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Items: 1 to 20 of 73

1.

Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores.

Lee CH, Cook S, Lee JS, Han B.

Genomics Inform. 2016 Dec;14(4):173-180. doi: 10.5808/GI.2016.14.4.173.

2.

Collapsed methylation quantitative trait loci analysis for low frequency and rare variants.

Richardson TG, Shihab HA, Hemani G, Zheng J, Hannon E, Mill J, Carnero-Montoro E, Bell JT, Lyttleton O, McArdle WL, Ring SM, Rodriguez S, Campbell C, Smith GD, Relton CL, Timpson NJ, Gaunt TR.

Hum Mol Genet. 2016 Oct 1;25(19):4339-4349. doi: 10.1093/hmg/ddw283.

3.

Meta-Analysis of Genome-Wide Association Studies with Correlated Individuals: Application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Sofer T, Shaffer JR, Graff M, Qi Q, Stilp AM, Gogarten SM, North KE, Isasi CR, Laurie CC, Szpiro AA.

Genet Epidemiol. 2016 Sep;40(6):492-501. doi: 10.1002/gepi.21981.

PMID:
27256683
4.

Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

Jiang Y, He Y, Zhang H.

J Am Stat Assoc. 2016;111(513):355-376.

PMID:
27217599
5.

Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies.

Hong J, Lunetta KL, Cupples LA, Dupuis J, Liu CT.

Genet Epidemiol. 2016 May;40(4):284-92. doi: 10.1002/gepi.21963.

PMID:
27061095
6.

A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping.

Han B, Duong D, Sul JH, de Bakker PI, Eskin E, Raychaudhuri S.

Hum Mol Genet. 2016 May 1;25(9):1857-66. doi: 10.1093/hmg/ddw049.

PMID:
26908615
7.

Gender-Dependent Association of FTO Polymorphisms with Body Mass Index in Mexicans.

Saldaña-Alvarez Y, Salas-Martínez MG, García-Ortiz H, Luckie-Duque A, García-Cárdenas G, Vicenteño-Ayala H, Cordova EJ, Esparza-Aguilar M, Contreras-Cubas C, Carnevale A, Chávez-Saldaña M, Orozco L.

PLoS One. 2016 Jan 4;11(1):e0145984. doi: 10.1371/journal.pone.0145984.

8.

FRMD3 gene: its role in diabetic kidney disease. A narrative review.

Buffon MP, Sortica DA, Gerchman F, Crispim D, Canani LH.

Diabetol Metab Syndr. 2015 Dec 30;7:118. doi: 10.1186/s13098-015-0114-4. Review.

9.

Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.

Fan R, Wang Y, Chiu CY, Chen W, Ren H, Li Y, Boehnke M, Amos CI, Moore JH, Xiong M.

Genetics. 2016 Feb;202(2):457-70. doi: 10.1534/genetics.115.180869.

10.

Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis.

Pei YF, Tian Q, Zhang L, Deng HW.

Ann Hum Genet. 2016 Mar;80(2):113-22. doi: 10.1111/ahg.12143.

PMID:
26686198
11.

FAPI: Fast and accurate P-value Imputation for genome-wide association study.

Kwan JS, Li MX, Deng JE, Sham PC.

Eur J Hum Genet. 2016 May;24(5):761-6. doi: 10.1038/ejhg.2015.190.

PMID:
26306642
12.

A Multi-Breed Genome-Wide Association Analysis for Canine Hypothyroidism Identifies a Shared Major Risk Locus on CFA12.

Bianchi M, Dahlgren S, Massey J, Dietschi E, Kierczak M, Lund-Ziener M, Sundberg K, Thoresen SI, Kämpe O, Andersson G, Ollier WE, Hedhammar Å, Leeb T, Lindblad-Toh K, Kennedy LJ, Lingaas F, Rosengren Pielberg G.

PLoS One. 2015 Aug 11;10(8):e0134720. doi: 10.1371/journal.pone.0134720.

13.

Statistical and Computational Methods for Genetic Diseases: An Overview.

Camastra F, Di Taranto MD, Staiano A.

Comput Math Methods Med. 2015;2015:954598. doi: 10.1155/2015/954598. Review.

14.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

Fan R, Wang Y, Boehnke M, Chen W, Li Y, Ren H, Lobach I, Xiong M.

Genetics. 2015 Aug;200(4):1089-104. doi: 10.1534/genetics.115.178343.

15.

Identification of a novel FGFRL1 MicroRNA target site polymorphism for bone mineral density in meta-analyses of genome-wide association studies.

Niu T, Liu N, Zhao M, Xie G, Zhang L, Li J, Pei YF, Shen H, Fu X, He H, Lu S, Chen XD, Tan LJ, Yang TL, Guo Y, Leo PJ, Duncan EL, Shen J, Guo YF, Nicholson GC, Prince RL, Eisman JA, Jones G, Sambrook PN, Hu X, Das PM, Tian Q, Zhu XZ, Papasian CJ, Brown MA, Uitterlinden AG, Wang YP, Xiang S, Deng HW.

Hum Mol Genet. 2015 Aug 15;24(16):4710-27. doi: 10.1093/hmg/ddv144.

16.
17.

Big data challenges in bone research: genome-wide association studies and next-generation sequencing.

Alonso N, Lucas G, Hysi P.

Bonekey Rep. 2015 Feb 11;4:635. doi: 10.1038/bonekey.2015.2.

18.

Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets.

Gross AL, Sherva R, Mukherjee S, Newhouse S, Kauwe JS, Munsie LM, Waterston LB, Bennett DA, Jones RN, Green RC, Crane PK; Alzheimer's Disease Neuroimaging Initiative.; GENAROAD Consortium.; AD Genetics Consortium..

Neuroepidemiology. 2014;43(3-4):194-205. doi: 10.1159/000367970.

19.

Systematic assessment of imputation performance using the 1000 Genomes reference panels.

Liu Q, Cirulli ET, Han Y, Yao S, Liu S, Zhu Q.

Brief Bioinform. 2015 Jul;16(4):549-62. doi: 10.1093/bib/bbu035.

20.

Using population isolates in genetic association studies.

Hatzikotoulas K, Gilly A, Zeggini E.

Brief Funct Genomics. 2014 Sep;13(5):371-7. doi: 10.1093/bfgp/elu022. Review.

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