Computational Systems Biology Methods and Protocols.7z

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HapMap to estimate the effective numbernof independent
SNPs [20].


  1. Well-designed replication studies are crucial to validate or
    refute the initial positive association.

  2. Fine mapping is a process of refining the associated variants to a
    credible set most likely to include the causal variant, which
    requires (1) that all common SNPs in the associated region
    are genotyped or imputed with high confidence, (2) very strin-
    gent quality control, and (3) large sample sizes sufficient in
    separating out SNPs in high LD.

  3. Meta-analysis methodology of GWA studies has successfully
    improved the power for detecting and validating disease-gene
    associations in some conditions, such as type 2 diabetes. There
    is a wide array of approaches, including fixed effects, random
    effects, Bayesian meta-analysis, and trans-ethnic meta-analysis,
    you can apply in your study for particular needs [21].


References



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    genome-wide association studies. Genet Epi-
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  2. Visscher PM, Brown MA, McCarthy MI, Yang
    J (2012) Five years of GWAS discovery. Am J
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  3. Haldar T, Ghosh S (2011) Power comparison
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  4. Satagopan JM (2004) Two-stage designs for
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  5. Kronenberg F (2008) Genome-wide associa-
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    10.1016/j.exger.2007.09.005

  6. MacArthur J, Bowler E et al (2017) The new
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  7. Welter D, MacArthur J, Morales J, Burdett T,
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(Database issue):D1001–D1006.https://doi.
org/10.1093/nar/gkt1229


  1. Purcell S, Neale B, Todd-Brown K, Thomas L,
    Ferreira MA, Bender D, Maller J, Sklar P, de
    Bakker PI, Daly MJ, Sham PC (2007) PLINK:
    a tool set for whole-genome association and
    population-based linkage analyses. Am J Hum
    Genet 81(3):559–575. https://doi.org/10.
    1086/519795

  2. Zondervan KT, Cardon LR (2007) Designing
    candidate gene and genome-wide case-control
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    nprot.2007.366

  3. Hoggart CJ, Clark TG, De Iorio M, Whittaker
    JC, Balding DJ (2008) Genome-wide signifi-
    cance for dense SNP and resequencing data.
    Genet Epidemiol 32(2):179–185. https://
    doi.org/10.1002/gepi.20292

  4. Seng KC, Seng CK (2008) The success of the
    genome-wide association approach: a brief
    story of a long struggle. Eur J Hum Genet 16
    (5):554–564. Bush WS, Moore JH (2012)
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    PLoS Comput Biol 8 (12):e1002822.
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  5. Chanock S, NCI-NHGRI Working Group on
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  6. Barrett JC, Fry B, Maller J, Daly MJ (2005)
    Haploview: analysis and visualization of LD


An Overview of Genome-Wide Association Studies 107
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