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SNPTransformer: a lightweight toolkit for genome-wide association studies.

Genomics, proteomics & bioinformatics

Dong C.
PMID: 21382596
Genomics Proteomics Bioinformatics. 2010 Dec;8(4):268-73. doi: 10.1016/S1672-0229(10)60029-0.

High-throughput genotyping chips have produced huge datasets for genome-wide association studies (GWAS) that have contributed greatly to discovering susceptibility genes for complex diseases. There are two strategies for performing data analysis for GWAS. One strategy is to use open-source...

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Dong C. SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics Proteomics Bioinformatics. 2010;8(4):268-73doi: 10.1016/S1672-0229(10)60029-0.
Dong, C. (2010). SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics, proteomics & bioinformatics, 8(4), 268-73. https://doi.org/10.1016/S1672-0229(10)60029-0
Dong, Changzheng. "SNPTransformer: a lightweight toolkit for genome-wide association studies." Genomics, proteomics & bioinformatics vol. 8,4 (2010): 268-73. doi: https://doi.org/10.1016/S1672-0229(10)60029-0
Dong C. SNPTransformer: a lightweight toolkit for genome-wide association studies. Genomics Proteomics Bioinformatics. 2010 Dec;8(4):268-73. doi: 10.1016/S1672-0229(10)60029-0. PMID: 21382596; PMCID: PMC5054149.
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GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits.

BMC genetics

Feng S, Wang S, Chen CC, Lan L.
PMID: 21255436
BMC Genet. 2011 Jan 21;12:12. doi: 10.1186/1471-2156-12-12.

BACKGROUND: In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies,...

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Feng S, Wang S, Chen CC, et al. GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC Genet. 2011;12:12doi: 10.1186/1471-2156-12-12.
Feng, S., Wang, S., Chen, C. C., & Lan, L. (2011). GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC genetics, 1212. https://doi.org/10.1186/1471-2156-12-12
Feng, Sheng, et al. "GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits." BMC genetics vol. 12 (2011): 12. doi: https://doi.org/10.1186/1471-2156-12-12
Feng S, Wang S, Chen CC, Lan L. GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC Genet. 2011 Jan 21;12:12. doi: 10.1186/1471-2156-12-12. PMID: 21255436; PMCID: PMC3036643.
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FAPI: Fast and accurate P-value Imputation for genome-wide association study.

European journal of human genetics : EJHG

Kwan JS, Li MX, Deng JE, Sham PC.
PMID: 26306642
Eur J Hum Genet. 2016 May;24(5):761-6. doi: 10.1038/ejhg.2015.190. Epub 2015 Aug 26.

Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be...

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Kwan JS, Li MX, Deng JE, et al. FAPI: Fast and accurate P-value Imputation for genome-wide association study. Eur J Hum Genet. 2015;24(5):761-6doi: 10.1038/ejhg.2015.190.
Kwan, J. S., Li, M. X., Deng, J. E., & Sham, P. C. (2016). FAPI: Fast and accurate P-value Imputation for genome-wide association study. European journal of human genetics : EJHG, 24(5), 761-6. https://doi.org/10.1038/ejhg.2015.190
Kwan, Johnny S H, et al. "FAPI: Fast and accurate P-value Imputation for genome-wide association study." European journal of human genetics : EJHG vol. 24,5 (2016): 761-6. doi: https://doi.org/10.1038/ejhg.2015.190
Kwan JS, Li MX, Deng JE, Sham PC. FAPI: Fast and accurate P-value Imputation for genome-wide association study. Eur J Hum Genet. 2016 May;24(5):761-6. doi: 10.1038/ejhg.2015.190. Epub 2015 Aug 26. PMID: 26306642; PMCID: PMC4930094.
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Towards an integrative genomics of lung function.

The Lancet. Respiratory medicine

Cho MH.
PMID: 26404119
Lancet Respir Med. 2015 Oct;3(10):739-41. doi: 10.1016/S2213-2600(15)00362-8. Epub 2015 Sep 21.

No abstract available.

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Cho MH. Towards an integrative genomics of lung function. Lancet Respir Med. 2015;3(10):739-41doi: 10.1016/S2213-2600(15)00362-8.
Cho, M. H. (2015). Towards an integrative genomics of lung function. The Lancet. Respiratory medicine, 3(10), 739-41. https://doi.org/10.1016/S2213-2600(15)00362-8
Cho, Michael H. "Towards an integrative genomics of lung function." The Lancet. Respiratory medicine vol. 3,10 (2015): 739-41. doi: https://doi.org/10.1016/S2213-2600(15)00362-8
Cho MH. Towards an integrative genomics of lung function. Lancet Respir Med. 2015 Oct;3(10):739-41. doi: 10.1016/S2213-2600(15)00362-8. Epub 2015 Sep 21. PMID: 26404119.
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A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures.

European journal of human genetics : EJHG

Greco B, Hainline A, Arbet J, Grinde K, Benitez A, Tintle N.
PMID: 26508571
Eur J Hum Genet. 2016 May;24(5):767-73. doi: 10.1038/ejhg.2015.194. Epub 2015 Oct 28.

The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular...

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Greco B, Hainline A, Arbet J, et al. A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures. Eur J Hum Genet. 2015;24(5):767-73doi: 10.1038/ejhg.2015.194.
Greco, B., Hainline, A., Arbet, J., Grinde, K., Benitez, A., & Tintle, N. (2016). A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures. European journal of human genetics : EJHG, 24(5), 767-73. https://doi.org/10.1038/ejhg.2015.194
Greco, Brian, et al. "A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures." European journal of human genetics : EJHG vol. 24,5 (2016): 767-73. doi: https://doi.org/10.1038/ejhg.2015.194
Greco B, Hainline A, Arbet J, Grinde K, Benitez A, Tintle N. A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures. Eur J Hum Genet. 2016 May;24(5):767-73. doi: 10.1038/ejhg.2015.194. Epub 2015 Oct 28. PMID: 26508571; PMCID: PMC4930097.
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P < 5 × 10(-8) has emerged as a standard of statistical significance for genome-wide association studies.

Journal of clinical epidemiology

Jannot AS, Ehret G, Perneger T.
PMID: 25666886
J Clin Epidemiol. 2015 Apr;68(4):460-5. doi: 10.1016/j.jclinepi.2015.01.001. Epub 2015 Jan 09.

OBJECTIVES: In genome-wide association studies (GWASs), the practice regarding the choice of thresholds of significance and of thresholds used to include single nucleotide polymorphisms (SNPs) in a further validation stage is not well known. Here, we performed a systematic...

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Jannot AS, Ehret G, Perneger T. P < 5 × 10(-8) has emerged as a standard of statistical significance for genome-wide association studies. J Clin Epidemiol. 2015;68(4):460-5doi: 10.1016/j.jclinepi.2015.01.001.
Jannot, A. S., Ehret, G., & Perneger, T. (2015). P < 5 × 10(-8) has emerged as a standard of statistical significance for genome-wide association studies. Journal of clinical epidemiology, 68(4), 460-5. https://doi.org/10.1016/j.jclinepi.2015.01.001
Jannot, Anne-Sophie, et al. "P < 5 × 10(-8) has emerged as a standard of statistical significance for genome-wide association studies." Journal of clinical epidemiology vol. 68,4 (2015): 460-5. doi: https://doi.org/10.1016/j.jclinepi.2015.01.001
Jannot AS, Ehret G, Perneger T. P < 5 × 10(-8) has emerged as a standard of statistical significance for genome-wide association studies. J Clin Epidemiol. 2015 Apr;68(4):460-5. doi: 10.1016/j.jclinepi.2015.01.001. Epub 2015 Jan 09. PMID: 25666886.
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DISEASES: text mining and data integration of disease-gene associations.

Methods (San Diego, Calif.)

Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ.
PMID: 25484339
Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05.

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for...

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Pletscher-Frankild S, Pallejà A, Tsafou K, et al. DISEASES: text mining and data integration of disease-gene associations. Methods. 2014;74:83-9doi: 10.1016/j.ymeth.2014.11.020.
Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X., & Jensen, L. J. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods (San Diego, Calif.), 7483-9. https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild, Sune, et al. "DISEASES: text mining and data integration of disease-gene associations." Methods (San Diego, Calif.) vol. 74 (2015): 83-9. doi: https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: text mining and data integration of disease-gene associations. Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05. PMID: 25484339.
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Exploring genetic susceptibility to cancer in diverse populations.

Current opinion in genetics & development

Haiman CA, Stram DO.
PMID: 20359883
Curr Opin Genet Dev. 2010 Jun;20(3):330-5. doi: 10.1016/j.gde.2010.02.007. Epub 2010 Mar 30.

Incidence rates for many cancers differ markedly by race/ethnicity and furthering our understanding of the genetic and environmental causes of such disparities is a scientific and public health need. Genome-wide association studies (GWAS) are widely acknowledged to provide important...

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Haiman CA, Stram DO. Exploring genetic susceptibility to cancer in diverse populations. Curr Opin Genet Dev. 2010;20(3):330-5doi: 10.1016/j.gde.2010.02.007.
Haiman, C. A., & Stram, D. O. (2010). Exploring genetic susceptibility to cancer in diverse populations. Current opinion in genetics & development, 20(3), 330-5. https://doi.org/10.1016/j.gde.2010.02.007
Haiman, Christopher A, and Stram, Daniel O. "Exploring genetic susceptibility to cancer in diverse populations." Current opinion in genetics & development vol. 20,3 (2010): 330-5. doi: https://doi.org/10.1016/j.gde.2010.02.007
Haiman CA, Stram DO. Exploring genetic susceptibility to cancer in diverse populations. Curr Opin Genet Dev. 2010 Jun;20(3):330-5. doi: 10.1016/j.gde.2010.02.007. Epub 2010 Mar 30. PMID: 20359883; PMCID: PMC4196678.
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iHAT: interactive hierarchical aggregation table for genetic association data.

BMC bioinformatics

Heinrich J, Vehlow C, Battke F, Jäger G, Weiskopf D, Nieselt K.
PMID: 22607364
BMC Bioinformatics. 2012;13:S2. doi: 10.1186/1471-2105-13-S8-S2. Epub 2012 May 18.

In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a...

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Heinrich J, Vehlow C, Battke F, et al. iHAT: interactive hierarchical aggregation table for genetic association data. BMC Bioinformatics. 2012;13:S2doi: 10.1186/1471-2105-13-S8-S2.
Heinrich, J., Vehlow, C., Battke, F., Jäger, G., Weiskopf, D., & Nieselt, K. (2012). iHAT: interactive hierarchical aggregation table for genetic association data. BMC bioinformatics, 13S2. https://doi.org/10.1186/1471-2105-13-S8-S2
Heinrich, Julian, et al. "iHAT: interactive hierarchical aggregation table for genetic association data." BMC bioinformatics vol. 13 (2012): S2. doi: https://doi.org/10.1186/1471-2105-13-S8-S2
Heinrich J, Vehlow C, Battke F, Jäger G, Weiskopf D, Nieselt K. iHAT: interactive hierarchical aggregation table for genetic association data. BMC Bioinformatics. 2012;13:S2. doi: 10.1186/1471-2105-13-S8-S2. Epub 2012 May 18. PMID: 22607364; PMCID: PMC3355335.
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Genome-wide analysis of fitness data and its application to improve metabolic models.

BMC bioinformatics

Vitkin E, Solomon O, Sultan S, Yakhini Z.
PMID: 30305012
BMC Bioinformatics. 2018 Oct 10;19(1):368. doi: 10.1186/s12859-018-2341-9.

BACKGROUND: Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence.RESULTS: We develop statistical and computational pipelines and frameworks for analyzing high...

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Vitkin E, Solomon O, Sultan S, et al. Genome-wide analysis of fitness data and its application to improve metabolic models. BMC Bioinformatics. 2018;19(1):368doi: 10.1186/s12859-018-2341-9.
Vitkin, E., Solomon, O., Sultan, S., & Yakhini, Z. (2018). Genome-wide analysis of fitness data and its application to improve metabolic models. BMC bioinformatics, 19(1), 368. https://doi.org/10.1186/s12859-018-2341-9
Vitkin, Edward, et al. "Genome-wide analysis of fitness data and its application to improve metabolic models." BMC bioinformatics vol. 19,1 (2018): 368. doi: https://doi.org/10.1186/s12859-018-2341-9
Vitkin E, Solomon O, Sultan S, Yakhini Z. Genome-wide analysis of fitness data and its application to improve metabolic models. BMC Bioinformatics. 2018 Oct 10;19(1):368. doi: 10.1186/s12859-018-2341-9. PMID: 30305012; PMCID: PMC6180484.
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Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing.

Mutation research

Ryu S, Han J, Norden-Krichmar TM, Schork NJ, Suh Y.
PMID: 29677560
Mutat Res. 2018 May;809:24-31. doi: 10.1016/j.mrfmmm.2018.03.007. Epub 2018 Mar 30.

Identification of all genetic variants associated with complex traits is one of the most important goals in modern human genetics. Genome-wide association studies (GWAS) have been successfully applied to identify common variants, which thus far explain only small portion...

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Ryu S, Han J, Norden-Krichmar TM, et al. Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing. Mutat Res. 2018;809:24-31doi: 10.1016/j.mrfmmm.2018.03.007.
Ryu, S., Han, J., Norden-Krichmar, T. M., Schork, N. J., & Suh, Y. (2018). Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing. Mutation research, 80924-31. https://doi.org/10.1016/j.mrfmmm.2018.03.007
Ryu, Seungjin, et al. "Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing." Mutation research vol. 809 (2018): 24-31. doi: https://doi.org/10.1016/j.mrfmmm.2018.03.007
Ryu S, Han J, Norden-Krichmar TM, Schork NJ, Suh Y. Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing. Mutat Res. 2018 May;809:24-31. doi: 10.1016/j.mrfmmm.2018.03.007. Epub 2018 Mar 30. PMID: 29677560; PMCID: PMC5962423.
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Multi-omics approaches to disease.

Genome biology

Hasin Y, Seldin M, Lusis A.
PMID: 28476144
Genome Biol. 2017 May 05;18(1):83. doi: 10.1186/s13059-017-1215-1.

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of "integrative genetics". Other omics technologies, such as proteomics and...

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Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83doi: 10.1186/s13059-017-1215-1.
Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome biology, 18(1), 83. https://doi.org/10.1186/s13059-017-1215-1
Hasin, Yehudit, et al. "Multi-omics approaches to disease." Genome biology vol. 18,1 (2017): 83. doi: https://doi.org/10.1186/s13059-017-1215-1
Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017 May 05;18(1):83. doi: 10.1186/s13059-017-1215-1. PMID: 28476144; PMCID: PMC5418815.
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Showing 1 to 12 of 108 entries