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Showing 1 to 12 of 22 entries
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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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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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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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Next steps in the identification of gene targets for type 1 diabetes.

Diabetologia

Grant SFA, Wells AD, Rich SS.
PMID: 32797243
Diabetologia. 2020 Nov;63(11):2260-2269. doi: 10.1007/s00125-020-05248-8. Epub 2020 Aug 14.

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical...

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Grant SFA, Wells AD, Rich SS. Next steps in the identification of gene targets for type 1 diabetes. Diabetologia. 2020;63(11):2260-2269doi: 10.1007/s00125-020-05248-8.
Grant, S. F. A., Wells, A. D., & Rich, S. S. (2020). Next steps in the identification of gene targets for type 1 diabetes. Diabetologia, 63(11), 2260-2269. https://doi.org/10.1007/s00125-020-05248-8
Grant, Struan F A, et al. "Next steps in the identification of gene targets for type 1 diabetes." Diabetologia vol. 63,11 (2020): 2260-2269. doi: https://doi.org/10.1007/s00125-020-05248-8
Grant SFA, Wells AD, Rich SS. Next steps in the identification of gene targets for type 1 diabetes. Diabetologia. 2020 Nov;63(11):2260-2269. doi: 10.1007/s00125-020-05248-8. Epub 2020 Aug 14. PMID: 32797243; PMCID: PMC7527360.
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Genome-wide association study and transcriptome comparison reveal novel QTL and candidate genes that control petal size in rapeseed.

Journal of experimental botany

Qian M, Fan Y, Li Y, Liu M, Sun W, Duan H, Yu M, Chang W, Niu Y, Li X, Liang Y, Qu C, Li J, Lu K.
PMID: 33712842
J Exp Bot. 2021 May 04;72(10):3597-3610. doi: 10.1093/jxb/erab105.

Petal size determines the value of ornamental plants, and thus their economic value. However, the molecular mechanisms controlling petal size remain unclear in most non-model species. To identify quantitative trait loci and candidate genes controlling petal size in rapeseed...

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Qian M, Fan Y, Li Y, et al. Genome-wide association study and transcriptome comparison reveal novel QTL and candidate genes that control petal size in rapeseed. J Exp Bot. 2021;72(10):3597-3610doi: 10.1093/jxb/erab105.
Qian, M., Fan, Y., Li, Y., Liu, M., Sun, W., Duan, H., Yu, M., Chang, W., Niu, Y., Li, X., Liang, Y., Qu, C., Li, J., & Lu, K. (2021). Genome-wide association study and transcriptome comparison reveal novel QTL and candidate genes that control petal size in rapeseed. Journal of experimental botany, 72(10), 3597-3610. https://doi.org/10.1093/jxb/erab105
Qian, Mingchao, et al. "Genome-wide association study and transcriptome comparison reveal novel QTL and candidate genes that control petal size in rapeseed." Journal of experimental botany vol. 72,10 (2021): 3597-3610. doi: https://doi.org/10.1093/jxb/erab105
Qian M, Fan Y, Li Y, Liu M, Sun W, Duan H, Yu M, Chang W, Niu Y, Li X, Liang Y, Qu C, Li J, Lu K. Genome-wide association study and transcriptome comparison reveal novel QTL and candidate genes that control petal size in rapeseed. J Exp Bot. 2021 May 04;72(10):3597-3610. doi: 10.1093/jxb/erab105. PMID: 33712842.
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Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm.

The New phytologist

Bilbrey EA, Williamson K, Hatzakis E, Miller DD, Fresnedo-Ramírez J, Cooperstone JL.
PMID: 34472097
New Phytol. 2021 Dec;232(5):1944-1958. doi: 10.1111/nph.17693. Epub 2021 Oct 04.

Apple (Malus × domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker-assisted selection minimises these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype-phytochemical associations in apples, we have...

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Bilbrey EA, Williamson K, Hatzakis E, et al. Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm. New Phytol. 2021;232(5):1944-1958doi: 10.1111/nph.17693.
Bilbrey, E. A., Williamson, K., Hatzakis, E., Miller, D. D., Fresnedo-Ramírez, J., & Cooperstone, J. L. (2021). Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm. The New phytologist, 232(5), 1944-1958. https://doi.org/10.1111/nph.17693
Bilbrey, Emma A, et al. "Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm." The New phytologist vol. 232,5 (2021): 1944-1958. doi: https://doi.org/10.1111/nph.17693
Bilbrey EA, Williamson K, Hatzakis E, Miller DD, Fresnedo-Ramírez J, Cooperstone JL. Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm. New Phytol. 2021 Dec;232(5):1944-1958. doi: 10.1111/nph.17693. Epub 2021 Oct 04. PMID: 34472097.
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From Genetic Mapping to Molecular Breeding: Genomics Have Paved the Highway.

Molecular plant

Huang X.
PMID: 27268287
Mol Plant. 2016 Jul 06;9(7):959-60. doi: 10.1016/j.molp.2016.06.001. Epub 2016 Jun 04.

No abstract available.

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Huang X. From Genetic Mapping to Molecular Breeding: Genomics Have Paved the Highway. Mol Plant. 2016;9(7):959-60doi: 10.1016/j.molp.2016.06.001.
Huang, X. (2016). From Genetic Mapping to Molecular Breeding: Genomics Have Paved the Highway. Molecular plant, 9(7), 959-60. https://doi.org/10.1016/j.molp.2016.06.001
Huang, Xuehui. "From Genetic Mapping to Molecular Breeding: Genomics Have Paved the Highway." Molecular plant vol. 9,7 (2016): 959-60. doi: https://doi.org/10.1016/j.molp.2016.06.001
Huang X. From Genetic Mapping to Molecular Breeding: Genomics Have Paved the Highway. Mol Plant. 2016 Jul 06;9(7):959-60. doi: 10.1016/j.molp.2016.06.001. Epub 2016 Jun 04. PMID: 27268287.
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eQTL analysis in humans.

Methods in molecular biology (Clifton, N.J.)

Franke L, Jansen RC.
PMID: 19763935
Methods Mol Biol. 2009;573:311-28. doi: 10.1007/978-1-60761-247-6_17.

Improving human health is a major aim of medical research, but it requires that variation between individuals be taken into account since each person carries a different combination of gene variants and is exposed to different environmental conditions, which...

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Franke L, Jansen RC. eQTL analysis in humans. Methods Mol Biol. 2009;573:311-28doi: 10.1007/978-1-60761-247-6_17.
Franke, L., & Jansen, R. C. (2009). eQTL analysis in humans. Methods in molecular biology (Clifton, N.J.), 573311-28. https://doi.org/10.1007/978-1-60761-247-6_17
Franke, Lude, and Jansen, Ritsert C. "eQTL analysis in humans." Methods in molecular biology (Clifton, N.J.) vol. 573 (2009): 311-28. doi: https://doi.org/10.1007/978-1-60761-247-6_17
Franke L, Jansen RC. eQTL analysis in humans. Methods Mol Biol. 2009;573:311-28. doi: 10.1007/978-1-60761-247-6_17. PMID: 19763935.
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Finding alternative expression quantitative trait loci by exploring sparse model space.

Journal of computational biology : a journal of computational molecular cell biology

Wang Z, Xu J, Shi X.
PMID: 24689773
J Comput Biol. 2014 May;21(5):385-93. doi: 10.1089/cmb.2014.0026. Epub 2014 Apr 01.

Sparse modeling, a feature selection method widely used in the machine-learning community, has been recently applied to identify associations in genetic studies including expression quantitative trait locus (eQTL) mapping. These genetic studies usually involve high dimensional data where the...

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Wang Z, Xu J, Shi X. Finding alternative expression quantitative trait loci by exploring sparse model space. J Comput Biol. 2014;21(5):385-93doi: 10.1089/cmb.2014.0026.
Wang, Z., Xu, J., & Shi, X. (2014). Finding alternative expression quantitative trait loci by exploring sparse model space. Journal of computational biology : a journal of computational molecular cell biology, 21(5), 385-93. https://doi.org/10.1089/cmb.2014.0026
Wang, Zhiyong, et al. "Finding alternative expression quantitative trait loci by exploring sparse model space." Journal of computational biology : a journal of computational molecular cell biology vol. 21,5 (2014): 385-93. doi: https://doi.org/10.1089/cmb.2014.0026
Wang Z, Xu J, Shi X. Finding alternative expression quantitative trait loci by exploring sparse model space. J Comput Biol. 2014 May;21(5):385-93. doi: 10.1089/cmb.2014.0026. Epub 2014 Apr 01. PMID: 24689773; PMCID: PMC4010169.
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Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments.

Nature biotechnology

Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C.
PMID: 23873083
Nat Biotechnol. 2013 Aug;31(8):748-52. doi: 10.1038/nbt.2642. Epub 2013 Jul 21.

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of...

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Wills QF, Livak KJ, Tipping AJ, et al. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013;31(8):748-52doi: 10.1038/nbt.2642.
Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. (2013). Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nature biotechnology, 31(8), 748-52. https://doi.org/10.1038/nbt.2642
Wills, Quin F, et al. "Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments." Nature biotechnology vol. 31,8 (2013): 748-52. doi: https://doi.org/10.1038/nbt.2642
Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol. 2013 Aug;31(8):748-52. doi: 10.1038/nbt.2642. Epub 2013 Jul 21. PMID: 23873083.
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Showing 1 to 12 of 22 entries