Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 13 to 24 of 131 entries
Sorted by: Best Match Show Resources per page
On the usage of HWE for identifying genotyping errors.

Annals of human genetics

Teo YY, Fry AE, Clark TG, Tai ES, Seielstad M.
PMID: 17388941
Ann Hum Genet. 2007 Sep;71:701-3; author reply 704. doi: 10.1111/j.1469-1809.2007.00356.x. Epub 2007 Mar 27.

No abstract available.

Cite
Teo YY, Fry AE, Clark TG, et al. On the usage of HWE for identifying genotyping errors. Ann Hum Genet. 2007;71:701-3; author reply 704doi: 10.1111/j.1469-1809.2007.00356.x.
Teo, Y. Y., Fry, A. E., Clark, T. G., Tai, E. S., & Seielstad, M. (2007). On the usage of HWE for identifying genotyping errors. Annals of human genetics, 71701-3; author reply 704. https://doi.org/10.1111/j.1469-1809.2007.00356.x
Teo, Yik Y, et al. "On the usage of HWE for identifying genotyping errors." Annals of human genetics vol. 71 (2007): 701-3; author reply 704. doi: https://doi.org/10.1111/j.1469-1809.2007.00356.x
Teo YY, Fry AE, Clark TG, Tai ES, Seielstad M. On the usage of HWE for identifying genotyping errors. Ann Hum Genet. 2007 Sep;71:701-3; author reply 704. doi: 10.1111/j.1469-1809.2007.00356.x. Epub 2007 Mar 27. PMID: 17388941.
Copy Download .nbib Format: NLM
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...

Cite
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.
Copy Download .nbib Format: NLM
Current software for genotype imputation.

Human genomics

Ellinghaus D, Schreiber S, Franke A, Nothnagel M.
PMID: 19706367
Hum Genomics. 2009 Jul;3(4):371-80. doi: 10.1186/1479-7364-3-4-371.

Genotype imputation for single nucleotide polymorphisms (SNPs) has been shown to be a powerful means to include genetic markers in exploratory genetic association studies without having to genotype them, and is becoming a standard procedure. A number of different...

Cite
Ellinghaus D, Schreiber S, Franke A, et al. Current software for genotype imputation. Hum Genomics. 2009;3(4):371-80doi: 10.1186/1479-7364-3-4-371.
Ellinghaus, D., Schreiber, S., Franke, A., & Nothnagel, M. (2009). Current software for genotype imputation. Human genomics, 3(4), 371-80. https://doi.org/10.1186/1479-7364-3-4-371
Ellinghaus, David, et al. "Current software for genotype imputation." Human genomics vol. 3,4 (2009): 371-80. doi: https://doi.org/10.1186/1479-7364-3-4-371
Ellinghaus D, Schreiber S, Franke A, Nothnagel M. Current software for genotype imputation. Hum Genomics. 2009 Jul;3(4):371-80. doi: 10.1186/1479-7364-3-4-371. PMID: 19706367; PMCID: PMC3525198.
Copy Download .nbib Format: NLM
High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays.

BMC genomics

Wang J, Lin M, Crenshaw A, Hutchinson A, Hicks B, Yeager M, Berndt S, Huang WY, Hayes RB, Chanock SJ, Jones RC, Ramakrishnan R.
PMID: 19943955
BMC Genomics. 2009 Nov 28;10:561. doi: 10.1186/1471-2164-10-561.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. There is a need...

Cite
Wang J, Lin M, Crenshaw A, et al. High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays. BMC Genomics. 2009;10:561doi: 10.1186/1471-2164-10-561.
Wang, J., Lin, M., Crenshaw, A., Hutchinson, A., Hicks, B., Yeager, M., Berndt, S., Huang, W. Y., Hayes, R. B., Chanock, S. J., Jones, R. C., & Ramakrishnan, R. (2009). High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays. BMC genomics, 10561. https://doi.org/10.1186/1471-2164-10-561
Wang, Jun, et al. "High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays." BMC genomics vol. 10 (2009): 561. doi: https://doi.org/10.1186/1471-2164-10-561
Wang J, Lin M, Crenshaw A, Hutchinson A, Hicks B, Yeager M, Berndt S, Huang WY, Hayes RB, Chanock SJ, Jones RC, Ramakrishnan R. High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays. BMC Genomics. 2009 Nov 28;10:561. doi: 10.1186/1471-2164-10-561. PMID: 19943955; PMCID: PMC2789104.
Copy Download .nbib Format: NLM
Investigating gene environment interaction in complex diseases: increasing power by selective sampling for environmental exposure.

International journal of epidemiology

Boks MP, Schipper M, Schubart CD, Sommer IE, Kahn RS, Ophoff RA.
PMID: 17971387
Int J Epidemiol. 2007 Dec;36(6):1363-9. doi: 10.1093/ije/dym215. Epub 2007 Oct 30.

BACKGROUND: The often limited influence of disease associated alleles on the vulnerability to complex diseases has lead to increased interest in environmental interaction with genotype. However, gene environmental interactions (GEIs) are not easily studied, since high numbers of subjects...

Cite
Boks MP, Schipper M, Schubart CD, et al. Investigating gene environment interaction in complex diseases: increasing power by selective sampling for environmental exposure. Int J Epidemiol. 2007;36(6):1363-9doi: 10.1093/ije/dym215.
Boks, M. P., Schipper, M., Schubart, C. D., Sommer, I. E., Kahn, R. S., & Ophoff, R. A. (2007). Investigating gene environment interaction in complex diseases: increasing power by selective sampling for environmental exposure. International journal of epidemiology, 36(6), 1363-9. https://doi.org/10.1093/ije/dym215
Boks, M P M, et al. "Investigating gene environment interaction in complex diseases: increasing power by selective sampling for environmental exposure." International journal of epidemiology vol. 36,6 (2007): 1363-9. doi: https://doi.org/10.1093/ije/dym215
Boks MP, Schipper M, Schubart CD, Sommer IE, Kahn RS, Ophoff RA. Investigating gene environment interaction in complex diseases: increasing power by selective sampling for environmental exposure. Int J Epidemiol. 2007 Dec;36(6):1363-9. doi: 10.1093/ije/dym215. Epub 2007 Oct 30. PMID: 17971387.
Copy Download .nbib Format: NLM
Confirmation of novel type 1 diabetes risk loci in families.

Diabetologia

Cooper JD, Howson JM, Smyth D, Walker NM, Stevens H, Yang JH, She JX, Eisenbarth GS, Rewers M, Todd JA, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Rich SS.
PMID: 22278338
Diabetologia. 2012 Apr;55(4):996-1000. doi: 10.1007/s00125-012-2450-3. Epub 2012 Jan 26.

AIMS/HYPOTHESIS: Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from...

Cite
Cooper JD, Howson JM, Smyth D, et al. Confirmation of novel type 1 diabetes risk loci in families. Diabetologia. 2012;55(4):996-1000doi: 10.1007/s00125-012-2450-3.
Cooper, J. D., Howson, J. M., Smyth, D., Walker, N. M., Stevens, H., Yang, J. H., She, J. X., Eisenbarth, G. S., Rewers, M., Todd, J. A., Akolkar, B., Concannon, P., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Pociot, F., Rich, S. S. (2012). Confirmation of novel type 1 diabetes risk loci in families. Diabetologia, 55(4), 996-1000. https://doi.org/10.1007/s00125-012-2450-3
Cooper, J D, et al. "Confirmation of novel type 1 diabetes risk loci in families." Diabetologia vol. 55,4 (2012): 996-1000. doi: https://doi.org/10.1007/s00125-012-2450-3
Cooper JD, Howson JM, Smyth D, Walker NM, Stevens H, Yang JH, She JX, Eisenbarth GS, Rewers M, Todd JA, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, Nerup J, Nierras C, Pociot F, Rich SS. Confirmation of novel type 1 diabetes risk loci in families. Diabetologia. 2012 Apr;55(4):996-1000. doi: 10.1007/s00125-012-2450-3. Epub 2012 Jan 26. PMID: 22278338; PMCID: PMC3296014.
Copy Download .nbib Format: NLM
Confronting social disparities in child health: a critical appraisal of life-course science and research.

Pediatrics

Wise PH.
PMID: 19861471
Pediatrics. 2009 Nov;124:S203-11. doi: 10.1542/peds.2009-1100H.

The utility of the life-course framework to address disparities in child health is based on its ability to integrate the science of child development with the requirements of effective and just public policy. I argue that the life-course framework...

Cite
Wise PH. Confronting social disparities in child health: a critical appraisal of life-course science and research. Pediatrics. 2009;124:S203-11doi: 10.1542/peds.2009-1100H.
Wise, P. H. (2009). Confronting social disparities in child health: a critical appraisal of life-course science and research. Pediatrics, 124S203-11. https://doi.org/10.1542/peds.2009-1100H
Wise, Paul H. "Confronting social disparities in child health: a critical appraisal of life-course science and research." Pediatrics vol. 124 (2009): S203-11. doi: https://doi.org/10.1542/peds.2009-1100H
Wise PH. Confronting social disparities in child health: a critical appraisal of life-course science and research. Pediatrics. 2009 Nov;124:S203-11. doi: 10.1542/peds.2009-1100H. PMID: 19861471.
Copy Download .nbib Format: NLM
Genotype by environment interaction and neurodevelopment III. Focus on the child's broader social ecology.

Epidemiology and psychiatric sciences

Bellani M, Nobile M, Bianchi V, van Os J, Brambilla P.
PMID: 23402645
Epidemiol Psychiatr Sci. 2013 Jun;22(2):125-9. doi: 10.1017/S2045796013000061. Epub 2013 Feb 12.

In a short series of articles, we will review the evidence for genotype by environment interaction (G × E) in developmental psychopathology. We will focus specifically on the characteristics of types of exposure assessed with respect to both their...

Cite
Bellani M, Nobile M, Bianchi V, et al. Genotype by environment interaction and neurodevelopment III. Focus on the child's broader social ecology. Epidemiol Psychiatr Sci. 2013;22(2):125-9doi: 10.1017/S2045796013000061.
Bellani, M., Nobile, M., Bianchi, V., van Os, J., & Brambilla, P. (2013). Genotype by environment interaction and neurodevelopment III. Focus on the child's broader social ecology. Epidemiology and psychiatric sciences, 22(2), 125-9. https://doi.org/10.1017/S2045796013000061
Bellani, M, et al. "Genotype by environment interaction and neurodevelopment III. Focus on the child's broader social ecology." Epidemiology and psychiatric sciences vol. 22,2 (2013): 125-9. doi: https://doi.org/10.1017/S2045796013000061
Bellani M, Nobile M, Bianchi V, van Os J, Brambilla P. Genotype by environment interaction and neurodevelopment III. Focus on the child's broader social ecology. Epidemiol Psychiatr Sci. 2013 Jun;22(2):125-9. doi: 10.1017/S2045796013000061. Epub 2013 Feb 12. PMID: 23402645; PMCID: PMC6998122.
Copy Download .nbib Format: NLM
Performance of genotype imputations using data from the 1000 Genomes Project.

Human heredity

Sung YJ, Wang L, Rankinen T, Bouchard C, Rao DC.
PMID: 22212296
Hum Hered. 2012;73(1):18-25. doi: 10.1159/000334084. Epub 2011 Dec 30.

Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly...

Cite
Sung YJ, Wang L, Rankinen T, et al. Performance of genotype imputations using data from the 1000 Genomes Project. Hum Hered. 2011;73(1):18-25doi: 10.1159/000334084.
Sung, Y. J., Wang, L., Rankinen, T., Bouchard, C., & Rao, D. C. (2012). Performance of genotype imputations using data from the 1000 Genomes Project. Human heredity, 73(1), 18-25. https://doi.org/10.1159/000334084
Sung, Yun Ju, et al. "Performance of genotype imputations using data from the 1000 Genomes Project." Human heredity vol. 73,1 (2012): 18-25. doi: https://doi.org/10.1159/000334084
Sung YJ, Wang L, Rankinen T, Bouchard C, Rao DC. Performance of genotype imputations using data from the 1000 Genomes Project. Hum Hered. 2012;73(1):18-25. doi: 10.1159/000334084. Epub 2011 Dec 30. PMID: 22212296; PMCID: PMC3322630.
Copy Download .nbib Format: NLM
Gene flow between the Korean peninsula and its neighboring countries.

PloS one

Jung J, Kang H, Cho YS, Oh JH, Ryu MH, Chung HW, Seo JS, Lee JE, Oh B, Bhak J, Kim HL.
PMID: 20686617
PLoS One. 2010 Jul 29;5(7):e11855. doi: 10.1371/journal.pone.0011855.

SNP markers provide the primary data for population structure analysis. In this study, we employed whole-genome autosomal SNPs as a marker set (54,836 SNP markers) and tested their possible effects on genetic ancestry using 320 subjects covering 24 regional...

Cite
Jung J, Kang H, Cho YS, et al. Gene flow between the Korean peninsula and its neighboring countries. PLoS One. 2010;5(7):e11855doi: 10.1371/journal.pone.0011855.
Jung, J., Kang, H., Cho, Y. S., Oh, J. H., Ryu, M. H., Chung, H. W., Seo, J. S., Lee, J. E., Oh, B., Bhak, J., & Kim, H. L. (2010). Gene flow between the Korean peninsula and its neighboring countries. PloS one, 5(7), e11855. https://doi.org/10.1371/journal.pone.0011855
Jung, Jongsun, et al. "Gene flow between the Korean peninsula and its neighboring countries." PloS one vol. 5,7 (2010): e11855. doi: https://doi.org/10.1371/journal.pone.0011855
Jung J, Kang H, Cho YS, Oh JH, Ryu MH, Chung HW, Seo JS, Lee JE, Oh B, Bhak J, Kim HL. Gene flow between the Korean peninsula and its neighboring countries. PLoS One. 2010 Jul 29;5(7):e11855. doi: 10.1371/journal.pone.0011855. PMID: 20686617; PMCID: PMC2912326.
Copy Download .nbib Format: NLM
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...

Cite
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.
Copy Download .nbib Format: NLM
A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning.

PloS one

Bennett BD, Xiong Q, Mukherjee S, Furey TS.
PMID: 23028573
PLoS One. 2012;7(9):e44635. doi: 10.1371/journal.pone.0044635. Epub 2012 Sep 13.

Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these...

Cite
Bennett BD, Xiong Q, Mukherjee S, et al. A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning. PLoS One. 2012;7(9):e44635doi: 10.1371/journal.pone.0044635.
Bennett, B. D., Xiong, Q., Mukherjee, S., & Furey, T. S. (2012). A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning. PloS one, 7(9), e44635. https://doi.org/10.1371/journal.pone.0044635
Bennett, Brian D, et al. "A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning." PloS one vol. 7,9 (2012): e44635. doi: https://doi.org/10.1371/journal.pone.0044635
Bennett BD, Xiong Q, Mukherjee S, Furey TS. A predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learning. PLoS One. 2012;7(9):e44635. doi: 10.1371/journal.pone.0044635. Epub 2012 Sep 13. PMID: 23028573; PMCID: PMC3441565.
Copy Download .nbib Format: NLM
Showing 13 to 24 of 131 entries