ArticleGenetic epidemiology2016
Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model.
Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genome-wide association scan and candidate gene analysis for seed coat color in sesame (Frontiers in plant science · 2025Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement.Journal of the World Aquaculture Society · 2021Article
- Learning gene networks underlying clinical phenotypes using SNP perturbation.PLoS computational biology · 2020Article
- Multi-trait multi-locus SEM model discriminates SNPs of different effects.BMC genomics · 2020Article
- LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies.BMC genomics · 2018Article
- Association Analysis in Rice: From Application to Utilization.Frontiers in plant science · 2016Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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