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@ARTICLE{Naylor:136111,
      author       = {Naylor, Melissa G and Weiss, Scott T and Lange, Christoph},
      title        = {{A} {B}ayesian approach to genetic association studies with
                      family-based designs.},
      journal      = {Genetic epidemiology},
      volume       = {34},
      number       = {6},
      issn         = {0741-0395},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {DZNE-2020-02433},
      pages        = {569-574},
      year         = {2010},
      abstract     = {For genome-wide association studies with family-based
                      designs, we propose a Bayesian approach. We show that
                      standard transmission disequilibrium test and family-based
                      association test statistics can naturally be implemented in
                      a Bayesian framework, allowing flexible specification of the
                      likelihood and prior odds. We construct a Bayes factor
                      conditional on the offspring phenotype and parental genotype
                      data and then use the data we conditioned on to inform the
                      prior odds for each marker. In the construction of the prior
                      odds, the evidence for association for each single marker is
                      obtained at the population-level by estimating its genetic
                      effect size by fitting the conditional mean model. Since
                      such genetic effect size estimates are statistically
                      independent of the effect size estimation within the
                      families, the actual data set can inform the construction of
                      the prior odds without any statistical penalty. In contrast
                      to Bayesian approaches that have recently been proposed for
                      genome-wide association studies, our approach does not
                      require assumptions about the genetic effect size; this
                      makes the proposed method entirely data-driven. The power of
                      the approach was assessed through simulation. We then
                      applied the approach to a genome-wide association scan to
                      search for associations between single nucleotide
                      polymorphisms and body mass index in the Childhood Asthma
                      Management Program data.},
      keywords     = {Asthma: genetics / Bayes Theorem / Body Mass Index / Child
                      / Genome-Wide Association Study: methods / Genotype / Humans
                      / Linkage Disequilibrium / Models, Genetic / Models,
                      Statistical / Phenotype / Polymorphism, Single Nucleotide},
      cin          = {U T4 Researchers - Bonn},
      ddc          = {610},
      cid          = {I:(DE-2719)7000008},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:20818722},
      pmc          = {pmc:PMC3349938},
      doi          = {10.1002/gepi.20513},
      url          = {https://pub.dzne.de/record/136111},
}