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Impact of Population Stratification on Family-Based Association in an Admixed Population.

International journal of genomics

Mersha TB, Ding L, He H, Alexander ES, Zhang X, Kurowski BG, Pilipenko V, Kottyan L, Martin LJ, Fardo DW.
PMID: 26064873
Int J Genomics. 2015;2015:501617. doi: 10.1155/2015/501617. Epub 2015 May 04.

Population substructure is a well-known confounder in population-based case-control genetic studies, but its impact in family-based studies is unclear. We performed population substructure analysis using extended families of admixed population to evaluate power and Type I error in an...

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Mersha TB, Ding L, He H, et al. Impact of Population Stratification on Family-Based Association in an Admixed Population. Int J Genomics. 2015;2015:501617doi: 10.1155/2015/501617.
Mersha, T. B., Ding, L., He, H., Alexander, E. S., Zhang, X., Kurowski, B. G., Pilipenko, V., Kottyan, L., Martin, L. J., & Fardo, D. W. (2015). Impact of Population Stratification on Family-Based Association in an Admixed Population. International journal of genomics, 2015501617. https://doi.org/10.1155/2015/501617
Mersha, T B, et al. "Impact of Population Stratification on Family-Based Association in an Admixed Population." International journal of genomics vol. 2015 (2015): 501617. doi: https://doi.org/10.1155/2015/501617
Mersha TB, Ding L, He H, Alexander ES, Zhang X, Kurowski BG, Pilipenko V, Kottyan L, Martin LJ, Fardo DW. Impact of Population Stratification on Family-Based Association in an Admixed Population. Int J Genomics. 2015;2015:501617. doi: 10.1155/2015/501617. Epub 2015 May 04. PMID: 26064873; PMCID: PMC4434195.
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10 Years of GWAS Discovery: Biology, Function, and Translation.

American journal of human genetics

Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J.
PMID: 28686856
Am J Hum Genet. 2017 Jul 06;101(1):5-22. doi: 10.1016/j.ajhg.2017.06.005.

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation...

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Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5-22doi: 10.1016/j.ajhg.2017.06.005.
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 Years of GWAS Discovery: Biology, Function, and Translation. American journal of human genetics, 101(1), 5-22. https://doi.org/10.1016/j.ajhg.2017.06.005
Visscher, Peter M, et al. "10 Years of GWAS Discovery: Biology, Function, and Translation." American journal of human genetics vol. 101,1 (2017): 5-22. doi: https://doi.org/10.1016/j.ajhg.2017.06.005
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017 Jul 06;101(1):5-22. doi: 10.1016/j.ajhg.2017.06.005. PMID: 28686856; PMCID: PMC5501872.
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Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test.

BMC proceedings

Peralta JM, Almeida M, Abraham LJ, Moses E, Blangero J.
PMID: 27980619
BMC Proc. 2016 Oct 18;10:103-108. doi: 10.1186/s12919-016-0013-1. eCollection 2016.

We present a novel approach to detect potential

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Peralta JM, Almeida M, Abraham LJ, et al. Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test. BMC Proc. 2016;10:103-108doi: 10.1186/s12919-016-0013-1.
Peralta, J. M., Almeida, M., Abraham, L. J., Moses, E., & Blangero, J. (2016). Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test. BMC proceedings, 10103-108. https://doi.org/10.1186/s12919-016-0013-1
Peralta, Juan Manuel, et al. "Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test." BMC proceedings vol. 10 (2016): 103-108. doi: https://doi.org/10.1186/s12919-016-0013-1
Peralta JM, Almeida M, Abraham LJ, Moses E, Blangero J. Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test. BMC Proc. 2016 Oct 18;10:103-108. doi: 10.1186/s12919-016-0013-1. eCollection 2016. PMID: 27980619; PMCID: PMC5133489.
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A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.

BMC proceedings

Green A, Cook K, Grinde K, Valcarcel A, Tintle N.
PMID: 27980630
BMC Proc. 2016 Oct 18;10:165-170. doi: 10.1186/s12919-016-0024-y. eCollection 2016.

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal...

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Green A, Cook K, Grinde K, et al. A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures. BMC Proc. 2016;10:165-170doi: 10.1186/s12919-016-0024-y.
Green, A., Cook, K., Grinde, K., Valcarcel, A., & Tintle, N. (2016). A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures. BMC proceedings, 10165-170. https://doi.org/10.1186/s12919-016-0024-y
Green, Alden, et al. "A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures." BMC proceedings vol. 10 (2016): 165-170. doi: https://doi.org/10.1186/s12919-016-0024-y
Green A, Cook K, Grinde K, Valcarcel A, Tintle N. A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures. BMC Proc. 2016 Oct 18;10:165-170. doi: 10.1186/s12919-016-0024-y. eCollection 2016. PMID: 27980630; PMCID: PMC5133502.
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On combining family- and population-based sequencing data.

BMC proceedings

Katsumata Y, Fardo DW.
PMID: 27980632
BMC Proc. 2016 Oct 18;10:175-179. doi: 10.1186/s12919-016-0026-9. eCollection 2016.

Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet...

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Katsumata Y, Fardo DW. On combining family- and population-based sequencing data. BMC Proc. 2016;10:175-179doi: 10.1186/s12919-016-0026-9.
Katsumata, Y., & Fardo, D. W. (2016). On combining family- and population-based sequencing data. BMC proceedings, 10175-179. https://doi.org/10.1186/s12919-016-0026-9
Katsumata, Yuriko, and Fardo, David W. "On combining family- and population-based sequencing data." BMC proceedings vol. 10 (2016): 175-179. doi: https://doi.org/10.1186/s12919-016-0026-9
Katsumata Y, Fardo DW. On combining family- and population-based sequencing data. BMC Proc. 2016 Oct 18;10:175-179. doi: 10.1186/s12919-016-0026-9. eCollection 2016. PMID: 27980632; PMCID: PMC5133531.
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Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar.

Journal of the American College of Cardiology

Kim DS, Gloyn AL, Knowles JW.
PMID: 34325839
J Am Coll Cardiol. 2021 Aug 03;78(5):496-512. doi: 10.1016/j.jacc.2021.03.346.

Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic...

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Kim DS, Gloyn AL, Knowles JW. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. J Am Coll Cardiol. 2021;78(5):496-512doi: 10.1016/j.jacc.2021.03.346.
Kim, D. S., Gloyn, A. L., & Knowles, J. W. (2021). Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. Journal of the American College of Cardiology, 78(5), 496-512. https://doi.org/10.1016/j.jacc.2021.03.346
Kim, Daniel Seung, et al. "Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar." Journal of the American College of Cardiology vol. 78,5 (2021): 496-512. doi: https://doi.org/10.1016/j.jacc.2021.03.346
Kim DS, Gloyn AL, Knowles JW. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. J Am Coll Cardiol. 2021 Aug 03;78(5):496-512. doi: 10.1016/j.jacc.2021.03.346. PMID: 34325839; PMCID: PMC8328195.
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Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees.

Statistics in biosciences

Jiang Y, Conneely KN, Epstein MP.
PMID: 30643591
Stat Biosci. 2018 Dec;10(3):491-505. doi: 10.1007/s12561-017-9197-9. Epub 2017 Jun 05.

Next generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case-control or population-based studies, with few methods that are applicable...

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Jiang Y, Conneely KN, Epstein MP. Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees. Stat Biosci. 2017;10(3):491-505doi: 10.1007/s12561-017-9197-9.
Jiang, Y., Conneely, K. N., & Epstein, M. P. (2018). Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees. Statistics in biosciences, 10(3), 491-505. https://doi.org/10.1007/s12561-017-9197-9
Jiang, Yunxuan, et al. "Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees." Statistics in biosciences vol. 10,3 (2018): 491-505. doi: https://doi.org/10.1007/s12561-017-9197-9
Jiang Y, Conneely KN, Epstein MP. Robust Rare-Variant Association Tests For Quantitative Traits in General Pedigrees. Stat Biosci. 2018 Dec;10(3):491-505. doi: 10.1007/s12561-017-9197-9. Epub 2017 Jun 05. PMID: 30643591; PMCID: PMC6329454.
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Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19.

BMC proceedings

Blangero J, Teslovich TM, Sim X, Almeida MA, Jun G, Dyer TD, Johnson M, Peralta JM, Manning A, Wood AR, Fuchsberger C, Kent JW, Aguilar DA, Below JE, Farook VS, Arya R, Fowler S, Blackwell TW, Puppala S, Kumar S, Glahn DC, Moses EK, Curran JE, Thameem F, Jenkinson CP, DeFronzo RA, Lehman DM, Hanis C, Abecasis G, Boehnke M, Göring H, Duggirala R, Almasy L.
PMID: 27980614
BMC Proc. 2016 Oct 18;10:71-77. doi: 10.1186/s12919-016-0008-y. eCollection 2016.

BACKGROUND: The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed...

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Blangero J, Teslovich TM, Sim X, et al. Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19. BMC Proc. 2016;10:71-77doi: 10.1186/s12919-016-0008-y.
Blangero, J., Teslovich, T. M., Sim, X., Almeida, M. A., Jun, G., Dyer, T. D., Johnson, M., Peralta, J. M., Manning, A., Wood, A. R., Fuchsberger, C., Kent, J. W., Aguilar, D. A., Below, J. E., Farook, V. S., Arya, R., Fowler, S., Blackwell, T. W., Puppala, S., Kumar, S., Glahn, D. C., Moses, E. K., Curran, J. E., Thameem, F., Jenkinson, C. P., DeFronzo, R. A., Lehman, D. M., Hanis, C., Abecasis, G., Boehnke, M., Göring, H., Duggirala, R., Almasy, L. (2016). Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19. BMC proceedings, 1071-77. https://doi.org/10.1186/s12919-016-0008-y
Blangero, John, et al. "Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19." BMC proceedings vol. 10 (2016): 71-77. doi: https://doi.org/10.1186/s12919-016-0008-y
Blangero J, Teslovich TM, Sim X, Almeida MA, Jun G, Dyer TD, Johnson M, Peralta JM, Manning A, Wood AR, Fuchsberger C, Kent JW, Aguilar DA, Below JE, Farook VS, Arya R, Fowler S, Blackwell TW, Puppala S, Kumar S, Glahn DC, Moses EK, Curran JE, Thameem F, Jenkinson CP, DeFronzo RA, Lehman DM, Hanis C, Abecasis G, Boehnke M, Göring H, Duggirala R, Almasy L. Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19. BMC Proc. 2016 Oct 18;10:71-77. doi: 10.1186/s12919-016-0008-y. eCollection 2016. PMID: 27980614; PMCID: PMC5133484.
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Independent test assessment using the extreme value distribution theory.

BMC proceedings

Almeida M, Blondell L, Peralta JM, Kent JW, Jun G, Teslovich TM, Fuchsberger C, Wood AR, Manning AK, Frayling TM, Cingolani PE, Sladek R, Dyer TD, Abecasis G, Duggirala R, Blangero J.
PMID: 27980644
BMC Proc. 2016 Oct 18;10:245-249. doi: 10.1186/s12919-016-0038-5. eCollection 2016.

The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of...

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Almeida M, Blondell L, Peralta JM, et al. Independent test assessment using the extreme value distribution theory. BMC Proc. 2016;10:245-249doi: 10.1186/s12919-016-0038-5.
Almeida, M., Blondell, L., Peralta, J. M., Kent, J. W., Jun, G., Teslovich, T. M., Fuchsberger, C., Wood, A. R., Manning, A. K., Frayling, T. M., Cingolani, P. E., Sladek, R., Dyer, T. D., Abecasis, G., Duggirala, R., & Blangero, J. (2016). Independent test assessment using the extreme value distribution theory. BMC proceedings, 10245-249. https://doi.org/10.1186/s12919-016-0038-5
Almeida, Marcio, et al. "Independent test assessment using the extreme value distribution theory." BMC proceedings vol. 10 (2016): 245-249. doi: https://doi.org/10.1186/s12919-016-0038-5
Almeida M, Blondell L, Peralta JM, Kent JW, Jun G, Teslovich TM, Fuchsberger C, Wood AR, Manning AK, Frayling TM, Cingolani PE, Sladek R, Dyer TD, Abecasis G, Duggirala R, Blangero J. Independent test assessment using the extreme value distribution theory. BMC Proc. 2016 Oct 18;10:245-249. doi: 10.1186/s12919-016-0038-5. eCollection 2016. PMID: 27980644; PMCID: PMC5133519.
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Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation.

BMC proceedings

González Silos R, Karadag Ö, Peil B, Fischer C, Kabisch M, Legrand C, Lorenzo Bermejo J.
PMID: 27980668
BMC Proc. 2016 Oct 18;10:397-404. doi: 10.1186/s12919-016-0062-5. eCollection 2016.

The relationship between genetic variability and individual phenotypes is usually investigated by testing for association relying on called genotypes. Allele counts obtained from next-generation sequence data could be used for this purpose too. Genetic association can be examined by...

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González Silos R, Karadag Ö, Peil B, et al. Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation. BMC Proc. 2016;10:397-404doi: 10.1186/s12919-016-0062-5.
González Silos, R., Karadag, �. �., Peil, B., Fischer, C., Kabisch, M., Legrand, C., & Lorenzo Bermejo, J. (2016). Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation. BMC proceedings, 10397-404. https://doi.org/10.1186/s12919-016-0062-5
González Silos, Rosa, et al. "Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation." BMC proceedings vol. 10 (2016): 397-404. doi: https://doi.org/10.1186/s12919-016-0062-5
González Silos R, Karadag Ö, Peil B, Fischer C, Kabisch M, Legrand C, Lorenzo Bermejo J. Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation. BMC Proc. 2016 Oct 18;10:397-404. doi: 10.1186/s12919-016-0062-5. eCollection 2016. PMID: 27980668; PMCID: PMC5133473.
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Comparing performance of non-tree-based and tree-based association mapping methods.

BMC proceedings

Thompson KL, Fardo DW.
PMID: 27980669
BMC Proc. 2016 Oct 18;10:405-410. doi: 10.1186/s12919-016-0063-4. eCollection 2016.

A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced in recent decades, along with the statistical methodology to analyze...

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Thompson KL, Fardo DW. Comparing performance of non-tree-based and tree-based association mapping methods. BMC Proc. 2016;10:405-410doi: 10.1186/s12919-016-0063-4.
Thompson, K. L., & Fardo, D. W. (2016). Comparing performance of non-tree-based and tree-based association mapping methods. BMC proceedings, 10405-410. https://doi.org/10.1186/s12919-016-0063-4
Thompson, Katherine L, and Fardo, David W. "Comparing performance of non-tree-based and tree-based association mapping methods." BMC proceedings vol. 10 (2016): 405-410. doi: https://doi.org/10.1186/s12919-016-0063-4
Thompson KL, Fardo DW. Comparing performance of non-tree-based and tree-based association mapping methods. BMC Proc. 2016 Oct 18;10:405-410. doi: 10.1186/s12919-016-0063-4. eCollection 2016. PMID: 27980669; PMCID: PMC5133494.
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Identifying the Lipidomic Effects of a Rare Loss-of-Function Deletion in .

Circulation. Genomic and precision medicine

Blackburn NB, Meikle PJ, Peralta JM, Kumar S, Leandro AC, Bellinger MA, Giles C, Huynh K, Mahaney MC, Göring HHH, VandeBerg JL, Williams-Blangero S, Glahn DC, Duggirala R, Blangero J, Michael LF, Curran JE.
PMID: 33887960
Circ Genom Precis Med. 2021 Jun;14(3):e003232. doi: 10.1161/CIRCGEN.120.003232. Epub 2021 Apr 22.

BACKGROUND: The identification and understanding of therapeutic targets for atherosclerotic cardiovascular disease is of fundamental importance given its global health and economic burden. Inhibition of ANGPTL3 (angiopoietin-like 3) has demonstrated a cardioprotective effect, showing promise for atherosclerotic cardiovascular disease...

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Blackburn NB, Meikle PJ, Peralta JM, et al. Identifying the Lipidomic Effects of a Rare Loss-of-Function Deletion in . Circ Genom Precis Med. 2021;14(3):e003232doi: 10.1161/CIRCGEN.120.003232.
Blackburn, N. B., Meikle, P. J., Peralta, J. M., Kumar, S., Leandro, A. C., Bellinger, M. A., Giles, C., Huynh, K., Mahaney, M. C., Göring, H. H. H., VandeBerg, J. L., Williams-Blangero, S., Glahn, D. C., Duggirala, R., Blangero, J., Michael, L. F., & Curran, J. E. (2021). Identifying the Lipidomic Effects of a Rare Loss-of-Function Deletion in . Circulation. Genomic and precision medicine, 14(3), e003232. https://doi.org/10.1161/CIRCGEN.120.003232
Blackburn, Nicholas B, et al. "Identifying the Lipidomic Effects of a Rare Loss-of-Function Deletion in ." Circulation. Genomic and precision medicine vol. 14,3 (2021): e003232. doi: https://doi.org/10.1161/CIRCGEN.120.003232
Blackburn NB, Meikle PJ, Peralta JM, Kumar S, Leandro AC, Bellinger MA, Giles C, Huynh K, Mahaney MC, Göring HHH, VandeBerg JL, Williams-Blangero S, Glahn DC, Duggirala R, Blangero J, Michael LF, Curran JE. Identifying the Lipidomic Effects of a Rare Loss-of-Function Deletion in . Circ Genom Precis Med. 2021 Jun;14(3):e003232. doi: 10.1161/CIRCGEN.120.003232. Epub 2021 Apr 22. PMID: 33887960; PMCID: PMC8206021.
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Showing 1 to 12 of 27 entries