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Showing 1 to 12 of 58 entries
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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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TreeQTL: hierarchical error control for eQTL findings.

Bioinformatics (Oxford, England)

Peterson CB, Bogomolov M, Benjamini Y, Sabatti C.
PMID: 27153635
Bioinformatics. 2016 Aug 15;32(16):2556-8. doi: 10.1093/bioinformatics/btw198. Epub 2016 Apr 19.

UNLABELLED: : Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative...

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Peterson CB, Bogomolov M, Benjamini Y, et al. TreeQTL: hierarchical error control for eQTL findings. Bioinformatics. 2016;32(16):2556-8doi: 10.1093/bioinformatics/btw198.
Peterson, C. B., Bogomolov, M., Benjamini, Y., & Sabatti, C. (2016). TreeQTL: hierarchical error control for eQTL findings. Bioinformatics (Oxford, England), 32(16), 2556-8. https://doi.org/10.1093/bioinformatics/btw198
Peterson, C B, et al. "TreeQTL: hierarchical error control for eQTL findings." Bioinformatics (Oxford, England) vol. 32,16 (2016): 2556-8. doi: https://doi.org/10.1093/bioinformatics/btw198
Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. TreeQTL: hierarchical error control for eQTL findings. Bioinformatics. 2016 Aug 15;32(16):2556-8. doi: 10.1093/bioinformatics/btw198. Epub 2016 Apr 19. PMID: 27153635; PMCID: PMC4978936.
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[Strategies for elucidating functional significance of complex disease related loci].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]

Jin G, Du J, Hu Z, Shen H.
PMID: 26081530
Zhonghua Yu Fang Yi Xue Za Zhi. 2015 Apr;49(4):299-302.

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with complex diseases or traits. However, the exact biological functions of these loci are largely unknown. Recent functional annotation indicates that the majority of disease/trait associated loci are...

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Jin G, Du J, Hu Z, et al. [Strategies for elucidating functional significance of complex disease related loci]. Zhonghua Yu Fang Yi Xue Za Zhi. 2015;49(4):299-302
Jin, G., Du, J., Hu, Z., & Shen, H. (2015). [Strategies for elucidating functional significance of complex disease related loci]. Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine], 49(4), 299-302.
Jin, Guangfu, et al. "[Strategies for elucidating functional significance of complex disease related loci]." Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] vol. 49,4 (2015): 299-302.
Jin G, Du J, Hu Z, Shen H. [Strategies for elucidating functional significance of complex disease related loci]. Zhonghua Yu Fang Yi Xue Za Zhi. 2015 Apr;49(4):299-302. Chinese. PMID: 26081530.
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Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data.

Genetics

Zhao W, Li H, Hou W, Wu R.
PMID: 17435222
Genetics. 2007 Jul;176(3):1879-92. doi: 10.1534/genetics.107.070920. Epub 2007 Apr 15.

The biological and statistical advantages of functional mapping result from joint modeling of the mean-covariance structures for developmental trajectories of a complex trait measured at a series of time points. While an increased number of time points can better...

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Zhao W, Li H, Hou W, et al. Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data. Genetics. 2007;176(3):1879-92doi: 10.1534/genetics.107.070920.
Zhao, W., Li, H., Hou, W., & Wu, R. (2007). Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data. Genetics, 176(3), 1879-92. https://doi.org/10.1534/genetics.107.070920
Zhao, Wei, et al. "Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data." Genetics vol. 176,3 (2007): 1879-92. doi: https://doi.org/10.1534/genetics.107.070920
Zhao W, Li H, Hou W, Wu R. Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data. Genetics. 2007 Jul;176(3):1879-92. doi: 10.1534/genetics.107.070920. Epub 2007 Apr 15. PMID: 17435222; PMCID: PMC1931562.
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Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies.

Bioinformatics (Oxford, England)

Yang TP, Beazley C, Montgomery SB, Dimas AS, Gutierrez-Arcelus M, Stranger BE, Deloukas P, Dermitzakis ET.
PMID: 20702402
Bioinformatics. 2010 Oct 01;26(19):2474-6. doi: 10.1093/bioinformatics/btq452. Epub 2010 Aug 10.

UNLABELLED: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL)...

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Yang TP, Beazley C, Montgomery SB, et al. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics. 2010;26(19):2474-6doi: 10.1093/bioinformatics/btq452.
Yang, T. P., Beazley, C., Montgomery, S. B., Dimas, A. S., Gutierrez-Arcelus, M., Stranger, B. E., Deloukas, P., & Dermitzakis, E. T. (2010). Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics (Oxford, England), 26(19), 2474-6. https://doi.org/10.1093/bioinformatics/btq452
Yang, Tsun-Po, et al. "Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies." Bioinformatics (Oxford, England) vol. 26,19 (2010): 2474-6. doi: https://doi.org/10.1093/bioinformatics/btq452
Yang TP, Beazley C, Montgomery SB, Dimas AS, Gutierrez-Arcelus M, Stranger BE, Deloukas P, Dermitzakis ET. Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics. 2010 Oct 01;26(19):2474-6. doi: 10.1093/bioinformatics/btq452. Epub 2010 Aug 10. PMID: 20702402; PMCID: PMC2944204.
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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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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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Synthetic biology of metabolism: using natural variation to reverse engineer systems.

Current opinion in plant biology

Kliebenstein DJ.
PMID: 24699221
Curr Opin Plant Biol. 2014 Jun;19:20-6. doi: 10.1016/j.pbi.2014.03.008. Epub 2014 Apr 01.

A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models...

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Kliebenstein DJ. Synthetic biology of metabolism: using natural variation to reverse engineer systems. Curr Opin Plant Biol. 2014;19:20-6doi: 10.1016/j.pbi.2014.03.008.
Kliebenstein, D. J. (2014). Synthetic biology of metabolism: using natural variation to reverse engineer systems. Current opinion in plant biology, 1920-6. https://doi.org/10.1016/j.pbi.2014.03.008
Kliebenstein, Daniel J. "Synthetic biology of metabolism: using natural variation to reverse engineer systems." Current opinion in plant biology vol. 19 (2014): 20-6. doi: https://doi.org/10.1016/j.pbi.2014.03.008
Kliebenstein DJ. Synthetic biology of metabolism: using natural variation to reverse engineer systems. Curr Opin Plant Biol. 2014 Jun;19:20-6. doi: 10.1016/j.pbi.2014.03.008. Epub 2014 Apr 01. PMID: 24699221.
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Finding invisible quantitative trait loci with missing data.

Plant biotechnology journal

Gabur I, Chawla HS, Liu X, Kumar V, Faure S, von Tiedemann A, Jestin C, Dryzska E, Volkmann S, Breuer F, Delourme R, Snowdon R, Obermeier C.
PMID: 29729219
Plant Biotechnol J. 2018 Dec;16(12):2102-2112. doi: 10.1111/pbi.12942. Epub 2018 May 28.

Evolutionary processes during plant polyploidization and speciation have led to extensive presence-absence variation (PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high-resolution genetic analysis in major crops frequently implements simple, cost-effective,...

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Gabur I, Chawla HS, Liu X, et al. Finding invisible quantitative trait loci with missing data. Plant Biotechnol J. 2018;16(12):2102-2112doi: 10.1111/pbi.12942.
Gabur, I., Chawla, H. S., Liu, X., Kumar, V., Faure, S., von Tiedemann, A., Jestin, C., Dryzska, E., Volkmann, S., Breuer, F., Delourme, R., Snowdon, R., & Obermeier, C. (2018). Finding invisible quantitative trait loci with missing data. Plant biotechnology journal, 16(12), 2102-2112. https://doi.org/10.1111/pbi.12942
Gabur, Iulian, et al. "Finding invisible quantitative trait loci with missing data." Plant biotechnology journal vol. 16,12 (2018): 2102-2112. doi: https://doi.org/10.1111/pbi.12942
Gabur I, Chawla HS, Liu X, Kumar V, Faure S, von Tiedemann A, Jestin C, Dryzska E, Volkmann S, Breuer F, Delourme R, Snowdon R, Obermeier C. Finding invisible quantitative trait loci with missing data. Plant Biotechnol J. 2018 Dec;16(12):2102-2112. doi: 10.1111/pbi.12942. Epub 2018 May 28. PMID: 29729219; PMCID: PMC6230954.
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Transcriptome skimming of lentil (Lens culinaris Medikus) cultivars with contrast reaction to salt stress.

Functional & integrative genomics

Singh D, Singh CK, Taunk J, Sharma S, Gaikwad K, Singh V, Sanwal SK, Singh D, Sharma PC, Pal M.
PMID: 33389259
Funct Integr Genomics. 2021 Jan;21(1):139-156. doi: 10.1007/s10142-020-00766-5. Epub 2021 Jan 03.

Extensive transcriptomic skimming was conducted to decipher molecular, morphological, physiological, and biochemical responses in salt-tolerant (PDL-1) and salt-sensitive (L-4076) cultivars under control (0 mM NaCl) and salinity stress (120 mM NaCl) conditions at seedling stage. Morphological, physiological, and biochemical...

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Singh D, Singh CK, Taunk J, et al. Transcriptome skimming of lentil (Lens culinaris Medikus) cultivars with contrast reaction to salt stress. Funct Integr Genomics. 2021;21(1):139-156doi: 10.1007/s10142-020-00766-5.
Singh, D., Singh, C. K., Taunk, J., Sharma, S., Gaikwad, K., Singh, V., Sanwal, S. K., Singh, D., Sharma, P. C., & Pal, M. (2021). Transcriptome skimming of lentil (Lens culinaris Medikus) cultivars with contrast reaction to salt stress. Functional & integrative genomics, 21(1), 139-156. https://doi.org/10.1007/s10142-020-00766-5
Singh, Dharmendra, et al. "Transcriptome skimming of lentil (Lens culinaris Medikus) cultivars with contrast reaction to salt stress." Functional & integrative genomics vol. 21,1 (2021): 139-156. doi: https://doi.org/10.1007/s10142-020-00766-5
Singh D, Singh CK, Taunk J, Sharma S, Gaikwad K, Singh V, Sanwal SK, Singh D, Sharma PC, Pal M. Transcriptome skimming of lentil (Lens culinaris Medikus) cultivars with contrast reaction to salt stress. Funct Integr Genomics. 2021 Jan;21(1):139-156. doi: 10.1007/s10142-020-00766-5. Epub 2021 Jan 03. PMID: 33389259.
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Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections.

Current protocols

Guerra-García A, Gioia T, von Wettberg E, Logozzo G, Papa R, Bitocchi E, Bett KE.
PMID: 34004055
Curr Protoc. 2021 May;1(5):e134. doi: 10.1002/cpz1.134.

The genetic and phenotypic characterization of crops allows us to elucidate their evolutionary and domestication history, the genetic basis of important traits, and the use of variation present in landraces and wild relatives to enhance resilience. In this context,...

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Guerra-García A, Gioia T, von Wettberg E, et al. Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections. Curr Protoc. 2021;1(5):e134doi: 10.1002/cpz1.134.
Guerra-García, A., Gioia, T., von Wettberg, E., Logozzo, G., Papa, R., Bitocchi, E., & Bett, K. E. (2021). Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections. Current protocols, 1(5), e134. https://doi.org/10.1002/cpz1.134
Guerra-García, Azalea, et al. "Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections." Current protocols vol. 1,5 (2021): e134. doi: https://doi.org/10.1002/cpz1.134
Guerra-García A, Gioia T, von Wettberg E, Logozzo G, Papa R, Bitocchi E, Bett KE. Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections. Curr Protoc. 2021 May;1(5):e134. doi: 10.1002/cpz1.134. PMID: 34004055.
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Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci.

Clinical and translational medicine

Wang Z, Zhu Q, Liu Y, Chen S, Zhang Y, Ma Q, Chen X, Liu C, Lei H, Chen H, Wang J, Zheng S, Li Z, Xiong L, Lai W, Zhong S.
PMID: 33634981
Clin Transl Med. 2021 Feb;11(2):e290. doi: 10.1002/ctm2.290.

No abstract available.

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Wang Z, Zhu Q, Liu Y, et al. Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci. Clin Transl Med. 2021;11(2):e290doi: 10.1002/ctm2.290.
Wang, Z., Zhu, Q., Liu, Y., Chen, S., Zhang, Y., Ma, Q., Chen, X., Liu, C., Lei, H., Chen, H., Wang, J., Zheng, S., Li, Z., Xiong, L., Lai, W., & Zhong, S. (2021). Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci. Clinical and translational medicine, 11(2), e290. https://doi.org/10.1002/ctm2.290
Wang, Zixian, et al. "Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci." Clinical and translational medicine vol. 11,2 (2021): e290. doi: https://doi.org/10.1002/ctm2.290
Wang Z, Zhu Q, Liu Y, Chen S, Zhang Y, Ma Q, Chen X, Liu C, Lei H, Chen H, Wang J, Zheng S, Li Z, Xiong L, Lai W, Zhong S. Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci. Clin Transl Med. 2021 Feb;11(2):e290. doi: 10.1002/ctm2.290. PMID: 33634981; PMCID: PMC7839954.
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Showing 1 to 12 of 58 entries