Academic journal article Genetics

Association Mapping of Complex Trait Loci with Context-Dependent Effects and Unknown Context Variable

Academic journal article Genetics

Association Mapping of Complex Trait Loci with Context-Dependent Effects and Unknown Context Variable

Article excerpt


A novel method for Bayesian analysis of genetic heterogeneity and multilocus association in random population samples is presented. The method is valid for quantitative and binary traits as well as for multiallelic markers. In the method, individuals are stochastically assigned into two etiological groups that can have both their own, and possibly different, subsets of trait-associated (disease-predisposing) loci or alleles. The method is favorable especially in situations when etiological models are stratified by the factors that are unknown or went unmeasured, that is, if genetic heterogeneity is due to, for example, unknown genes × environment or genes × gene interactions. Additionally, a heterogeneity structure for the phenotype does not need to follow the structure of the general population; it can have a distinct selection history. The performance of the method is illustrated with simulated example of genes × environment interaction (quantitative trait with loosely linked markers) and compared to the results of singlegroup analysis in the presence of missing data. Additionally, example analyses with previously analyzed cystic fibrosis and type 2 diabetes data sets (binary traits with closely linked markers) are presented. The implementation (written in WinBUGS) is freely available for research purposes from

(ProQuest Information and Learning: ... denotes formulae omitted.)

WITH the wide availability of markers, association mapping has been increasingly recognized as a primary tool to identify parts of chromosomes that may show a functional relationship to the phenotype (RISCH and MERIKANGAS 1996; FLINT and MOTT 2001; LOHMUELLER et al. 2003). Population-based association studies suffer from confounding due to population stratification (inability to divide variance into withinand among-population components) and genetic heterogeneity (trait loci or their alleles are not unique for the trait). If not accounted for properly, hidden population structure (stratification) may give rise to false positives (LANDER and SCHORK 1994; CARDON and PALMER 2003) and genetic heterogeneity can dramatically disturb or mask the mapping signals (TERWILLIGER and WEISS 1998; THORTON-WELLS et al. 2004). This is why both confounding and heterogeneity are probable contributors to the problem of nonreplication in genetic studies of complex traits (SILLANPÄÄ and Auranen 2004).

Techniques suchas stratified analysis (CLAYTON 2001), matching (HINDS et al. 2004), genomic controls (DEVLIN and ROEDER 1999; MARCHINI et al. 2004), structured association (PRITCHARD et al. 2000a; SILLANPÄÄ et al. 2001; HOGGART et al. 2003), smoothing (CONTI and WITTE 2003; SILLANPÄÄ and BHATTACHARJEE 2005), use of secondary samples (EPSTEIN et al. 2005; KAZEEM and FARRELL 2005), or approaches based on knowledge of relatives (EWENS and SPIELMAN 1995; THOMSON 1995; KNAPP and BECKER 2003) have been used to overcome the problem of population stratification. (For extensive comparison, see SETAKIS et al. 2006.) Similarly, there are approaches based on relationship information (linkage analysis and identity-by-descent methods), haplotype frequency profiles (LONGMATE 2001), or smoothing/partition/clustering of haplotypes or alleles (THOMAS et al. 2001; MORRIS et al. 2002, 2003; SEAMAN et al. 2002; MOLITOR et al. 2003a,b; DURRANT et al. 2004; YU et al. 2004a,b) that are robust to allelic heterogeneity.

Several model-based and model-free methods consider locus heterogeneity in the context of linkage analysis or family data (SMITH 1963; LEAL 1997; GRIGULL et al. 2001; PROVINCE et al. 2001; SCHAID et al. 2001; SHANNON et al. 2001; WHITTEMORE and HALPERN 2001; HODGE et al. 2002; BULL et al. 2003; EKSTROM and DALGAARD 2003; HAUSER et al. 2004; HOTI et al. 2004); however, few consider locus heterogeneity in association analysis or case-control data. To prevent confounding due to locus heterogeneity in association analysis, one may apply a subset analysis (LEAL and OTT 2000; REBBECK et al. …

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