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Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.

Statistics and its interface

Zhao J, Zhang H.
PMID: 26543519
Stat Interface. 2016;9(1):47-56. doi: 10.4310/SII.2016.v9.n1.a5.

The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been...

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Zhao J, Zhang H. Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing. Stat Interface. 2016;9(1):47-56doi: 10.4310/SII.2016.v9.n1.a5.
Zhao, J., & Zhang, H. (2016). Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing. Statistics and its interface, 9(1), 47-56. https://doi.org/10.4310/SII.2016.v9.n1.a5
Zhao, Jiwei, and Zhang, Heping. "Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing." Statistics and its interface vol. 9,1 (2016): 47-56. doi: https://doi.org/10.4310/SII.2016.v9.n1.a5
Zhao J, Zhang H. Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing. Stat Interface. 2016;9(1):47-56. doi: 10.4310/SII.2016.v9.n1.a5. PMID: 26543519; PMCID: PMC4629876.
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Gene-based and pathway-based genome-wide association study of alcohol dependence.

Shanghai archives of psychiatry

Zuo L, Zhang CK, Sayward FG, Cheung KH, Wang K, Krystal JH, Zhao H, Luo X.
PMID: 26120261
Shanghai Arch Psychiatry. 2015 Apr 25;27(2):111-8. doi: 10.11919/j.issn.1002-0829.215031.

BACKGROUND: The organization of risk genes within signaling pathways may provide clues about the converging neurobiological effects of risk genes for alcohol dependence.AIM: Identify risk genes and risk gene pathways for alcohol dependence.METHODS: We conducted a pathway-based genome-wide association...

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Zuo L, Zhang CK, Sayward FG, et al. Gene-based and pathway-based genome-wide association study of alcohol dependence. Shanghai Arch Psychiatry. 2015;27(2):111-8doi: 10.11919/j.issn.1002-0829.215031.
Zuo, L., Zhang, C. K., Sayward, F. G., Cheung, K. H., Wang, K., Krystal, J. H., Zhao, H., & Luo, X. (2015). Gene-based and pathway-based genome-wide association study of alcohol dependence. Shanghai archives of psychiatry, 27(2), 111-8. https://doi.org/10.11919/j.issn.1002-0829.215031
Zuo, Lingjun, et al. "Gene-based and pathway-based genome-wide association study of alcohol dependence." Shanghai archives of psychiatry vol. 27,2 (2015): 111-8. doi: https://doi.org/10.11919/j.issn.1002-0829.215031
Zuo L, Zhang CK, Sayward FG, Cheung KH, Wang K, Krystal JH, Zhao H, Luo X. Gene-based and pathway-based genome-wide association study of alcohol dependence. Shanghai Arch Psychiatry. 2015 Apr 25;27(2):111-8. doi: 10.11919/j.issn.1002-0829.215031. PMID: 26120261; PMCID: PMC4466852.
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Evaluating methods for modeling epistasis networks with application to head and neck cancer.

Cancer informatics

Talluri R, Shete S.
PMID: 25733798
Cancer Inform. 2015 Feb 10;14:17-23. doi: 10.4137/CIN.S17289. eCollection 2015.

Epistasis helps to explain how multiple single-nucleotide polymorphisms (SNPs) interact to cause disease. A variety of tools have been developed to detect epistasis. In this article, we explore the strengths and weaknesses of an information theory approach for detecting...

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Talluri R, Shete S. Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Inform. 2015;14:17-23doi: 10.4137/CIN.S17289.
Talluri, R., & Shete, S. (2015). Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer informatics, 1417-23. https://doi.org/10.4137/CIN.S17289
Talluri, Rajesh, and Shete, Sanjay. "Evaluating methods for modeling epistasis networks with application to head and neck cancer." Cancer informatics vol. 14 (2015): 17-23. doi: https://doi.org/10.4137/CIN.S17289
Talluri R, Shete S. Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Inform. 2015 Feb 10;14:17-23. doi: 10.4137/CIN.S17289. eCollection 2015. PMID: 25733798; PMCID: PMC4332043.
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Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial.

Frontiers in public health

Keene KL, Chen WM, Chen F, Williams SR, Elkhatib SD, Hsu FC, Mychaleckyj JC, Doheny KF, Pugh EW, Ling H, Laurie CC, Gogarten SM, Madden EB, Worrall BB, Sale MM.
PMID: 25147783
Front Public Health. 2014 Aug 06;2:112. doi: 10.3389/fpubh.2014.00112. eCollection 2014.

BACKGROUND: B vitamins play an important role in homocysteine metabolism, with vitamin deficiencies resulting in increased levels of homocysteine and increased risk for stroke. We performed a genome-wide association study (GWAS) in 2,100 stroke patients from the Vitamin Intervention...

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Keene KL, Chen WM, Chen F, et al. Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial. Front Public Health. 2014;2:112doi: 10.3389/fpubh.2014.00112.
Keene, K. L., Chen, W. M., Chen, F., Williams, S. R., Elkhatib, S. D., Hsu, F. C., Mychaleckyj, J. C., Doheny, K. F., Pugh, E. W., Ling, H., Laurie, C. C., Gogarten, S. M., Madden, E. B., Worrall, B. B., & Sale, M. M. (2014). Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial. Frontiers in public health, 2112. https://doi.org/10.3389/fpubh.2014.00112
Keene, Keith L, et al. "Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial." Frontiers in public health vol. 2 (2014): 112. doi: https://doi.org/10.3389/fpubh.2014.00112
Keene KL, Chen WM, Chen F, Williams SR, Elkhatib SD, Hsu FC, Mychaleckyj JC, Doheny KF, Pugh EW, Ling H, Laurie CC, Gogarten SM, Madden EB, Worrall BB, Sale MM. Genetic Associations with Plasma B12, B6, and Folate Levels in an Ischemic Stroke Population from the Vitamin Intervention for Stroke Prevention (VISP) Trial. Front Public Health. 2014 Aug 06;2:112. doi: 10.3389/fpubh.2014.00112. eCollection 2014. PMID: 25147783; PMCID: PMC4123605.
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Association of maternal CNVs in GSTT1/GSTT2 with smoking, preterm delivery, and low birth weight.

Frontiers in genetics

Zheng X, Feingold E, Ryckman KK, Shaffer JR, Boyd HA, Feenstra B, Melbye M, Marazita ML, Murray JC, Cuenco KT.
PMID: 24194744
Front Genet. 2013 Oct 28;4:196. doi: 10.3389/fgene.2013.00196. eCollection 2013.

Preterm delivery (PTD) is an adverse birth outcome associated with increased infant mortality and negative lifelong health consequences. PTD may be the result of interactions between genetics and maternal/fetal environmental factors including smoking exposure (SMK). A common deletion in...

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Zheng X, Feingold E, Ryckman KK, et al. Association of maternal CNVs in GSTT1/GSTT2 with smoking, preterm delivery, and low birth weight. Front Genet. 2013;4:196doi: 10.3389/fgene.2013.00196.
Zheng, X., Feingold, E., Ryckman, K. K., Shaffer, J. R., Boyd, H. A., Feenstra, B., Melbye, M., Marazita, M. L., Murray, J. C., & Cuenco, K. T. (2013). Association of maternal CNVs in GSTT1/GSTT2 with smoking, preterm delivery, and low birth weight. Frontiers in genetics, 4196. https://doi.org/10.3389/fgene.2013.00196
Zheng, Xiaojing, et al. "Association of maternal CNVs in GSTT1/GSTT2 with smoking, preterm delivery, and low birth weight." Frontiers in genetics vol. 4 (2013): 196. doi: https://doi.org/10.3389/fgene.2013.00196
Zheng X, Feingold E, Ryckman KK, Shaffer JR, Boyd HA, Feenstra B, Melbye M, Marazita ML, Murray JC, Cuenco KT. Association of maternal CNVs in GSTT1/GSTT2 with smoking, preterm delivery, and low birth weight. Front Genet. 2013 Oct 28;4:196. doi: 10.3389/fgene.2013.00196. eCollection 2013. PMID: 24194744; PMCID: PMC3809558.
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Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium.

Behavior genetics

van den Berg SM, de Moor MH, Verweij KJ, Krueger RF, Luciano M, Arias Vasquez A, Matteson LK, Derringer J, Esko T, Amin N, Gordon SD, Hansell NK, Hart AB, Seppälä I, Huffman JE, Konte B, Lahti J, Lee M, Miller M, Nutile T, Tanaka T, Teumer A, Viktorin A, Wedenoja J, Abdellaoui A, Abecasis GR, Adkins DE, Agrawal A, Allik J, Appel K, Bigdeli TB, Busonero F, Campbell H, Costa PT, Smith GD, Davies G, de Wit H, Ding J, Engelhardt BE, Eriksson JG, Fedko IO, Ferrucci L, Franke B, Giegling I, Grucza R, Hartmann AM, Heath AC, Heinonen K, Henders AK, Homuth G, Hottenga JJ, Iacono WG, Janzing J, Jokela M, Karlsson R, Kemp JP, Kirkpatrick MG, Latvala A, Lehtimäki T, Liewald DC, Madden PA, Magri C, Magnusson PK, Marten J, Maschio A, Mbarek H, Medland SE, Mihailov E, Milaneschi Y, Montgomery GW, Nauck M, Nivard MG, Ouwens KG, Palotie A, Pettersson E, Polasek O, Qian Y, Pulkki-Råback L, Raitakari OT, Realo A, Rose RJ, Ruggiero D, Schmidt CO, Slutske WS, Sorice R, Starr JM, St Pourcain B, Sutin AR, Timpson NJ, Trochet H, Vermeulen S, Vuoksimaa E, Widen E, Wouda J, Wright MJ, Zgaga L, Porteous D, Minelli A, Palmer AA, Rujescu D, Ciullo M, Hayward C, Rudan I, Metspalu A, Kaprio J, Deary IJ, Räikkönen K, Wilson JF, Keltikangas-Järvinen L, Bierut LJ, Hettema JM, Grabe HJ, Penninx BW, van Duijn CM, Evans DM, Schlessinger D, Pedersen NL, Terracciano A, McGue M, Martin NG, Boomsma DI.
PMID: 26362575
Behav Genet. 2016 Mar;46(2):170-82. doi: 10.1007/s10519-015-9735-5. Epub 2015 Sep 11.

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively...

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van den Berg SM, de Moor MH, Verweij KJ, et al. Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav Genet. 2015;46(2):170-82doi: 10.1007/s10519-015-9735-5.
van den Berg, S. M., de Moor, M. H., Verweij, K. J., Krueger, R. F., Luciano, M., Arias Vasquez, A., Matteson, L. K., Derringer, J., Esko, T., Amin, N., Gordon, S. D., Hansell, N. K., Hart, A. B., Seppälä, I., Huffman, J. E., Konte, B., Lahti, J., Lee, M., Miller, M., Nutile, T., Tanaka, T., Teumer, A., Viktorin, A., Wedenoja, J., Abdellaoui, A., Abecasis, G. R., Adkins, D. E., Agrawal, A., Allik, J., Appel, K., Bigdeli, T. B., Busonero, F., Campbell, H., Costa, P. T., Smith, G. D., Davies, G., de Wit, H., Ding, J., Engelhardt, B. E., Eriksson, J. G., Fedko, I. O., Ferrucci, L., Franke, B., Giegling, I., Grucza, R., Hartmann, A. M., Heath, A. C., Heinonen, K., Henders, A. K., Homuth, G., Hottenga, J. J., Iacono, W. G., Janzing, J., Jokela, M., Karlsson, R., Kemp, J. P., Kirkpatrick, M. G., Latvala, A., Lehtimäki, T., Liewald, D. C., Madden, P. A., Magri, C., Magnusson, P. K., Marten, J., Maschio, A., Mbarek, H., Medland, S. E., Mihailov, E., Milaneschi, Y., Montgomery, G. W., Nauck, M., Nivard, M. G., Ouwens, K. G., Palotie, A., Pettersson, E., Polasek, O., Qian, Y., Pulkki-Råback, L., Raitakari, O. T., Realo, A., Rose, R. J., Ruggiero, D., Schmidt, C. O., Slutske, W. S., Sorice, R., Starr, J. M., St Pourcain, B., Sutin, A. R., Timpson, N. J., Trochet, H., Vermeulen, S., Vuoksimaa, E., Widen, E., Wouda, J., Wright, M. J., Zgaga, L., Porteous, D., Minelli, A., Palmer, A. A., Rujescu, D., Ciullo, M., Hayward, C., Rudan, I., Metspalu, A., Kaprio, J., Deary, I. J., Räikkönen, K., Wilson, J. F., Keltikangas-Järvinen, L., Bierut, L. J., Hettema, J. M., Grabe, H. J., Penninx, B. W., van Duijn, C. M., Evans, D. M., Schlessinger, D., Pedersen, N. L., Terracciano, A., McGue, M., Martin, N. G., & Boomsma, D. I. (2016). Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behavior genetics, 46(2), 170-82. https://doi.org/10.1007/s10519-015-9735-5
van den Berg, Stéphanie M, et al. "Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium." Behavior genetics vol. 46,2 (2016): 170-82. doi: https://doi.org/10.1007/s10519-015-9735-5
van den Berg SM, de Moor MH, Verweij KJ, Krueger RF, Luciano M, Arias Vasquez A, Matteson LK, Derringer J, Esko T, Amin N, Gordon SD, Hansell NK, Hart AB, Seppälä I, Huffman JE, Konte B, Lahti J, Lee M, Miller M, Nutile T, Tanaka T, Teumer A, Viktorin A, Wedenoja J, Abdellaoui A, Abecasis GR, Adkins DE, Agrawal A, Allik J, Appel K, Bigdeli TB, Busonero F, Campbell H, Costa PT, Smith GD, Davies G, de Wit H, Ding J, Engelhardt BE, Eriksson JG, Fedko IO, Ferrucci L, Franke B, Giegling I, Grucza R, Hartmann AM, Heath AC, Heinonen K, Henders AK, Homuth G, Hottenga JJ, Iacono WG, Janzing J, Jokela M, Karlsson R, Kemp JP, Kirkpatrick MG, Latvala A, Lehtimäki T, Liewald DC, Madden PA, Magri C, Magnusson PK, Marten J, Maschio A, Mbarek H, Medland SE, Mihailov E, Milaneschi Y, Montgomery GW, Nauck M, Nivard MG, Ouwens KG, Palotie A, Pettersson E, Polasek O, Qian Y, Pulkki-Råback L, Raitakari OT, Realo A, Rose RJ, Ruggiero D, Schmidt CO, Slutske WS, Sorice R, Starr JM, St Pourcain B, Sutin AR, Timpson NJ, Trochet H, Vermeulen S, Vuoksimaa E, Widen E, Wouda J, Wright MJ, Zgaga L, Porteous D, Minelli A, Palmer AA, Rujescu D, Ciullo M, Hayward C, Rudan I, Metspalu A, Kaprio J, Deary IJ, Räikkönen K, Wilson JF, Keltikangas-Järvinen L, Bierut LJ, Hettema JM, Grabe HJ, Penninx BW, van Duijn CM, Evans DM, Schlessinger D, Pedersen NL, Terracciano A, McGue M, Martin NG, Boomsma DI. Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav Genet. 2016 Mar;46(2):170-82. doi: 10.1007/s10519-015-9735-5. Epub 2015 Sep 11. PMID: 26362575; PMCID: PMC4751159.
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Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

Cite
Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
Copy Download .nbib Format: NLM
Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

Cite
Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
Copy Download .nbib Format: NLM
Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BioData mining

Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD.
PMID: 28770004
BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol...

Cite
Holzinger ER, Verma SS, Moore CB, et al. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017;10:25doi: 10.1186/s13040-017-0145-5.
Holzinger, E. R., Verma, S. S., Moore, C. B., Hall, M., De, R., Gilbert-Diamond, D., Lanktree, M. B., Pankratz, N., Amuzu, A., Burt, A., Dale, C., Dudek, S., Furlong, C. E., Gaunt, T. R., Kim, D. S., Riess, H., Sivapalaratnam, S., Tragante, V., van Iperen, E. P. A., Brautbar, A., Carrell, D. S., Crosslin, D. R., Jarvik, G. P., Kuivaniemi, H., Kullo, I. J., Larson, E. B., Rasmussen-Torvik, L. J., Tromp, G., Baumert, J., Cruickshanks, K. J., Farrall, M., Hingorani, A. D., Hovingh, G. K., Kleber, M. E., Klein, B. E., Klein, R., Koenig, W., Lange, L. A., Mӓrz, W., North, K. E., Charlotte Onland-Moret, N., Reiner, A. P., Talmud, P. J., van der Schouw, Y. T., Wilson, J. G., Kivimaki, M., Kumari, M., Moore, J. H., Drenos, F., Asselbergs, F. W., Keating, B. J., & Ritchie, M. D. (2017). Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData mining, 1025. https://doi.org/10.1186/s13040-017-0145-5
Holzinger, Emily R, et al. "Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals." BioData mining vol. 10 (2017): 25. doi: https://doi.org/10.1186/s13040-017-0145-5
Holzinger ER, Verma SS, Moore CB, Hall M, De R, Gilbert-Diamond D, Lanktree MB, Pankratz N, Amuzu A, Burt A, Dale C, Dudek S, Furlong CE, Gaunt TR, Kim DS, Riess H, Sivapalaratnam S, Tragante V, van Iperen EPA, Brautbar A, Carrell DS, Crosslin DR, Jarvik GP, Kuivaniemi H, Kullo IJ, Larson EB, Rasmussen-Torvik LJ, Tromp G, Baumert J, Cruickshanks KJ, Farrall M, Hingorani AD, Hovingh GK, Kleber ME, Klein BE, Klein R, Koenig W, Lange LA, Mӓrz W, North KE, Charlotte Onland-Moret N, Reiner AP, Talmud PJ, van der Schouw YT, Wilson JG, Kivimaki M, Kumari M, Moore JH, Drenos F, Asselbergs FW, Keating BJ, Ritchie MD. Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. BioData Min. 2017 Jul 24;10:25. doi: 10.1186/s13040-017-0145-5. eCollection 2017. PMID: 28770004; PMCID: PMC5525436.
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Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis.

Lupus

Kawai VK, Shi M, Liu G, Feng Q, Wei W, Chung CP, Walunas TL, Gordon AS, Linneman JG, Hebbring SJ, Harley JB, Cox NJ, Roden DM, Stein CM, Mosley JD.
PMID: 33977795
Lupus. 2021 Jul;30(8):1264-1272. doi: 10.1177/09612033211014952. Epub 2021 May 12.

OBJECTIVES: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders.METHODS: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In...

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Kawai VK, Shi M, Liu G, et al. Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus. 2021;30(8):1264-1272doi: 10.1177/09612033211014952.
Kawai, V. K., Shi, M., Liu, G., Feng, Q., Wei, W., Chung, C. P., Walunas, T. L., Gordon, A. S., Linneman, J. G., Hebbring, S. J., Harley, J. B., Cox, N. J., Roden, D. M., Stein, C. M., & Mosley, J. D. (2021). Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus, 30(8), 1264-1272. https://doi.org/10.1177/09612033211014952
Kawai, Vivian K, et al. "Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis." Lupus vol. 30,8 (2021): 1264-1272. doi: https://doi.org/10.1177/09612033211014952
Kawai VK, Shi M, Liu G, Feng Q, Wei W, Chung CP, Walunas TL, Gordon AS, Linneman JG, Hebbring SJ, Harley JB, Cox NJ, Roden DM, Stein CM, Mosley JD. Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus. 2021 Jul;30(8):1264-1272. doi: 10.1177/09612033211014952. Epub 2021 May 12. PMID: 33977795; PMCID: PMC8205989.
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Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence.

The annals of applied statistics

Zhang H, Liu D, Zhao J, Bi X.
PMID: 30666272
Ann Appl Stat. 2018 Dec;12(4):2359-2378. doi: 10.1214/18-AOAS1156. Epub 2018 Nov 13.

We propose a novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is a common phenomenon in mental health in which an individual suffers from multiple disorders simultaneously. For example, in the Study...

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Zhang H, Liu D, Zhao J, et al. Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence. Ann Appl Stat. 2018;12(4):2359-2378doi: 10.1214/18-AOAS1156.
Zhang, H., Liu, D., Zhao, J., & Bi, X. (2018). Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence. The annals of applied statistics, 12(4), 2359-2378. https://doi.org/10.1214/18-AOAS1156
Zhang, Heping, et al. "Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence." The annals of applied statistics vol. 12,4 (2018): 2359-2378. doi: https://doi.org/10.1214/18-AOAS1156
Zhang H, Liu D, Zhao J, Bi X. Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence. Ann Appl Stat. 2018 Dec;12(4):2359-2378. doi: 10.1214/18-AOAS1156. Epub 2018 Nov 13. PMID: 30666272; PMCID: PMC6338437.
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Selection of X-chromosome Inactivation Model.

Cancer informatics

Wang J, Talluri R, Shete S.
PMID: 29308008
Cancer Inform. 2017 Dec 17;16:1176935117747272. doi: 10.1177/1176935117747272. eCollection 2017.

To address the complexity of the X-chromosome inactivation (XCI) process, we previously developed a unified approach for the association test for X-chromosomal single-nucleotide polymorphisms (SNPs) and the disease of interest, accounting for different biological possibilities of XCI: random, skewed,...

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Wang J, Talluri R, Shete S. Selection of X-chromosome Inactivation Model. Cancer Inform. 2017;16:1176935117747272doi: 10.1177/1176935117747272.
Wang, J., Talluri, R., & Shete, S. (2017). Selection of X-chromosome Inactivation Model. Cancer informatics, 161176935117747272. https://doi.org/10.1177/1176935117747272
Wang, Jian, et al. "Selection of X-chromosome Inactivation Model." Cancer informatics vol. 16 (2017): 1176935117747272. doi: https://doi.org/10.1177/1176935117747272
Wang J, Talluri R, Shete S. Selection of X-chromosome Inactivation Model. Cancer Inform. 2017 Dec 17;16:1176935117747272. doi: 10.1177/1176935117747272. eCollection 2017. PMID: 29308008; PMCID: PMC5751921.
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