, L, White, E, Zanke, BW, Abecasis, GR, Carlson, CS, Peters, U, Nickerson, DA, Kundaje, A, and Hsu, L

Abstract

BACKGROUND: The evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues. METHODS: Active regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency <1%. Gene sets were defined as ARE variants within 200 kilobases of a transcription start site (TSS) using either CR ARE or ARE from non-digestive tissues. CRC-set association p-values were used to evaluate enrichment of less frequent variant associations in CR ARE versus non-digestive ARE. RESULTS: ARE from 126/127 tissues and cell-types were significantly enriched for stronger CRC-variant associations. Strongest enrichment was observed for digestive tissues and immune cell types. CR-specific ARE were also enriched for stronger CRC-variant associations compared to ARE combined across non-digestive tissues (p-value = 9.6 × 10-4). Additionally, we found enrichment of stronger CRC association p-values for rare variant sets of CR ARE compared to non-digestive ARE (p-value = 0.029). CONCLUSIONS: Integrative epigenomic RVAM may enable discovery of less frequent variants associated with CRC, and ARE of digestive and immune tissues are most informative. Although distance-based aggregation of less frequent variants in CR ARE surrounding TSS showed modest enrichment, future asso

Published
2017

16. CellML 2.0.1

Author

Clerx Michael, Cooling Michael T., Cooper Jonathan, Garny Alan, Moyle Keri, Nickerson David P., Nielsen Poul M. F., and Sorby Hugh

Subjects
Biotechnology, TP248.13-248.65
Abstract

We present here CellML 2.0.1, an XML-based language for describing and exchanging mathematical models of physiological systems. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of reusable components, each with variables and equations giving relationships between those variables. Models may import other models to create systems of increasing complexity. CellML 2.0.1 is defined by the normative specification presented here, prescribing the CellML syntax and the rules by which it should be used. The normative specification is intended primarily for the developers of software tools which directly consume CellML syntax. Users of CellML models may prefer to browse the informative rendering of the specification (https://cellml.org/specifications/cellml_2.0/) which extends the normative specification with explanations of the rules combined with examples of their usage. This version improves the identification of rule statements and corrects errata present in the CellML 2.0 specification.

Published
2023
Full Text
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17. Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022

Author

König Matthias, Gleeson Padraig, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., Myers Chris J., Nickerson David P., Sommer Björn, Waltemath Dagmar, and Schreiber Falk

Subjects
Biotechnology, TP248.13-248.65
Abstract

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.

Published
2023
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18. Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants

Author

Wang, J, Du, M, Jiao, S, Bien, SA, Gala, M, Abecasis, G, Bezieau, S, Brenner, H, Butterbach, K, Caan, BJ, Carlson, CS, Casey, G, Chang-Claude, J, Conti, DV, Curtis, KR, Duggan, D, Gallinger, S, Haile, RW, Harrison, TA, Hayes, RB, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jenkins, MA, Kury, S, Le Marchand, L, Leal, SM, Newcomb, PA, Nickerson, DA, Potter, JD, Schoen, RE, Schumacher, FR, Seminara, D, Slattery, ML, Hsu, L, Chan, AT, White, E, Berndt, SI, Peters, U, Wang, J, Du, M, Jiao, S, Bien, SA, Gala, M, Abecasis, G, Bezieau, S, Brenner, H, Butterbach, K, Caan, BJ, Carlson, CS, Casey, G, Chang-Claude, J, Conti, DV, Curtis, KR, Duggan, D, Gallinger, S, Haile, RW, Harrison, TA, Hayes, RB, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jenkins, MA, Kury, S, Le Marchand, L, Leal, SM, Newcomb, PA, Nickerson, DA, Potter, JD, Schoen, RE, Schumacher, FR, Seminara, D, Slattery, ML, Hsu, L, Chan, AT, White, E, Berndt, SI, and Peters, U

Abstract

Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s).

Published
2016

19. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

Author

Postmus, I, Warren, HR, Trompet, S, Arsenault, BJ, Avery, CL, Bis, JC, Chasman, DI, de Keyser, Toke, Deshmukh, HA, Evans, DS, Feng, Q, Li, X, Smit, RAJ, Smith, AV, Sun, F, Taylor, KD, Arnold, AM, Barnes, MR, Barratt, BJ, Betteridge, J, Boekholdt, SM, Boerwinkle, E, Buckley, BM, Chen, YDI, de Craen, AJM, Cummings, SR, Denny, JC, Dube, MP, Durrington, PN, Eiriksdottir, G, Ford, I, Guo, X, Harris, TB, Heckbert, SR, Hofman, Bert, Hovingh, GK, Kastelein, JJP, Launer, LJ, Liu, C-T, Liu, Y, Lumley, T, Mckeigue, PM, Munroe, PB, Neil, A, Nickerson, DA, Nyberg, F, O'Brien, E, O'Donnell, CJ, Post, W, Poulter, N, Vasan, RS, Rice, K, Rich, SS, Rivadeneira, Fernando, Sattar, N, Sever, P, Shaw-Hawkins, S, Shields, DC, Slagboom, PE (Eline), Smith, NL, Smith, JD, Sotoodehnia, N, Stanton, A, Stott, DJ, Stricker, Bruno, Sturmer, T, Uitterlinden, André, Wei, W-Q, Westendorp, RGJ, Whitsel, EA, Wiggins, KL, Wilke, RA, Ballantyne, CM, Colhoun, HM, Cupples, LA, Franco Duran, OH, Gudnason, V, Hitman, G, Palmer, CNA, Psaty, BM, Ridker, PM, Stafford, JM, Stein, CM, Tardif, J-C, Caulfield, MJ, Jukema, JW, Rotter, JI, Krauss, RM, Postmus, I, Warren, HR, Trompet, S, Arsenault, BJ, Avery, CL, Bis, JC, Chasman, DI, de Keyser, Toke, Deshmukh, HA, Evans, DS, Feng, Q, Li, X, Smit, RAJ, Smith, AV, Sun, F, Taylor, KD, Arnold, AM, Barnes, MR, Barratt, BJ, Betteridge, J, Boekholdt, SM, Boerwinkle, E, Buckley, BM, Chen, YDI, de Craen, AJM, Cummings, SR, Denny, JC, Dube, MP, Durrington, PN, Eiriksdottir, G, Ford, I, Guo, X, Harris, TB, Heckbert, SR, Hofman, Bert, Hovingh, GK, Kastelein, JJP, Launer, LJ, Liu, C-T, Liu, Y, Lumley, T, Mckeigue, PM, Munroe, PB, Neil, A, Nickerson, DA, Nyberg, F, O'Brien, E, O'Donnell, CJ, Post, W, Poulter, N, Vasan, RS, Rice, K, Rich, SS, Rivadeneira, Fernando, Sattar, N, Sever, P, Shaw-Hawkins, S, Shields, DC, Slagboom, PE (Eline), Smith, NL, Smith, JD, Sotoodehnia, N, Stanton, A, Stott, DJ, Stricker, Bruno, Sturmer, T, Uitterlinden, André, Wei, W-Q, Westendorp, RGJ, Whitsel, EA, Wiggins, KL, Wilke, RA, Ballantyne, CM, Colhoun, HM, Cupples, LA, Franco Duran, OH, Gudnason, V, Hitman, G, Palmer, CNA, Psaty, BM, Ridker, PM, Stafford, JM, Stein, CM, Tardif, J-C, Caulfield, MJ, Jukema, JW, Rotter, JI, and Krauss, RM

Published
2016

20. Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences

Author

Colonna, V, Ayub, Q, Chen, Y, Pagani, L, Luisi, P, Pybus, M, Garrison, E, Xue, Y, Tyler-Smith, C, Abecasis, GR, Auton, A, Brooks, LD, Depristo, MA, Durbin, RM, Handsaker, RE, Kang, HM, Marth, GT, McVean, G, Altshuler, DM, Bentley, DR, Chakravarti, A, Clark, AG, Donnelly, P, Eichler, EE, Flicek, P, Gabriel, SB, Gibbs, RA, Green, ED, Hurles, ME, Knoppers, BM, Korbel, JO, Lander, ES, Lee, C, Lehrach, H, Mardis, ER, McVean, GA, Nickerson, DA, Schmidt, JP, Sherry, ST, Wang, J, Wilson, RK, Dinh, H, Kovar, C, Lee, S, Lewis, L, Muzny, D, Reid, J, Wang, M, Fang, X, Guo, X, Jian, M, Jiang, H, Jin, X, Li, G, Li, J, Li, Y, Li, Z, Liu, X, Lu, Y, Ma, X, Su, Z, Tai, S, Tang, M, Wang, B, Wang, G, Wu, H, Wu, R, Yin, Y, Zhang, W, Zhao, J, Zhao, M, Zheng, X, Zhou, Y, Gupta, N, Clarke, L, Leinonen, R, Smith, RE, Zheng-Bradley, X, Grocock, R, Humphray, S, James, T, Kingsbury, Z, Sudbrak, R, Albrecht, MW, Amstislavskiy, VS, Borodina, TA, Lienhard, M, Mertes, F, Sultan, M, Timmermann, B, Yaspo, ML, Fulton, L, Fulton, R, Weinstock, GM, Balasubramaniam, S, Burton, J, Danecek, P, Keane, TM, Kolb-Kokocinski, A, McCarthy, S, Molecular Dynamics, Biomimetics, Urban and Regional Studies Institute, Nanomedicine & Drug Targeting, Artificial Intelligence, Micromechanics, Molecular Cell Biology, Van Swinderen Institute for Particle Physics and G, Archaeology of Northwestern Europe, Polymer Chemistry and Bioengineering, Christianity and the History of Ideas, Scientific Visualization and Computer Graphics, Chemical Technology, Macromolecular Chemistry & New Polymeric Materials, Bernoulli Institute, Surfaces and Thin Films, Hemelrijk group, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Falcao Salles lab, Synthetic Organic Chemistry, Psychometrics and Statistics, Bio-inspired systems and circuits, Advanced Production Engineering, Drug Design, The 1000 Genomes Project Consortium, Faculteit Medische Wetenschappen/UMCG, Wellcome Trust, Consiglio Nazionale delle Ricerche, EMBO, and 1000 Genomes Project Consortium

Subjects
Historia y Arqueología, lactase persistence, POSITIVE SELECTION, BALANCING SELECTION, SOFT SWEEP, Biología, standing variation, Population, Biology, Balancing selection, Genome, Polymorphism, Single Nucleotide, Genètica de poblacions humanes, Ciencias Biológicas, purl.org/becyt/ford/1 [https], selective sweep, functional annotation cluster, Genética y Herencia, HUMANIDADES, Genetic drift, Gene Frequency, INDEL Mutation, Humans, Selection, Genetic, education, purl.org/becyt/ford/1.6 [https], Selection (genetic algorithm), education.field_of_study, purl.org/becyt/ford/6 [https], Genome, Human, Research, Genetic Drift, Levenshtein distance, Selecció natural, Sequence Analysis, DNA, Human genetics, Otras Historia y Arqueología, Evolutionary biology, Human genome, purl.org/becyt/ford/6.1 [https], Selective sweep, Genètica humana -- Variació, CIENCIAS NATURALES Y EXACTAS
Abstract

It contains associated material.-- The 1000 Genomes Project Consortium, [Background] Population differentiation has proved to be effective for identifying loci under geographically localized positive selection, and has the potential to identify loci subject to balancing selection. We have previously investigated the pattern of genetic differentiation among human populations at 36.8 million genomic variants to identify sites in the genome showing high frequency differences. Here, we extend this dataset to include additional variants, survey sites with low levels of differentiation, and evaluate the extent to which highly differentiated sites are likely to result from selective or other processes., [Results] We demonstrate that while sites with low differentiation represent sampling effects rather than balancing selection, sites showing extremely high population differentiation are enriched for positive selection events and that one half may be the result of classic selective sweeps. Among these, we rediscover known examples, where we actually identify the established functional SNP, and discover novel examples including the genes ABCA12, CALD1 and ZNF804, which we speculate may be linked to adaptations in skin, calcium metabolism and defense, respectively., [Conclusions] We identify known and many novel candidate regions for geographically restricted positive selection, and suggest several directions for further research. © 2014 Colonna et al., This work was supported by The Wellcome Trust (098051), an Italian National Research Council (CNR) short-term mobility fellowship from the 2013 program to VC, and an EMBO Short Term Fellowship ASTF 324–2010 to VC.

Published
2014

21. A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

Author

Aspichueta, P, Shim, H, Chasman, DI, Smith, JD, Mora, S, Ridker, PM, Nickerson, DA, Krauss, RM, Stephens, M, Aspichueta, P, Shim, H, Chasman, DI, Smith, JD, Mora, S, Ridker, PM, Nickerson, DA, Krauss, RM, and Stephens, M

Abstract

We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log(10)Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns--both in our original data and in a replication cohort--consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log(10)Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses

Published
2015

22. Specifications of standards in systems and synthetic biology: status and developments in 2021

Author

Schreiber Falk, Gleeson Padraig, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., König Matthias, Myers Chris J., Nickerson David P., Sommer Björn, and Waltemath Dagmar

Subjects
Biotechnology, TP248.13-248.65
Abstract

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.

Published
2021
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23. An integrated map of genetic variation from 1,092 human genomes

Author

Altshuler, DM, Durbin, RM, Abecasis, GR, Bentley, DR, Chakravarti, A, Clark, AG, Donnelly, P, Eichler, EE, Flicek, P, Gabriel, SB, Gibbs, RA, Green, ED, Hurles, ME, Knoppers, BM, Korbel, JO, Lander, ES, Lee, C, Lehrach, H, Mardis, ER, Marth, GT, McVean, GA, Nickerson, DA, Schmidt, JP, Sherry, ST, Wang, J, Wilson, RK, Dinh, H, Kovar, C, Lee, S, Lewis, L, Muzny, D, Reid, J, Wang, M, Fang, X, Guo, X, Jian, M, Jiang, H, Jin, X, Li, G, Li, J, Li, Y, Li, Z, Liu, X, Lu, Y, Ma, X, Su, Z, Tai, S, Tang, M, Wang, B, Wang, G, Wu, H, Wu, R, Yin, Y, Zhang, W, Zhao, J, Zhao, M, Zheng, X, Zhou, Y, Gupta, N, Clarke, L, Leinonen, R, Smith, RE, Zheng-Bradley, X, Grocock, R, Humphray, S, James, T, Kingsbury, Z, Sudbrak, R, Albrecht, MW, Amstislavskiy, VS, Borodina, TA, Lienhard, M, Mertes, F, Sultan, M, Timmermann, B, Yaspo, M-L, Fulton, L, Fulton, R, Weinstock, GM, Balasubramaniam, S, Burton, J, Danecek, P, Keane, TM, Kolb-Kokocinski, A, McCarthy, S, Stalker, J, Quail, M, Davies, CJ, Gollub, J, Webster, T, Wong, B, Zhan, Y, Auton, A, Yu, F, Bainbridge, M, Challis, D, Evani, US, Lu, J, Nagaswamy, U, Sabo, A, Wang, Y, Yu, J, Coin, LJM, Fang, L, Li, Q, Lin, H, Liu, B, Luo, R, Qin, N, Shao, H, Xie, Y, Ye, C, Yu, C, Zhang, F, Zheng, H, Zhu, H, Garrison, EP, Kural, D, Lee, W-P, Leong, WF, Ward, AN, Wu, J, Zhang, M, Griffin, L, Hsieh, C-H, Mills, RE, Shi, X, Von Grotthuss, M, Zhang, C, Daly, MJ, DePristo, MA, Banks, E, Bhatia, G, Carneiro, MO, Del Angel, G, Genovese, G, Handsaker, RE, Hartl, C, McCarroll, SA, Nemesh, JC, Poplin, RE, Schaffner, SF, Shakir, K, Yoon, SC, Lihm, J, Makarov, V, Jin, H, Kim, W, Kim, KC, Rausch, T, Beal, K, Cunningham, F, Herrero, J, McLaren, WM, Ritchie, GRS, Gottipati, S, Keinan, A, Rodriguez-Flores, JL, Sabeti, PC, Grossman, SR, Tabrizi, S, Tariyal, R, Cooper, DN, Ball, EV, Stenson, PD, Barnes, B, Bauer, M, Cheetham, RK, Cox, T, Eberle, M, Kahn, S, Murray, L, Peden, J, Shaw, R, Ye, K, Batzer, MA, Konkel, MK, Walker, JA, MacArthur, DG, Lek, M, Herwig, R, Shriver, MD, Bustamante, CD, Byrnes, JK, De la Vega, FM, Gravel, S, Kenny, EE, Kidd, JM, Lacroute, P, Maples, BK, Moreno-Estrada, A, Zakharia, F, Halperin, E, Baran, Y, Craig, DW, Christoforides, A, Homer, N, Izatt, T, Kurdoglu, AA, Sinari, SA, Squire, K, Xiao, C, Sebat, J, Bafna, V, Burchard, EG, Hernandez, RD, Gignoux, CR, Haussler, D, Katzman, SJ, Kent, WJ, Howie, B, Ruiz-Linares, A, Dermitzakis, ET, Lappalainen, T, Devine, SE, Maroo, A, Tallon, LJ, Rosenfeld, JA, Michelson, LP, Kang, HM, Anderson, P, Angius, A, Bigham, A, Blackwell, T, Busonero, F, Cucca, F, Fuchsberger, C, Jones, C, Jun, G, Lyons, R, Maschio, A, Porcu, E, Reinier, F, Sanna, S, Schlessinger, D, Sidore, C, Tan, A, Trost, MK, Awadalla, P, Hodgkinson, A, Lunter, G, Marchini, JL, Myers, S, Churchhouse, C, Delaneau, O, Gupta-Hinch, A, Iqbal, Z, Mathieson, I, Rimmer, A, Xifara, DK, Oleksyk, TK, Fu, Y, Xiong, M, Jorde, L, Witherspoon, D, Xing, J, Browning, BL, Alkan, C, Hajirasouliha, I, Hormozdiari, F, Ko, A, Sudmant, PH, Chen, K, Chinwalla, A, Ding, L, Dooling, D, Koboldt, DC, McLellan, MD, Wallis, JW, Wendl, MC, Zhang, Q, Tyler-Smith, C, Albers, CA, Ayub, Q, Chen, Y, Coffey, AJ, Colonna, V, Huang, N, Jostins, L, Li, H, Scally, A, Walter, K, Xue, Y, Zhang, Y, Gerstein, MB, Abyzov, A, Balasubramanian, S, Chen, J, Clarke, D, Habegger, L, Harmanci, AO, Jin, M, Khurana, E, Mu, XJ, Sisu, C, Degenhardt, J, Stuetz, AM, Church, D, Michaelson, JJ, Ben, B, Lindsay, SJ, Ning, Z, Frankish, A, Harrow, J, Fowler, G, Hale, W, Kalra, D, Barker, J, Kelman, G, Kulesha, E, Radhakrishnan, R, Roa, A, Smirnov, D, Streeter, I, Toneva, I, Vaughan, B, 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, O'Sullivan, C, Ostapchuk, Y, Phan, L, Ponomarov, S, Schneider, V, Shekhtman, E, Sirotkin, K, Slotta, D, Zhang, H, Barnes, KC, Beiswanger, C, Cai, H, Cao, H, Gharani, N, Henn, B, Jones, D, Kaye, JS, Kent, A, Kerasidou, A, Mathias, R, Ossorio, PN, Parker, M, Reich, D, Rotimi, CN, Royal, CD, Sandoval, K, Su, Y, Tian, Z, Tishkoff, S, Toji, LH, Via, M, Yang, H, Yang, L, Zhu, J, Bodmer, W, Bedoya, G, Ming, CZ, Yang, G, You, CJ, Peltonen, L, Garcia-Montero, A, Orfao, A, Dutil, J, Martinez-Cruzado, JC, Brooks, LD, Felsenfeld, AL, McEwen, JE, Clemm, NC, Duncanson, A, Dunn, M, Guyer, MS, Peterson, JL, 1000 Genomes Project Consortium, Dermitzakis, Emmanouil, Universitat de Barcelona, Massachusetts Institute of Technology. Department of Biology, Altshuler, David, and Lander, Eric S.

Subjects
Natural selection, LOCI, Genome-wide association study, Evolutionary biology, Continental Population Groups/genetics, Human genetic variation, VARIANTS, Genoma humà, Binding Sites/genetics, 0302 clinical medicine, RARE, Sequence Deletion/genetics, WIDE ASSOCIATION, ddc:576.5, Copy-number variation, MUTATION, Exome sequencing, transcription factor, Conserved Sequence, Human evolution, Sequence Deletion, Genetics, RISK, 0303 health sciences, Multidisciplinary, Continental Population Groups, 1000 Genomes Project Consortium, Genetic analysis, Genomics, Polymorphism, Single Nucleotide/genetics, Research Highlight, 3. Good health, Algorithm, Multidisciplinary Sciences, Genetic Variation/genetics, Map, Science & Technology - Other Topics, Conserved Sequence/genetics, Integrated approach, General Science & Technology, Genetics, Medical, Haplotypes/genetics, Biology, Polymorphism, Single Nucleotide, Evolution, Molecular, 03 medical and health sciences, Genetic variation, Humans, Transcription Factors/metabolism, POPULATION-STRUCTURE, 1000 Genomes Project, Polymorphism, Nucleotide Motifs, Alleles, 030304 developmental biology, COPY NUMBER VARIATION, Science & Technology, Binding Sites, Human genome, Genome, Human, Racial Groups, Genetic Variation, Genetics, Population, Haplotypes, Genome, Human/genetics, untranslated RNA, 030217 neurology & neurosurgery, Transcription Factors, Genome-Wide Association Study
Abstract

By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations., National Institutes of Health (U.S.) (Grant RC2HL102925), National Institutes of Health (U.S.) (Grant U54HG3067)

Published
2012

24. Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting

Author

Kalman, LV, primary, Agúndez, JAG, additional, Appell, M Lindqvist, additional, Black, JL, additional, Bell, GC, additional, Boukouvala, S, additional, Bruckner, C, additional, Bruford, E, additional, Caudle, K, additional, Coulthard, SA, additional, Daly, AK, additional, Tredici, AL Del, additional, den Dunnen, JT, additional, Drozda, K, additional, Everts, RE, additional, Flockhart, D, additional, Freimuth, RR, additional, Gaedigk, A, additional, Hachad, H, additional, Hartshorne, T, additional, Ingelman‐Sundberg, M, additional, Klein, TE, additional, Lauschke, VM, additional, Maglott, DR, additional, McLeod, HL, additional, McMillin, GA, additional, Meyer, UA, additional, Müller, DJ, additional, Nickerson, DA, additional, Oetting, WS, additional, Pacanowski, M, additional, Pratt, VM, additional, Relling, MV, additional, Roberts, A, additional, Rubinstein, WS, additional, Sangkuhl, K, additional, Schwab, M, additional, Scott, SA, additional, Sim, SC, additional, Thirumaran, RK, additional, Toji, LH, additional, Tyndale, RF, additional, van Schaik, RHN, additional, Whirl‐Carrillo, M, additional, Yeo, KTJ, additional, and Zanger, UM, additional

Published
2015
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25. Characterization of statin dose response in electronic medical records.

Author

Wei, W-Q, Wei, W-Q, Feng, Q, Jiang, L, Waitara, MS, Iwuchukwu, OF, Roden, DM, Jiang, M, Xu, H, Krauss, RM, Rotter, JI, Nickerson, DA, Davis, RL, Berg, RL, Peissig, PL, McCarty, CA, Wilke, RA, Denny, JC, Wei, W-Q, Wei, W-Q, Feng, Q, Jiang, L, Waitara, MS, Iwuchukwu, OF, Roden, DM, Jiang, M, Xu, H, Krauss, RM, Rotter, JI, Nickerson, DA, Davis, RL, Berg, RL, Peissig, PL, McCarty, CA, Wilke, RA, and Denny, JC

Abstract

Efforts to define the genetic architecture underlying variable statin response have met with limited success, possibly because previous studies were limited to effect based on a single dose. We leveraged electronic medical records (EMRs) to extract potency (ED50) and efficacy (Emax) of statin dose-response curves and tested them for association with 144 preselected variants. Two large biobanks were used to construct dose-response curves for 2,026 and 2,252 subjects on simvastatin and atorvastatin, respectively. Atorvastatin was more efficacious, was more potent, and demonstrated less interindividual variability than simvastatin. A pharmacodynamic variant emerging from randomized trials (PRDM16) was associated with Emax for both. For atorvastatin, Emax was 51.7 mg/dl in subjects homozygous for the minor allele vs. 75.0 mg/dl for those homozygous for the major allele. We also identified several loci associated with ED50. The extraction of rigorously defined traits from EMRs for pharmacogenetic studies represents a promising approach to further understand the genetic factors contributing to drug response.

Published
2014

26. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

Author

Postmus, I, Trompet, S, Deshmukh, HA, Barnes, MR, Li, XH, Warren, HR, Chasman, DI, Zhou, KX, Arsenault, BJ, Donnelly, LA, Wiggins, KL, Avery, CL, Griffin, P, Feng, QP, Taylor, KD, Li, G, Evans, DS, Smith, AV, de Keyser, Toke, Johnson, AD, de Craen, AJM, Stott, DJ, Buckley, BM, Ford, I, Westendorp, RGJ, Slagboom, PE (Eline), Sattar, N, Munroe, PB, Sever, P, Poulter, N, Stanton, A, Shields, DC, O'Brien, E, Shaw-Hawkins, S, Chen, YDI, Nickerson, DA, Smith, JD, Dube, MP, Boekholdt, SM, Hovingh, GK, Kastelein, JJP, Mckeigue, PM, Betteridge, J, Neil, A, Durrington, PN, Doney, A, Carr, F, Morris, A, McCarthy, MI, Groop, L, Ahlqvist, E, Bis, JC, Rice, K, Smith, NL, Lumley, T, Whitsel, EA, Sturmer, T, Boerwinkle, E, Ngwa, JS, O'Donnell, CJ, Vasan, RS, Wei, WQ, Wilke, RA, Liu, CT, Sun, FG, Guo, XQ, Heckbert, SR, Post, W, Sotoodehnia, N, Arnold, AM, Stafford, JM, Ding, JZ (Jing Zhong), Herrington, DM, Kritchevsky, SB, Eiriksdottir, G, Launer, LJ (Lenore), Harris, TB, Chu, AY, Giulianini, F, MacFadyen, JG, Barratt, BJ, Nyberg, F, Stricker, Bruno, Uitterlinden, André, Hofman, Bert, Rivadeneira, Fernando, Emilsson, V, Franco Duran, OH, Ridker, PM, Gudnason, V, Liu, YM, Denny, JC, Ballantyne, CM, Rotter, JI, Cupples, LA, Psaty, BM, Palmer, CNA, Tardif, JC, Colhoun, HM, Hitman, G, Krauss, RM, Jukema, JW, Caulfield, MJ, Postmus, I, Trompet, S, Deshmukh, HA, Barnes, MR, Li, XH, Warren, HR, Chasman, DI, Zhou, KX, Arsenault, BJ, Donnelly, LA, Wiggins, KL, Avery, CL, Griffin, P, Feng, QP, Taylor, KD, Li, G, Evans, DS, Smith, AV, de Keyser, Toke, Johnson, AD, de Craen, AJM, Stott, DJ, Buckley, BM, Ford, I, Westendorp, RGJ, Slagboom, PE (Eline), Sattar, N, Munroe, PB, Sever, P, Poulter, N, Stanton, A, Shields, DC, O'Brien, E, Shaw-Hawkins, S, Chen, YDI, Nickerson, DA, Smith, JD, Dube, MP, Boekholdt, SM, Hovingh, GK, Kastelein, JJP, Mckeigue, PM, Betteridge, J, Neil, A, Durrington, PN, Doney, A, Carr, F, Morris, A, McCarthy, MI, Groop, L, Ahlqvist, E, Bis, JC, Rice, K, Smith, NL, Lumley, T, Whitsel, EA, Sturmer, T, Boerwinkle, E, Ngwa, JS, O'Donnell, CJ, Vasan, RS, Wei, WQ, Wilke, RA, Liu, CT, Sun, FG, Guo, XQ, Heckbert, SR, Post, W, Sotoodehnia, N, Arnold, AM, Stafford, JM, Ding, JZ (Jing Zhong), Herrington, DM, Kritchevsky, SB, Eiriksdottir, G, Launer, LJ (Lenore), Harris, TB, Chu, AY, Giulianini, F, MacFadyen, JG, Barratt, BJ, Nyberg, F, Stricker, Bruno, Uitterlinden, André, Hofman, Bert, Rivadeneira, Fernando, Emilsson, V, Franco Duran, OH, Ridker, PM, Gudnason, V, Liu, YM, Denny, JC, Ballantyne, CM, Rotter, JI, Cupples, LA, Psaty, BM, Palmer, CNA, Tardif, JC, Colhoun, HM, Hitman, G, Krauss, RM, Jukema, JW, and Caulfield, MJ

Abstract

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

Published
2014

27. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy

Author

Mangravite, LM, Engelhardt, BE, Medina, MW, Smith, JD, Brown, CD, Chasman, DI, Mecham, BH, Howie, B, Shim, H, Naidoo, D, Feng, Q, Rieder, MJ, Chen, Y-DI, Rotter, JI, Ridker, PM, Hopewell, JC, Parish, S, Armitage, J, Collins, R, Wilke, RA, Nickerson, DA, Stephens, M, Krauss, RM, Mangravite, LM, Engelhardt, BE, Medina, MW, Smith, JD, Brown, CD, Chasman, DI, Mecham, BH, Howie, B, Shim, H, Naidoo, D, Feng, Q, Rieder, MJ, Chen, Y-DI, Rotter, JI, Ridker, PM, Hopewell, JC, Parish, S, Armitage, J, Collins, R, Wilke, RA, Nickerson, DA, Stephens, M, and Krauss, RM

Abstract

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Published
2013

28. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

Author

Waltemath Dagmar, Golebiewski Martin, Blinov Michael L, Gleeson Padraig, Hermjakob Henning, Hucka Michael, Inau Esther Thea, Keating Sarah M, König Matthias, Krebs Olga, Malik-Sheriff Rahuman S, Nickerson David, Oberortner Ernst, Sauro Herbert M, Schreiber Falk, Smith Lucian, Stefan Melanie I, Wittig Ulrike, and Myers Chris J

Subjects
combine, community building, meeting report, standardization, Biotechnology, TP248.13-248.65
Abstract

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.

Published
2020
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29. Specifications of standards in systems and synthetic biology: status and developments in 2020

Author

Schreiber Falk, Sommer Björn, Czauderna Tobias, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., König Matthias, Myers Chris, Nickerson David, and Waltemath Dagmar

Subjects
ontologies, standards, systems biology, synthetic biology, Biotechnology, TP248.13-248.65
Abstract

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.

Published
2020
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30. Genome-Wide Association Study Identifies Genetic Loci Associated with Iron Deficiency

Author

Tjwa, M, McLaren, CE, Garner, CP, Constantine, CC, McLachlan, S, Vulpe, CD, Snively, BM, Gordeuk, VR, Nickerson, DA, Cook, JD, Leiendecker-Foster, C, Beckman, KB, Eckfeldt, JH, Barcellos, LF, Murray, JA, Adams, PC, Acton, RT, Killeen, AA, McLaren, GD, Tjwa, M, McLaren, CE, Garner, CP, Constantine, CC, McLachlan, S, Vulpe, CD, Snively, BM, Gordeuk, VR, Nickerson, DA, Cook, JD, Leiendecker-Foster, C, Beckman, KB, Eckfeldt, JH, Barcellos, LF, Murray, JA, Adams, PC, Acton, RT, Killeen, AA, and McLaren, GD

Abstract

The existence of multiple inherited disorders of iron metabolism in man, rodents and other vertebrates suggests genetic contributions to iron deficiency. To identify new genomic locations associated with iron deficiency, a genome-wide association study (GWAS) was performed using DNA collected from white men aged≥25 y and women≥50 y in the Hemochromatosis and Iron Overload Screening (HEIRS) Study with serum ferritin (SF)≤12 µg/L (cases) and iron replete controls (SF>100 µg/L in men, SF>50 µg/L in women). Regression analysis was used to examine the association between case-control status (336 cases, 343 controls) and quantitative serum iron measures and 331,060 single nucleotide polymorphism (SNP) genotypes, with replication analyses performed in a sample of 71 cases and 161 controls from a population of white male and female veterans screened at a US Veterans Affairs (VA) medical center. Five SNPs identified in the GWAS met genome-wide statistical significance for association with at least one iron measure, rs2698530 on chr. 2p14; rs3811647 on chr. 3q22, a known SNP in the transferrin (TF) gene region; rs1800562 on chr. 6p22, the C282Y mutation in the HFE gene; rs7787204 on chr. 7p21; and rs987710 on chr. 22q11 (GWAS observed P<1.51×10(-7) for all). An association between total iron binding capacity and SNP rs3811647 in the TF gene (GWAS observed P=7.0×10(-9), corrected P=0.012) was replicated within the VA samples (observed P=0.012). Associations with the C282Y mutation in the HFE gene also were replicated. The joint analysis of the HEIRS and VA samples revealed strong associations between rs2698530 on chr. 2p14 and iron status outcomes. These results confirm a previously-described TF polymorphism and implicate one potential new locus as a target for gene identification.

Published
2011

31. Biological, clinical and population relevance of 95 loci for blood lipids

Author

Teslovich, TM, Musunuru, K, Smith, AV, Edmondson, AC, Stylianou, IM, Koseki, M, Pirruccello, JP, Ripatti, S, Chasman, DI, Willer, CJ, Johansen, CT, Fouchier, SW, Isaacs, A, Peloso, GM, Barbalic, M, Ricketts, SL, Bis, JC, Aulchenko, YS, Thorleifsson, G, Feitosa, MF, Chambers, J, Orho-Melander, M, Melander, O, Johnson, T, Li, X, Guo, X, Li, M, Cho, YS, Go, MJ, Kim, YJ, Lee, J-Y, Park, T, Kim, K, Sim, X, Ong, RT-H, Croteau-Chonka, DC, Lange, LA, Smith, JD, Song, K, Zhao, JH, Yuan, X, Luan, J, Lamina, C, Ziegler, A, Zhang, W, Zee, RYL, Wright, AF, Witteman, JCM, Wilson, JF, Willemsen, G, Wichmann, H-E, Whitfield, JB, Waterworth, DM, Wareham, NJ, Waeber, G, Vollenweider, P, Voight, BF, Vitart, V, Uitterlinden, AG, Uda, M, Tuomilehto, J, Thompson, JR, Tanaka, T, Surakka, I, Stringham, HM, Spector, TD, Soranzo, N, Smit, JH, Sinisalo, J, Silander, K, Sijbrands, EJG, Scuteri, A, Scott, J, Schlessinger, D, Sanna, S, Salomaa, V, Saharinen, J, Sabatti, C, Ruokonen, A, Rudan, I, Rose, LM, Roberts, R, Rieder, M, Psaty, BM, Pramstaller, PP, Pichler, I, Perola, M, Penninx, BWJH, Pedersen, NL, Pattaro, C, Parker, AN, Pare, G, Oostra, BA, O'Donnell, CJ, Nieminen, MS, Nickerson, DA, Montgomery, GW, Meitinger, T, McPherson, R, McCarthy, MI, McArdle, W, Masson, D, Martin, NG, Marroni, F, Mangino, M, Magnusson, PKE, Lucas, G, Luben, R, Loos, RJF, Lokki, M-L, Lettre, G, Langenberg, C, Launer, LJ, Lakatta, EG, Laaksonen, R, Kyvik, KO, Kronenberg, F, Koenig, IR, Khaw, K-T, Kaprio, J, Kaplan, LM, Johansson, A, Jarvelin, M-R, Janssens, ACJW, Ingelsson, E, Igi, W, Hovingh, GK, Hottenga, J-J, Hofman, A, Hicks, AA, Hengstenberg, C, Heid, IM, Hayward, C, Havulinna, AS, Hastie, ND, Harris, TB, Haritunians, T, Hall, AS, Gyllensten, U, Guiducci, C, Groop, LC, Gonzalez, E, Gieger, C, Freimer, NB, Ferrucci, L, Erdmann, J, Elliott, P, Ejebe, KG, Doering, A, Dominiczak, AF, Demissie, S, Deloukas, P, de Geus, EJC, de Faire, U, Crawford, G, Collins, FS, Chen, Y-DI, Caulfield, MJ, Campbell, H, Burtt, NP, Bonnycastle, LL, Boomsma, DI, Boekholdt, SM, Bergman, RN, Barroso, I, Bandinelli, S, Ballantyne, CM, Assimes, TL, Quertermous, T, Altshuler, D, Seielstad, M, Wong, TY, Tai, E-S, Feranil, AB, Kuzawa, CW, Adair, LS, Taylor, HA, Borecki, IB, Gabriel, SB, Wilson, JG, Holm, H, Thorsteinsdottir, U, Gudnason, V, Krauss, RM, Mohlke, KL, Ordovas, JM, Munroe, PB, Kooner, JS, Tall, AR, Hegele, RA, Kastelein, JJP, Schadt, EE, Rotter, JI, Boerwinkle, E, Strachan, DP, Mooser, V, Stefansson, K, Reilly, MP, Samani, NJ, Schunkert, H, Cupples, LA, Sandhu, MS, Ridker, PM, Rader, DJ, van Duijn, CM, Peltonen, L, Abecasis, GR, Boehnke, M, Kathiresan, S, Teslovich, TM, Musunuru, K, Smith, AV, Edmondson, AC, Stylianou, IM, Koseki, M, Pirruccello, JP, Ripatti, S, Chasman, DI, Willer, CJ, Johansen, CT, Fouchier, SW, Isaacs, A, Peloso, GM, Barbalic, M, Ricketts, SL, Bis, JC, Aulchenko, YS, Thorleifsson, G, Feitosa, MF, Chambers, J, Orho-Melander, M, Melander, O, Johnson, T, Li, X, Guo, X, Li, M, Cho, YS, Go, MJ, Kim, YJ, Lee, J-Y, Park, T, Kim, K, Sim, X, Ong, RT-H, Croteau-Chonka, DC, Lange, LA, Smith, JD, Song, K, Zhao, JH, Yuan, X, Luan, J, Lamina, C, Ziegler, A, Zhang, W, Zee, RYL, Wright, AF, Witteman, JCM, Wilson, JF, Willemsen, G, Wichmann, H-E, Whitfield, JB, Waterworth, DM, Wareham, NJ, Waeber, G, Vollenweider, P, Voight, BF, Vitart, V, Uitterlinden, AG, Uda, M, Tuomilehto, J, Thompson, JR, Tanaka, T, Surakka, I, Stringham, HM, Spector, TD, Soranzo, N, Smit, JH, Sinisalo, J, Silander, K, Sijbrands, EJG, Scuteri, A, Scott, J, Schlessinger, D, Sanna, S, Salomaa, V, Saharinen, J, Sabatti, C, Ruokonen, A, Rudan, I, Rose, LM, Roberts, R, Rieder, M, Psaty, BM, Pramstaller, PP, Pichler, I, Perola, M, Penninx, BWJH, Pedersen, NL, Pattaro, C, Parker, AN, Pare, G, Oostra, BA, O'Donnell, CJ, Nieminen, MS, Nickerson, DA, Montgomery, GW, Meitinger, T, McPherson, R, McCarthy, MI, McArdle, W, Masson, D, Martin, NG, Marroni, F, Mangino, M, Magnusson, PKE, Lucas, G, Luben, R, Loos, RJF, Lokki, M-L, Lettre, G, Langenberg, C, Launer, LJ, Lakatta, EG, Laaksonen, R, Kyvik, KO, Kronenberg, F, Koenig, IR, Khaw, K-T, Kaprio, J, Kaplan, LM, Johansson, A, Jarvelin, M-R, Janssens, ACJW, Ingelsson, E, Igi, W, Hovingh, GK, Hottenga, J-J, Hofman, A, Hicks, AA, Hengstenberg, C, Heid, IM, Hayward, C, Havulinna, AS, Hastie, ND, Harris, TB, Haritunians, T, Hall, AS, Gyllensten, U, Guiducci, C, Groop, LC, Gonzalez, E, Gieger, C, Freimer, NB, Ferrucci, L, Erdmann, J, Elliott, P, Ejebe, KG, Doering, A, Dominiczak, AF, Demissie, S, Deloukas, P, de Geus, EJC, de Faire, U, Crawford, G, Collins, FS, Chen, Y-DI, Caulfield, MJ, Campbell, H, Burtt, NP, Bonnycastle, LL, Boomsma, DI, Boekholdt, SM, Bergman, RN, Barroso, I, Bandinelli, S, Ballantyne, CM, Assimes, TL, Quertermous, T, Altshuler, D, Seielstad, M, Wong, TY, Tai, E-S, Feranil, AB, Kuzawa, CW, Adair, LS, Taylor, HA, Borecki, IB, Gabriel, SB, Wilson, JG, Holm, H, Thorsteinsdottir, U, Gudnason, V, Krauss, RM, Mohlke, KL, Ordovas, JM, Munroe, PB, Kooner, JS, Tall, AR, Hegele, RA, Kastelein, JJP, Schadt, EE, Rotter, JI, Boerwinkle, E, Strachan, DP, Mooser, V, Stefansson, K, Reilly, MP, Samani, NJ, Schunkert, H, Cupples, LA, Sandhu, MS, Ridker, PM, Rader, DJ, van Duijn, CM, Peltonen, L, Abecasis, GR, Boehnke, M, and Kathiresan, S

Abstract

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.

Published
2010

32. The simulation experiment description markup language (SED-ML): language specification for level 1 version 4

Author

Smith Lucian P., Bergmann Frank T., Garny Alan, Helikar Tomáš, Karr Jonathan, Nickerson David, Sauro Herbert, Waltemath Dagmar, and König Matthias

Subjects
computational modeling, reproducibility, simulation experiment, Biotechnology, TP248.13-248.65
Abstract

Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Published
2021
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33. OMEX metadata specification (version 1.2)

Author

Gennari John H., König Matthias, Misirli Goksel, Neal Maxwell L., Nickerson David P., and Waltemath Dagmar

Subjects
biosimulation modeling, combine standards, metadata, semantics, Biotechnology, TP248.13-248.65
Abstract

A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. This document details version 1.2 of the specification, which builds on version 1.0 published last year in this journal. In particular, this version includes a set of initial model-level annotations (whereas v 1.0 described exclusively annotations at a smaller scale). Additionally, this version uses best practices for namespaces, and introduces omex-library.org as a common root for all annotations. Distributing modeling projects within an OMEX archive is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model representations. This specification acts as a technical guideline for developing software tools that can support this standard, and thereby encourages broad advances in model reuse, discovery, and semantic analyses.

Published
2021
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34. The International HapMap Project

Author

Gibbs, RA, Belmont, JW, Hardenbol, P., Willis, TD, Yu, FL, Yang, HM, Ch'ang, LY, Huang, W., Liu, B., Shen, Y., Tam, PKH, Foster, MW, Jasperse, M., Knoppers, BM, Kwok, PY, Licinio, J., Long, JC, Marshall, PA, Ossorio, PN, Wang, VO, Rotimi, CN, Tsui, LC, Royal, CDM, Spallone, P., Terry, SF, Lander, ES, Lai, EH, Nickerson, DA, Abecasis, GR, Altshuler, D., Bentley, DR, Boehnke, M., Waye, MMY, Cardon, LR, Daly, MJ, Deloukas, P., Douglas, JA, Gabriel, SB, Hudson, RR, Hudson, TJ, Kruglyak, L., Nakamura, Y., Wong, JTF, Nussbaum, RL, Schaffner, SF, Sherry, ST, Stein, LD, Tanaka, T., Zeng, CQ, Zhang, QR, Chee, MS, Galver, LM, Kruglyak, S., Murray, SS, Oliphant, AR, Montpetit, A., Chagnon, F., Ferretti, V., Leboeuf, M., Phillips, MS, Verner, A., Duan, SH, Lind, DL, Miller, RD, Rice, JP, Saccone, NL, Taillon-Miller, P., Xiao, M., Sekine, A., Sorimachi, K., Tanaka, Y., Tsunoda, T., Yoshino, E., Hunt, S., Powell, D., Qiu, RZ, Ken, A., Dunston, GM, Kato, K., Niikawa, N., Clayton, EW, Watkin, J., Sodergren, E., Weinstock, GM, Wilson, RK, Fulton, LL, Rogers, J., Birren, BW, Han, H., Wang, HG, Godbout, M., Wallenburg, JC, L'Archeveque, P., Bellemare, G., Todani, K., Fujita, T., Tanaka, S., Holden, AL, Collins, FS, Brooks, LD, McEwen, JE, Guyer, MS, Jordan, E., Peterson, JL, Spiegel, J., Sung, LM, Zacharia, LF, Kennedy, K., Dunn, MG, Seabrook, R., Shillito, M., Skene, B., Stewart, JG, Valle, DL, Jorde, LB, Chakravarti, A., Cho, MK, Duster, T., Gibbs, RA, Belmont, JW, Hardenbol, P., Willis, TD, Yu, FL, Yang, HM, Ch'ang, LY, Huang, W., Liu, B., Shen, Y., Tam, PKH, Foster, MW, Jasperse, M., Knoppers, BM, Kwok, PY, Licinio, J., Long, JC, Marshall, PA, Ossorio, PN, Wang, VO, Rotimi, CN, Tsui, LC, Royal, CDM, Spallone, P., Terry, SF, Lander, ES, Lai, EH, Nickerson, DA, Abecasis, GR, Altshuler, D., Bentley, DR, Boehnke, M., Waye, MMY, Cardon, LR, Daly, MJ, Deloukas, P., Douglas, JA, Gabriel, SB, Hudson, RR, Hudson, TJ, Kruglyak, L., Nakamura, Y., Wong, JTF, Nussbaum, RL, Schaffner, SF, Sherry, ST, Stein, LD, Tanaka, T., Zeng, CQ, Zhang, QR, Chee, MS, Galver, LM, Kruglyak, S., Murray, SS, Oliphant, AR, Montpetit, A., Chagnon, F., Ferretti, V., Leboeuf, M., Phillips, MS, Verner, A., Duan, SH, Lind, DL, Miller, RD, Rice, JP, Saccone, NL, Taillon-Miller, P., Xiao, M., Sekine, A., Sorimachi, K., Tanaka, Y., Tsunoda, T., Yoshino, E., Hunt, S., Powell, D., Qiu, RZ, Ken, A., Dunston, GM, Kato, K., Niikawa, N., Clayton, EW, Watkin, J., Sodergren, E., Weinstock, GM, Wilson, RK, Fulton, LL, Rogers, J., Birren, BW, Han, H., Wang, HG, Godbout, M., Wallenburg, JC, L'Archeveque, P., Bellemare, G., Todani, K., Fujita, T., Tanaka, S., Holden, AL, Collins, FS, Brooks, LD, McEwen, JE, Guyer, MS, Jordan, E., Peterson, JL, Spiegel, J., Sung, LM, Zacharia, LF, Kennedy, K., Dunn, MG, Seabrook, R., Shillito, M., Skene, B., Stewart, JG, Valle, DL, Jorde, LB, Chakravarti, A., Cho, MK, and Duster, T.

Published
2003

35. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019

Author

Schreiber Falk, Sommer Björn, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Keating Sarah M., König Matthias, Myers Chris, Nickerson David, and Waltemath Dagmar

Subjects
Biotechnology, TP248.13-248.65
Abstract

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.

Published
2019
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37. CellML 2.0

Author

Clerx Michael, Cooling Michael T., Cooper Jonathan, Garny Alan, Moyle Keri, Nickerson David P., Nielsen Poul M. F., and Sorby Hugh

Subjects
computational physiology, modularity, physiome project, reproducibility, reusability, Biotechnology, TP248.13-248.65
Abstract

We present here CellML 2.0, an XML-based language for describing and exchanging mathematical models of physiological systems. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of reusable components, each with variables and equations giving relationships between those variables. Models may import other models to create systems of increasing complexity. CellML 2.0 is defined by the normative specification presented here, prescribing the CellML syntax and the rules by which it should be used. The normative specification is intended primarily for the developers of software tools which directly consume CellML syntax. Users of CellML models may prefer to browse the informative rendering of the specification (https://cellml.org/specifications/cellml_2.0/) which extends the normative specification with explanations of the rules combined with examples of their usage.

Published
2020
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38. Open modeling and exchange (OMEX) metadata specification version 1.0

Author

Neal Maxwell L., Gennari John H., Waltemath Dagmar, Nickerson David P., and König Matthias

Subjects
data integration, metadata, semantics, simulation, Biotechnology, TP248.13-248.65
Abstract

A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. Distributing modeling projects within these archives is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model and data representation formats within an archive. The specification primarily includes technical guidelines for encoding archive metadata, so that software tools can more easily utilize and exchange it, thereby spurring broad advancements in model reuse, discovery, and semantic analyses.

Published
2020
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39. Toll-like receptor 1 polymorphisms affect innate immune responses and outcomes in sepsis.

Author

Wurfel MM, Gordon AC, Holden TD, Radella F, Strout J, Kajikawa O, Ruzinski JT, Rona G, Black RA, Stratton S, Jarvik GP, Hajjar AM, Nickerson DA, Rieder M, Sevransky J, Maloney JP, Moss M, Martin G, Shanholtz C, and Garcia JG

Abstract

Rationale: Polymorphisms affecting Toll-like receptor (TLR)-mediated responses could predispose to excessive inflammation during an infection and contribute to an increased risk for poor outcomes in patients with sepsis.Objectives: To identify hypermorphic polymorphisms causing elevated TLR-mediated innate immune cytokine and chemokine responses and to test whether these polymorphisms are associated with increased susceptibility to death, organ dysfunction, and infections in patients with sepsis.Methods: We screened single-nucleotide polymorphisms (SNPs) in 43 TLR-related genes to identify variants affecting TLR-mediated inflammatory responses in blood from healthy volunteers ex vivo. The SNP associated most strongly with hypermorphic responses was tested for associations with death, organ dysfunction, and type of infection in two studies: a nested case-control study in a cohort of intensive care unit patients with sepsis, and a case-control study using patients with sepsis, patients with sepsis-related acute lung injury, and healthy control subjects.Measurements and Main Results: The SNP demonstrating the most hypermorphic effect was the G allele of TLR1(-7202A/G) (rs5743551), which associated with elevated TLR1-mediated cytokine production (P < 2 x 10(-20)). TLR1(-7202G) marked a coding SNP that causes higher TLR1-induced NF-kappaB activation and higher cell surface TLR1 expression. In the cohort of patients with sepsis TLR1(-7202G) predicted worse organ dysfunction and death (odds ratio, 1.82; 95% confidence interval, 1.07-3.09). In the case-control study TLR1(-7202G) was associated with sepsis-related acute lung injury (odds ratio, 3.40; 95% confidence interval, 1.59-7.27). TLR1(-7202G) also associated with a higher prevalence of gram-positive cultures in both clinical studies.Conclusions: Hypermorphic genetic variation in TLR1 is associated with increased susceptibility to organ dysfunction, death, and gram-positive infection in sepsis. [ABSTRACT FROM AUTHOR]

Published
2008
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42. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017

Author

Schreiber Falk, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Keating Sarah M., Novère Nicolas Le, Myers Chris, Nickerson David, Sommer Björn, and Waltemath Dagmar

Subjects
combine, systems biology, synthetic biology, standards, Biotechnology, TP248.13-248.65
Abstract

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.

Published
2018
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43. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Author

Schreiber Falk, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Novère Nicolas Le, Myers Chris, Nickerson David, Sommer Björn, and Waltemath Dagmar

Subjects
Biotechnology, TP248.13-248.65
Abstract

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.

Published
2016
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44. Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)

Author

Bergmann Frank T., Cooper Jonathan, König Matthias, Moraru Ion, Nickerson David, Le Novère Nicolas, Olivier Brett G., Sahle Sven, Smith Lucian, and Waltemath Dagmar

Subjects
simulation experiment, computational modeling, reproducibility, Biotechnology, TP248.13-248.65
Abstract

The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.

Published
2018
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45. Maternal Pulmonary Embolism by Amniotic Fluid

Author

Sparling Hj, Nickerson Da, Blackburn N, and Mallory Gk

Subjects
Pregnancy, medicine.medical_specialty, Amniotic fluid, Lung, business.industry, Embolism, General Medicine, Amniotic Fluid, medicine.disease, Surgery, Pulmonary embolism, medicine.anatomical_structure, Meconium, Anesthesia, medicine, Humans, Family, Pulmonary Embolism, Complication, business
Abstract

MATERNAL pulmonary embolism by amniotic fluid was first described by Steiner and Lushbaugh1 in 1941. The authors based this study on 8 cases in human beings and successfully produced the picture by intravenous inoculation of amniotic fluid and meconium into dogs and rabbits. In the following year they2 presented 2 additional cases. Since then other studies3 4 5 6 have brought the total number of cases reported up to the time of this writing to 17. It is our purpose to present 3 additional cases of this unusual complication of pregnancy to emphasize the characteristic symptomatology and pathology. To the best of our knowledge, . . .

Published
1950

46. The CellML 1.1 Specification

Author

Cuellar Autumn, Hedley Warren, Nelson Melanie, Lloyd Catherine, Halstead Matt, Bullivant David, Nickerson David, Hunter Peter, and Nielsen Poul

Subjects
Biotechnology, TP248.13-248.65
Abstract

This document specifies CellML 1.1, an XML-based language for describing and exchanging models of cellular and subcellular processes. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of re- usable components, each with variables and equations manipulating those variables. Models may import other models to create systems of increasing complexity. Metadata may be embedded in CellML documents using RDF.

Published
2015
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47. Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 2

Author

Bergmann Frank T., Cooper Jonathan, Le Novère Nicolas, Nickerson David, and Waltemath Dagmar

Subjects
Biotechnology, TP248.13-248.65
Abstract

The number, size and complexity of computational models of biological systems are growing at an ever increasing pace. It is imperative to build on existing studies by reusing and adapting existing models and parts thereof. The description of the structure of models is not sufficient to enable the reproduction of simulation results. One also needs to describe the procedures the models are subjected to, as recommended by the Minimum Information About a Simulation Experiment (MIASE) guidelines.

Published
2015
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48. Specifications of Standards in Systems and Synthetic Biology

Author

Schreiber Falk, Bader Gary D., Golebiewski Martin, Hucka Michael, Kormeier Benjamin, Le Novère Nicolas, Myers Chris, Nickerson David, Sommer Björn, Waltemath Dagmar, and Weise Stephan

Subjects
Biotechnology, TP248.13-248.65
Abstract

Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation).

Published
2015
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49. Lidocaine in Liposuction Wetting Solution has a Minimal Effect on Analgesic Requirements when used in Conjunction with General Anesthesia

Author

Nickerson, Da, Clugston, Pa, and Warren, Rj

Abstract

Lidocaine has become a common additive to liposuction wetting solutions, even for liposuction conducted under general anesthesia. Recent focus on the morbidity associated with liposuction has prompted a review of this practice. The analgesic requirements of patients undergoing liposuction of multiple sites under general anesthetic, with and without a lidocaine-containing wetting solution, were reviewed. The results suggest that while lidocaine does provide a reduction in analgesic requirements, this reduction is small, is readily compensated for by oral analgesics, and may not balance the potential risks associated with lidocaine.

Published
2002
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50. Revision history aware repositories of computational models of biological systems

Author

Nickerson David P, Hunter Peter J, Halstead Matt DB, Garny Alan, Cowan Dougal, Lawson James, Cooling Mike T, Britten Randall, Yu Tommy, Miller Andrew K, Nunns Geo, Wimalaratne Sarala M, and F Nielsen Poul M

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. Results We have extended the Physiome Model Repository software to be fully revision history aware, by building it on top of Mercurial, an existing DVCS. We have demonstrated the utility of this approach, when used in conjunction with the model composition facilities in CellML, to build and understand more complex models. We have also demonstrated the ability of the repository software to present version history to casual users over the web, and to highlight specific versions which are likely to be useful to users. Conclusions Providing facilities for maintaining and using revision history information is an important part of building a useful repository of computational models, as this information is useful both for understanding the source of and justification for parts of a model, and to facilitate automated processes such as merges. The availability of fully revision history aware repositories, and associated tools, will therefore be of significant benefit to the community.

Published
2011
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