BACKGROUND: The discovery of genetic associations is an important factor in the understanding of @entity1 illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, @entity165 /lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset @entity195 (LOAD), a percentage of @entity195 heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods. METHODS: Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis ( @entity19528 ( @entity19528 )). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the @entity19528 classifier construction step. RESULTS: The first approach gave 199 SNPs mostly associated with genes in @entity460 signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with @entity2204 and @entity69468 SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in @entity19528 modeling. Nineteen variants in LD with @entity39649 , @entity7012 , @entity10616 , @entity69469 , @entity69470 , and @entity24304 respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included @entity2204 and @entity69468 SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling. CONCLUSIONS: With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of @entity195 .

Data mining of high density genomic variant data for prediction of XXXX risk.

@entity1 :: ('9606', 'Species') :: ['human'] @entity39649 :: ('6764', 'Gene') :: ['ST5'] @entity2204 :: ('348', 'Gene') :: ['APOE'] @entity7012 :: ('7220', 'Gene') :: ['TRPC1'] @entity69470 :: ('55967', 'Gene') :: ['NDUFA12'] @entity10616 :: ('83734', 'Gene') :: ['ATG10'] @entity24304 :: ('11188', 'Gene') :: ['NISCH'] @entity69468 :: ('9846', 'Gene') :: ['GAB2'] @entity69469 :: ('63982', 'Gene') :: ['ANO3'] @entity460 :: ('MESH:D002118', 'Chemical') :: ['calcium'] @entity195 :: ('MESH:D000544', 'Disease') :: ['AD', "Alzheimer's disease"] @entity19528 :: ('MESH:D007733', 'Disease') :: ['random forest', 'RF'] @entity165 :: ('MESH:D002784', 'Chemical') :: ['cholesterol']
@entity195 :: (MESH:D000544,Disease) :: ['Alzheimer's disease']
