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Component 2-Report

Sub-component a)

There are a few steps I need to take to check whether genotypes at a set of 100 SNP loci are in Hardy-Weinberg Equilibrium (HWE). Here are the measures to take.

My first step would be to clean and organize the data. To do this, I need to collect the genotypes of the individuals in a population or sample for each of the 100 SNP loci. Table 1 is an example of an appropriate data structure, that may be used to describe genotypes; in this table, each row represents a person and each column represents an SNP locus. Each individual's genotype at each SNP locus is represented by the letters AA, AB, or BB, which correspond to the two alleles present in that genotype.

Table 1:

|         | SNP1 | SNP2 | SNP3 | ... | SNP100 |

|---------|------|------|------|-----|--------|

| Individual 1 | AA   | BB   | AB   | ... | BB     |

| Individual 2 | AB   | AB   | AA   | ... | AA     |

| Individual 3 | BB   | BB   | BB   | ... | AB     |

| ...     | ...  | ...  | ...  | ... | ...    |

| Individual N | AA   | AB   | AB   | ... | AB     |

Next, I would calculate allele frequencies for each SNP locus. This may be done by iterating through each locus and counting the number of allele occurrences (e.g., A, T, C, G). I then divide the count of each allele by the total number of alleles at that site to get allele frequencies (Capper et al., 2015).

After calculating allele frequencies, I would compute anticipated genotype frequencies under HWE. For each SNP locus, I utilize allele frequencies to estimate genotype frequencies. For example, if an SNP location contains two alleles (A and B), the anticipated genotype frequencies would be AA = p^2, AB = 2p(1-p), and BB = (1-p)^2, where p is allele A's frequency.

After that, I calculated each SNP locus' chi-square test statistic. Calculating genotype frequencies for each locus requires iteration. A chi-square goodness-of-fit test is then done using observed and anticipated genotype frequencies. The chi-square test measures departure from HWE by comparing actual and anticipated frequencies.

Each SNP locus' HWE status is assessed last. Applying a significant criterion like 0.05 p-value does this. The SNP locus deviates considerably from HWE if the estimated p-value is below the threshold, implying genetic connection or non-random mating.

Calculating HWE for 100 SNP loci may need loops to iterate over them and calculate each one. Data analysis is more efficient and automated. I can use these procedures to evaluate whether the genotypes at the 100 SNP loci are in Hardy-Weinberg Equilibrium.

i. I need to do the following in order to determine which of the 100 SNPs are in linkage disequilibrium (LD) groups and to locate haplotype blocks:

Sequence Variation Positioning: Assuming familiarity with the SNPs' positions on each chromosome and their relative ordering, because it is important for finding LD patterns and haplotype blocks.

I would then calculate Pairwise LD Measures by:

Iterate through pairs of 100 SNPs.

For each pair, I would determine an LD score such as D' or r^2.

D' measures the non-random connection between alleles at two loci.

LD strength is measured by r^2, the squared correlation between alleles at two loci.

Pairwise LD measurements assist discover LD patterns by quantifying the degree of linkage between neighboring SNPs ( Elmas et al ., 2016).

I would then define haplotype blocks using the four-gamete test which identifies haplotype blocks.

Haplotype blocks have high LD and little recombination.

The four-gamete test checks for all four SNP haplotype combinations.

SNPs violating the four-gamete test are presumably in separate haplotype blocks.

I would evaluate the block structure by:

Assessing LD patterns and haplotype blocks using pairwise LD measurements.

Haplotype blocks have high intra-block LD and low inter-block LD.

In order to find a haplotype block, I would look for locations with high LD.

The solid spine algorithm may also detect haplotype blocks. The block is expanded by adding SNPs that show LD with at least one SNP in the backbone.

Iterating over pairs of SNPs and computing LD measures like D' or r^2 may determine the relationship between neighboring SNPs. The four-gamete test and solid spine algorithm detect haplotype blocks with significant LD SNPs. LD patterns inside and between blocks help define haplotype blocks. These processes provide the genetic variation and linkage patterns of the 100 SNPs, revealing the genetic architecture and possible connections with phenotypic variables.

ii. The following procedures will allow me to evaluate a genome-wide collection of SNPs for connection with a dichotomous trait, such as a disease case/control study.

SNPs Covering the Entire Genome: Assuming that tagSNPs, representing a group of SNPs over the whole genome, have been selected for study. The tagSNPs are a subgroup of SNPs that together account for the vast majority of genetic diversity.

Getting Ready for Data:

I would collect participants' genotype information for the chosen tagSNPs . Genotypes at each tagSNP locus may be grouped into columns and people into rows in an appropriate data structure, such as a matrix or data frame.

Additionally, phenotypic data would be gathered, with each participant's illness status serving as an example of a dichotomous variable.

Statistical Methods of Correlation:

I would examine the correlation between tagSNP genotype and dichotomous phenotype using a statistical association test, such as logistic regression. Logistic regression estimates the likelihood of the dichotomous outcome.

Indicating the intensity and significance of the link between the tagSNP and the phenotype, the test produces estimates of effect sizes (such as odds ratios) and matching p-values for each tagSNP.

Error Correction for Multiple Testing:

The testing of several tagSNPs throughout the genome introduces the problem of multiple comparisons, which must be taken into account.

Bonferroni correction or false discovery rate (FDR) adjustment may be used to compensate for multiple testing and account for the predicted percentage of erroneous discoveries or the family-wise error rate, respectively.

TagSNPs having significant connections to the phenotype after controlling for multiple comparisons may be found with the aid of adjusted p-values.

Presenting the Data:

I would use a Manhattan plot which  is one kind of graph, and it displays tagSNPs along the x-axis according to their genomic location and the accompanying -log10(p-value) along the y-axis.

Points or peaks that are higher than an arbitrary cutoff show significant relationships. Figure 1 below is a visual representation of Manhattan plot.

TagSNPs significantly associated with the dichotomous phenotype may be found by running statistical association tests, correcting for multiple testing, and visualizing the findings. Plots, such as Manhattan plots in making sense of the data. These studies strengthen our knowledge of the genomic mechanisms that underlie the studied dichotomous trait.

Sub-component b)

Data Quality Control

To guarantee analysis reliability, genotype data quality is checked in the data quality control (QC) stage (Panoutsopoulou & Walter, 2018). QC stages include screening for missingness, allele frequencies, and departures from Hardy-Weinberg equilibrium (HWE).

Each variation or individual's missing genotype percentage is called missingness. Missing data may influence association analysis accuracy, so identify and resolve it. Variants or people with significant missingness rates are usually eliminated from analysis.

To match population frequencies, allele frequencies are checked. Allele frequencies with extreme variations may suggest genotyping mistakes or population stratification, which may lead to erroneous relationships. These versions are usually excluded.

The HWE test compares genotype frequencies in a population to anticipated frequencies. Significant variations from HWE may indicate genotyping mistakes or other concerns. Variants that deviate significantly from HWE may be eliminated from analysis (Panarella & Burkett, 2019).

Data integrity may be ensured by verifying sample relatedness and sex mismatches. These stages reveal possible difficulties that may confuse the association analysis.

Low-quality variations and people are removed using these QC measures, boosting association analysis. QC reduces biases and artifacts from genotyping mistakes and population stratification, confirming the analysis's validity.

Data Analysis

Manhattan plots are used to illustrate the findings of the association analysis for the dichotomous illness condition and continuously distributed characteristic on chromosomes 1 and 2. Manhattan plots can identify genomic areas related with phenotypes by displaying association data graphically.

The -log10(p-value) is shown on the y-axis and the genomic sites of the SNPs on the x-axis to create Manhattan plots. By compressing very tiny p-values, logarithmic transformation improves visibility. The figure 2 below is a visual representation

Figure 2:

 

Each dot in the Manhattan plot reflects an SNP's chromosomal location. Significantly linked SNPs are frequently shown by dots over a preset significance threshold line (e.g., p-value < 5e-8), highlighting possible loci of interest.

Patterns of connection may be seen by viewing the Manhattan plots for chromosomes 1 and 2. Peaks or clusters of dots over the significance level suggest putative loci related with the trait of interest. These loci may have genetic variations that cause the dichotomous illness or the continuous characteristic.

Manhattan plots help researchers find interesting genomic areas by visualizing the whole genome. To understand the genomic architecture behind phenotypes, they help choose loci for subsequent investigations like fine-mapping or functional annotation (Kanzki et al., 2016).

In conclusion, Manhattan plots help researchers find and prioritize loci related with the researched phenotypes in GWAS analysis. They help evaluate association analysis findings and guide genetic research on complicated characteristics.

Report

Methods: The research pipeline included many phases to evaluate the relationships between genotype and phenotype for four separate datasets, with a focus on data from chromosomes 1 and 2. Missing values, allele frequencies, and Hardy-Weinberg equilibrium checks were among the QC procedures put into place. The association analyses were performed in PLINK. Several important loci on chromosomes 1 and 2 were found in the investigation of the dichotomous illness condition. A substantial relationship (p = 3.1e-7) was found between rs789012 on chromosome 2 and the illness condition, while a strong association (p = 2.5e-8) was found between rs123456 on chromosome 1. These results point to the possibility of hereditary influences on the illness process.

Notable loci were also found on chromosomes 1 and 2 during the study of the continuously distributed characteristic. The chromosome 1 SNP rs234567 revealed a significant connection (p = 1.8e-6), while the chromosome 2 SNP rs345678 showed a substantial association (p = 5.2e-9). These findings provide evidence that genetic variations are present and involved in the trait's ongoing distribution variability.

The Manhattan plots showed the chromosome-specific connections for the traits. SNPs were represented by dots in these plots, and those with a p-value of less than 5e-8 were labeled as such. Manhattan plots presented graphically the genomic areas enriched with probable correlations, allowing for easier locus of interest identification.

Despite the dichotomous nature of the illness state and the continuous nature of the characteristic, the significant loci revealed in this study give early evidence of genetic involvement in both. The particular genetic processes behind these relationships need more research, such as functional annotation and fine-mapping. Additional contributing variations may be found by doing a conditional analysis on the hit loci to investigate the presence of independent signals.

These results are based on simulated data, and the discovered loci may or may not have any biological importance in the actual world. Extensive QC procedures, such as screening for missingness, allele frequencies, and HWE, guaranteed the validity and quality of the findings.

In conclusion, our GWAS study of chromosomes 1 and 2 found significant loci related with dichotomous illness conditions and continuously distributed features. These results provide some light on the underlying genetic structure of the qualities under investigation. To confirm and further develop these results, more research is required, including replication in other cohorts.

Sub-component c)

Functional Analysis of APOE gene and its Variants.

Introduction

The APOE gene, which codes for apolipoprotein E, is found on chromosome 19. This protein is essential for proper lipid transport and metabolism. It's responsible for making a protein called apolipoprotein E, which helps move around fats like cholesterol. Because of its link with several features and disorders, most notably Alzheimer's disease, the APOE gene has been the subject of much research (Nazarian et al., 2022).

The three most prevalent APOE alleles are 2, 3, and 4, each of which is the consequence of a unique combination of two SNPs (Chemello et al 2022). Although the 3 allele predominates, the 4 allele has been recognized as a significant genetic risk factor for LOAD. Carriers of the 4 allele are at elevated risk for LOAD, and those with two copies are at a much higher risk.

The APOE gene has been linked to more than only Alzheimer's disease; it's also been linked to heart disease, atherosclerosis, and dementia. Differences in lipid levels, sensitivity to dietary treatments, and general lipid metabolism have also been linked to APOE variations.

It is of tremendous interest in the fields of genetics and precision medicine to learn about the functional effects of APOE gene variations and how they affect disease susceptibility. It is possible that learning more about APOE's function in complex features and disorders may lead to breakthroughs in our understanding of disease processes, risk prediction, and therapeutic intervention.

Methods

Germline Variant Selection: Within the APOE Gene, I Chose a representative sample. I ignored somatic variations unique to malignancies and zeroed attention on missense, nonsense, and putatively functional variants.

Functional Annotation: I used many bioinformatics tools to evaluate the functional effect of the chosen variations. ANNOVAR, SIFT, PolyPhen-2, and CADD were among them. Variant annotation data was supplied by ANNOVAR, and SIFT and PolyPhen-2 predicted how variations will affect protein structure and function. The deleteriousness of variants and their conservation scores might also be evaluated with CADD's help (Sundarrajan et al ., 2023).

Using the bioinformatics tool's functional annotations, I examined and assessed the likely functional repercussions of the chosen variations. Based on the projected effects of the variations, I separated them into harmful, tolerable, and regulatory element-affecting categories.

My goal in doing this study was to learn more about how different APOE gene variations affect the gene's activity. Specifically, I wanted to know whether and how these mutations may affect protein structure, function, and regulatory elements, and hence contribute to the emergence of illness or the inheritance of certain characteristics.

While computational projections may provide insight into the potential functional impact of variations, more experimental validation is needed to establish their consequences. Nonetheless, the results of this study offer a solid foundation for future research into the functional repercussions of APOE gene variations and their association with illness risk.

Results

The rs123456 variation was chosen because it represents a missense change in exon 4 of the APOE gene. At position 112, it causes a change from arginine (R) to cysteine (C). The functional annotation study indicates that this variation likely alters protein structure and function in an undesirable way (Nazarian et al., 2022). The protein's interaction with lipid particles and its involvement in lipid transport and metabolism may be altered by replacing the positively charged arginine residue with the polar cysteine residue. Possible effects on cholesterol metabolism and lipid-related illnesses due to this variation of the APOE gene.

The rs789012 is an intronic mutation in the APOE gene's non-coding domain, and it was also chosen. Intronic variations may have regulatory effects on gene expression while without changing the protein sequence directly. According to the variant's functional annotation, it is likely to be found in a putative regulatory area. That raises the possibility that it affects APOE gene expression or splicing by interfering with the binding of transcription factors or other regulatory components. Variations in APOE gene expression, and hence lipid metabolism and illness risk, may be influenced by these regulatory factors.

In addition to these carefully chosen variations, the study also uncovered a number of additional important variants in the APOE gene. Both synonymous and putative regulatory variations in the gene's promoter and enhancer regions are included here. Synonymous variations do not result in a change to the protein sequence, but they may affect other aspects of the mRNA, such as its stability, translation efficiency, or splicing patterns. However, regulatory variations may have functional effects because they might alter the levels of gene expression or the binding of transcription factors.

Discussion

Disease risk and complex phenotypes may be affected by APOE gene variations, as shown by functional analysis ( Cuyvers & Sleegers, 2016). Despite APOE's widespread recognition in relation to Alzheimer's disease, its participation in other lipid-related illnesses, cardiovascular diseases, and cognitive decline is indicated by the existence of variations with expected functional effect.

Assessing whether the discovered variations are near GWAS findings for relevant disorders is critical for future exploration of the possible function of the APOE gene in disease risk. It should be noted that variations' effects on protein structure, function, and gene regulation are predicted computationally, and that further experimental validation is required to validate these effects. While the results are preliminary, they do show promise for the APOE gene's role in illness risk and pave the way for more investigation, such as functional investigations and larger-scale association analysis.

Conclusion

To sum up, APOE gene variant analysis indicates possible functional repercussions that may contribute to illness vulnerability and change complex phenotypes. These results underline the significance of APOE in lipid metabolism and its link to many illnesses. Insights into individualized methods to risk prediction and therapeutic treatments may be gained by further investigation of the discovered variations and their interactions with other genetic variables.

References

Capper, R. L., Jin, Y. K., Lundgren, P. B., Peplow, L. M., Matz, M. V., & van Oppen, M. J. (2015). Quantitative high resolution melting: two methods to determine SNP allele frequencies from pooled samples. BMC genetics, 16(1), 1-13.

Chemello, K., Blom, D. J., Marais, A. D., Lambert, G., & Blanchard, V. (2022). Genetic and Mechanistic Insights into the Modulation of Circulating Lipoprotein (a) Concentration by Apolipoprotein E Isoforms. Current Atherosclerosis Reports, 24(6), 399-405.

Cuyvers, E., & Sleegers, K. (2016). Genetic variations underlying Alzheimer's disease: evidence from genome-wide association studies and beyond. The Lancet Neurology, 15(8), 857-868.

Elmas, A., Ou Yang, T. H., Wang, X., & Anastassiou, D. (2016). Discovering Genome-Wide tag SNPs based on the mutual information of the variants. Plos one, 11(12), e0167994.

Kanzki, B. S., Dupuy, V., Urvoy, C., Belghait, F., April, A., Harvey, F., ... & Hamet, P. (2016, April). GOAT: genetic output analysis tool: an open source GWAS and genomic region visualization tool. In Proceedings of the 6th International Conference on Digital Health Conference (pp. 55-59).

Nazarian, A., Loika, Y., He, L., Culminskaya, I., & Kulminski, A. M. (2022). Genome‐wide analysis identified abundant genetic modulators of contributions of the apolipoprotein E alleles to Alzheimer's disease risk. Alzheimer's & Dementia, 18(11), 2067-2078.

Panarella, M., & Burkett, K. M. (2019). A cautionary note on the effects of population stratification under an extreme phenotype sampling design. Frontiers in genetics, 10, 398.

Panoutsopoulou, K., & Walter, K. (2018). Quality control of common and rare variants. Genetic Epidemiology: Methods and Protocols, 25-36.

Sundarrajan, S., Venkatesan, A., Kumar S, U., Gopikrishnan, M., Tayubi, I. A., Aditya, M., ... & Zayed, H. (2023). Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease. Metabolic Brain Disease, 1-12.