Endrin
Endrin is an organochlorine compound historically used as a broad-spectrum insecticide and rodenticide. Developed in the 1950s, it belongs to the cyclodiene pesticide family, known for its persistence in the environment. Due to its high toxicity and environmental stability, endrin has been classified as a persistent organic pollutant (POP) and is now largely banned globally.
Biological Basis
Section titled “Biological Basis”Endrin primarily exerts its toxic effects by acting as a neurotoxin. It interferes with the central nervous system by binding to the picrotoxinin binding site of the GABA-A receptor complex, which is responsible for inhibiting neuronal activity. This binding blocks the influx of chloride ions into neurons, leading to hyperexcitability, tremors, convulsions, and seizures. Endrin is also known for its lipophilicity, meaning it readily dissolves in fats, allowing it to bioaccumulate in the fatty tissues of organisms and biomagnify up the food chain.
Clinical Relevance
Section titled “Clinical Relevance”Exposure to endrin can have severe health consequences. Acute poisoning typically manifests with rapid onset of neurological symptoms, including headache, dizziness, nausea, vomiting, confusion, and muscle twitching, progressing to convulsions, seizures, and potentially death. Chronic exposure, even at low levels, has been associated with a range of adverse effects, including neurological damage, reproductive issues, and developmental problems. It is also classified as a probable human carcinogen. Medical management of endrin poisoning focuses on supportive care, controlling seizures, and preventing further absorption.
Social Importance
Section titled “Social Importance”The widespread use of endrin in agriculture during the mid-20th century led to significant environmental contamination. Its persistence in soil, water, and the atmosphere, coupled with its ability to bioaccumulate, raised global concerns about its long-term impact on ecosystems and human health. The international community recognized endrin’s dangers, leading to its inclusion in the Stockholm Convention on Persistent Organic Pollutants, which aims to eliminate or restrict the production and use of such chemicals worldwide. Despite its ban, endrin remains an environmental legacy pollutant, requiring ongoing monitoring and remediation efforts to mitigate its continued presence in the environment and food supply.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Several methodological and statistical factors limit the interpretability and robustness of findings from these genome-wide association studies. Many studies employed cohorts of moderate size, which inherently limits statistical power and increases the susceptibility to false negative findings, where true associations might be missed.[1] The practice of sex-pooled analyses, while mitigating multiple testing burdens, means that genetic associations unique to either males or females may remain undetected, potentially overlooking important sex-specific genetic influences on phenotypes. [2] Furthermore, the reliance on a subset of available SNPs, as well as the use of specific genotyping chips like the Affymetrix 100K GeneChip, means that not all genetic variations are comprehensively covered. This incomplete coverage can lead to missing genes or loci that influence a phenotype and may not provide sufficient data for a thorough examination of candidate genes. [2]
The process of imputing missing genotypes, while expanding genomic coverage, introduces a potential for error, with estimated error rates ranging from 1.46% to 2.14% per allele in some instances. [3] This imputation inaccuracy could affect the precision of genotype-phenotype associations. Moreover, the challenge of replicating initial findings is a recurring issue, with some meta-analyses demonstrating replication for only about one-third of reported associations. [1] This lack of replication across studies can stem from inadequate statistical power in follow-up cohorts or differences in study populations, highlighting the potential for false positive findings and the need for rigorous validation. [1]
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”A significant limitation across several studies is the restricted generalizability of their findings, primarily due to the demographic characteristics of the study cohorts. Many cohorts were largely composed of individuals of white European descent, often middle-aged to elderly. [1] This demographic homogeneity means that the observed genetic associations may not be broadly applicable to younger populations or individuals of other ethnic or racial backgrounds, underscoring a critical gap in understanding genetic influences across diverse human populations. [1] The timing of DNA collection, sometimes occurring at later examinations, also introduces a potential survival bias, as only individuals who survived to those later time points were included, possibly skewing genetic associations. [1]
Challenges in phenotype characterization and measurement also impact the reliability of results. For instance, averaging echocardiographic traits over long periods, sometimes spanning decades and involving different equipment, could introduce misclassification or mask age-dependent gene effects, as the genetic and environmental influences on traits may change with age. [4] Similarly, variations in the time of day blood samples are collected, or the menopausal status of participants, are known to influence serum markers, potentially confounding genetic associations if not consistently controlled. [5]Furthermore, the use of specific biomarkers, such as cystatin C for kidney function or TSH for thyroid function, without comprehensive measures like free thyroxine or a full assessment of disease, may not fully capture the underlying physiological processes, limiting the scope of genetic insights.[6]
Confounding Factors and Unexplained Variation
Section titled “Confounding Factors and Unexplained Variation”The intricate interplay of environmental factors and gene-environment interactions presents a complex challenge in precisely attributing phenotypic variation to genetic loci. Differences in environmental exposures or lifestyle factors between study cohorts can modify genotype-phenotype associations, making replication difficult and indicating that genetic effects are not always isolated but rather context-dependent.[1] For example, variations in lipid-lowering therapies among participants or the time of blood collection can significantly influence phenotypic values, acting as confounding factors that need careful adjustment. [5] The assumption that similar genetic and environmental factors influence traits across a wide age range may not hold true, potentially masking age-dependent gene effects when observations are averaged across different age groups. [4]
Despite identifying numerous genetic associations, these studies often only explain a fraction of the observed heritability for complex traits, pointing to significant remaining knowledge gaps. The current GWAS approaches, even with imputation, may not fully capture the entire spectrum of genetic variation, including rare variants or structural changes not present on genotyping arrays, which could contribute to the unexplained heritability. [2] The focus on multivariable models might also lead to overlooking important bivariate associations between individual SNPs and phenotypes. [6] Consequently, while these studies reveal key genetic contributors, a substantial portion of the genetic architecture underlying complex traits remains to be elucidated, necessitating further research into diverse genetic variants, gene-gene interactions, and comprehensive environmental factors.
Variants
Section titled “Variants”Genetic variations across the human genome contribute to individual differences in susceptibility to environmental factors and various health outcomes. Single nucleotide polymorphisms (SNPs) such as*rs10491442 * in the _PDE4D_ gene, *rs114726772 * in _USH2A_, and *rs72607877 * in _FGF12_ are examples of variations that can influence crucial biological pathways. _PDE4D_(Phosphodiesterase 4D) plays a vital role in regulating intracellular cyclic AMP (cAMP) levels, a secondary messenger involved in numerous cellular processes, including neuronal signaling, inflammation, and smooth muscle relaxation. Variants in_PDE4D_can alter the efficiency of cAMP breakdown, potentially affecting brain function and cardiovascular health._USH2A_ (Usherin) is primarily known for its role in the development and function of the inner ear and retina, but its broader functions in cell adhesion and signaling suggest potential indirect roles in cellular responses to stress. _FGF12_(Fibroblast Growth Factor 12) belongs to a family of proteins that regulate cell growth, differentiation, and development, particularly important in the nervous system for neuronal excitability and synaptic plasticity. Alterations in these genes could modify an individual’s neurophysiological resilience, potentially influencing their response to neurotoxic compounds like endrin, which targets the nervous system and can disrupt normal signaling pathways.
Other variants, such as *rs17122597 * in _CDC14A_, *rs8021014 * in _SYNJ2BP-COX16_ and _COX16_, and *rs7607266 * in _COMMD1_, are associated with genes involved in fundamental cellular processes like cell cycle control, mitochondrial function, and homeostasis. _CDC14A_ (Cell Division Cycle 14A) is a phosphatase that plays a critical role in regulating the cell cycle, DNA repair, and maintaining genomic stability. Variations in _CDC14A_ might affect cellular proliferation and the ability to repair DNA damage, which are crucial for maintaining tissue integrity against external stressors. The _COX16_ gene, part of the _SYNJ2BP-COX16_ locus, encodes a protein essential for the assembly of cytochrome c oxidase, a key enzyme complex in the mitochondrial electron transport chain responsible for cellular energy production. Variants in _COX16_ could impact mitochondrial efficiency and cellular metabolism, leading to altered energy states and increased susceptibility to oxidative stress. _COMMD1_ (COMM Domain Containing 1) is involved in diverse cellular functions, including copper homeostasis, regulation of the NF-κB signaling pathway (a central mediator of inflammation and immune responses), and endosomal trafficking. Genetic differences in _COMMD1_could influence an individual’s capacity to manage heavy metal toxicity or modulate inflammatory responses, both of which are relevant to the body’s reaction to xenobiotics like endrin.[7]
Furthermore, variants including *rs6022454 * in _TSHZ2_, *rs7867688 * in _PLPPR1_, *rs72942461 * in _LINC00607_, and *rs115347967 * within the _LINC02462 - EEF1A1P35_ region highlight the diverse genetic landscape influencing health. _TSHZ2_ (Teashirt Zinc Finger Homeobox 2) is a transcription factor involved in embryonic development and tissue patterning, suggesting potential roles in regulating the expression of other genes important for organogenesis and tissue maintenance. _PLPPR1_ (Phospholipid Phosphatase Related 1) is involved in lipid metabolism and signaling, with implications for neuronal migration and axon guidance, linking it to brain development and function. _LINC00607_ and _LINC02462_ represent long intergenic non-coding RNAs (lncRNAs), which are increasingly recognized for their regulatory roles in gene expression, chromatin remodeling, and various cellular processes, including stress responses. _EEF1A1P35_ is a pseudogene related to _EEF1A1_, which encodes a protein involved in protein synthesis. Variations in these regulatory and metabolic genes could subtly alter an individual’s baseline physiological state, potentially affecting their ability to metabolize, detoxify, or repair cellular damage caused by environmental toxins like endrin, thereby influencing individual susceptibility to its adverse effects.[7]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10491442 | PDE4D | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs17122597 | CDC14A | environmental exposure measurement chlorpyrifos measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs114726772 | USH2A | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement |
| rs72607877 | FGF12 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs8021014 | SYNJ2BP-COX16, COX16 | cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs6022454 | TSHZ2 | cadmium chloride measurement chlorpyrifos measurement azinphos methyl measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs7607266 | COMMD1 | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement |
| rs72942461 | LINC00607 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement 2,4,5-trichlorophenol measurement |
| rs7867688 | PLPPR1 | lipid measurement cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement |
| rs115347967 | LINC02462 - EEF1A1P35 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
Clinical Relevance
Section titled “Clinical Relevance”References
Section titled “References”[1] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S11.
[2] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S9.
[3] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-69.
[4] Vasan, R. S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S12.
[5] Benyamin, B., et al. “Variants in TF and HFEexplain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 83, no. 6, 2008, pp. 758-66.
[6] Hwang, S. J., et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S10.
[7] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, vol. 40, no. 2, 2008, pp. 189-97.