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000138071 0247_ $$2doi$$a10.1186/s12881-015-0198-6
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000138071 037__ $$aDZNE-2020-04393
000138071 041__ $$aEnglish
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000138071 1001_ $$0P:(DE-HGF)0$$aPark, Suyeon$$b0
000138071 245__ $$aAdjusting heterogeneous ascertainment bias for genetic association analysis with extended families.
000138071 260__ $$aHeidelberg$$bSpringer$$c2015
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000138071 520__ $$aIn family-based association analysis, each family is typically ascertained from a single proband, which renders the effects of ascertainment bias heterogeneous among family members. This is contrary to case-control studies, and may introduce sample or ascertainment bias. Statistical efficiency is affected by ascertainment bias, and careful adjustment can lead to substantial improvements in statistical power. However, genetic association analysis has often been conducted using family-based designs, without addressing the fact that each proband in a family has had a great influence on the probability for each family member to be affected.We propose a powerful and efficient statistic for genetic association analysis that considered the heterogeneity of ascertainment bias among family members, under the assumption that both prevalence and heritability of disease are available. With extensive simulation studies, we showed that the proposed method performed better than the existing methods, particularly for diseases with large heritability.We applied the proposed method to the genome-wide association analysis of Alzheimer's disease. Four significant associations with the proposed method were found.Our significant findings illustrated the practical importance of this new analysis method.
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000138071 650_2 $$2MeSH$$aAlzheimer Disease: genetics
000138071 650_2 $$2MeSH$$aComputer Simulation
000138071 650_2 $$2MeSH$$aData Interpretation, Statistical
000138071 650_2 $$2MeSH$$aFamily
000138071 650_2 $$2MeSH$$aGene Frequency
000138071 650_2 $$2MeSH$$aGenetic Association Studies: methods
000138071 650_2 $$2MeSH$$aGenetic Heterogeneity
000138071 650_2 $$2MeSH$$aHumans
000138071 650_2 $$2MeSH$$aSelection Bias
000138071 7001_ $$0P:(DE-HGF)0$$aLee, Sungyoung$$b1
000138071 7001_ $$0P:(DE-HGF)0$$aLee, Young$$b2
000138071 7001_ $$0P:(DE-2719)2802016$$aHerold, Christine$$b3$$udzne
000138071 7001_ $$0P:(DE-HGF)0$$aHooli, Basavaraj$$b4
000138071 7001_ $$0P:(DE-HGF)0$$aMullin, Kristina$$b5
000138071 7001_ $$0P:(DE-HGF)0$$aPark, Taesung$$b6
000138071 7001_ $$0P:(DE-HGF)0$$aPark, Changsoon$$b7
000138071 7001_ $$0P:(DE-HGF)0$$aBertram, Lars$$b8
000138071 7001_ $$0P:(DE-2719)9000181$$aLange, Christoph$$b9$$udzne
000138071 7001_ $$0P:(DE-HGF)0$$aTanzi, Rudolph$$b10$$eCorresponding author
000138071 7001_ $$0P:(DE-HGF)0$$aWon, Sungho$$b11
000138071 77318 $$2Crossref$$3journal-article$$a10.1186/s12881-015-0198-6$$b : Springer Science and Business Media LLC, 2015-08-19$$n1$$p62$$tBMC Medical Genetics$$v16$$x1471-2350$$y2015
000138071 773__ $$0PERI:(DE-600)2041359-2$$a10.1186/s12881-015-0198-6$$gVol. 16, no. 1, p. 62$$n1$$p62$$q16:1<62$$tBMC medical genetics$$v16$$x1471-2350$$y2015
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