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Items: 1 to 20 of 81

1.

Genetics of the Framingham Heart Study population.

Govindaraju DR, Cupples LA, Kannel WB, O'Donnell CJ, Atwood LD, D'Agostino RB Sr, Fox CS, Larson M, Levy D, Murabito J, Vasan RS, Splansky GL, Wolf PA, Benjamin EJ.

Adv Genet. 2008;62:33-65. doi: 10.1016/S0065-2660(08)00602-0. Review.

2.

Design considerations for genetic linkage and association studies.

Nsengimana J, Bishop DT.

Methods Mol Biol. 2012;850:237-62. doi: 10.1007/978-1-61779-555-8_13.

PMID:
22307702
3.

Genetic determinants of mortality. Can findings from genome-wide association studies explain variation in human mortality?

Ganna A, Rivadeneira F, Hofman A, Uitterlinden AG, Magnusson PK, Pedersen NL, Ingelsson E, Tiemeier H.

Hum Genet. 2013 May;132(5):553-61. doi: 10.1007/s00439-013-1267-6. Epub 2013 Jan 25.

PMID:
23354976
4.

Finding genes influencing susceptibility to complex diseases in the post-genome era.

Rannala B.

Am J Pharmacogenomics. 2001;1(3):203-21. Review.

PMID:
12083968
5.

Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family.

Nolan D, Kraus WE, Hauser E, Li YJ, Thompson DK, Johnson J, Chen HC, Nelson S, Haynes C, Gregory SG, Kraus VB, Shah SH.

PLoS One. 2013 Aug 2;8(8):e71779. doi: 10.1371/journal.pone.0071779. Print 2013.

6.

Linkage analysis of factors underlying insulin resistance: Strong Heart Family Study.

North KE, Almasy L, Göring HH, Cole SA, Diego VP, Laston S, Cantu T, Williams JT, Howard BV, Lee ET, Best LG, Fabsitz RR, MacCluer JW.

Obes Res. 2005 Nov;13(11):1877-84.

7.

Genome-wide linkage meta-analysis identifies susceptibility loci at 2q34 and 13q31.3 for genetic generalized epilepsies.

EPICURE Consortium, Leu C, de Kovel CG, Zara F, Striano P, Pezzella M, Robbiano A, Bianchi A, Bisulli F, Coppola A, Giallonardo AT, Beccaria F, Trenité DK, Lindhout D, Gaus V, Schmitz B, Janz D, Weber YG, Becker F, Lerche H, Kleefuss-Lie AA, Hallman K, Kunz WS, Elger CE, Muhle H, Stephani U, Møller RS, Hjalgrim H, Mullen S, Scheffer IE, Berkovic SF, Everett KV, Gardiner MR, Marini C, Guerrini R, Lehesjoki AE, Siren A, Nabbout R, Baulac S, Leguern E, Serratosa JM, Rosenow F, Feucht M, Unterberger I, Covanis A, Suls A, Weckhuysen S, Kaneva R, Caglayan H, Turkdogan D, Baykan B, Bebek N, Ozbek U, Hempelmann A, Schulz H, Rüschendorf F, Trucks H, Nürnberg P, Avanzini G, Koeleman BP, Sander T.

Epilepsia. 2012 Feb;53(2):308-18. doi: 10.1111/j.1528-1167.2011.03379.x. Epub 2012 Jan 13.

8.

Consistency of genetic analyses in longitudinal data: observations from the GAW13 Framingham Heart Study data.

Diego VP, Atwood L, Mathias RA, Almasy L.

Genet Epidemiol. 2003;25 Suppl 1:S29-35.

PMID:
14635166
9.

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk.

Simonson MA, Wills AG, Keller MC, McQueen MB.

BMC Med Genet. 2011 Oct 26;12:146. doi: 10.1186/1471-2350-12-146.

10.

Cardiovascular disease risk factors, type 2 diabetes mellitus, and the Framingham Heart Study.

Fox CS.

Trends Cardiovasc Med. 2010 Apr;20(3):90-5. doi: 10.1016/j.tcm.2010.08.001. Review.

11.

Genotype-environment interactions for quantitative traits in Korea Associated Resource (KARE) cohorts.

Kim J, Lee T, Lee HJ, Kim H.

BMC Genet. 2014 Feb 4;15:18. doi: 10.1186/1471-2156-15-18.

12.
13.

The sex-specific genetic architecture of quantitative traits in humans.

Weiss LA, Pan L, Abney M, Ober C.

Nat Genet. 2006 Feb;38(2):218-22. Epub 2006 Jan 22.

PMID:
16429159
14.

Adaptive genetic variation and heart disease risk.

Parnell LD, Lee YC, Lai CQ.

Curr Opin Lipidol. 2010 Apr;21(2):116-22. doi: 10.1097/MOL.0b013e3283378e42. Review.

15.

Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study.

Cupples LA.

Curr Opin Lipidol. 2008 Apr;19(2):144-50. doi: 10.1097/MOL.0b013e3282f73746. Review.

PMID:
18388694
17.

The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports.

Cupples LA, Arruda HT, Benjamin EJ, D'Agostino RB Sr, Demissie S, DeStefano AL, Dupuis J, Falls KM, Fox CS, Gottlieb DJ, Govindaraju DR, Guo CY, Heard-Costa NL, Hwang SJ, Kathiresan S, Kiel DP, Laramie JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey M, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf PA, Yang Q, Atwood LD.

BMC Med Genet. 2007;8 Suppl 1:S1.

18.

In search of causal variants: refining disease association signals using cross-population contrasts.

Saccone NL, Saccone SF, Goate AM, Grucza RA, Hinrichs AL, Rice JP, Bierut LJ.

BMC Genet. 2008 Aug 29;9:58. doi: 10.1186/1471-2156-9-58.

19.

A genome-wide scan to identify loci for smoking rate in the Framingham Heart Study population.

Li MD, Ma JZ, Cheng R, Dupont RT, Williams NJ, Crews KM, Payne TJ, Elston RC; Framingham Heart Study.

BMC Genet. 2003 Dec 31;4 Suppl 1:S103.

20.

Evidence for a gene influencing haematocrit on chromosome 6q23-24: genomewide scan in the Framingham Heart Study.

Lin JP, O'Donnell CJ, Levy D, Cupples LA.

J Med Genet. 2005 Jan;42(1):75-9. No abstract available.

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