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Cloettapreis_2013_Nr_41

23 been excluded a priori38 . It remains to be shown whether these exhaust­ ive approaches will prove feasible and successful in the study of the com- plex genetic architecture of episodic memory. Gene clusters Rather than computing complex gene-gene interactions, another approach to capture the interplay between genetic variants and its impact on epi- sodic memory focuses on the computation of compound gene and SNP clusters based on multi-locus analyses. For example, a permutation-based method, termed set association, evaluates sets of polymorphic markers and provides a cluster of significant alleles and genotypes with a single test statistic. Importantly, such compound analyses defining gene clus- ters can be used for the calculation of aggregate, individual genetic scores, which principally reflect a person’s number of trait-associated genetic variants weighted by the effect size of each variant. With regard to epi- sodic memory, such approaches have proven feasible39 and extendable to conditions of pathological cognition40 . These multi-locus methods repre- sent an extension of the candidate gene approach and deal with sets of genes in biologically meaningful candidate pathways. A pre-selection of the human homologues of 47 genes with well-established molecular and biological functions in synaptic plasticity and animal memory led to the identification of a 7-gene-cluster associated with episodic memory39 . This gene cluster represents such important memory-related molecules as ade- nylyl cyclases, kinases, and glutamate receptors.An aggregate, individu- al gene score based on the 7-gene-cluster was also associated with acti- vations in memory-related brain regions, such as the hippocampus and parahipocampal gyrus (Figure 1). The computation of aggregate genetic scores based on genetic clusters has hitherto relied on pre-existing infor- mation (i.e., a candidate pathway approach). Recently, similar gene clus- tering methods have been used for the calculation of genetic risk profiles by utilizing GWAS data41 . Most probably, capitalizing on a combination of GWAS data and gene clustering methods will also facilitate the un­ biased identification of novel gene clusters related to episodic memory capacity.

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