A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data

Division of Statistical Genomics, Washington University School of Medicine, 4444 Forest Park Boulevard, Campus Box 8506, St, Louis, Missouri 63108 USA. .
BMC proceedings 12/2009; 3 Suppl 7(Suppl 7):S98. DOI: 10.1186/1753-6561-3-s7-s98
Source: PubMed


ABSTRACT : We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h2 of 0.0015 to 0.0002).

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    • "In general, this replication or incorporation of additional information into analytical methods proved fruitful, and integration of linkage and association analyses showed promise. However, random effects often determined which loci with very small effects were detectable[Daw et al., 2009;Hendricks et al., 2009]. Association testing while controlling for relatedness within a sample proved valuable: evidence of cryptic relatedness was identified within the FHS[Marchani et al., 2009]. "
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    ABSTRACT: Group 12 evaluated approaches to incorporate outside information or otherwise optimize traditional linkage and association analyses. The abundance of available data allowed exploration of identity-by-descent (IBD) estimation, score statistics, formal combination of linkage and association testing, significance estimation, and replication. We observed that IBD estimation can be optimized with a subset of marker data while estimation of inheritance vectors can provide both IBD estimates and a measure of their uncertainty. Score statistics incorporating covariates or combining association and linkage information performed at least as well as standard approaches while requiring less computation time. The formal combination of linkage and association methods may be fruitful, although the nature of the simulated data limited our conclusions. Estimation of significance may be improved through simulation, correction for cryptic relatedness, and the inclusion of prior information. Replication using real data provided consistent results, though the same was not true of simulated data replicates. Overall, we found that increasing the amount of available data limits analyses due to computational constraints and motivates the need to improve methods for the identification of complex-trait genes.
    Full-text · Article · Jan 2009 · Genetic Epidemiology