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001     136111
005     20240321220019.0
024 7 _ |a 10.1002/gepi.20513
|2 doi
024 7 _ |a pmid:20818722
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024 7 _ |a pmc:PMC3349938
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024 7 _ |a 0741-0395
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024 7 _ |a 1098-2272
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024 7 _ |a altmetric:79866364
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037 _ _ |a DZNE-2020-02433
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Naylor, Melissa G
|0 P:(DE-HGF)0
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|e Corresponding author
245 _ _ |a A Bayesian approach to genetic association studies with family-based designs.
260 _ _ |a New York, NY
|c 2010
|b Wiley-Liss
264 _ 1 |3 online
|2 Crossref
|b Wiley
|c 2010-08-30
264 _ 1 |3 print
|2 Crossref
|b Wiley
|c 2010-09-01
336 7 _ |a article
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336 7 _ |a Output Types/Journal article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a Journal Article
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520 _ _ |a 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.
536 _ _ |a 345 - Population Studies and Genetics (POF3-345)
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542 _ _ |i 2015-09-01
|2 Crossref
|u http://doi.wiley.com/10.1002/tdm_license_1.1
588 _ _ |a Dataset connected to CrossRef, PubMed,
650 _ 2 |a Asthma: genetics
|2 MeSH
650 _ 2 |a Bayes Theorem
|2 MeSH
650 _ 2 |a Body Mass Index
|2 MeSH
650 _ 2 |a Child
|2 MeSH
650 _ 2 |a Genome-Wide Association Study: methods
|2 MeSH
650 _ 2 |a Genotype
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Linkage Disequilibrium
|2 MeSH
650 _ 2 |a Models, Genetic
|2 MeSH
650 _ 2 |a Models, Statistical
|2 MeSH
650 _ 2 |a Phenotype
|2 MeSH
650 _ 2 |a Polymorphism, Single Nucleotide
|2 MeSH
700 1 _ |a Weiss, Scott T
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Lange, Christoph
|0 P:(DE-2719)9000181
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|e Last author
773 1 8 |a 10.1002/gepi.20513
|b : Wiley, 2010-08-30
|n 6
|p 569-574
|3 journal-article
|2 Crossref
|t Genetic Epidemiology
|v 34
|y 2010
|x 0741-0395
773 _ _ |a 10.1002/gepi.20513
|g Vol. 34, no. 6, p. 569 - 574
|0 PERI:(DE-600)1492643-X
|n 6
|q 34:6<569 - 574
|p 569-574
|t Genetic epidemiology
|v 34
|y 2010
|x 0741-0395
856 7 _ |2 Pubmed Central
|u http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3349938
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
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999 C 5 |a 10.1016/S0197-2456(98)00044-0
|9 -- missing cx lookup --
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|o 10.1016/S0197-2456(98)00044-0
999 C 5 |a 10.1038/nature05911
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|o 10.1038/nature05911
999 C 5 |y 2000
|2 Crossref
|o Kuczmarski 2000
999 C 5 |a 10.1159/000073728
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|o 10.1159/000073728
999 C 5 |y 2007
|2 Crossref
|t R: A language and environment for statistical computing
|o R Development Core Team R: A language and environment for statistical computing 2007
999 C 5 |a 10.1038/ng1582
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|o 10.1038/ng1582
999 C 5 |y 2008a
|2 Crossref
|o Wakefield 2008a
999 C 5 |a 10.1093/ije/dym257
|9 -- missing cx lookup --
|2 Crossref
|o 10.1093/ije/dym257

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