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Genome-Wide Association Analysis

A variety of public data analysis packages are available for genomic mapping applications in both CC and DO populations. Several methodologies also exist for QTL mapping, but discussion of the analysis particulars is beyond the scope of this chapter. Here, a few of the analysis packages that have been utilized in recent investigations are presented. [Pg.324]

Umicevic Mirkov M, Cui J, Vermeulen SH, Stahl EA, Toonen EJ, Makkinje RRet al (2013) Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis. Ann Rheum Dis 72 1375-1381... [Pg.659]

Ferreira, M.A., et al. (2008). Collaborative genome-wide association analysis supports a role for ANK3 and CACNAIC in bipolar disorder. Nat Genet, in press. [Pg.304]

Lasky-Su J, Lange N, Brehm JM, Damask A, Soto-Quiros M, Avila L, Celedon JC, Canino G, Cloutier MM, Hollis BW, Weiss ST, Litonjua AA (2012) Genome-wide association analysis of circulating vitamin D levels in children with asthma. Hum Genet 131 1495-1505... [Pg.720]

Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904-909. Available at http //genepath.med.Harvard.edu/ reich/ EIGENSTRAT.htm. [Pg.40]

Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. [Pg.478]

Kapur K, Johnson T, Beckmann ND et al (2010) Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing gene. PLoS Genet 6 el001035... [Pg.182]

Estrada K, Styrkarsdottir U, Evangelou E et al (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 44 491-501... [Pg.669]

Zeggini, E., et al. (2008). Meta-analysis of genome-wide association data and large-scale repUcation identifies additional susceptibiUty loci for type 2 diabetes. Nat. Genet, 40 638-45. [Pg.307]

Abstract. The analysis of big data, particularly from the biosciences, provides unique challenges to the methods used to analyse such data. Datasets such as those used in genome-wide association studies can have a very high number of variables/dimensions (e.g. 400,000+) and therefore modifications are required to standard methods to allow them to function correctly. [Pg.232]

Barrett, J. C. et al. 2009. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41 703-707. [Pg.169]

Genomic mapping (QTL mapping, genome-wide association (GWA), genotype-phenotype association analysis) Haplotype... [Pg.316]

See other pages where Genome-Wide Association Analysis is mentioned: [Pg.232]    [Pg.685]    [Pg.324]    [Pg.1768]    [Pg.232]    [Pg.685]    [Pg.324]    [Pg.1768]    [Pg.339]    [Pg.175]    [Pg.571]    [Pg.599]    [Pg.101]    [Pg.6]    [Pg.374]    [Pg.610]    [Pg.663]    [Pg.48]    [Pg.262]    [Pg.331]    [Pg.563]    [Pg.742]    [Pg.115]    [Pg.149]    [Pg.407]    [Pg.315]    [Pg.13]    [Pg.459]    [Pg.1788]    [Pg.181]    [Pg.767]    [Pg.52]    [Pg.73]   


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Associative analysis

Genome analyses genomics

Genome analysis

Genome-wide

Genome-wide analyses

Genome-wide association

Genomics analysis

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