Concordant Association of Insulin Degrading Enzyme Gene (IDE) Variants with IDE mRNA, A beta, and Alzheimer's Disease

Background: The insulin-degrading enzyme gene (IDE) is a strong functional and positional candidate for late onset Alzheimer's disease (LOAD).

The role of ECE1 variants in cognitive ability in old age and Alzheimer's disease risk

The ß-amyloid peptide may play a central role in Alzheimer's disease (AD) pathogenesis. We have evaluated variants in seven Aß-degrading genes (ACE, ECE1, ECE2, IDE, MME, PLAU, and TF) for association with AD risk in the Genetic and Environmental Risk in Alzheimer's Disease Consortium 1 (GERAD1) cohort, and with three cognitive phenotypes in the Lothian Birth Cohort 1936 (LBC1936), using 128 and 121 SNPs, respectively. In GERAD1, we identified a significant association between a four-SNP intragenic ECE1 haplotype and risk of AD in individuals that carried at least one APOE e4 allele (P = 0.00035, odds ratio = 1.61). In LBC1936, we identified a significant association between a different two-SNP ECE1 intragenic haplotype and non-verbal reasoning in individuals lacking the APOE e4 allele (P = 0.00036, ß = -0.19). Both results showed a trend towards significance after permutation (0.05 <P <0.10). A follow-up cognitive genetic study evaluated the association of ECE1 SNPs in three additional cohorts of non-demented older people. Meta-analysis of the four cohorts identified the significant association (Z <0.05) of SNPs in the ECE-1b promoter with non-verbal reasoning scores, particularly in individuals lacking the APOE e4 allele. Our genetic findings are not wholly consistent. Nonetheless, the AD associated intronic haplotype is linked to the 338A variant of known ECE1b promoter variant, 338C>A (rs213045). We observed significantly less expression from the 338A variant in two human neuroblastoma cell lines and speculate that this promoter may be subject to tissue-specific regulation.

Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.