## 1 Introduction  
The existence of familial forms of Alzheimer's disease (AD) suggests that genetic factors play a crucial role in the pathogenesis of this disease. The heritability is 58-79% for late-onset AD and more than 90% for early-onset AD [1]. The most studied but not exhaustive genetic risk factor for AD is the \(\epsilon 4\) allele of the _APOE_ gene (_APOE-\(\epsilon 4\)_), which encodes apolipoprotein E. This allele increases the risk of AD by 3-fold in heterozygous carriers and 15-fold in homozygous carriers [2]. People who have the _APOE_ gene's _e2_ allele (_APOE-e2_) have a lower risk of AD. The search for reliable genetic risk factors beyond _APOE_ has been ongoing for some time. Large international consortia such as the International Genomics of Alzheimer's Project (IGAP) and the Alzheimer's Disease Genetics Consortium (ADGC) have been established to examine the genetic basis of Alzheimer's disease. Genome-wide association studies (GWAS) have accumulated vast amounts of data on AD genetics using samples from tens of thousands of AD patients and healthy donors [3,4]. These studies have identified over 40 loci associated with the disease [5]. This knowledge has significantly contributed to understanding the pathogenesis and polygenic nature of AD, forming a foundation for further research on the molecular processes of the disease's progression and the development of new therapeutic strategies. However, single-nucleotide polymorphisms (SNPs) typically have a limited impact on disease risk and cannot be utilized as independent prognostic markers. This problem is common among multifactorial diseases. To gauge the impact of genetic factors on disease development, a polygenic risk score (PRS) approach was developed. PRS models estimate the total (multiplicative) impact of several SNPs, which are typically selected based on GWAS using specialized algorithms [6]. Each SNP is assigned a coefficient (often a weighted odds ratio or hazard ratio), and the PRS is computed as the total number of risk alleles multiplied by their corresponding coefficients. The first PRS model for assessing the risk of developing AD was released in 2005, preceding the large-scale GWAS. This model consisted of nine SNPs, incorporating _APOE-e4_[7]. GWAS data have since given rise to PRS models comprising nineteen to hundreds of thousands of SNPs [8, 9, 10, 11, 12, 13]. Additional factors such as _APOE_ gene alleles, gender, age, and other social and physiological characteristics can also be incorporated. Studies on PRS models have indicated their correlation with the age and risk of AD and dementia [8, 9, 10, 11, 14] as well as their impact on the rate of progression of mild cognitive impairment (MCI) and the risk of its conversion to AD [15, 16, 17]. Additionally, the relationship between PRS and cognitive function in healthy individuals of various ages has been established [18, 19]. Moreover, PRS has been associated with structural and functional brain changes as well as some biochemical indicators related to signs of neurodegeneration [9, 20], such as amyloid-\(\beta\) (A\(\beta\)) and tau protein deposition [10, 21, 22, 23, 24]. A\(\beta\)40, A\(\beta\)42, phosphorylated tau 181 (pTau181), and total tau (tTau) serve as additional diagnostic markers for AD [25]. Therefore, exploring their correlation with PRS presents an interesting avenue for investigation. Thus, polygenic models offer a promising approach to identifying individuals at high risk for developing AD. These tests may have practical applications in selecting personalized preventive measures and developing screening strategies. In addition, PRS models could be utilized in the design of clinical trials for AD therapy aimed at preventing disease progression. It is suggested that supplementing study cohorts with individuals who have a high PRS and thus an elevated risk for developing AD may enhance the probability of identifying effective treatment regimens [6, 16, 26].