Mixed Modeling with Whole Genome Data
Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with who...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2012/485174 |
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| _version_ | 1849695453613916160 |
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| author | Jing Hua Zhao Jian'an Luan |
| author_facet | Jing Hua Zhao Jian'an Luan |
| author_sort | Jing Hua Zhao |
| collection | DOAJ |
| description | Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates. Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. The framework also extends to consortium meta-analysis involving both population-based and family-based studies. Results. Through examples we show that the framework can be furnished with general statistical packages whose great advantage lies in simplicity and exibility to study both genetic and environmental effects. Areas which require further work are also indicated. Conclusion. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available. |
| format | Article |
| id | doaj-art-d089a355a6a44d3ca558109d0e85feff |
| institution | DOAJ |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-d089a355a6a44d3ca558109d0e85feff2025-08-20T03:19:46ZengWileyJournal of Probability and Statistics1687-952X1687-95382012-01-01201210.1155/2012/485174485174Mixed Modeling with Whole Genome DataJing Hua Zhao0Jian'an Luan1MRC Epidemiology Unit & Institute of Metabolic Science, Addrenbrooke's Hospital, Box 285, Hills Road, Cambridge CB2 0QQ, UKMRC Epidemiology Unit & Institute of Metabolic Science, Addrenbrooke's Hospital, Box 285, Hills Road, Cambridge CB2 0QQ, UKObjective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates. Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. The framework also extends to consortium meta-analysis involving both population-based and family-based studies. Results. Through examples we show that the framework can be furnished with general statistical packages whose great advantage lies in simplicity and exibility to study both genetic and environmental effects. Areas which require further work are also indicated. Conclusion. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available.http://dx.doi.org/10.1155/2012/485174 |
| spellingShingle | Jing Hua Zhao Jian'an Luan Mixed Modeling with Whole Genome Data Journal of Probability and Statistics |
| title | Mixed Modeling with Whole Genome Data |
| title_full | Mixed Modeling with Whole Genome Data |
| title_fullStr | Mixed Modeling with Whole Genome Data |
| title_full_unstemmed | Mixed Modeling with Whole Genome Data |
| title_short | Mixed Modeling with Whole Genome Data |
| title_sort | mixed modeling with whole genome data |
| url | http://dx.doi.org/10.1155/2012/485174 |
| work_keys_str_mv | AT jinghuazhao mixedmodelingwithwholegenomedata AT jiananluan mixedmodelingwithwholegenomedata |