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BMC Med Genet. 2015 Aug 19;16:62. doi: 10.1186/s12881-015-0198-6.

Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families.

Park S1,2,3, Lee S4, Lee Y5,6, Herold C7,8, Hooli B9, Mullin K10, Park T11, Park C12, Bertram L13,14,15, Lange C16,17,18,19, Tanzi R20, Won S21,22,23.

Author information

1
Department of Applied Statistics, Chung-Ang University, Seoul, Korea. sooyeon1002@gamil.com.
2
Center for Genome Science, National Institute of Health, Osong Health Technology Administration complex, Chungcheongbuk-do, Seoul, Korea. sooyeon1002@gamil.com.
3
Department of Biostatistics, Soonchunhyang University, College of Medicine, Seoul, Korea. sooyeon1002@gamil.com.
4
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea. biznok@gmail.com.
5
Department of Applied Statistics, Chung-Ang University, Seoul, Korea. lyou7688@gmail.com.
6
Center for Genome Science, National Institute of Health, Osong Health Technology Administration complex, Chungcheongbuk-do, Seoul, Korea. lyou7688@gmail.com.
7
German Center for Neurodegenerative Diseases (DZNE), Sigmund-Freud-Str. 25, Bonn, 53127, Germany. christine.herold@dzne.de.
8
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. christine.herold@dzne.de.
9
Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Massachusetts, USA. bhooli@helix.mgh.harvard.edu.
10
Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Massachusetts, USA. kmullin1@mgh.harvard.edu.
11
Department of Statistics, Seoul National University, Seoul, Korea. tspark@snu.ac.kr.
12
Department of Applied Statistics, Chung-Ang University, Seoul, Korea. cspark@cau.ac.kr.
13
Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Massachusetts, USA. lars.bertram@uni-luebeck.de.
14
Department of Vertebrate Genomics, Neuropsychiatric Genetics Group, Max Planck Institute for Molecular Genetics, Berlin, Germany. lars.bertram@uni-luebeck.de.
15
Department of Medicine, School of Public Health, Imperial College London, London, UK. lars.bertram@uni-luebeck.de.
16
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. clange@hsph.harvard.edu.
17
Harvard Medical School, Boston, MA, USA. clange@hsph.harvard.edu.
18
Institute for Genomic Mathematics, University of Bonn, Bonn, Germany. clange@hsph.harvard.edu.
19
German Center for Neurodegenerative Diseases, Bonn, Germany. clange@hsph.harvard.edu.
20
Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Massachusetts, USA. rtanzi@mgh.harvard.edu.
21
Department of Public Health Science, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
22
Institute of Health and Environment, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
23
National Cancer Center, Seoul, Korea. won1@snu.ac.kr.

Abstract

BACKGROUND:

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.

METHOD:

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.

RESULTS:

We applied the proposed method to the genome-wide association analysis of Alzheimer's disease. Four significant associations with the proposed method were found.

CONCLUSION:

Our significant findings illustrated the practical importance of this new analysis method.

PMID:
26286599
PMCID:
PMC4593209
DOI:
10.1186/s12881-015-0198-6
[Indexed for MEDLINE]
Free PMC Article

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