Literature DB >> 25250827

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

Ren-Hua Chung1, Wei-Yun Tsai, Chang-Hsun Hsieh, Kuan-Yi Hung, Chao A Hsiung, Elizabeth R Hauser.   

Abstract

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.
© 2014 WILEY PERIODICALS, INC.

Keywords:  extended pedigree; genetic simulation tools; quantitative trait; sequencing

Mesh:

Year:  2014        PMID: 25250827     DOI: 10.1002/gepi.21850

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  11 in total

1.  An efficient gene-gene interaction test for genome-wide association studies in trio families.

Authors:  Pei-Yuan Sung; Yi-Ting Wang; Ya-Wen Yu; Ren-Hua Chung
Journal:  Bioinformatics       Date:  2016-02-11       Impact factor: 6.937

2.  A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.

Authors:  Ren-Hua Chung; Chen-Yu Kang
Journal:  Gigascience       Date:  2019-05-01       Impact factor: 6.524

3.  The statistical power of genome-wide association studies for threshold traits with different frequencies of causal variants.

Authors:  Hassan Khanzadeh; Navid Ghavi Hossein-Zadeh; Shahrokh Ghovvati
Journal:  Genetica       Date:  2021-10-27       Impact factor: 1.082

4.  KNOWLEDGE DRIVEN BINNING AND PHEWAS ANALYSIS IN MARSHFIELD PERSONALIZED MEDICINE RESEARCH PROJECT USING BIOBIN.

Authors:  Anna O Basile; John R Wallace; Peggy Peissig; Catherine A McCarty; Murray Brilliant; Marylyn D Ritchie
Journal:  Pac Symp Biocomput       Date:  2016

5.  Adaptive combination of P-values for family-based association testing with sequence data.

Authors:  Wan-Yu Lin
Journal:  PLoS One       Date:  2014-12-26       Impact factor: 3.240

6.  GESDB: a platform of simulation resources for genetic epidemiology studies.

Authors:  Po-Ju Yao; Ren-Hua Chung
Journal:  Database (Oxford)       Date:  2016-05-30       Impact factor: 3.451

7.  A biologically informed method for detecting rare variant associations.

Authors:  Carrie Colleen Buchanan Moore; Anna Okula Basile; John Robert Wallace; Alex Thomas Frase; Marylyn DeRiggi Ritchie
Journal:  BioData Min       Date:  2016-08-30       Impact factor: 2.522

8.  A Powerful Gene-Based Test Accommodating Common and Low-Frequency Variants to Detect Both Main Effects and Gene-Gene Interaction Effects in Case-Control Studies.

Authors:  Ren-Hua Chung; Chen-Yu Kang
Journal:  Front Genet       Date:  2018-01-08       Impact factor: 4.599

9.  r2VIM: A new variable selection method for random forests in genome-wide association studies.

Authors:  Silke Szymczak; Emily Holzinger; Abhijit Dasgupta; James D Malley; Anne M Molloy; James L Mills; Lawrence C Brody; Dwight Stambolian; Joan E Bailey-Wilson
Journal:  BioData Min       Date:  2016-02-01       Impact factor: 2.522

10.  Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Authors:  Peng-Lin Lin; Ya-Wen Yu; Ren-Hua Chung
Journal:  PLoS One       Date:  2016-09-13       Impact factor: 3.240

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