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A comparison of software packages that assess linkage using a variance components approach
E J Atkinson1, D Hall, M de Andrade
1Department of Health Sciences Research, Section of Biostatistics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Genetic Epidemiology
|January 17, 2002
Summary
Statistical genetics software packages GENEHUNTER, SOLAR, and ACT show agreement for immunoglobulin E (IgE) quantitative trait analysis on chromosome 5. However, discrepancies arise in genome-wide analyses on other chromosomes.
Area of Science:
- Statistical genetics
- Quantitative trait analysis
- Bioinformatics software comparison
Background:
- Quantitative traits, such as immunoglobulin E (IgE) levels, are influenced by genetic factors.
- Statistical genetics software packages are crucial for analyzing complex genetic data.
- Variance components models are widely used to estimate genetic and environmental influences on traits.
Purpose of the Study:
- To compare the performance of three statistical genetics software packages: GENEHUNTER, SOLAR, and ACT.
- To evaluate the consistency of results from these packages for quantitative trait analysis.
- To assess the impact of different datasets and chromosomal regions on software performance.
Main Methods:
- Utilizing a variance components approach for quantitative trait analysis.
- Applying single-point and multipoint linkage analysis.
- Comparing results from GENEHUNTER, SOLAR, and ACT using familial asthma data and genome-wide data.
Main Results:
- Software packages showed agreement for immunoglobulin E (IgE) quantitative trait analysis on a specific region of chromosome 5.
- Larger disagreements were observed among GENEHUNTER, SOLAR, and ACT results for genome-wide German data on chromosomes 1, 9, and 14.
- The effect of covariates was investigated where applicable.
Conclusions:
- GENEHUNTER, SOLAR, and ACT generally provide consistent results for limited genetic analyses.
- Discrepancies among software packages can emerge in more extensive, genome-wide genetic analyses.
- Careful consideration of software choice and data characteristics is important for reliable quantitative trait analysis.