Journal article
Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model
Genetic epidemiology, Vol.40(5), pp.382-393
07/2016
DOI: 10.1002/gepi.21975
PMCID: PMC5582543
PMID: 27247027
Abstract
Genome-wide association studies (GWAS) have led to the identification of many genetic variants associated with complex diseases in the past 10 years. Penalization methods, with significant numerical and statistical advantages, have been extensively adopted in analyzing GWAS. This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, we develop a penalized multitrait mixed modeling approach. It accommodates the two different types of correlations and includes several existing methods as special cases. Effective penalization is adopted for marker selection. Simulation demonstrates its satisfactory performance. The GAW 18 data are analyzed using the proposed method.
Details
- Title: Subtitle
- Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model
- Creators
- Jin Liu - National University of SingaporeCan Yang - Hong Kong Baptist UniversityXingjie Shi - Nanjing University of Finance and EconomicsCong Li - Yale UniversityJian Huang - University of IowaHongyu Zhao - Yale UniversityShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Genetic epidemiology, Vol.40(5), pp.382-393
- DOI
- 10.1002/gepi.21975
- PMID
- 27247027
- PMCID
- PMC5582543
- NLM abbreviation
- Genet Epidemiol
- ISSN
- 0741-0395
- eISSN
- 1098-2272
- Publisher
- Blackwell Publishing Ltd
- Number of pages
- 12
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China (NSFC), award: 61501389; name: Hong Kong Research Grant Council, award: HKBU_22302815, HKBU_12202114; DOI: 10.13039/501100001747, name: Hong Kong Baptist University, award: FRG2/14-15/069, FRG2/14-15/077
- Language
- English
- Date published
- 07/2016
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257625102771
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