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Background: The Apolipoprotein E (APOE) gene is the main risk factor for late-onset Alzheimer disease (LOAD). Genetic variants and haplotypes in regions near the APOE locus may be associated with LOAD in the Colombian population. Objective: We evaluated frequencies and risk of genetic variants and haplotypes in APOE, TOMM40, and APOC1 promoters, also in putative regulatory enhancer elements (TOMM40 IVS2-4 and TOMM40 IVS6), and in cis-regulatory elements (ME1 and BCR). Materials and Methods: Our case-control association study was carried out in 50 patients with LOAD and 50 controls. We determined frequencies and odd ratios for genetic variants and haplotypes. Results: We found a significant association between LOAD and genetic variants at the TOMM40 promoter, at TOMM40 IVS2-4 and TOMM40 IVS6 regulatory enhancer elements, and at the APOC1 promoter. Particularly, variants of Poly-T and APOC1 promoter could anticipate the age of onset of LOAD in our population. We identified three risk haplotypes in TOMM40 (ACGGAG, ACGGGG, and ATAGGC) related to LOAD’s age of onset. We also found other risk or protection haplotypes at the TOMM40 and APOE promoters, at TOMM40 IVS2-4, TOMM40 IVS6 regulatory enhancer elements, and at ME1. Conclusion: Genetic variants and haplotypes near the APOE locus are related to LOAD risk and accelerated onset of LOAD in the Colombian population.

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Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.