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  • Analysis of Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging

    The article describes two main types of epigenetic changes, referred to as chromatin modifications or chromatin marks. They specifically analyze post-translational modifications (PTMs) of histone proteins and the incorporation of histone variants. Chromatin modifications encompass a wide range of chemical changes to histones, including phosphorylation, acetylation, and methylation. This refers to the exchange of standard histone proteins in the nucleosome with different versions, such as H3.3, H2A.Z, and macroH2A.

    According to the article, aging has several effects on epigenetic modifications in immune cells. For one, the variation in chromatin marks, both between different individuals and within single cells of the same individual, increases with age. Another effect mentioned in the article is that the overall levels of most chromatin marks were found to be elevated in the immune cells of older subjects compared to younger subjects. An increased cell-to-cell variability in chromatin marks is referred to as “epigenomic noise.” The authors suggest this leads to higher “transcriptional noise,” meaning gene expression becomes less precise and more variable in aging cells. A study of twins revealed that these aging-related epigenetic alterations are predominantly driven by non-heritable, environmental influences accumulated over a lifetime.

    In Figure 3, the paper analyzes the heterogeneity of chromatin in different lymphocyte subsets using the EpiTOF platform. The figure groups different T cell subsets based on their chromatin modification profiles to see which cell types are most similar epigenetically. It also directly compares the levels of specific chromatin marks between different functional subsets, such as naive vs. memory T cells and regulatory T cells vs. other T cells. The researchers used PCA to separate NK cell subsets based on the variation in their chromatin marks. The main result reported in Figure 3 is that different functional subsets of lymphocytes have distinct, specialized chromatin modification profiles.

    The immune system comprises many distinct cell types, each with a unique epigenetic profile that defines its identity and function. Aging disrupts the usually stable and precise epigenetic regulation. As people age, the epigenetic profiles within their immune cells become more variable and less consistent, both within individuals and across different cells. These epigenetic changes are a molecular feature of aging in the immune system. The increased epigenomic noise that accumulates with age is linked to increased variability in gene expression. As aging occurs, the loss of precise epigenetic control could be an underlying mechanism for the decline in immune function that is a hallmark of the aging process.

    References

    Peggie Cheung, F. V. (2018). Single-Cell Chromatin Modification Profiling Reveals Increased Epigenetic Variations with Aging. Cell, 173(6), 1385-1397. https://doi.org/10.1016/j.cell.2018.03.079.

  • Analysis of self-reported eye color in a familial cohort

    This observational study investigated self-reported eye color across three generations of a primary family unit, supplemented by data from unrelated individuals. The initial assessment revealed variation in eye color, even within the core family, suggesting a complex inheritance pattern rather than a simple Mendelian model. The first generation (n=4) exhibited a spectrum of blue, green, and brown eyes. The second generation, comprising five subjects, predominantly displayed blue and green eyes, with only one individual reporting brown eyes. Interestingly, the third generation (n=5) showed a different distribution, with individuals having either blue (n=3) or brown (n=2) eyes, and no reported green eyes in this cohort.

    These findings align with the established understanding that human eye color is a polygenic trait, influenced by the interplay of multiple genes rather than a single gene locus (Sturm & Duffy, 2012). The primary genes involved regulate melanin production and distribution in the iris, leading to the observed phenotypic diversity (Sturm & Duffy, 2012). The variation seen within the family, where individuals across generations exhibit different eye colors despite familial relationships, is a hallmark of polygenic inheritance. Each parent contributes a unique set of alleles from these multiple genes, and the specific combination inherited by an offspring determines their eye color. For instance, parents with blue or green eyes can carry alleles that, when combined in their children, may result in brown eyes, or vice versa, depending on the complex interactions of these numerous genetic factors (White & Rabago-Smith, 2011). The shift in observed eye colors between the second and third generations in this study likely reflects the specific allelic combinations passed down, demonstrating how different phenotypic ratios can emerge due to the segregation and independent assortment of multiple contributing genes.

  • Analysis of Epidemiological Statistics for Apolipoprotein E in Alzheimer’s Disease

    The communication of epidemiological statistics in genetic research carries significant weight, especially for conditions like Alzheimer’s disease, where public anxiety is high. The scientific review on the APOE gene and AD is a model of responsible communication because it is transparent about the limitations of its data. The primary limitation is generalizability; the measurements of risk associated with APOE variants are not universal and depend heavily on context.

    A major limitation is the effect of population stratification. While the APOE ε4 allele is the strongest genetic risk factor for late-onset AD, the article qualifies that this risk varies significantly across ancestral populations. The risk is highest for individuals of East Asian ancestry, lower for non-Hispanic Whites, and further “attenuated” for those of African origin. This reality makes applying a single global risk statistic imprecise. The true measurement of risk is conditional upon an individual’s genetic ancestry.

    A second limitation involves gene-sex interaction. The article reports that while the APOE ε2 allele is generally protective against AD, this effect is not uniform. The data indicate the benefit is “strong in non-Hispanic White men but not observed in women or in Black individuals.” This finding suggests that the measurement of the allele’s effect is conditional on both sex and ancestry, rendering any generalized statement about its protective quality oversimplified.

    Within its intended scientific context, the article is clear and concise. The potential for misleading use arises if these statistics are presented to a lay audience without careful framing, as this can promote a sense of genetic determinism. For example, reporting that APOE ε4 “considerably increases the odds of disease onset” without the context that many carriers never develop AD could cause undue alarm. While the article itself is not misleading, the statistics it contains are susceptible to misinterpretation through oversimplification.

    To mitigate this risk, the statistical findings can be rewritten for a public audience to prioritize clarity. The following is a proposed revision adapted for a public health brochure or news feature.

    “A person’s genetics can play a role in their likelihood of developing Alzheimer’s disease. One of the most extensively studied genes is Apolipoprotein E, also known as APOE. We each inherit two copies of this gene, which comes in several versions, most commonly ε2, ε3, and ε4.

    Scientific studies have shown these versions are linked to different levels of risk. The ε4 version is associated with a higher risk of developing late-onset Alzheimer’s, while the ε2 version is linked to a lower risk. The ε3 version, the most common, is considered to have a neutral effect.

    However, these are not one-size-fits-all rules. The influence of the ε4 gene varies among individuals. Its effect is most potent in people of East Asian descent and weaker in those of African ancestry. This means a person’s complete genetic background adds to the risk associated with the individual genes present.

    Also, the ε2 version can be considered protective, but doesn’t offer the same benefit to everyone. Research shows its protective effect is most visible in studies of White men and is not as apparent in women or in Black individuals.

    These findings show that genetics are just one piece of a complex puzzle. Having an APOE ε4 copy does not mean a person will develop Alzheimer’s, and having an ε2 copy does not guarantee protection. Our knowledge is constantly evolving as we learn more about how our genes interact with our ancestry, sex, and lifestyle.”

                This revised version avoids jargon and addresses the limitations related to ancestry and sex. This version also frames genetic risk as probability rather than certainty, providing a more accurate and useful summary for the public.

    References

    Belaidi, A.A., Bush, A.I. & Ayton, S. Apolipoprotein E in Alzheimer’s disease: molecular insights and therapeutic opportunities. Mol Neurodegeneration 20, 47 (2025). https://doi.org/10.1186/s13024-025-00843-y

  • A Reflection on Alignment and Conflict in Precision Medicine and Public Health

    The Precision Medicine Initiative, which tailors treatment and prevention to an individual’s unique characteristics, represents a significant shift from traditional public health approaches. However, precision medicine both aligns with and conflicts with public health goals.

    Precision medicine offers powerful tools that align with the public health mission of disease prevention and health equity. By using genetic screening, public health can identify subgroups at high risk for diseases like hereditary breast and ovarian cancer (HBOC) due to BRCA1/2 mutations. This allows for targeted, efficient prevention strategies, such as increased surveillance or risk-reducing surgeries, which is a more sophisticated form of risk stratification that public health has always practiced. Large-scale research programs, such as All of Us, seek to address health disparities by building diverse genomic databases. The knowledge gained helps ensure new diagnostics and treatments are effective for everyone, directly supporting the goal of achieving health equity. On the other hand, the individualized focus of precision medicine creates tension with public health’s population-level orientation. Public health often relies on low-cost, high-impact interventions, such as vaccination and sanitation.

    This cost issue can exacerbate health inequities. Access to genetic screening is often limited by socioeconomic status. As studies on BRCA testing show, Black women are less likely than White women to be referred for genetic counseling, even when they meet the same clinical criteria (Cragun et al., 2017). Unless equitable access is guaranteed, precision medicine risks creating a two-tiered system of healthcare, which is directly opposed to the public health goal of ensuring health for all. Finally, a focus on genetic risk can divert attention from the social and environmental determinants of health, such as poverty and pollution. This creates a conflict in priorities, pitting a molecular view of health against a socio-ecological one.

    For a condition like HBOC, public understanding is vital for informed decision-making. Two ideal online resources provide information and support for HBOC. The CDC website and the website for Facing Our Risk of Cancer Empowered (FORCE) are both great resources for those with questions regarding HBOC.

    The CDC’s webpage on BRCA gene mutations is an authoritative, evidence-based resource (CDC, 2024). Its primary strength is its credibility. The content is structured logically, explaining what HBOC is, who is at risk, and the guidelines for testing. However, its main weakness is its clinical and impersonal tone. While factual, it lacks the personal stories and emotional context that can help people process the challenging experience of discovering a high genetic cancer risk.

    FORCE is a nonprofit organization with a patient-centered website (FORCE, 2024). Its greatest strength is its focus on the lived experience of having a BRCA mutation. It excels at providing personal stories, support networks, and practical advice on navigating risk-management options. It creates a sense of community that can be invaluable. Its weakness is that, as an advocacy organization, its content is naturally framed to empower patients and may not present epidemiological data with the same neutral tone as the CDC.

    References

    Centers for Disease Control and Prevention. (2024, May 30). People at Increased Risk for BRCA Gene Mutations. https://www.cdc.gov/breast-ovarian-cancer-hereditary/risk-factors/

    Cragun, D., Weidner, A., Lewis, C., Bonner, D., Kim, J., Vadaparampil, S. T., & Pal, T. (2017). Racial disparities in BRCA testing and cancer risk management across a population-based sample of young breast cancer survivors. Cancer, 123(13), 2497–2505. https://doi.org/10.1002/cncr.30621

    Facing Our Risk of Cancer Empowered. (2024). Overview of Hereditary Cancer Treatment. https://www.facingourrisk.org/info/risk-management-and-treatment/cancer-treatment/overview

  • DNA in Forensics

                In criminology, forensic investigators have used DNA analysis for some time. They apply genetic analysis to solve crimes where biological materials serve as evidence. This type of analysis remains a powerful forensic tool despite its drawbacks. Advances in DNA testing have made it so precise and highly sensitive that handlers risk contaminating samples (Butler, 2015). Environmental damage can also render DNA useless as a forensic tool. In such cases, conducting a genomic analysis of the entire genome helps as a control. Even with its high sensitivity and reliability, forensic experts have encountered cases where authorities falsely imprisoned or released suspects due to faulty or contaminated evidence.

    Historically, forensic teams have used genetic and genomic samples to catch criminals and exonerate the innocent. In the well-publicized case of Joseph James DeAngelo, the Golden State Killer, investigators used consumer DNA testing to compare forensic evidence with familial DNA (Wikipedia contributors, 2025). The company’s privacy policy in DNA analysis prevented this type of use. However, the investigators used evidence to create a fake profile and matched their evidence with that of DeAngelo’s family member, a service user. Although their methodology was unethical, they matched the evidence to DeAngelo, leading to his arrest (St. John, 2020). Investigators can use familial lines to convict criminals effectively, provided they compare evidence against an ethically maintained database.

    Conversely, DNA evidence has played a crucial role in exonerating the innocent. Authorities convicted Kirk Bloodsworth, a U.S. Marine, of rape and murder, sentencing him to death based on circumstantial evidence. Investigators believed he was responsible for the murder of a 9-year-old girl. However, after Bloodsworth spent nine years in prison, forensic analysts compared DNA from the crime scene with his and the victim’s, proving his innocence (The Innocence Project, 2025). Cases like these punctuate the importance of genetic testing in criminology.

    Forensic scientists retrieve genetic evidence from various sources, including non-visible biological material left on surfaces through skin contact (Alketbi, et al., 2025). Today, investigators primarily use trace samples of biological material in evidence-gathering. They extract and amplify DNA from these samples, achieving an overall success rate of 64% (Alketbi, et al., 2025). Although different surfaces yield varying degrees of success, most samples remain viable as evidence. While forensic teams predominantly use trace samples in criminal investigations, they also rely on them to identify disaster victims and exonerate falsely accused individuals. However, such small samples remain highly vulnerable to contamination or environmental degradation.

    Dr. Edmond Locard, known as the “Sherlock Holmes” of Lyon, France, developed the exchange principle, which states that “every contact leaves a trace.” His principle asserts that every criminal leaves something behind at a crime scene, forming the foundation of modern forensics, particularly DNA forensics (Naqvi, et al., 2024). Today forensic analysts have refined their techniques to such a degree that they can extract DNA evidence from trace amounts of biological material. Although these traces remain invisible to the eye, biological material is present whenever someone touches a surface. Environmental elements pose a high risk of DNA degradation, with exposure to water, temperature fluctuations, humidity, UV radiation, and time affecting the sample. Among these factors, extreme temperatures and UV radiation present the most significant threats to DNA evidence (Naqvi, et al., 2024). In light of these facts, forensic teams must secure samples as quickly as possible after discovery.

    DNA forensics has revolutionized criminal investigations, providing a powerful tool for both convicting the guilty and exonerating the innocent. Genetic and genomic analysis advancements have enhanced forensic precision, allowing investigators to extract valuable evidence from even trace biological material. However, the heightened sensitivity of DNA testing introduces risks, including contamination and environmental degradation, which can compromise results. Ethical concerns also arise in familial DNA search cases, emphasizing the need for responsible forensic practices. Despite these challenges, DNA evidence remains a fundamental element of modern criminology, reinforcing the importance of meticulous handling, ethical application, and continued advancements in forensic science.

    References

    Alketbi, S. K., Goodwin, W., Alghanim, H. J., Abdullahi, A. A., Aidarous, N. I., Alawadhi, H. M., . . . Almheiri, M. A. (2025). Trace DNA Recovery: Insights from Dubai Police Casework. Perspectives in Legal and Forensic Sciences; 2(1):10001, https://doi.org/10.70322/plfs.2025.10001.

    Butler, J. M. (2015). The Future of DNA Analysis. Phil. Trans. R. Soc. B; 37020140252, http://doi.org/10.1098/rstb.2014.0252.

    Naqvi, S. Z., Ahmed, U., Daga, S. S., Rawat, P., Singhal, G., & Patil, B. (2024). Examining the Impact of Environmental Variables on DNA Extraction Efficiency in Forensic Blood Samples. Periodico di Mineralogia; 93 (5), 440-458.

    St. John, P. (2020, 12 08). The untold story of how the Golden State Killer was found: A covert operation and private DNA. Retrieved from LA Times: https://www.latimes.com/california/story/2020-12-08/man-in-the-window

    The Innocence Project. (2025, 02 09). Kirk Bloodsworth. Retrieved from Innocence Project: https://innocenceproject.org/cases/kirk-bloodsworth/

    Wikipedia contributors. (2025, 01 12). Joseph James DeAngelo. Retrieved from Wikipedia: https://en.wikipedia.org/w/index.php?title=Special:CiteThisPage&page=Joseph_James_DeAngelo&id=1268931776&wpFormIdentifier=titleform

  • 1000 Genomes Project and Haplotypes

    Genome-wide association studies (GWAS) attempt to identify genetic variations associated with specific traits or diseases across the human genome (Manolio, 2010). An effective GWAS typically requires dense marker coverage across the genome to capture patterns of genetic variation linked to the phenotype of interest. Genotype imputation is a statistical technique that is used to infer genotypes at untyped markers. Impuitation is commonly used to increase marker density in GWAS datasets (Marchini & Howie, 2010). Genotype imputation relies heavily on high-quality haplotype reference panels, which catalog patterns of genetic variation in reference populations (Spencer, Su, Donnelly, & Marchini, 2009). The 1000 Genomes Project (1KGP) generated a comprehensive catalog of human genetic variation using sequencing technologies, which provided a powerful resource for creating improved haplotype reference panels (Delaneau, Marchini, & Consortium, 2014).

    A haplotype represents a specific set of genetic alleles at multiple loci, or variatns, which are close together on the same chromosome and tend to be inherited as a single unit (Ziegler, 2011). Variants often include single nucleotide polymorphisms (SNPs). Linkage disequilibrium (LD) is the phenomenon underlying haplotypes which describes the non-random association of alleles at different loci on the same chromosome (Ziegler, 2011). Due to limited recombination between closely spaced variants over generations, specific combinations of haplotypes persist in populations. Regions of the genome characterized by strong LD and limited haplotype diversity are often referred to as haplotype blocks.

    Haplotypes contribute significantly to human genetic diversity beyond the variation captured by individual SNPs alone  (Manolio, 2010). Different combinations of alleles across linked loci create distinct haplotype patterns within and between populations and the specific structure and frequency of haplotypes can vary across different global populations due to distinct demographic histories and evolutionary changes (Delaneau, Marchini, & Consortium, 2014). Specific haplotypes may carry functional variants, either coding changes within genes or regulatory variants affecting gene expression, that make changes to traits or disease susceptibility (Manolio, 2010).

    A haplotype reference panel serves as a detailed catalog of common haplotypes observed within one or more reference populations  (Marchini & Howie, 2010). These panels are constructed using dense genotype or sequence data from individuals representing the populations of interest (Spencer, Su, Donnelly, & Marchini, 2009). In GWAS, researchers typically genotype study participants using SNP arrays, which capture only a subset of common genomic variation (Spencer, Su, Donnelly, & Marchini, 2009).

    Genotype imputation uses the study participants’ typed SNP data along with the haplotype reference panel to statistically infer genotypes at SNPs not directly measured on the array (Marchini & Howie, 2010). Imputation effectively increases the density of genetic markers analyzed in the GWAS, often from hundreds of thousands to millions of variants (Manolio, 2010). The increased marker density improves the power of GWAS to detect association signals and facilitates fine-mapping efforts to identify potential causal variants within associated regions (Marchini & Howie, 2010).

    The 1KGP sought to create a deep catalog of human genetic variation, including SNPs, indels, and structural variants, by sequencing individuals from diverse global populations (Delaneau, Marchini, & Consortium, 2014). They used sophisticated statistical methods in order to integrate low-coverage whole-genome sequencing, high-coverage exome sequencing, and dense SNP array data from 1KGP participants. This integration allowed for the construction of a highly accurate and detailed haplotype reference panel (Delaneau, Marchini, & Consortium, 2014). Compared to previous reference panels primarily based on SNP array data, the 1KGP panel offered several improvements.

    These improvements included much better representation and phasing accuracy for lower-frequency and rare variants. The panel also provided enhanced coverage across a wider range of diverse global populations included in the 1KGP. The use of sequence data directly improved the accuracy of estimated haplotypes compared to methods relying solely on array data (Delaneau, Marchini, & Consortium, 2014).

    Applying the improved 1KGP haplotype reference panel for imputation significantly enhances GWAS utility. The increased accuracy of imputation, especially for less common variants, allows researchers to test a larger proportion of genomic variation for association with diseases and traits (Marchini & Howie, 2010). The improved power increases the likelihood of discovering novel genetic associations, especially those driven by lower-frequency variants that might have been poorly imputed using older panels  (Manolio, 2010).

    Better representation of diverse populations in the 1KGP panel improves imputation quality and facilitates discoveries in non-European ancestry groups, contributing to more equitable genetic research  (Delaneau, Marchini, & Consortium, 2014). The higher density of accurately imputed variants resulting from the 1KGP panel also aids in fine-mapping association signals, which helps to narrow down the set of causal variants within a genomic region linked to a disease (Manolio, 2010).

    Accurate and comprehensive haplotype reference panels are important tools for modern GWAS, enabling genotype imputation to maximize marker coverage. The 1KGP produced an improved reference panel with better representation of rare variants and diverse populations (Delaneau, Marchini, & Consortium, 2014). Utilizing the 1KGP panel for imputation boosts the power and resolution of GWAS, enhancing our ability to discover novel genetic associations and understand the genetic underpinnings of complex human diseases and traits.

    References

    Delaneau, O., Marchini, J., & Consortium, T. 1. (2014). Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel. Nature Communications; 5(3934), https://www.nature.com/articles/ncomms4934.

    Manolio, T. (2010). Genomewide association studies and assessment of the risk of disease. New England Journal of Medicine, 363(2), 166–176. https://doi.org/10.1056/NEJMra0905980.

    Marchini, J., & Howie, B. (2010). Genotype imputation for genome-wide association studies. Nature Reviews Genetics, 11(7), 499–511. https://doi.org/10.1038/nrg2796.

    Spencer, C., Su, Z., Donnelly, P., & Marchini, J. (2009). Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genetics, 5(5), e1000477, https://doi.org/10.1371/journal.pgen.1000477.

    Ziegler, A. (2011). A Statistical Approach to Genetic Epidemiology (2nd ed.). Wiley Professional Development (P&T), https://bookshelf.vitalsource.com/books/9783527633661.