Dementia occurs in various forms, with the most common being Alzheimer’s disease (AD). AD is a degenerative neurological condition that is marked by gradual loss of memory, which occurs due to buildup of plaque in the brain, particularly the toxic amyloid β peptides. This accumulation in the brain is majorly attributed to genetic predispositions (70%) and environmental factors (30%) like age, head trauma, level of fitness, and cardiovascular infections.

It is noted that the disease is most prevalent to patients older than 65 years of age, with late-onset Alzheimer’s disease (LOAD) and Sporadic Alzheimer’s disease (SAD) accounting for more than 99% of AD. Research studies indicate that predisposition of a person to SAD is influenced by various genes, nonetheless, there is insufficient documentation on these genes. Sufficient documentation only exists for the ε4 allele (APOE gene). 

Genome-wide association studies depend on the statistics for phenotypic/genotypic identification in a population study, where the availability of recurrent alleles amidst Alzheimer’s disease patients is of significance in the provision of diagnostic values. The Restriction Fragment Length Polymorphisms (RFLPs), Single Nucleotide Polymorphisms (SNPs), and microsatellites are utilized in analysis to expose inheritance patterns that are correlated with the disease.

A blend of healthy participants and SAD patients aged 65 years and above are needed for a study on AD to warrant a marginal genetic error. It is important to maintain a larger size of the group (usually more than 1000) to enhance accuracy for the identification of the influences reconciled by smaller genes. Consequently, ethnicity can cause a genetic difference, and thus should be taken into consideration.

Through the utilization of the Hardy-Weinberg equilibrium, calculation of genetic difference is possible as well as the establishment of control group equilibrium. This allows for the removal of markers that do not match with the equation and thus allowing for allele frequency evaluation in a population. For instance, there are 2 alleles in Locus A, alleles 1 and 2.

P2 + 2PQ + G2 = 1                                                                                                     [1]

Above is the Hardy-Weinberg equation for calculation of the genetic difference, where p represents allele 1 frequency and Q allele 2 frequency.

The significance of the results achieved from the Hardy-Weinberg equation is then confirmed with the chi-squared (χ2) test.

χ2 = (Observed -Expected)2 / Expected                                                                         [2]

If the result achieved from equation 2 above is ≥ 3.84, then the there is significant variance between the two alleles and they are removed, and if the result is ≤ 3.81, then there is no significant variance.

            SAD study analyzes the genetic markers to reveal allele frequency changes while chi test confirms the availability of significant variance in the groups. P-value, on the other hand, indicates the probability of the tested hypothesis, where p-value ≤ 0.05 proves that there is a significant variance and thus the marker directly correlates with AD. Additionally, p-value ≥ 0.05 indicates that there is no significant variation and therefore, the hypothesis is removed.

            The achieved disease markers are then documented and amassed in an odds ratio to define the chances of a person with the marker being affected.

Odds ratio = total value of SAD cases / total value of healthy controls                             [3]

Indirect associated markers transpire concurrently to the functional variant while the direct associated markers are not inevitably pathological despite their direct correlation with the disease.