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Current Progress in Bioinformatics 2016.

Briefings in bioinformatics

Altman R.
PMID: 26628559
Brief Bioinform. 2016 Jan;17(1):1. doi: 10.1093/bib/bbv105. Epub 2015 Nov 30.

No abstract available.

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Altman R. Current Progress in Bioinformatics 2016. Brief Bioinform. 2015;17(1):1doi: 10.1093/bib/bbv105.
Altman, R. (2016). Current Progress in Bioinformatics 2016. Briefings in bioinformatics, 17(1), 1. https://doi.org/10.1093/bib/bbv105
Altman, Russ. "Current Progress in Bioinformatics 2016." Briefings in bioinformatics vol. 17,1 (2016): 1. doi: https://doi.org/10.1093/bib/bbv105
Altman R. Current Progress in Bioinformatics 2016. Brief Bioinform. 2016 Jan;17(1):1. doi: 10.1093/bib/bbv105. Epub 2015 Nov 30. PMID: 26628559.
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Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption.

International journal of epidemiology

Bowden J.
PMID: 29025021
Int J Epidemiol. 2017 Dec 01;46(6):2097-2099. doi: 10.1093/ije/dyx192.

No abstract available.

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Bowden J. Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. Int J Epidemiol. 2017;46(6):2097-2099doi: 10.1093/ije/dyx192.
Bowden, J. (2017). Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. International journal of epidemiology, 46(6), 2097-2099. https://doi.org/10.1093/ije/dyx192
Bowden, Jack. "Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption." International journal of epidemiology vol. 46,6 (2017): 2097-2099. doi: https://doi.org/10.1093/ije/dyx192
Bowden J. Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption. Int J Epidemiol. 2017 Dec 01;46(6):2097-2099. doi: 10.1093/ije/dyx192. PMID: 29025021; PMCID: PMC5837449.
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Human genetics: pleiotropic mutations.

Nature reviews. Genetics

Stower H.
PMID: 22143242
Nat Rev Genet. 2011 Dec 06;13(1):5. doi: 10.1038/nrg3132.

No abstract available.

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Stower H. Human genetics: pleiotropic mutations. Nat Rev Genet. 2011;13(1):5doi: 10.1038/nrg3132.
Stower, H. (2011). Human genetics: pleiotropic mutations. Nature reviews. Genetics, 13(1), 5. https://doi.org/10.1038/nrg3132
Stower, Hannah. "Human genetics: pleiotropic mutations." Nature reviews. Genetics vol. 13,1 (2011): 5. doi: https://doi.org/10.1038/nrg3132
Stower H. Human genetics: pleiotropic mutations. Nat Rev Genet. 2011 Dec 06;13(1):5. doi: 10.1038/nrg3132. PMID: 22143242.
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Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits.

Psychosomatic medicine

Haljas K, Amare AT, Alizadeh BZ, Hsu YH, Mosley T, Newman A, Murabito J, Tiemeier H, Tanaka T, van Duijn C, Ding J, Llewellyn DJ, Bennett DA, Terracciano A, Launer L, Ladwig KH, Cornelis MC, Teumer A, Grabe H, Kardia SLR, Ware EB, Smith JA, Snieder H, Eriksson JG, Groop L, Räikkönen K, Lahti J.
PMID: 29280852
Psychosom Med. 2018 Apr;80(3):242-251. doi: 10.1097/PSY.0000000000000555.

OBJECTIVE: Shared genetic background may explain phenotypic associations between depression and Type 2 diabetes (T2D). We aimed to study, on a genome-wide level, if genetic correlation and pleiotropic loci exist between depressive symptoms and T2D or glycemic traits.METHODS: We...

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Haljas K, Amare AT, Alizadeh BZ, et al. Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits. Psychosom Med. 2018;80(3):242-251doi: 10.1097/PSY.0000000000000555.
Haljas, K., Amare, A. T., Alizadeh, B. Z., Hsu, Y. H., Mosley, T., Newman, A., Murabito, J., Tiemeier, H., Tanaka, T., van Duijn, C., Ding, J., Llewellyn, D. J., Bennett, D. A., Terracciano, A., Launer, L., Ladwig, K. H., Cornelis, M. C., Teumer, A., Grabe, H., Kardia, S. L. R., Ware, E. B., Smith, J. A., Snieder, H., Eriksson, J. G., Groop, L., Räikkönen, K., & Lahti, J. (2018). Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits. Psychosomatic medicine, 80(3), 242-251. https://doi.org/10.1097/PSY.0000000000000555
Haljas, Kadri, et al. "Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits." Psychosomatic medicine vol. 80,3 (2018): 242-251. doi: https://doi.org/10.1097/PSY.0000000000000555
Haljas K, Amare AT, Alizadeh BZ, Hsu YH, Mosley T, Newman A, Murabito J, Tiemeier H, Tanaka T, van Duijn C, Ding J, Llewellyn DJ, Bennett DA, Terracciano A, Launer L, Ladwig KH, Cornelis MC, Teumer A, Grabe H, Kardia SLR, Ware EB, Smith JA, Snieder H, Eriksson JG, Groop L, Räikkönen K, Lahti J. Bivariate Genome-Wide Association Study of Depressive Symptoms With Type 2 Diabetes and Quantitative Glycemic Traits. Psychosom Med. 2018 Apr;80(3):242-251. doi: 10.1097/PSY.0000000000000555. PMID: 29280852; PMCID: PMC6051528.
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10 Years of GWAS Discovery: Biology, Function, and Translation.

American journal of human genetics

Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J.
PMID: 28686856
Am J Hum Genet. 2017 Jul 06;101(1):5-22. doi: 10.1016/j.ajhg.2017.06.005.

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation...

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Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5-22doi: 10.1016/j.ajhg.2017.06.005.
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American journal of human genetics, 101(1), 5-22. https://doi.org/10.1016/j.ajhg.2017.06.005
Visscher, Peter M, et al. "10 Years of GWAS Discovery: Biology, Function, and Translation." American journal of human genetics vol. 101,1 (2017): 5-22. doi: https://doi.org/10.1016/j.ajhg.2017.06.005
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017 Jul 06;101(1):5-22. doi: 10.1016/j.ajhg.2017.06.005. PMID: 28686856; PMCID: PMC5501872.
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The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies.

Perspectives on psychological science : a journal of the Association for Psychological Science

Avinun R.
PMID: 31558103
Perspect Psychol Sci. 2020 Jan;15(1):81-89. doi: 10.1177/1745691619867107. Epub 2019 Sep 27.

Genome-wide association studies (GWASs) have shown that pleiotropy is widespread (i.e., the same genetic variants affect multiple traits) and that complex traits are polygenic (i.e., affected by many genetic variants with very small effect sizes). However, despite the growing...

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Avinun R. The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies. Perspect Psychol Sci. 2019;15(1):81-89doi: 10.1177/1745691619867107.
Avinun, R. (2020). The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies. Perspectives on psychological science : a journal of the Association for Psychological Science, 15(1), 81-89. https://doi.org/10.1177/1745691619867107
Avinun, Reut. "The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies." Perspectives on psychological science : a journal of the Association for Psychological Science vol. 15,1 (2020): 81-89. doi: https://doi.org/10.1177/1745691619867107
Avinun R. The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies. Perspect Psychol Sci. 2020 Jan;15(1):81-89. doi: 10.1177/1745691619867107. Epub 2019 Sep 27. PMID: 31558103; PMCID: PMC6980468.
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Antagonistic pleiotropy can maintain fitness variation in annual plants.

Journal of evolutionary biology

Brown KE, Kelly JK.
PMID: 29030895
J Evol Biol. 2018 Jan;31(1):46-56. doi: 10.1111/jeb.13192. Epub 2017 Nov 08.

Antagonistic pleiotropy (AP) is a genetic trade-off between different fitness components. In annual plants, a trade-off between days to flower (DTF) and reproductive capacity often determines how many individuals survive to flower in a short growing season, and also...

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Brown KE, Kelly JK. Antagonistic pleiotropy can maintain fitness variation in annual plants. J Evol Biol. 2017;31(1):46-56doi: 10.1111/jeb.13192.
Brown, K. E., & Kelly, J. K. (2018). Antagonistic pleiotropy can maintain fitness variation in annual plants. Journal of evolutionary biology, 31(1), 46-56. https://doi.org/10.1111/jeb.13192
Brown, K E, and Kelly, J K. "Antagonistic pleiotropy can maintain fitness variation in annual plants." Journal of evolutionary biology vol. 31,1 (2018): 46-56. doi: https://doi.org/10.1111/jeb.13192
Brown KE, Kelly JK. Antagonistic pleiotropy can maintain fitness variation in annual plants. J Evol Biol. 2018 Jan;31(1):46-56. doi: 10.1111/jeb.13192. Epub 2017 Nov 08. PMID: 29030895.
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Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression.

International journal of epidemiology

Bowden J, Spiller W, Del Greco M F, Sheehan N, Thompson J, Minelli C, Davey Smith G.
PMID: 29961852
Int J Epidemiol. 2018 Aug 01;47(4):1264-1278. doi: 10.1093/ije/dyy101.

BACKGROUND: data furnishing a two-sample Mendelian randomization (MR) study are often visualized with the aid of a scatter plot, in which single-nucleotide polymorphism (SNP)-outcome associations are plotted against the SNP-exposure associations to provide an immediate picture of the causal-effect...

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Bowden J, Spiller W, Del Greco M F, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47(4):1264-1278doi: 10.1093/ije/dyy101.
Bowden, J., Spiller, W., Del Greco M, F., Sheehan, N., Thompson, J., Minelli, C., & Davey Smith, G. (2018). Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International journal of epidemiology, 47(4), 1264-1278. https://doi.org/10.1093/ije/dyy101
Bowden, Jack, et al. "Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression." International journal of epidemiology vol. 47,4 (2018): 1264-1278. doi: https://doi.org/10.1093/ije/dyy101
Bowden J, Spiller W, Del Greco M F, Sheehan N, Thompson J, Minelli C, Davey Smith G. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018 Aug 01;47(4):1264-1278. doi: 10.1093/ije/dyy101. PMID: 29961852; PMCID: PMC6124632.
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Inferring pleiotropy by network analysis: linked diseases in the human PPI network.

BMC systems biology

Nguyen TP, Liu WC, Jordán F.
PMID: 22034985
BMC Syst Biol. 2011 Oct 31;5:179. doi: 10.1186/1752-0509-5-179.

BACKGROUND: Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with...

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Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179doi: 10.1186/1752-0509-5-179.
Nguyen, T. P., Liu, W. C., & Jordán, F. (2011). Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC systems biology, 5179. https://doi.org/10.1186/1752-0509-5-179
Nguyen, Thanh-Phuong, et al. "Inferring pleiotropy by network analysis: linked diseases in the human PPI network." BMC systems biology vol. 5 (2011): 179. doi: https://doi.org/10.1186/1752-0509-5-179
Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011 Oct 31;5:179. doi: 10.1186/1752-0509-5-179. PMID: 22034985; PMCID: PMC3231966.
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Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies.

European heart journal

Labrecque JA, Kaufman JS, Huang JY.
PMID: 31960031
Eur Heart J. 2020 Feb 14;41(7):892-893. doi: 10.1093/eurheartj/ehz943.

No abstract available.

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Labrecque JA, Kaufman JS, Huang JY. Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies. Eur Heart J. 2020;41(7):892-893doi: 10.1093/eurheartj/ehz943.
Labrecque, J. A., Kaufman, J. S., & Huang, J. Y. (2020). Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies. European heart journal, 41(7), 892-893. https://doi.org/10.1093/eurheartj/ehz943
Labrecque, Jeremy A, et al. "Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies." European heart journal vol. 41,7 (2020): 892-893. doi: https://doi.org/10.1093/eurheartj/ehz943
Labrecque JA, Kaufman JS, Huang JY. Genetic instruments with too many strings: acknowledging pleiotropy and population structure in Mendelian randomization studies. Eur Heart J. 2020 Feb 14;41(7):892-893. doi: 10.1093/eurheartj/ehz943. PMID: 31960031.
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Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA.

International journal of cardiology

Jia X, Yang Y, Chen Y, Xia Z, Zhang W, Feng Y, Li Y, Tan J, Xu C, Zhang Q, Deng H, Shi X.
PMID: 30459114
Int J Cardiol. 2019 May 15;283:144-150. doi: 10.1016/j.ijcard.2018.10.102. Epub 2018 Oct 31.

BACKGROUND: Although genome-wide association studies (GWAS) have been extensively applied in identifying SNP associated with metabolic diseases, the SNPs identified by this prevailing univariate approach only explain a small percentage of the genetic variance of traits. The extensive previous...

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Jia X, Yang Y, Chen Y, et al. Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA. Int J Cardiol. 2018;283:144-150doi: 10.1016/j.ijcard.2018.10.102.
Jia, X., Yang, Y., Chen, Y., Xia, Z., Zhang, W., Feng, Y., Li, Y., Tan, J., Xu, C., Zhang, Q., Deng, H., & Shi, X. (2019). Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA. International journal of cardiology, 283144-150. https://doi.org/10.1016/j.ijcard.2018.10.102
Jia, XiaoCan, et al. "Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA." International journal of cardiology vol. 283 (2019): 144-150. doi: https://doi.org/10.1016/j.ijcard.2018.10.102
Jia X, Yang Y, Chen Y, Xia Z, Zhang W, Feng Y, Li Y, Tan J, Xu C, Zhang Q, Deng H, Shi X. Multivariate analysis of genome-wide data to identify potential pleiotropic genes for type 2 diabetes, obesity and coronary artery disease using MetaCCA. Int J Cardiol. 2019 May 15;283:144-150. doi: 10.1016/j.ijcard.2018.10.102. Epub 2018 Oct 31. PMID: 30459114.
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Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders.

Cell

[No authors listed]
PMID: 31835028
Cell. 2019 Dec 12;179(7):1469-1482.e11. doi: 10.1016/j.cell.2019.11.020.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia...

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Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders. Cell. 2019;179(7):1469-1482.e11doi: 10.1016/j.cell.2019.11.020.
(2019). Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders. Cell, 179(7), 1469-1482.e11. https://doi.org/10.1016/j.cell.2019.11.020
"Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders." Cell vol. 179,7 (2019): 1469-1482.e11. doi: https://doi.org/10.1016/j.cell.2019.11.020
Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders. Cell. 2019 Dec 12;179(7):1469-1482.e11. doi: 10.1016/j.cell.2019.11.020. PMID: 31835028; PMCID: PMC7077032.
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Showing 1 to 12 of 14 entries