Biomedical subjects

Shaun Purcell

Publications and source records attributed to Shaun Purcell.

21 records · Page 2Linked to original sources

PubMed2002

Optimal selection strategies for QTL mapping using pooled DNA samples.

The cost of large-scale association studies may be reduced substantially by analysis of pooled DNA from multiple individuals. Here we examine the optimal symmetric and asymmetric designs for pooling experiments for quantitative traits under a range of assumptions about the underlying genetic model and the sources of experimental errors in allele frequency estimation. The results indicate that, in the absence of experimental errors and for common alleles with additive effects, a symmetric pooling scheme comparing the top 27% with the bottom 27% of the trait distribution is optimal, extracting 80% the total information available. A symmetric design is not optimal for rare or recessive alleles, which require asymmetric (or other) pooling strategies. Allele frequency measurement errors reduce the optimal pooling fraction as well as the overall efficiency of the pooling design. In contrast, random variation in the amount of DNA contributed by individuals to a pool reduces only the overall efficiency of the pooling design. Our results emphasize the importance of minimising experimental errors and suggest a pooling fraction of around 20%.

Analysis of Variance↗

PubMed2002

Variance components models for gene-environment interaction in twin analysis.

Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.

Computer Simulation↗

PubMed2002

Variance components models for gene-environment interaction in quantitative trait locus linkage analysis.

Gene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.

Computer Simulation↗