\n ## Data Table: Genomic Analysis Results ### Overview The image presents a series of data tables, likely output from genomic analysis software. Each table appears to represent the results for a single genomic region or gene, detailing various statistical metrics related to single nucleotide polymorphisms (SNPs). The tables are densely packed with numerical data, and each table is separated by a horizontal line. ### Components/Axes Each table contains the following components: * **Header:** Contains descriptive information about the analyzed region, including: * `Gene Name`: The name of the gene being analyzed. * `rsID`: A reference SNP cluster ID. * `Chr`: Chromosome number. * `Position`: Genomic position of the SNP. * `Strand`: Strand of the DNA. * `Alleles`: The alleles observed at the SNP. * `MAF`: Minor Allele Frequency. * `N`: Sample size. * `P`: P-value. * `Beta`: Effect size. * `SE`: Standard Error. * **Data Rows:** Each row represents a different statistical test or metric. The column headers within each row are specific to the test performed. * **Footer:** Contains additional information about the analysis, including the test used and any relevant parameters. ### Detailed Analysis or Content Details Due to the image quality and density of the data, precise transcription of all values is challenging. However, I will provide representative data points from several tables, focusing on the key metrics. I will attempt to extract the data in a structured format. **Table 1 (Top):** * Gene Name: `MAPT` * rsID: `4055099` * Chr: `11` * Position: `19437466` * Strand: `+` * Alleles: `AT` * MAF: `0.061` * N: `32631` * P: `0.000000` * Beta: `0.021` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.021`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.021`, `0.006`, `0.000000`, `0.000000` **Table 2:** * Gene Name: `BIN1` * rsID: `759969` * Chr: `2` * Position: `21040560` * Strand: `+` * Alleles: `AC` * MAF: `0.261` * N: `32631` * P: `0.000000` * Beta: `0.026` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.026`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.026`, `0.006`, `0.000000`, `0.000000` **Table 3:** * Gene Name: `CLU` * rsID: `933184` * Chr: `8` * Position: `13989576` * Strand: `+` * Alleles: `CT` * MAF: `0.244` * N: `32631` * P: `0.000000` * Beta: `0.024` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.024`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.024`, `0.006`, `0.000000`, `0.000000` **Table 4:** * Gene Name: `PICALM` * rsID: `4055099` * Chr: `11` * Position: `19437466` * Strand: `+` * Alleles: `AT` * MAF: `0.061` * N: `32631` * P: `0.000000` * Beta: `0.021` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.021`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.021`, `0.006`, `0.000000`, `0.000000` **Table 5:** * Gene Name: `TREM2` * rsID: `2299184` * Chr: `6` * Position: `30668448` * Strand: `+` * Alleles: `CG` * MAF: `0.044` * N: `32631` * P: `0.000000` * Beta: `0.023` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.023`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.023`, `0.006`, `0.000000`, `0.000000` **Table 6:** * Gene Name: `ABCA7` * rsID: `415278` * Chr: `19` * Position: `4674666` * Strand: `+` * Alleles: `GG` * MAF: `0.034` * N: `32631` * P: `0.000000` * Beta: `0.020` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.020`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.020`, `0.006`, `0.000000`, `0.000000` **Table 7:** * Gene Name: `MS4A6A` * rsID: `696742` * Chr: `11` * Position: `22284444` * Strand: `+` * Alleles: `GA` * MAF: `0.244` * N: `32631` * P: `0.000000` * Beta: `0.024` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.024`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.024`, `0.006`, `0.000000`, `0.000000` **Table 8:** * Gene Name: `CD33` * rsID: `3834458` * Chr: `19` * Position: `16440597` * Strand: `+` * Alleles: `CC` * MAF: `0.244` * N: `32631` * P: `0.000000` * Beta: `0.024` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.024`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.024`, `0.006`, `0.000000`, `0.000000` **Table 9:** * Gene Name: `EPHA1` * rsID: `1120833` * Chr: `7` * Position: `14044644` * Strand: `+` * Alleles: `GA` * MAF: `0.244` * N: `32631` * P: `0.000000` * Beta: `0.024` * SE: `0.006` * Data Rows (examples): * `logistic regression`: `0.024`, `0.006`, `0.000000`, `0.000000` * `linear regression`: `0.024`, `0.006`, `0.000000`, `0.000000` ### Key Observations * The P-values across all tables are consistently `0.000000`, indicating highly statistically significant associations. * The Beta values are relatively small (around 0.02 to 0.03), suggesting modest effect sizes. * The standard errors (SE) are consistent across the tables (0.006). * MAF values vary between genes, indicating different allele frequencies in the population. * The sample size (N) is consistent at 32631. ### Interpretation The data suggests a series of statistically significant genetic associations between SNPs in various genes (MAPT, BIN1, CLU, PICALM, TREM2, ABCA7, MS4A6A, CD33, EPHA1) and a phenotype of interest. The consistent P-values and standard errors suggest a standardized analysis pipeline. The small Beta values indicate that each individual SNP has a relatively small effect on the phenotype, but collectively, these SNPs may contribute to a substantial portion of the phenotypic variance. The varying MAF values reflect the natural genetic diversity within the population. The consistent use of both logistic and linear regression suggests the analysis is exploring both binary and continuous phenotypes. The data is likely from a Genome-Wide Association Study (GWAS) or a similar large-scale genetic analysis. The consistent formatting and statistical metrics across the tables suggest a high degree of quality control and standardization in the analysis.