as genomic data scientist and translator , translate to persian with explation :
## ADNI BED/BIM/FAM Genetic Data Workflow for Prediction Model

Here is a workflow for ADNI BED/BIM/FAM genetic data, from step 0 to the final prediction model: 

*   **Step 0: Data Acquisition and Preparation**
    *   Obtain the ADNI BED/BIM/FAM genetic data. 
        *   The sources do not mention where to get ADNI BED/BIM/FAM genetic data, but they do discuss that genetic data was collected on NeuroX or NeuroChip arrays. 
        *   You may want to find out if ADNI data is available in this format.
    *   Prepare the data for quality control and analysis using PLINK, an open-source genome association analysis tool.
        *   Convert the data to binary files (BED/BIM/FAM).

*   **Step 1: Quality Control**
    *   **Sample-Level QC**
        *   **Sample Call Rate:** Remove samples with low call rates, meaning a large proportion of missing genotypes.
        *   **Gender Mismatch:**  Identify and address discrepancies between reported and genetically inferred sex.
        *   **Relatedness:**  Remove closely related individuals to avoid bias.
        *   **Ancestry:** Ensure that all individuals are from the same ancestry group to avoid population stratification bias.
        *   **Heterozygosity:** Detect and remove samples with extreme heterozygosity rates, which may indicate poor data quality. 
    *   **SNP-Level QC**
        *   **SNP Call Rate:** Remove SNPs with low call rates. 
        *   **Hardy-Weinberg Equilibrium (HWE):** Remove SNPs that significantly deviate from HWE.
        *   **Minor Allele Frequency (MAF):** Remove SNPs with very low MAF.

*   **Step 2: Imputation (Optional)**
    *   **Purpose:** Imputation infers missing genotypes based on a reference panel, increasing the number of SNPs available for analysis. 
    *   **Software:** Use the Michigan Imputation Server with Minimac4 software.
    *   **Pre-Imputation QC:** Perform quality control steps on the data before imputation.
    *   **Post-Imputation QC:** Filter imputed SNPs based on imputation quality scores. 

*   **Step 3: Polygenic Risk Score (PRS) Calculation**
    *   **Software:** Use PRSice-2 software, designed for calculating PRS at a large scale. 
    *   **Base Dataset:** Obtain GWAS summary statistics for AD as the base dataset.
        *   The sources used summary statistics from the IGAP consortium. 
    *   **Clumping:**  
        *   Account for linkage disequilibrium (LD) between SNPs, where SNPs located close to each other on the genome are often inherited together.
        *   Use a clumping algorithm to select the most significant SNP in each LD block to avoid over-representation of specific genomic regions.
        *   PRSice-2 uses PLINK for LD calculation. 
    *   **Threshold Selection:** Test various p-value thresholds to determine the best model fit based on Nagelkerke’s R2 and the number of SNPs included.
        *   Start with SNPs with high significance in the base dataset.
        *   Incrementally increase the threshold to include less significant SNPs.
    *   **Covariates:** Incorporate relevant covariates such as age, sex, and APOE genotype to improve model accuracy. 
        *   The APOE locus is often excluded during initial PRS calculation and included later.
        *   The sources used TaqMan assays to determine APOE genotype. 

*   **Step 4: Prediction Model Evaluation**
    *   **Logistic Regression:** Use logistic regression to build the prediction model, with the calculated PRS and selected covariates as predictors, and disease status (AD or control) as the outcome. 
        *   The sources used SPSS software to perform logistic regression.  
    *   **Model Performance Metrics:** Assess the predictive ability of the model using metrics such as the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC).
    *   **Interpretation:**  
        *   Evaluate the significance of the included SNPs and their potential biological relevance to AD. 
        *   Compare the performance of models with different thresholds and covariate combinations.

*   **Step 5: Application of the Model**
    *   **Risk Prediction:** Use the developed model to predict the risk of AD for new, unseen individuals. 
    *   **Clinical Utility:** If the model has sufficiently high predictive ability, it can be used as a tool to aid in clinical decision-making.

The sources provided are based on retrospective studies using samples with known disease status (AD cases and controls). Predicting AD risk for individuals in a real-world setting, where disease status is unknown, may require further model validation and refinement.