SeqSIMLA2: simulating correlated quantitative traits accounting for shared environmental effects in user-specified pedigree structure.

Journal Article (Journal Article)

Simulation tools that simulate sequence data in unrelated cases and controls or in families with quantitative traits or disease status are important for genetic studies. The simulation tools can be used to evaluate the statistical power for detecting the causal variants when planning a genetic epidemiology study, or to evaluate the statistical properties for new methods. We previously developed SeqSIMLA version 1 (SeqSIMLA1), which simulates family or case-control data with a disease or quantitative trait model. SeqSIMLA1, and several other tools that simulate quantitative traits, do not specifically model the shared environmental effects among relatives on a trait. However, shared environmental effects are commonly observed for some traits in families, such as body mass index. SeqSIMLA1 simulates a fixed three-generation family structure. However, it would be ideal to simulate prespecified pedigree structures for studies involving large pedigrees. Thus, we extended SeqSIMLA1 to create SeqSIMLA2, which can simulate correlated traits and considers the shared environmental effects. SeqSIMLA2 can also simulate prespecified large pedigree structures. There are no restrictions on the number of individuals that can be simulated in a pedigree. We used a blood pressure example to demonstrate that SeqSIMLA2 can simulate realistic correlation structures between the systolic and diastolic blood pressure among relatives. We also showed that SeqSIMLA2 can simulate large pedigrees with large chromosomal regions in a reasonable time frame.

Full Text

Duke Authors

Cited Authors

  • Chung, R-H; Tsai, W-Y; Hsieh, C-H; Hung, K-Y; Hsiung, CA; Hauser, ER

Published Date

  • January 2015

Published In

Volume / Issue

  • 39 / 1

Start / End Page

  • 20 - 24

PubMed ID

  • 25250827

Electronic International Standard Serial Number (EISSN)

  • 1098-2272

Digital Object Identifier (DOI)

  • 10.1002/gepi.21850


  • eng

Conference Location

  • United States