ArticleBMC medical genetics2015
Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families.
Article in BMC medical genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Article
- Article
- Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting.American journal of human genetics · 2019Article
- WISARD: workbench for integrated superfast association studies for related datasets.BMC medical genomics · 2018Article
- Arrhythmogenic Cardiomyopathy: Electrical and Structural Phenotypes.Arrhythmia & electrophysiology review · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundIn 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.
methodWe 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.
resultsWe applied the proposed method to the genome-wide association analysis of Alzheimer's disease. Four significant associations with the proposed method were found.
conclusionOur significant findings illustrated the practical importance of this new analysis method.
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Registered trials
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