Disease Recurrence
Disease recurrence refers to the return of a disease after a period of remission or successful treatment. This phenomenon is a significant challenge across various medical fields, from oncology and autoimmune disorders to infectious diseases and mental health conditions. Understanding the mechanisms behind why diseases reappear is crucial for developing more effective long-term treatments and improving patient outcomes.
Background
Section titled “Background”Many chronic and acute conditions, despite initial successful management, can manifest again, sometimes years after apparent recovery. This can involve the return of the original disease at the same site, or its emergence in a different part of the body, often with varying severity. The unpredictable nature of recurrence profoundly impacts patients’ lives, leading to prolonged treatment, increased healthcare burden, and psychological distress.
Biological Basis
Section titled “Biological Basis”The biological underpinnings of disease recurrence are complex and multifaceted. They often involve a combination of genetic predispositions, epigenetic changes, persistence of minimal residual disease, and immune system factors. For instance, in certain cancers, a small population of drug-resistant cancer stem cells may survive initial therapy, leading to relapse. In autoimmune diseases, the immune system’s memory may reactivate under specific triggers. Genetic variations, such as Single Nucleotide Polymorphisms (SNPs), can play a role in an individual’s susceptibility to a disease and potentially influence its likelihood of recurrence. Research utilizing genome-wide association studies (GWAS) has identified specific genetic loci associated with diseases known for recurrence, such as shared risk loci for Crohn’s disease and celiac disease, includingIL18RAP, PTPN2, TAGAP, and PUS10.[1] Other studies have identified genes like NELL1as novel susceptibility genes for inflammatory bowel disease (IBD), which often has a relapsing-remitting course.[2] Similarly, genetic regions and SNPs like rs13358880 on chromosome 5 have been implicated in conditions like bipolar disorder, which is characterized by recurrent episodes.[3]These genetic insights can shed light on pathways that contribute to disease persistence or vulnerability to relapse.
Clinical Relevance
Section titled “Clinical Relevance”The clinical relevance of understanding disease recurrence is paramount. It directly informs treatment strategies, patient surveillance, and the development of personalized medicine. Identifying individuals at higher risk of recurrence allows for targeted preventative measures, more aggressive follow-up, or alternative therapeutic approaches. Genetic markers, when validated, can serve as prognostic indicators, guiding clinicians in tailoring treatment plans to minimize recurrence risk. For example, knowing a patient’s genetic profile might influence the choice of chemotherapy, the frequency of diagnostic screenings, or the duration of remission-maintaining therapies. Early detection of recurrence through advanced monitoring techniques can lead to timelier interventions, potentially improving prognosis and reducing morbidity.
Social Importance
Section titled “Social Importance”The social importance of addressing disease recurrence extends to public health, economic stability, and the overall well-being of affected communities. High rates of recurrence can place a substantial burden on healthcare systems due to repeated hospitalizations, costly treatments, and long-term care needs. For patients and their families, recurrence can lead to significant psychological stress, impacting quality of life, employment, and social engagement. By advancing our understanding of recurrence, society can benefit from more effective public health strategies, reduced healthcare expenditures, and improved support systems for those living with chronic and relapsing conditions. Research into genetic factors influencing recurrence, and the development of new diagnostic and therapeutic tools, offers hope for reducing the personal and societal impact of these challenging conditions.
Limitations
Section titled “Limitations”Understanding the genetic underpinnings of disease recurrence is subject to several methodological and analytical limitations inherent in large-scale genetic studies. These limitations can influence the interpretation of findings, particularly regarding the generalizability and completeness of identified genetic associations.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic studies, including those investigating disease recurrence, often face challenges related to study design and statistical power that can impact the reliability and reproducibility of findings. A significant limitation is the frequent failure of replication, where initially identified risk alleles may not show consistent associations in subsequent studies. For instance, low allele frequency of a variant has been observed to contribute to replication failure, with a substantial number of risk alleles failing to replicate with the same effect direction in subsequent GWAS.[4] The observed effect sizes for variants can also vary between discovery and replication cohorts, sometimes reflecting an overestimation in initial studies, which can be influenced by differences in statistical power and study design.[5] Furthermore, the choice of discovery P-value thresholds can profoundly affect replication rates, necessitating careful adjustment to ensure consistent reproducibility across different analytical methods.[6] These statistical challenges mean that not all associations identified in initial screens may represent true underlying genetic effects, making robust replication critical for confirming findings.[7]
Ancestry Specificity and Generalizability
Section titled “Ancestry Specificity and Generalizability”Many large-scale genetic studies are conducted within specific ancestral populations, which can limit the generalizability of their findings to broader global populations. For example, a significant genome-wide association study that identified novel susceptibility loci for various diseases was conducted exclusively in a Japanese population.[4] While valuable, such population-specific discoveries may not directly translate to individuals of different ancestries due to variations in allele frequencies, linkage disequilibrium patterns, and genetic architecture. Research indicates that genetic associations often replicate more effectively within the same ancestral group where they were initially discovered.[8]This ancestral specificity means that genetic risk factors for disease recurrence identified in one population may not be universally applicable, highlighting the need for diverse cohorts to fully understand the global genetic landscape of disease.
Complex Genetic Architecture and Unexplained Variance
Section titled “Complex Genetic Architecture and Unexplained Variance”The genetic basis of complex traits, including susceptibility to disease recurrence, is often polygenic, involving numerous genetic variants, each contributing a small effect. This complex architecture means that even large-scale studies may only identify a fraction of the total genetic influences. For instance, studies have confirmed the polygenetic nature of diseases, identifying many susceptibility alleles with individually small effects.[8] Moreover, different studies might identify associations with distinct SNPs within the same gene region, suggesting that the directly genotyped variants may not be the true causal ones, or that multiple causal variants exist within a gene.[5]This intricate genetic landscape, characterized by many variants of small effect and potential heterogeneity in causal variants, contributes to remaining knowledge gaps and the challenge of fully accounting for the heritability of disease recurrence.
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing an individual’s susceptibility to various diseases, including cancer, and can impact the likelihood of disease recurrence. These variants, often single nucleotide polymorphisms (SNPs), can alter gene expression, protein function, or regulatory processes, thereby modulating biological pathways involved in disease development and progression. Extensive genome-wide association studies (GWAS) have identified numerous genetic loci associated with different cancer types, highlighting the complex interplay between genetics and disease outcomes.[9]Understanding the specific impact of these variants can provide insights into disease mechanisms and potential therapeutic targets.
Several variants are associated with non-coding RNA genes, which play diverse regulatory roles without directly coding for proteins. For instance, rs11104986 is linked to RNU1-117P and LINC02458. RNU1-117P is a pseudogene of RNU1 (U1 snRNA), involved in pre-mRNA splicing, while LINC02458 is a long intergenic non-coding RNA (lincRNA) whose function is still being elucidated but may regulate gene expression in cis or trans. Similarly, rs141641519 is associated with KRT19P1, a pseudogene of KRT19 (Keratin 19), and RNU4-66P, another small nuclear RNA pseudogene, while rs140432770 is found near Y_RNA and LINC02549. Variants in these non-coding regions can affect RNA stability, processing, or interaction with other molecules, subtly altering gene regulatory networks that may contribute to cancer predisposition or influence how a disease responds to treatment, thus affecting recurrence.[9] Another intriguing variant, rs79096001 , is located within VLDLR-AS1, an antisense long non-coding RNA that overlaps with the VLDLRgene (Very Low Density Lipoprotein Receptor). Antisense lncRNAs likeVLDLR-AS1 can modulate the expression of their corresponding sense genes, in this case, VLDLR, which is involved in lipid metabolism and signaling. Alterations in VLDLR-AS1expression or function due to a variant could impact lipid processing pathways, cell growth, and survival, which are critical in various cancers. Such dysregulation might contribute to a cellular environment conducive to tumor progression or resistance to therapy, potentially increasing the risk of disease recurrence.[10]Variants affecting kinase genes, central to cell signaling, are also highly relevant to disease recurrence. The variantrs187043769 is associated with MAP3K4 (Mitogen-Activated Protein Kinase Kinase Kinase 4), a key component of cellular stress response and developmental signaling pathways. MAP3K4 regulates cell proliferation, differentiation, and apoptosis. Similarly, rs139807789 is linked to STK33(Serine/Threonine Kinase 33), a kinase involved in cell survival pathways, particularly in cancer cells. Polymorphisms in these genes can lead to altered kinase activity or expression, potentially driving uncontrolled cell growth, enabling tumor survival, or promoting metastatic potential, thereby influencing the aggressiveness of a disease and its propensity to recur after initial treatment.[9]Other variants impact genes crucial for cell adhesion, migration, and developmental signaling, which are fundamental processes in cancer biology. The variantrs11871306 is associated with WNT9B(Wnt Family Member 9B), a ligand in the Wnt signaling pathway, which is vital for embryonic development and tissue homeostasis, and frequently dysregulated in cancer. Alterations in Wnt signaling can promote cell proliferation, stemness, and epithelial-mesenchymal transition (EMT), all contributing to tumor invasiveness and recurrence. Furthermore,rs58443603 is linked to MACF1 (Microtubule Actin Crosslinking Factor 1), a cytolinker protein important for cytoskeletal integrity and cell migration, while rs184111067 is near SPATA13 (Spermatogenesis Associated 13), which is involved in cell migration and invasion, and rs554254417 is associated with CNTN3(Contactin 3), a cell adhesion molecule in the nervous system. Variants affecting these genes can modify cellular architecture, motility, and intercellular communication, potentially enhancing a tumor’s ability to metastasize and evade immune surveillance, thereby increasing the risk of disease relapse.[9]
Key Variants
Section titled “Key Variants”Causes of Disease Recurrence
Section titled “Causes of Disease Recurrence”Disease recurrence is a complex phenomenon influenced by a confluence of genetic predispositions, environmental factors, and the inherent variability in populations and study methodologies. Understanding these contributing elements is crucial for elucidating the underlying mechanisms of relapse.
Genetic Predisposition
Section titled “Genetic Predisposition”Genetic factors play a significant role in determining an individual’s susceptibility to disease recurrence. Genome-wide association studies (GWAS) have been instrumental in identifying specific inherited variants and risk loci associated with various conditions. For instance, meta-analyses have uncovered new risk loci for diseases like rheumatoid arthritis.[7]These genetic markers, which can include single nucleotide polymorphisms (SNPs), contribute to polygenic risk, where the cumulative effect of multiple genes, rather than a single Mendelian form, influences recurrence likelihood. Further research aims to identify “replicable risk regions” where numerous genetic markers consistently associate with a phenotype across diverse populations, providing robust evidence for their involvement.[11]
Environmental Modifiers and Population Heterogeneity
Section titled “Environmental Modifiers and Population Heterogeneity”Environmental factors, encompassing lifestyle, diet, and various exposures, can significantly interact with genetic predispositions to influence disease recurrence. Such factors can act as triggers or modifiers, altering disease progression even in individuals with a genetic susceptibility. The impact of these environmental influences can vary greatly across different populations, meaning findings from one cohort might not be generalizable to others.[12] Differences in “key factors” between study cohorts, which may include environmental or socioeconomic disparities, can modify phenotype-genotype associations and thus affect the observed risk for recurrence.[12]These variations highlight the importance of considering the diverse environmental and demographic contexts in which disease recurrence manifests.
Clinical and Methodological Influences
Section titled “Clinical and Methodological Influences”Beyond direct biological mechanisms, clinical characteristics and methodological limitations in research can also influence our understanding and prediction of disease recurrence. Age-related changes are a known factor, as study cohorts often comprise individuals who are middle-aged to elderly, potentially limiting the generalizability of findings to younger populations.[12] Furthermore, challenges in replicating previously reported phenotype-genotype associations can arise from several methodological issues, including the presence of false positive findings in initial studies or inadequate statistical power in subsequent replication attempts.[12] Factors such as survival bias, introduced when DNA collection occurs at later examinations, can also obscure the true causal pathways for recurrence.[12]
Genetic Insights for Prognosis and Risk Stratification
Section titled “Genetic Insights for Prognosis and Risk Stratification”The identification of genetic risk loci through genome-wide association studies (GWAS) and meta-analyses provides crucial prognostic value in understanding disease recurrence. By analyzing large cohorts and employing rigorous statistical methods, researchers can pinpoint specific genetic variants that predict the likelihood of disease progression, treatment response, and long-term implications for patient health.[7]For instance, robustly replicated associations, such as those for rheumatoid arthritis or large artery atherosclerotic stroke, can help anticipate whether a disease is likely to return or worsen, enabling clinicians to prepare for potential future challenges.[7] These genetic insights are instrumental in risk stratification, allowing for personalized medicine approaches. Predictive models that incorporate both clinical and genetic risk factors, evaluated using metrics like the area under the receiver operator characteristic (ROC) curve, can effectively discriminate between individuals at varying risks of recurrence.[13]Identifying high-risk individuals, such as those carrying common variants at 6p21.1 associated with stroke (rs556621 ), enables the implementation of targeted prevention strategies and intensified monitoring protocols, aiming to either prevent recurrence or detect it early for more effective intervention.[13]
Guiding Clinical Management and Treatment Decisions
Section titled “Guiding Clinical Management and Treatment Decisions”Genetic discoveries hold significant clinical utility in refining diagnostic approaches and guiding treatment selection for diseases prone to recurrence. The validation of genetic associations through replication studies, often involving large independent cohorts, strengthens their potential as diagnostic markers.[1] For example, the identification of shared risk loci like IL18RAP and PTPN2for Crohn’s disease and celiac disease, confirmed through rigorous replication, provides proof of concept for using genetic information to enhance diagnostic precision and establish a baseline risk for future recurrence.[1]Furthermore, understanding an individual’s genetic predisposition can inform tailored treatment strategies and monitoring protocols. For conditions such as testicular germ cell cancer or postmenopausal breast cancer, genetic variants may influence disease subtype or response to specific therapies, necessitating a personalized approach to prevent recurrence.[14] Studies that fit additive genetic risk models in replication cohorts highlight the potential for genetic profiles to guide not only initial treatment choices but also the intensity and duration of post-treatment surveillance, optimizing patient care by aligning interventions with individual genetic vulnerabilities.[9]
Understanding Overlapping Disease Etiologies
Section titled “Understanding Overlapping Disease Etiologies”Genetic research frequently uncovers comorbidities and associations, revealing how shared genetic factors can contribute to related conditions or overlapping phenotypes, which is critical for managing disease recurrence. The discovery of pleiotropic effects, where specific genetic variants increase the risk for multiple diseases, such as the shared loci for Crohn’s disease and celiac disease, underscores the interconnectedness of disease etiologies.[1] This means that a patient experiencing recurrence of one condition might also possess genetic predispositions to other related diseases or complications, requiring a comprehensive clinical perspective.
Large-scale meta-analyses across diverse populations and longitudinal studies are instrumental in elucidating the complex genetic architecture underlying various diseases and their potential for recurrence.[15]By identifying common variants that influence the susceptibility to conditions like rheumatoid arthritis or keratinocyte cancers, researchers gain a deeper understanding of the biological pathways that, when dysregulated, can lead to both initial disease onset and subsequent recurrence.[7] This knowledge can facilitate the development of novel prevention strategies and therapeutic targets that address shared genetic mechanisms, ultimately improving long-term patient outcomes by mitigating the risk of recurrence and related complications.
Longitudinal Cohort Investigations and Temporal Patterns
Section titled “Longitudinal Cohort Investigations and Temporal Patterns”Large-scale prospective cohort studies are instrumental in understanding the natural history of diseases and temporal patterns relevant to outcomes such as disease recurrence. Several prominent cohorts have been utilized for extensive longitudinal research, including the Atherosclerosis Risk in Communities (ARIC) study, the Framingham Heart Study (FHS) cohorts, the Multi-Ethnic Study of Atherosclerosis (MESA), the Cardiovascular Health Study (CHS), and the Health and Retirement Study (HRS).[15]These studies involve repeated measurements over extended periods, with FHS cohort 1 featuring 28 visits, CHS 22 examinations, ARIC 4 visits, and MESA 5 visits, providing rich datasets for tracking disease progression and potential recurrence.[15] Genotyping efforts across these cohorts, such as in 12,771 ARIC participants, 8224 MESA participants, and 9167 FHS participants, further enable the investigation of genetic predispositions and their influence on long-term health trajectories.[15]The extensive follow-up data from these cohorts are crucial for identifying individuals who experience subsequent disease events, thereby informing our understanding of recurrence risk over time.
Cross-Population Comparisons and Epidemiological Insights
Section titled “Cross-Population Comparisons and Epidemiological Insights”Population studies also highlight the importance of investigating disease patterns across diverse demographic groups, which is critical for understanding variations in recurrence rates and risk factors. While many foundational studies, such as the Framingham Heart Study, have provided comprehensive insights into community-based health, their cohorts were predominantly composed of individuals of white European descent, largely middle-aged to elderly.[12]This demographic profile, while valuable, limits the generalizability of findings regarding disease prevalence, incidence, and recurrence to younger populations or those of differing ethnic and racial backgrounds.[12]For instance, analyses in cohorts like ARIC, MESA, and FHS often specify the number of “whites included” in genotyping, indicating a focus that may not fully capture the genetic and environmental diversity relevant to disease recurrence across broader populations.[15]These observations underscore the need for more inclusive studies to elucidate population-specific effects and address potential disparities in disease recurrence.
Methodological Considerations and Generalizability
Section titled “Methodological Considerations and Generalizability”The robust design of population studies is paramount for drawing valid conclusions about disease recurrence, though inherent methodological limitations must be considered. Studies frequently employ sophisticated approaches like pleiotropic meta-analyses of longitudinal data to identify genetic variants associated with various traits, requiring careful attention to data quality and consistency across multiple cohorts.[15]For instance, the Framingham Heart Study, while comprehensively characterized, faced limitations due to its moderate cohort size, which could lead to inadequate statistical power and a susceptibility to false negative findings.[12] Furthermore, the timing of DNA collection, such as at later examinations in FHS, may introduce a survival bias, potentially skewing observations related to long-term outcomes.[12]Ensuring representativeness across age groups and ancestries, as well as accounting for potential generation confounders by analyzing distinct cohorts separately, are crucial steps for enhancing the generalizability of findings on disease recurrence.[15]
Frequently Asked Questions About Disease Recurrence
Section titled “Frequently Asked Questions About Disease Recurrence”These questions address the most important and specific aspects of disease recurrence based on current genetic research.
1. My disease came back after treatment; why did it work for others but not me?
Section titled “1. My disease came back after treatment; why did it work for others but not me?”It’s frustrating when that happens, and often it comes down to subtle biological differences. Your unique genetic makeup, including specific variations, can influence how your body responds to treatment and whether a small number of disease cells survive or your immune system remains susceptible to reactivation. This means that even with the same treatment, some individuals are simply more predisposed to recurrence.
2. Will my kids be more likely to have my disease come back if I did?
Section titled “2. Will my kids be more likely to have my disease come back if I did?”Yes, there can be an increased likelihood. Many diseases with recurrence have a genetic component, meaning certain predispositions can be passed down. For instance, specific shared risk loci like IL18RAP and PTPN2are associated with recurrent conditions like Crohn’s disease and celiac disease, and genes likeNELL1for inflammatory bowel disease, which often runs in families.
3. Can a DNA test tell me if my illness is likely to return?
Section titled “3. Can a DNA test tell me if my illness is likely to return?”Potentially, yes. Genetic markers, when validated, can serve as prognostic indicators, helping predict your risk of recurrence. Knowing your genetic profile might guide doctors in tailoring treatment plans, such as choosing specific therapies or determining the frequency of follow-up screenings, to minimize recurrence risk.
4. Does my family’s background affect my risk of recurrence?
Section titled “4. Does my family’s background affect my risk of recurrence?”Yes, your ancestral background can play a significant role. Genetic risk factors for disease recurrence can be specific to certain populations due to variations in allele frequencies and genetic architecture. Research conducted in one ancestral group may not directly translate to others, highlighting the importance of diverse studies to understand risks globally.
5. Why do some people’s autoimmune conditions stay away, but mine keeps flaring up?
Section titled “5. Why do some people’s autoimmune conditions stay away, but mine keeps flaring up?”Your immune system’s unique programming, influenced by your genetics, is key here. In autoimmune diseases, the immune system has a “memory” that can reactivate under specific triggers. Genetic variations can make some individuals’ immune systems more prone to this reactivation, leading to a relapsing-remitting course for conditions like Crohn’s disease, which has genetic links likeIL18RAP.
6. I try to stay healthy, but my disease still returned. Was it inevitable?
Section titled “6. I try to stay healthy, but my disease still returned. Was it inevitable?”While healthy habits are always beneficial, the complex biological underpinnings of recurrence mean it’s not always avoidable. Genetic predispositions, combined with factors like the persistence of minimal residual disease or specific immune system responses, can significantly influence whether your disease returns. Your genetic profile can make you more vulnerable despite your best efforts.
7. Can changing my diet or exercise stop my illness from coming back?
Section titled “7. Can changing my diet or exercise stop my illness from coming back?”Lifestyle choices like diet and exercise support overall health, but their direct impact on genetically influenced recurrence is complex. While they might help manage general health and reduce some triggers, your genetic predisposition plays a significant role in whether your disease recurs. Genetic insights can help identify pathways that contribute to disease persistence or vulnerability to relapse, which might not be fully overcome by lifestyle alone.
8. Why do some mental health issues, like bipolar, keep coming back even with therapy?
Section titled “8. Why do some mental health issues, like bipolar, keep coming back even with therapy?”Recurrent mental health conditions often have a strong genetic basis that influences their course. For example, specific genetic regions and SNPs, like rs13358880 on chromosome 5, have been implicated in conditions like bipolar disorder, which is characterized by recurrent episodes. These genetic factors contribute to the brain’s vulnerability to relapse, even with successful initial treatment.
9. If my disease returns, will it always be worse than before?
Section titled “9. If my disease returns, will it always be worse than before?”Not necessarily. Disease recurrence can manifest with varying severity, sometimes at the same site, or in a different part of the body. While genetics can influence the likelihood of recurrence, it doesn’t automatically mean a more severe episode. The specific genetic and biological factors involved in your individual case will determine the nature of the relapse.
10. Are there early warning signs I should look for to catch recurrence quickly?
Section titled “10. Are there early warning signs I should look for to catch recurrence quickly?”Early detection is crucial, and genetic insights can help guide this. Knowing your genetic profile might influence the frequency of diagnostic screenings your doctor recommends. While the specific signs vary by disease, advanced monitoring techniques based on your individual risk factors can lead to timelier interventions if recurrence is detected.
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
Section titled “References”[1] Festen, E. A. et al. “A meta-analysis of genome-wide association scans identifies IL18RAP, PTPN2, TAGAP, and PUS10 as shared risk loci for Crohn’s disease and celiac disease.”PLoS Genet, vol. 7, no. 2, 3 Feb. 2011, e1002003.
[2] Franke, A. et al. “Systematic association mapping identifies NELL1 as a novel IBD disease gene.”PLoS One, 2007.
[3] Smith, E. N. et al. “Genome-wide association study of bipolar disorder in European American and African American individuals.” Molecular Psychiatry, 2009.
[4] Ishigaki K. et al. “Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases.” Nat Genet. 2020.
[5] Sabatti C. et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.” Nat Genet. 2008.
[6] Loya H. et al. “A scalable variational inference approach for increased mixed-model association power.” Nat Genet. 2024.
[7] Stahl EA. et al. “Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci.” Nat Genet. 2010.
[8] Aberg KA. et al. “A comprehensive family-based replication study of schizophrenia genes.” JAMA Psychiatry. 2013.
[9] Hunter, D. J. et al. “A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.”Nat Genet, vol. 39, no. 7, 27 May 2007, pp. 870-874.
[10] Pardo, L. M. et al. “Genome-Wide Association Studies of Multiple Keratinocyte Cancers.” PLoS One, vol. 12, no. 1, 12 Jan. 2017, e0169212.
[11] Zuo, L., et al. “Genome-wide search for replicable risk gene regions in alcohol and nicotine co-dependence.” Am J Med Genet B Neuropsychiatr Genet, vol. 159B, no. 4, 2012, pp. 401-10.
[12] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, 2007, PMID: 17903293.
[13] Holliday, E. G. et al. “Common variants at 6p21.1 are associated with large artery atherosclerotic stroke.”Nat Genet, vol. 44, no. 10, 2 Sept. 2012, pp. 1149-1154.
[14] Kanetsky, P. A. et al. “Common variation in KITLG and at 5q31.3 predisposes to testicular germ cell cancer.”Nat Genet, vol. 41, no. 7, 31 May 2009, pp. 804-809.
[15] He, L. et al. “Pleiotropic Meta-Analyses of Longitudinal Studies Discover Novel Genetic Variants Associated with Age-Related Diseases.” Front Genet, vol. 7, 18 Oct. 2016, p. 182.