Journal Article

DZNE-2020-04393

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families.

Park, S. ; Lee, S. ; Lee, Y. ; Herold, C.DZNE* ; Hooli, B. ; Mullin, K. ; Park, T. ; Park, C. ; Bertram, L. ; Lange, C.DZNE* ; Tanzi, R. (Corresponding author) ; Won, S.

2015
Springer Heidelberg

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Please use a persistent id in citations: doi:10.1186/s12881-015-0198-6

Abstract: In 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.

Keyword(s): Alzheimer Disease: genetics (MeSH) ; Computer Simulation (MeSH) ; Data Interpretation, Statistical (MeSH) ; Family (MeSH) ; Gene Frequency (MeSH) ; Genetic Association Studies: methods (MeSH) ; Genetic Heterogeneity (MeSH) ; Humans (MeSH) ; Selection Bias (MeSH)

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Contributing Institute(s):

1. Genomische Mathematik in der Neuroepidemiologie (GenomMathematik)
2. U T4 Researchers - Bonn (U T4 Researchers - Bonn)

Research Program(s):

1. 345 - Population Studies and Genetics (POF3-345) (POF3-345)

Appears in the scientific report 2015

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Database coverage:
; ; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Ebsco Academic Search ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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