Pernicious Anemia

Pernicious anemia is an autoimmune condition characterized by the body’s inability to absorb vitamin B12 (cobalamin) from the gastrointestinal tract, leading to a deficiency. This deficiency results in megaloblastic anemia, a condition where red blood cells are abnormally large and immature, and can also cause neurological complications. The term “pernicious” historically referred to the severe, often fatal, nature of the disease before effective treatments were discovered.

Biological Basis

The primary biological basis of pernicious anemia is an autoimmune attack on the parietal cells in the stomach lining or on intrinsic factor, a protein produced by these cells. Intrinsic factor is essential for the absorption of vitamin B12 in the small intestine. Without sufficient intrinsic factor, dietary vitamin B12 cannot be absorbed, leading to a systemic deficiency. While the direct genetic variants specifically linked to pernicious anemia are diverse, genetic predisposition plays a significant role in autoimmune conditions. Studies in consumer genetics often utilize methods like Genome-Wide Association Studies (GWAS) to identify single nucleotide polymorphisms (SNPs) and other genetic variations associated with complex traits and diseases.^[1]These studies analyze millions of SNPs across the genome to pinpoint regions or specific variants that contribute to disease risk.^[1] For example, techniques like imputation are used to infer genotypes for unmeasured SNPs based on reference panels, enhancing the coverage of genetic variation analyzed.^[1] This approach allows researchers to understand the genetic architecture underlying conditions, including those with an autoimmune component.

Clinical Relevance

Pernicious anemia manifests with symptoms such as fatigue, weakness, shortness of breath, and pale skin due to anemia. Neurological symptoms are also common and can include numbness, tingling, balance problems, memory impairment, and cognitive difficulties, which can become irreversible if untreated. Diagnosis typically involves blood tests to measure vitamin B12 levels, along with tests for intrinsic factor antibodies and parietal cell antibodies. Early diagnosis and treatment are crucial to prevent severe complications. The condition is managed through lifelong vitamin B12 supplementation, usually administered via injections, to bypass the absorption defect.

Social Importance

Pernicious anemia, though not as common as some other forms of anemia, has significant social importance due to its chronic nature and potential for severe, debilitating symptoms if undiagnosed or untreated. It disproportionately affects older adults and certain ethnic groups. The need for lifelong treatment impacts patients’ quality of life and healthcare systems. Awareness and education about pernicious anemia are vital to ensure timely diagnosis, especially given that its symptoms can be non-specific and mimic other conditions. Understanding the genetic underpinnings of such autoimmune diseases through genetic research can lead to improved diagnostic tools, personalized treatment strategies, and potentially preventive measures in the future.

Methodological and Statistical Power Constraints

Studies investigating complex traits like pernicious anemia often encounter significant limitations related to statistical power and study design. Many analyses, particularly those focused on rare genetic variants or specific phenotypic subsets, are constrained by comparatively small sample sizes for genome-wide association studies (GWAS), which inherently leads to low statistical power and an increased susceptibility to false-positive findings, especially for infrequent variants (.^[2] ). Even in studies involving hundreds of thousands of individuals, the power to robustly detect rare variant associations can remain limited, suggesting that current methodologies may not fully capture their genetic contributions (.^[3] ). Furthermore, heritability estimates derived from certain study designs, such as twin- and family-based studies, may be artificially inflated, potentially overstating the overall genetic influence on the trait (.^[3] ).

The ability to consistently replicate findings across independent cohorts also presents a substantial challenge. Some research indicates that independent replication is not always feasible due to the scarcity of similarly sized genotype datasets or because studies did not incorporate an independent replication sample (.^[4] ). This lack of external validation can introduce bias, as selective participation in large biobanks may distort genetic associations and limit the generalizability of initial discoveries (.^[4] ). Additionally, cohorts composed primarily of healthy individuals, such as blood donors, might be depleted of individuals carrying rare, potentially “damaging” variants, thereby further limiting the generalizability and statistical power of rare variant association analyses (.^[5] ).

Phenotypic Complexity and Generalizability Across Ancestries

The accurate and consistent measurement of complex phenotypes constitutes a notable limitation in genetic studies. Phenotypic measurements can be inherently “noisy” due to confounding factors from various cell types or other biological determinants that are not fully accounted for, which can diminish the statistical power of association tests (.^[3] ). This imperfect measurement, coupled with potential hidden sources of variability, can limit the precise interpretation of rare genetic variants impacting the phenotype (.^[3] ). Moreover, differences in phenotype construction across studies can complicate comparisons and meta-analyses, and the reliance on unvalidated singleton variants in rare variant tests may introduce false positives (.^{[5], [6]} ).

Generalizability of genetic findings across diverse ancestral populations is another critical concern. Variants discovered in one population, such as European cohorts, may not generalize effectively to others, potentially due to differences in minor allele frequencies, underlying causal variants, or haplotype structures (.^[7] ). This can lead to variations in effect sizes across populations, as observed for certain iron-related traits (.^[7] ). While efforts are made to mitigate biases from population structure through stratified analyses and adjustment for principal components, the influence of population substructure on findings remains a plausible limitation, impacting the portability and broader applicability of genetic discoveries (.^[8] ).

Environmental Confounders and Remaining Knowledge Gaps

Genetic studies frequently contend with the challenge of comprehensively accounting for environmental factors or gene–environment interactions, which can act as significant confounders. Complex traits are influenced by a myriad of genetic and environmental factors, and heterogeneity in environmental exposures, such as dietary intake, can lead to variations in genetic effects across populations (.^{[1], [7]}). Many studies may not fully capture or adjust for all relevant non-genetic factors, including lifestyle, nutrition, physical activity, and psychological elements, potentially leading to inaccurate predictions and limiting the interpretation of genetic associations due to unconsidered gene-environment interactions or other unaccounted sources of variance (.^{[1], [7]} ).

Despite advances in identifying genetic variants, a substantial portion of the heritability for many complex traits remains unexplained, often referred to as “missing heritability.” This gap suggests that, beyond common variants, other sources of genetic variation, including rare variants and complex regulatory networks, contribute to the trait but are yet to be fully elucidated (.^[3] ). A significant proportion of detected variants are located in noncoding regions, highlighting a need for further research to understand their regulatory functions (.^[1]). Consequently, there remains a gap between current genetic discoveries and their direct translation into clinical practice or a complete understanding of the disease etiology (.^[1] ).

Variants

Genetic variations contribute to an individual’s susceptibility to various conditions, including autoimmune disorders like pernicious anemia, which is characterized by impaired vitamin B12 absorption often due to autoimmune destruction of gastric parietal cells. Several single nucleotide polymorphisms (SNPs) and their associated genes are implicated in pathways that could influence immune regulation, cell differentiation, or gastric function, thereby playing a role in the development or progression of pernicious anemia. These genetic markers offer insights into the complex etiology of the disease, highlighting potential biological mechanisms that, when disrupted, may increase risk.^[9] Variations within genes like LGR6(Leucine-rich repeat-containing G protein-coupled receptor 6) andUBE2T (Ubiquitin-conjugating enzyme E2T) are of particular interest, such as rs541051646 . LGR6is recognized for its role in stem cell maintenance and tissue regeneration, processes crucial for the repair and integrity of the gastric mucosa, which is a primary site of pathology in pernicious anemia. Meanwhile,UBE2T is involved in DNA repair pathways, and dysregulation in this area could lead to genomic instability or altered cellular responses that might predispose individuals to autoimmune conditions or affect the health of gastric cells. The interplay of these genes through variant rs541051646 could thus influence cellular resilience and immune homeostasis in the gastrointestinal tract.^[10] Long intergenic non-coding RNAs (lncRNAs) also play crucial regulatory roles, and variants like rs537837866 in LINC02028 and rs377695939 near LINC02713 and CNTN5 (Contactin 5) warrant investigation. LncRNAs such as LINC02028 can modulate gene expression, affecting cellular processes, including immune responses and the differentiation of gastric cells. Similarly, LINC02713, in proximity to CNTN5, a gene involved in neuronal adhesion, may have broader regulatory impacts. While CNTN5itself is not directly linked to gastric function, variations in these genomic regions could influence immune system development or metabolic pathways that are indirectly relevant to pernicious anemia.^[11] Further, genes involved in diverse cellular functions, such as ICE1 (Interactions with CSTF2 protein 1) and its associated pseudogene HMGB3P3 (High Mobility Group Box 3 Pseudogene 3) with variant rs575640865 , or ZNF731P (Zinc Finger Protein 731 Pseudogene) and VN1R17P (Vomeronasal 1 Receptor Pseudogene 17) with rs569863531 , contribute to the genetic landscape. ICE1 plays a role in RNA processing, and HMGB3P3relates to inflammatory responses, both of which are critical in autoimmune disease development. Pseudogenes likeZNF731P and VN1R17P can regulate the expression of functional genes, potentially influencing cell differentiation or immune regulation within the gastric environment. The MARCHF1 (Membrane Associated Ring-CH-type Finger 1) gene, with variant rs576359040 , encodes an E3 ubiquitin ligase that regulates the surface expression of key immune proteins, including MHC class II molecules; altered function could disrupt immune tolerance and contribute to autoimmune attack on parietal cells.^[12] Finally, TMEM72 (Transmembrane Protein 72) and its antisense RNA TMEM72-AS1, along with variant rs146734497 , highlight the importance of membrane proteins and their regulation in cellular health. TMEM72 is a transmembrane protein that may contribute to cell membrane integrity or signaling, which is essential for proper gastric cell function. Similarly, the transcription factor POU2F3 (POU Class 2 Homeobox 3), associated with rs567953174 , is vital for the differentiation of specific gut epithelial cells. Variations in these genes could compromise the structural or functional integrity of the gastric lining, increasing vulnerability to the autoimmune processes characteristic of pernicious anemia.^[13]

Key Variants

RS ID	Gene	Related Traits
rs541051646	LGR6 - UBE2T	pernicious anemia
rs537837866	LINC02028	pernicious anemia
rs377695939	LINC02713 - CNTN5	pernicious anemia
rs575640865	ICE1 - HMGB3P3	pernicious anemia
rs569863531	ZNF731P - VN1R17P	pernicious anemia
rs576359040	MARCHF1	pernicious anemia
rs146734497	TMEM72, TMEM72-AS1	pernicious anemia
rs567953174	POU2F3	pernicious anemia

Hematological Parameters and Their Assessment

The clinical presentation of pernicious anemia is characterized by distinctive changes in hematological parameters, which are objectively assessed through quantitative laboratory measurements, including complete blood count data.^[6]Key biomarkers for evaluating red blood cell traits and iron status, crucial in the diagnosis of anemia, encompass serum iron, ferritin, transferrin saturation, and total iron binding capacity (TIBC).^{[7], [14], [15]} When multiple measurements are available for an individual, the median value is frequently employed to ensure a representative assessment.^[6] These analyses are typically conducted using automated hematology analyzers, which are equipped with specific flow channels (e.g., PLT-F) and incorporate ‘flag’ variables to identify potential sample or measurement abnormalities, such as platelet clumping, thereby upholding data integrity and reliability.^[16]

Variability in Blood Trait Measurements

Hematological parameters exhibit considerable inter-individual variation, necessitating careful consideration of demographic and technical factors during assessment. Age and sex are significant covariates that influence these measurements and are routinely adjusted for in analytical models.^[6] For instance, studies investigating iron deficiency have recognized age and sex-specific patterns, often excluding pre-menopausal females due to the common prevalence of iron depletion from blood loss in this group.^[17] Beyond biological influences, technical factors such as the time of blood draw, fasting status, the specific assessment date, and even the collection center can introduce variability. These non-biological covariates are often statistically accounted for, sometimes using natural splines to model complex, non-linear relationships and seasonal variations in biomarker levels.^[18]Furthermore, some quantitative traits, like glycated hemoglobin (HbA1c), may present with skewed distributions, requiring mathematical transformations such as log-transformation to ensure accurate statistical analysis.^[11]

Diagnostic Interpretation of Hematological Biomarkers

The diagnostic significance of hematological biomarkers relies on the interpretation of measured values against established clinical thresholds and the rigorous management of data quality. For conditions like iron deficiency, which can mimic or coexist with pernicious anemia, specific diagnostic criteria are applied, such as a serum ferritin concentration equal to or less than 12 mg/L or a transferrin saturation below 15%.^[17]Conversely, iron-replete control individuals are characterized by higher ferritin levels, typically exceeding 100 mg/L in men and 50 mg/L in women.^[17] A critical aspect of diagnostic accuracy involves the exclusion of outliers from laboratory data, defined as values exceeding specific standard deviation thresholds (e.g., more than 7 standard deviations from the mean for general lab measurements or more than 3 standard deviations for blood cell counts) or those deviating by more than 8 median absolute deviations from the global median of analyzer measurements.^{[6], [8], [16]} Such meticulous data quality control and precise interpretation are fundamental for accurate diagnosis, differentiation from other conditions, and establishing meaningful clinical correlations.

Genetic Predisposition to Iron Dysregulation

The development of conditions affecting iron metabolism, which can lead to various forms of anemia, is significantly influenced by inherited genetic variants, ranging from Mendelian forms to polygenic risk factors. Mutations in genes such asHFE are well-documented contributors to hereditary hemochromatosis, a disorder characterized by iron overload, and these genetic variations also play a role in the broader polygenic background that influences iron stores in the general population . Understanding these underlying biological mechanisms, from molecular pathways to organ-level effects, is critical for comprehending the diverse manifestations of conditions impacting blood health.

Red Blood Cell Biology and Oxidative Stress

The health and function of red blood cells (RBCs) are paramount for oxygen delivery throughout the body. A key enzyme in RBC metabolism is glucose-6-phosphate dehydrogenase (G6PD), which is the rate-limiting enzyme of the pentose phosphate pathway. This pathway is the primary source of the reducing cofactor NADPH, essential for maintaining glutathione homeostasis and enabling various antioxidant enzymes to neutralize reactive oxygen species.^[19] Deficiencies in G6PDactivity, often due to genetic polymorphisms, lead to increased susceptibility of RBCs to oxidative stress and hemolysis, where red blood cells prematurely break down.^[19]Such deficiencies can manifest as hemolytic anemia and are associated with higher levels of metabolic markers indicative of cellular damage, including lipid oxidation and altered protein modification.^[19] The functional regulation of proteins like pre-B-cell leukemia homeobox interacting protein 1 (PBXIP1) also plays a role in erythroid differentiation, the process by which red blood cells mature from progenitor cells in the bone marrow.^[20]Any disruption in this carefully orchestrated cellular differentiation can impair the production of healthy red blood cells, contributing to an anemic state. Furthermore, enzymes like myeloperoxidase (MPO), while primarily known for their role in immune cells, can have functional polymorphic variants. Although MPO deficiency is distinct from processes directly affecting RBC formation, it highlights how genetic variations in enzyme activity can broadly impact cellular functions and systemic health.^[21]

Iron Metabolism and Homeostasis

Iron is a vital component of hemoglobin, the protein responsible for oxygen transport in red blood cells, and its metabolism is tightly regulated to prevent both deficiency and overload. Iron deficiency is a widespread nutritional disorder globally, significantly impacting erythropoiesis.^[22]The body regulates iron levels through critical proteins like transferrin (TF), which transports iron in the blood, and ferritin, which stores iron within cells.^[7] Genetic variants in genes involved in iron metabolism, such as TF polymorphisms, can influence iron levels and susceptibility to iron deficiency.^[23]Other genes, including transmembrane protease, serine 6 (TMPRSS6), hereditary hemochromatosis gene (HFE), protein phosphatase 1, regulatory subunit 3B (PPP1R3B), myelin regulatory factor (MYRF), and fatty acid desaturase 2 (FADS2), are also associated with various iron traits like serum iron, ferritin, transferrin saturation, and total iron binding capacity.^[7]These genetic influences underscore the complex regulatory networks governing iron uptake, distribution, and storage. Disruptions in these homeostatic mechanisms can lead to a spectrum of conditions, from iron deficiency anemia to iron overload disorders, both of which can have profound systemic consequences and may even contribute to neurodegenerative diseases.^[22]

Cellular Metabolism and DNA Synthesis

Cellular metabolism, particularly the synthesis of nucleic acids, is fundamental for cell proliferation and differentiation, including the rapid turnover of blood cells. Impaired purine synthesis, as seen in conditions like ATICdeficiency (a recessive genetic disease affecting phosphoribosylaminoimidazolecarboxamide formyltransferase/IMP cyclohydrolase), directly impacts the production of essential building blocks for DNA and RNA.^[24]Such metabolic disruptions can lead to significant cellular dysfunction, particularly in rapidly dividing cells like those in the bone marrow. The integrity of DNA synthesis is crucial for proper erythropoiesis, and defects can result in the production of abnormally large, immature red blood cells, a characteristic feature of megaloblastic anemias.

While distinct, conditions affecting heme synthesis, such as porphyrias caused by deficiency in enzymes like porphobilinogen deaminase (HMBS), highlight the importance of intricate metabolic pathways for blood components. HMBSdeficiency can lead to a neuropathy resembling human hepatic porphyria, demonstrating how metabolic errors can have systemic effects beyond the primary site of blood cell production.^{[25], [26]} These examples illustrate the interconnectedness of various metabolic pathways and their collective impact on cellular function and overall physiological health.

Systemic Consequences and Neurological Links

Anemia, irrespective of its specific cause, can have widespread systemic consequences due to reduced oxygen delivery to tissues and organs. Beyond the direct impact on blood cell function, disruptions in essential metabolic processes or genetic predispositions can manifest in various organ systems. For instance, some genetic conditions affecting cellular processes, such as a founder mutation inVPS11 linked to autophagic defects, can cause severe neurological disorders like autosomal recessive leukoencephalopathy.^[27] This illustrates how fundamental cellular mechanisms, when impaired, can lead to severe neurological dysfunction.

Similarly, other genetic disorders like GBAmutations are associated with neurodegenerative conditions such as Parkinson’s disease, further emphasizing the intricate links between genetic integrity, cellular health, and neurological well-being.^{[28], [29]} While these conditions are distinct, they underscore the broader principle that metabolic and genetic disruptions affecting cellular health can have far-reaching effects, including significant neurological impairment, a symptom that can also be observed in severe and prolonged deficiencies contributing to certain anemias.

Diagnostic Utility and Risk Stratification

Genetic studies play a crucial role in identifying variants associated with disease susceptibility, offering significant diagnostic utility and enabling robust risk stratification. Approaches like Genome-Wide Association Studies (GWAS) prioritize Single Nucleotide Polymorphisms (SNPs) based on factors such as minor allele frequency and P-value, thereby pinpointing genetic associations essential for understanding complex conditions.^[30]This systematic identification supports enhanced risk assessment, allowing for the proactive identification of high-risk individuals and the development of personalized prevention strategies. Polygenic Risk Score (PRS) models further refine this stratification, demonstrating improved predictive accuracy when integrated with clinical features like age and sex, enhancing their capability to predict disease predisposition beyond what standalone genetic markers can achieve.^[31]

Prognostic Insights and Treatment Monitoring

Integrating genetic data with comprehensive longitudinal health records provides valuable prognostic insights crucial for predicting disease progression, treatment response, and long-term outcomes. Rigorous analysis of quantitative laboratory measurements, involving steps like outlier removal and Box-Cox transformation, can uncover subtle yet significant indicators pertinent to disease activity or treatment efficacy.^[6]While PRS models alone might exhibit limited predictive robustness, their accuracy is considerably boosted by incorporating diverse clinical features, including age, sex, body mass index, blood pressure, glycated hemoglobin levels, and various biomarkers, alongside environmental factors. This multi-factorial approach facilitates the development of dynamic monitoring strategies, allowing for adaptive patient care and informing long-term therapeutic adjustments for conditions requiring ongoing management.^[31]

Understanding Comorbidities and Disease Architecture

Genetic and phenotypic analyses are instrumental in unraveling complex comorbidities and overlapping disease phenotypes, which is vital for holistic patient management. Utilizing large-scale health system data, where case status for specific phecodes is defined by repeated hospital visits, allows for the identification of genetic associations between various conditions.^[6]This comprehensive view helps in deciphering the intricate relationships between different health issues, providing insights into potential syndromic presentations and guiding the management of associated complications. Such detailed analyses underscore the necessity of considering a broad spectrum of factors—genetic, clinical, and environmental—to fully characterize disease associations, improve model accuracy, and ultimately foster a more nuanced understanding of disease heterogeneity.^[31]

Frequently Asked Questions About Pernicious Anemia

These questions address the most important and specific aspects of pernicious anemia based on current genetic research.

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1. If my parent has pernicious anemia, does that mean I’ll definitely get it too?

Not necessarily, but your risk is higher. Pernicious anemia has a significant genetic predisposition as an autoimmune condition, meaning certain genetic variations can increase your susceptibility. However, it’s not a simple inheritance pattern, and other factors also play a role.

2. Does my ethnic background affect my chances of getting pernicious anemia?

Yes, it can. Research indicates that pernicious anemia disproportionately affects certain ethnic groups. This is often linked to specific genetic variations that are more common in those populations, influencing their overall risk.

3. Could a DNA test tell me if I’m at risk for pernicious anemia before I have symptoms?

In the future, potentially. While there isn’t a routine diagnostic DNA test for pernicious anemia right now, genetic research is actively identifying specific genetic variations associated with increased risk. This understanding could eventually lead to personalized risk assessments and potentially earlier interventions.

4. Why did I get pernicious anemia, but my sibling with similar genes didn’t?

Genetic predisposition increases your risk, but it doesn’t guarantee you’ll develop the condition. Autoimmune diseases like pernicious anemia are complex, involving multiple genetic variations interacting with environmental factors. Even with similar genetic backgrounds, individual immune responses can differ significantly.

5. Will my pernicious anemia get worse over time, even if I take my shots?

No, with consistent lifelong vitamin B12 supplementation, your pernicious anemia should be well-controlled, and symptoms should not worsen. The treatment effectively bypasses the absorption defect, preventing the deficiency from progressing and helping to reverse most symptoms. Regular treatment is key to managing the condition effectively.

6. Can I do anything to prevent my kids from getting pernicious anemia if it runs in our family?

Currently, there aren’t specific proven lifestyle interventions to prevent pernicious anemia, especially given its autoimmune and genetic predisposition. However, ongoing genetic research aims to better understand the underlying causes, which may lead to preventive measures or early identification strategies in the future.

7. If I eat a lot of B12-rich foods, can I avoid the injections?

Unfortunately, no. Pernicious anemia is caused by your body’s inability to absorb vitamin B12 due to an autoimmune attack on intrinsic factor, not a lack of B12 in your diet. Even with a diet rich in B12, your body cannot absorb it, making injections essential to bypass this defect.

8. If I have another autoimmune disease, am I more likely to also get pernicious anemia?

Yes, you are. Having one autoimmune condition can increase your risk of developing others, including pernicious anemia. This is because many autoimmune diseases share common genetic predispositions and involve similar types of immune system dysregulation.

9. Is it true pernicious anemia is more common as I get older?

Yes, that’s true. Pernicious anemia disproportionately affects older adults. While the exact reasons are complex, age-related changes and the progressive nature of the autoimmune attack on stomach cells can contribute to its increased prevalence later in life.

10. Can my pernicious anemia affect my memory or focus at work?

Yes, it definitely can. If untreated, pernicious anemia can lead to neurological symptoms such as memory impairment and cognitive difficulties. Consistent B12 supplementation is crucial to manage these symptoms and help you maintain your focus and memory for daily tasks and work.

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This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

[1] Li, J. et al. “Genome-wide admixture and association study of serum iron, ferritin, transferrin saturation and total iron binding capacity in African Americans.”Hum Mol Genet, vol. 23, no. 24, 2014, pp. 6610–19.

[2] Homann, J., et al. “Genome-Wide Association Study of Alzheimer’s Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery Dataset.”Frontiers in Aging Neuroscience, vol. 14, 2022, p. 863471.

[3] Pillalamarri, V. et al. “Whole-exome sequencing in 415,422 individuals identifies rare variants associated with mitochondrial DNA copy number.”HGG Adv, vol. 4, no. 1, 2023, p. 100188.

[4] Schoeler, T., et al. “Participation bias in the UK Biobank distorts genetic associations and downstream analyses.” Nature Human Behaviour, vol. 7, no. 5, 2023, pp. 789–798.

[5] Riveros-Mckay, F., et al. “The influence of rare variants in circulating metabolic biomarkers.” PLoS Genetics, vol. 16, no. 3, 2020, p. e1008632.

[6] Zaidi, A. A., et al. “The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort.” HGG Adv, vol. 4, no. 3, 2023, p. 100224.

[7] Raffield, L. M. et al. “Genome-wide association study of iron traits and relation to diabetes in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL): potential genomic intersection of iron and glucose regulation?”Hum Mol Genet, vol. 26, no. 12, 2017, pp. 2327–42.

[8] Chong, M., et al. “GWAS and ExWAS of blood Mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia.”Elife, vol. 11, 2022, p. e72322.

[9] Comuzzie AG et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One.

[10] Raffler J et al. Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality. PLoS Genet.

[11] Li D et al. Progressive effects of single-nucleotide polymorphisms on 16 phenotypic traits based on longitudinal data. Genes Genomics.

[12] McQueen MB et al. The National Longitudinal Study of Adolescent to Adult Health (Add Health) sibling pairs genome-wide data. Behav Genet.

[13] Bacelis J et al. Literature-Informed Analysis of a Genome-Wide Association Study of Gestational Age in Norwegian Women and Children Suggests Involvement of Inflammatory Pathways. PLoS One.

[14] Li, J. “Genome-wide admixture and association study of serum iron, ferritin, transferrin saturation and total iron binding capacity in African Americans.”Hum Mol Genet, vol. 24, no. 3, 2015, pp. 883-890.

[15] Benyamin, B., et al. “Common variants in TMPRSS6 are associated with iron status and erythrocyte volume.” Nat. Genet., vol. 41, 2009, pp. 1173–1175.

[16] Akbari, P., et al. “A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology.”Nat Commun, vol. 14, no. 1, 2023, p. 5045.

[17] McLaren, C. E., et al. “Mixture distribution analysis of phenotypic markers reflecting HFE gene mutations.” Blood, vol. 102, no. 12, 2003, pp. 4563–4566.

[18] Gupta, R., et al. “Nuclear genetic control of mtDNA copy number and heteroplasmy in humans.” Nature, vol. 620, no. 7974, 2023, pp. 582-590.

[19] Moore, A., et al. “Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors.” J Biol Chem. 2023.

[20] Manavathi, B., et al. “Functional regulation of pre-B-cell leukemia homeoboxinteractingprotein1 (PBXIP1/HPIP) in erythroiddifferentiation.” J Biol Chem. 2012.

[21] Reiner, A.P., et al. “Genome-wide and gene-centric analyses of circulating myeloperoxidase levels in the charge and care consortia.” Hum Mol Genet. 2013.

[22] McLaren, C. E. et al. “Genome-wide association study identifies genetic loci associated with iron deficiency.” PLoS One, vol. 6, no. 3, 2011, e17390.

[23] Lee, P.L., et al. “The effect of transferrin polymorphisms on iron metabolism.” Blood Cells Mol Dis. 1999.

[24] Tahir, U.A., et al. “Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals.” Nat Commun. 2022.

[25] Lindberg, R.L., et al. “Porphobilinogen deaminase deficiency in mice causes a neuropathy resembling that of human hepatic porphyria.” Nature Genetics. 1996.

[26] Homedan, C., et al. “Mitochondrial energetic defects in muscle and brain of a hmbs -/- mouse model of acute intermittent porphyria.” Human Molecular Genetics. 2015.

[27] Zhang, J., et al. “A founder mutation in vps11 causes an autosomal recessive leukoencephalopathy linked to autophagic defects.” PLOS Genetics. 2016.

[28] Sidransky, E., et al. “Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease.” N Engl J Med. 2009.

[29] Chahine, L.M., et al. “Clinical and biochemical differences in patients having Parkinson disease with vs without GBA mutations.” JAMA Neurol. 2013.

[30] Divers, J. “GWAS for time to failure of kidney transplants from African American deceased donors.” Clin Transplant, PMID: 32080893.

[31] Liu, TY, et al. “Diversity and longitudinal records: Genetic architecture of disease associations and polygenic risk in the Taiwanese Han population.”Sci Adv, PMID: 40465716.