Table of Contents
Overview
Definition and Scope
Genetic epidemiology is a scientific discipline that emerged in the mid-1980s, integrating methodologies from traditional epidemiology and genetics to analyze the familial distribution of traits and diseases. It is defined as the study of the aetiology, distribution, and control of disease in groups of relatives, as well as the inherited causes of disease within populations.[4.1] This field focuses on understanding how genetic factors influence human traits, including health and disease, while also considering the interactions between genes and environmental factors.[3.1] The discipline has inherited key elements from its parent fields, emphasizing the importance of studying defined populations to investigate the roles of genetic factors in disease causation.[2.1] Genetic epidemiologists aim to elucidate the causes, distribution, and control of inherited diseases, thereby contributing to the broader understanding of public health and disease prevention.[3.1]Importance in Public Health
Genetic epidemiology is pivotal in public health by merging genetic insights with traditional epidemiological methods. This integration enhances the understanding of infectious diseases, leading to improved surveillance, outbreak investigations, and the development of more effective control strategies.[5.1] Researchers can explore both genetic and environmental factors, facilitating valid inferences about disease patterns within populations.[6.1] Over the past four decades, the field has advanced significantly, with institutions like Johns Hopkins pioneering the incorporation of genetic concepts into epidemiological studies. This progress has enabled the mapping of genetic variants and the prediction of disease risk based on genetic profiles, particularly for complex diseases influenced by both genetic and environmental factors, such as chronic obstructive pulmonary disease, diabetes, and various cancers.[7.1] The application of genetic epidemiology in clinical practice is increasingly important. For example, integrating genetic testing into routine oncology care has improved access to genetic counseling and testing, crucial for identifying hereditary cancer risks.[13.1] Additionally, pharmacogenetic testing across healthcare settings has shown potential to enhance drug prescribing practices and reduce adverse events.[14.1] As genetic epidemiology evolves, healthcare providers must understand the implications of genetic data in personalized medicine. By tailoring treatment strategies to individual genetic profiles, healthcare can transition from a one-size-fits-all approach to more precise and effective interventions.[18.1] This shift not only enhances patient care but also has the potential to reduce healthcare costs by enabling earlier diagnoses and more efficient use of therapeutic options.[20.1]In this section:
History
Early Developments
The field of genetic epidemiology emerged as a distinct discipline in the late 20th century, integrating genetics, epidemiology, and biostatistics to explore the interplay between genetic and environmental factors in the etiology of complex diseases. Approximately 35 years ago, the term "genetic epidemiology" was coined, marking its formal recognition within public health.[48.1] Early initiatives at institutions like Johns Hopkins incorporated genetic concepts and family history into epidemiological studies, laying the groundwork for mapping genetic variants and predicting disease risk based on genetic profiles. This pioneering work also investigated gene-environment interactions across diseases such as diabetes, cardiovascular disease, and schizophrenia.[47.1] Genetic epidemiology is defined as the scientific discipline focused on analyzing the familial distribution of traits to uncover potential genetic bases for diseases, emphasizing the importance of understanding both genetic and environmental influences and their interactions in producing various health outcomes.[49.1] The field has evolved to include advanced methodologies, such as genome-wide association studies (GWAS), which systematically scan the human genome for genetic variations associated with specific diseases. These studies often focus on populations or family groups, employing linkage analysis to identify inherited genetic factors linked to disease.[50.1] The completion of the Human Genome Project in 2001 significantly impacted genetic epidemiology by providing a comprehensive reference for human genetic variation. This milestone facilitated the rise of candidate gene studies and gene-environment research, aiming to elucidate the complex heritability of diseases and enhance public health interventions.[51.1] Despite its promise to transform public health, many findings in genetic epidemiology have yet to achieve the anticipated impact, highlighting ongoing challenges in translating genomic research into practical health solutions.Key Milestones in Genetic Epidemiology
The field of genetic epidemiology has undergone significant evolution over the past few decades, marked by key milestones that have shaped its methodologies and applications. A foundational event was the completion of the Human Genome Project in 2003, which provided a genome sequence covering over 90% of the human genome. This achievement set a precedent for future genetic research and international collaboration, accelerating the discovery of new genes and pathways involved in health and disease.[57.1] [56.1] Genetic epidemiology, a relatively young discipline, integrates genetics, epidemiology, and biostatistics to identify optimal study designs and statistical analyses for understanding the genetic basis of complex diseases, where both genetic and environmental factors contribute to etiology.[47.1] Approximately forty years ago, researchers at Johns Hopkins began incorporating genetic concepts and family history into epidemiologic studies, advancing the mapping of genetic variants and risk prediction based on genetic profiles.[47.1] This evolution has enabled the examination of interactions between environmental factors and genes across various complex diseases, including chronic obstructive pulmonary disease, autism, birth defects, HIV infections, diabetes, cardiovascular disease, schizophrenia, and inflammatory bowel disease.[47.1] The advancement of genetic testing has raised significant ethical implications, including issues of personal autonomy and social responsibility to ensure fair outcomes for individuals and society.[52.1] As genetic testing becomes more mainstream, driven by advancements like whole genome sequencing and commercial providers, addressing the risks of data misuse is crucial.[54.1] Each new genetic test introduces challenges for medicine, public health, and social policy, particularly regarding test utilization, implementation, and result usage.[53.1] Ethical and legal principles such as autonomy, confidentiality, privacy, and equity are vital in determining whether individuals should have the choice to accept or refuse testing and what protections are necessary to prevent genetic discrimination.[53.1] Integrating genetic data with environmental factors is increasingly recognized as essential for advancing public health initiatives. This approach leverages interactions between the genome, epigenome, and environment to enhance health outcomes at both individual and population levels, supporting comprehensive public health policies.[61.1] Historically, the call for greater integration between genetics and public health is not new; researchers like Galea et al. and Diez Roux advocated for complex systems as a pathway forward in epidemiology.[63.1] The explosion of health data and bioinformatics approaches has underscored the potential for real-time monitoring of health metrics, environmental factors, and behavioral data, informing actionable insights for patients and healthcare providers.[62.1]In this section:
Recent Advancements
Technological Innovations
Recent advancements in genetic epidemiology have been significantly influenced by technological innovations, particularly in the realms of sequencing and data analysis. One of the most notable developments is the advent of next-generation sequencing (NGS) technologies, which allow researchers to sequence millions of DNA molecules simultaneously. This high-throughput capability has transformed the analysis of genetic variation, enabling a more comprehensive understanding of the genetic factors associated with complex diseases.[107.1] Compared to traditional Sanger sequencing, NGS is approximately 50,000 times faster, facilitating rapid advancements in the field.[104.1] NGS has proven particularly beneficial in elucidating the genetic underpinnings of both Mendelian and complex neurological diseases. The technology provides detailed insights into genome structure, genetic variations, gene activity, and changes in gene behavior, which are crucial for understanding the multifaceted nature of genetic diseases.[107.1] For instance, Roche's innovative sequencing by expansion (SBX) technology exemplifies a significant leap forward in NGS, enhancing the ability to decode complex diseases such as cancer and neurodegenerative conditions.[106.1] In addition to NGS, genome-wide association studies (GWAS) have revolutionized genetic epidemiology methodologies. Since their inception, GWAS have led to a substantial increase in data regarding common genetic variants and their association with complex diseases, facilitating numerous discoveries of new loci linked to disease susceptibility.[110.1] The integration of diverse genetic data through multiancestry GWAS meta-analysis (MAGMA) addresses challenges related to small effect sizes and inadequate coverage of genetic variation, thereby enhancing the understanding of complex traits.[111.1]Impact on Clinical Practice
The introduction of diagnostic clinical genome and exome sequencing (CGES) is significantly transforming the practice of clinical geneticists. Many large healthcare institutions are investing heavily in the necessary infrastructure and technology to facilitate access to CGES, particularly as health-care coverage increasingly extends to genomic sequencing-based tests that are clinically indicated.[95.1] This shift is expected to enhance the integration of genetic epidemiology into routine clinical practice, thereby improving patient education and addressing ethical considerations surrounding genetic testing. Nurses play a crucial role in this evolving landscape, as they are essential for obtaining comprehensive family histories, identifying at-risk family members, and helping patients understand the implications of their genetic test results. The goal of nursing research in clinical genetics and genomics is to enhance the quality of healthcare for patients and their families, highlighting the international concern for preparing nurses to deliver genetic/genomic-based care.[96.1] Furthermore, primary care providers are uniquely positioned to refer patients to genetic services, which can facilitate the identification of pathogenic germline variants (PGVs) in cancer-susceptibility genes, ultimately saving lives.[97.1] Statistical genetics and genetic epidemiology are pivotal in defining the genetic basis of human traits and diseases, such as cancer and heart disease, by studying the interplay of genetic and environmental factors in populations.[98.1] To ensure that the benefits of genetic epidemiology are equitably distributed, it is essential to increase the representation of underrepresented groups in research. This includes prioritizing the recruitment of diverse participants, conducting studies in varied healthcare settings, and fostering community engagement to build trust and address local concerns.[120.1] Moreover, collaboration between medical genetics and other clinical specialties is vital for the advancement of best practice standards. Genetics experts, including board-certified genetic counselors, are specially trained to interpret and communicate genetic test results, which enhances the integration of genetic data into broader health management strategies.[122.1] This collaboration also involves overcoming barriers such as the redistribution of roles and the sharing of data among healthcare professionals, which is necessary for the effective implementation of genetic epidemiology in clinical practice.[125.1]In this section:
Methodological Approaches
Study Designs in Genetic Epidemiology
In genetic epidemiology, two primary study designs are employed to investigate the genetic basis of complex diseases: population-based and family-based designs. Population-based designs involve sampling affected and unaffected individuals who are unrelated, such as through population cohorts or case-control samples. This approach allows researchers to assess genetic variants across a broader population, facilitating the identification of associations between genetic factors and disease risk.[146.1] These studies often involve larger sample sizes, enabling valid inferences to the broader population and allowing for the examination of both genetic and environmental influences.[147.1] Conversely, family-based designs focus on familial relationships and are particularly useful for estimating the strength of genetic effects for a disease. They conduct genetic linkage analysis to localize genetic causes to specific chromosomal regions and perform association studies that are less confounded by ethnic background.[148.1] Family-based designs can provide stronger evidence for the association of genetic variants with diseases, especially when a variant co-segregates with a phenotype within a family, thereby enhancing the validity of the findings.[149.1] However, they may face limitations regarding sample size and generalizability compared to population-based approaches. Both study designs play crucial roles in exploring the interactions between genetic and environmental factors in disease etiology.[147.1] The integration of these approaches enhances our understanding of complex diseases, where both genetic predispositions and environmental factors contribute to risk. Family-based designs offer significant advantages when a genetic variant co-segregates with a phenotype within a family, strengthening the evidence for the variant's association with the disease.[149.1]Data Analysis Techniques
Statistical analysis is pivotal in genetic epidemiology for elucidating the connections between genetic factors and complex diseases. Selecting suitable statistical methods is vital, as it requires understanding the assumptions and conditions of various techniques. Two primary categories of statistical methods are employed: descriptive statistics, which summarize data through indexes such as mean and median, and inferential statistics, which include methods like regression analysis to test hypotheses about relationships between variables.[143.1] Regression analysis, particularly logistic regression, is frequently used to estimate the relationship between explanatory variables and the log odds ratio of a categorical response variable. The null hypothesis in this context asserts that the regression coefficient is zero, while the alternative hypothesis suggests otherwise. Rejecting the null hypothesis indicates a significant relationship between the explanatory variables and the response variable.[144.1] Various statistical methods are also employed in genetic association studies, including meta-analysis and expression quantitative trait loci, which target complex diseases.[142.1] These methods facilitate the analysis of sequence variants and the integration of data from mutation databases and single nucleotide polymorphisms (SNPs), enhancing our understanding of genetic and environmental interactions.[142.1] Genome-wide association studies (GWAS) have provided insights into the genetics of diseases, such as coronary artery disease, by linking genetic variations to practical implications like polygenic risk scores.[145.1] The field has evolved to incorporate advanced statistical methods that explore the relationships between genetic and environmental factors.[145.1] Improvements in sequencing technology and the optimization of bioinformatics algorithms have enhanced genomic epidemiology investigations, enabling real-time guidance during outbreaks and improving infection control interventions.[156.1] These advancements are crucial for identifying novel genetic variants associated with diseases and refining our understanding of complex disease etiology.[156.1] Emerging technologies, particularly in statistical learning, have gained importance in genetic epidemiology, especially concerning high-dimensional omics data. One application is using genetic variants and principal components analysis to define homogeneous subsets of individuals, reflecting different ethnicities within study samples.[160.1] Unsupervised learning methods like hierarchical clustering and k-means clustering contribute to understanding population structure and genetic diversity.[160.1] As genetic research data expands, developing more powerful machine learning methods is urgent.[159.1] Boulesteix et al. (2019) provide an overview of statistical learning approaches, comparing traditional regression analysis with machine learning methods, highlighting their transformative potential.[158.1] The field has evolved significantly since the Human Genome Project's first draft in 2001, which coincided with the rise of candidate gene studies.[167.1] This advancement integrates genetics, epidemiology, and biostatistics to enhance understanding of complex diseases. Gene-environment studies are valuable for identifying missing heritability and improving public health outcomes.[167.1] Despite potential translational applications, many findings have not yet achieved the anticipated public health impact expected after the human genome sequencing.[167.1]In this section:
Challenges And Limitations
Ethical Considerations
Ethical considerations in genetic epidemiology encompass challenges such as effectively communicating genetic risk information to the public. Public preferences for communication strategies are crucial for translating genome-based knowledge into improved population health outcomes.[174.1] However, many individuals struggle to understand the interaction between genetic and environmental influences on health outcomes.[175.1] To address this, educational interventions, like videos explaining gene-environment interactions, have been explored, particularly in the context of eating behavior and weight management.[175.1] These efforts underscore the need for effective communication methods to enhance public understanding and informed consent regarding genetic risks and benefits. Ethical issues related to genetic information often parallel those associated with other health data, suggesting that established ethical principles can be applied to genetic epidemiology.[177.1] The need for informed consent is critical, as participation in genetic studies may expose individuals to risks such as genetic discrimination and emotional distress. Ensuring a comprehensive consent process is essential to make potential subjects fully aware of these risks.[181.1] In genetics and genomics research (GGR), ethical considerations are vital, particularly concerning respect for persons, privacy, and confidentiality. The shared nature of genetic information means that genetic conditions can affect families, creating complex ethical dilemmas for health professionals who must prevent harm and avoid risks to individuals and their relatives.[180.1] Ethical and regulatory frameworks must align with these concerns to facilitate the development of biobanks and conduct genetic research while protecting individuals' rights and privacy.[191.1] As GGR expands globally, researchers and oversight entities face ethical challenges that require comprehensive policy frameworks capable of integrating with local environments.[193.1] The ethical, legal, and social implications (ELSIs) of genetic research also extend to public health regulations, especially concerning infectious diseases. Balancing public health interests with individual privacy rights presents unique challenges that require careful consideration.[194.1] As genetic research evolves, ethical frameworks must adapt to address these emerging dilemmas, ensuring the protection of individuals' rights while advancing public health initiatives.[193.1] Addressing health disparities involves enhancing genetic literacy and improving access to genetic technologies, particularly among minority populations. This approach not only addresses the ethical implications of genetic epidemiology but also promotes equity in health outcomes.[198.1]Interpretation of Genetic Data
Interpreting genetic data in epidemiological studies involves several challenges that can affect the validity and reliability of findings. A significant issue is the potential for bias introduced by study design and background information, which can influence gene-phenotype associations. This is particularly problematic in genome-wide association studies (GWAS), where distinguishing induced associations from true causal relationships complicates the interpretation of results.[188.1] Loss of follow-up in cohort studies can lead to the exclusion of informative genetic variants, skewing analysis and limiting generalizability.[189.1] The reliance on robust clinical databases underscores the importance of comprehensive data collection and management in genetic epidemiology.[189.1] Sample size is crucial, as many genetic variants identified in GWAS are associated with modest odds ratios (ORs) of 1.2 to 1.5, necessitating large sample sizes—around 4,000 cases and 4,000 controls—to achieve adequate statistical power.[190.1] The complexities of genetic architecture also pose challenges, as the relationship between genotypes and phenotypes remains elusive, complicating the establishment of causal links between genetic variants and disease outcomes.[202.1] Furthermore, the interplay between genetic and environmental factors adds complexity. Gene-environment interactions can significantly influence disease risk, but accurately measuring these interactions is challenging.[184.1] The multifactorial nature of diseases, involving multiple genes and environmental factors, complicates genetic data interpretation and requires a broad understanding of causation that considers various influences over the life course.[186.1]Future Directions
Emerging Trends in Research
Emerging trends in genetic epidemiology emphasize the integration of advanced methodologies and technologies to deepen the understanding of disease mechanisms. A key focus is the evaluation of candidate genes concerning disease emergence and maintenance, employing methods such as case/control designs, case-parent designs, haplotype-based tests, and variance component models, which are gaining prominence in the field.[206.1] This interdisciplinary approach seeks to identify and characterize population-level factors contributing to disease, drawing on principles from population genetics, epidemiology, molecular biology, and biostatistics.[208.1] The integration of multi-omics data is another significant trend, offering potential insights into the genetic basis of complex traits and diseases. This comprehensive approach facilitates the analysis of genetic and environmental interactions, crucial for understanding non-communicable diseases (NCDs) and their treatment responses.[211.1] However, integrating diverse data types presents challenges, as methods for multi-omics analysis must accurately represent molecular system dynamics, despite the complexity of biological systems and technological constraints.[212.1] Additionally, data annotation and storage are significant hurdles, often relying on proprietary software that may not be publicly accessible, creating bottlenecks in data analysis and interpretation.[213.1] These challenges hinder the practical application of multi-omics data in routine research.[214.1] Machine learning techniques are increasingly vital in genetic epidemiology, particularly for analyzing large-scale datasets. These models can predict the pathogenicity of genetic mutations and personalize medicine based on individual genetic profiles.[217.1] As genomic data generation technologies evolve, there is a growing demand for innovative machine learning methods and experts to apply these techniques to complex datasets.[218.1] The integration of artificial intelligence in genomics is expected to revolutionize research and clinical applications, although ethical considerations and data privacy issues remain significant challenges.[219.1]Potential for Precision Medicine
The integration of genetic epidemiology into public health frameworks holds significant potential for advancing precision medicine. However, challenges remain in effectively translating genetic findings into actionable health interventions. One major issue is the limited consideration of social and environmental contexts in genetic epidemiology studies, which can restrict the public health impact of genomic discoveries.[103.1] This divide persists despite the shared goals of both fields and the increasing availability of interdisciplinary data, largely due to a lack of common theoretical frameworks and differing terminologies.[103.1] The Human Genome Project (HGP), which published its first draft in 2001, has profoundly influenced genetic epidemiology practices. While gene-environment studies are crucial for identifying the heritability of complex diseases, the translation of genetic research into public health applications has not met initial expectations.[222.1] For instance, although certain genetic findings, such as those related to warfarin and clopidogrel, demonstrate translational potential, many discoveries have yet to significantly impact public health.[222.1] Public health genomics is concerned with the effective and responsible translation of genomic research into population health benefits.[239.1] To achieve this, there is an increasing need for population-based epidemiologic studies that quantify the impact of gene variants on the risk of disease, death, and disability, as well as identify and quantify the effects of modifiable risk factors.[238.1] Public health agencies must develop appropriate methods to collect and monitor the results of genome-based research and technologies, which will help identify information gaps at both the population and individual levels, ultimately aiding in the formulation of evidence-based strategies.[240.1] The ultimate goal of public health genomics is to devise effective preventive interventions targeted at individuals with specific genotypes, once the interactions between genetic and environmental factors in disease causation are understood.[240.1] Therefore, the public health community plays a crucial role in raising genomic literacy, developing targeted messages about the use of genetic information in disease prevention, and coordinating communication strategies with stakeholder groups.[240.1] Public health agencies must develop appropriate methods for collecting and monitoring the results of genome-based research and technologies to identify information gaps at both the population and individual levels. This effort is essential for formulating evidence-based strategies in public health genomics.[240.1] The ultimate goal is to devise effective preventive interventions targeted at individuals with specific genotypes, particularly as we gain a better understanding of the interactions between genetic and environmental factors in disease causation.[240.1] Furthermore, large-scale differences in environment and lifestyle may limit the transferability of genetic insights, such as Polygenic Risk Score models, from diaspora populations to continental African populations, highlighting the need for immediate measures to address the imbalance in genomic studies.[235.1]Applications In Public Health
Disease Prevention Strategies
Genetic epidemiology plays a crucial role in disease prevention by identifying genetic and environmental risk factors that influence health outcomes. This field examines the interaction between genetic factors and environmental exposures, which is essential for developing targeted prevention and treatment strategies for various diseases.[247.1] Case-control studies in genetic epidemiology provide insights into the genetic basis of diseases and assess the influence of environmental factors, thereby estimating disease risk associated with specific genetic variants.[249.1] Research into gene-environment interactions has shown that many health conditions result from a complex interplay of genetic predispositions and environmental exposures.[259.1] This knowledge enables public health officials to design more effective interventions by targeting at-risk populations based on their genetic profiles and environmental contexts. The Human Genome Project (HGP), which published its first draft in 2001, has significantly impacted genetic epidemiology, particularly in disease prevention strategies. It has facilitated candidate gene studies and provided a comprehensive understanding of human genetic variation. However, while genetic epidemiology holds potential for informing public health practices, its findings have yet to achieve the anticipated impact on public health outcomes. This is partly because environmental exposures are generally more amenable to intervention than genetic factors. Gene-environment studies may offer a promising avenue for addressing the complexities of heritability in diseases and enhancing the contributions of genomics research to public health.[250.1] Examples such as the use of warfarin and clopidogrel illustrate the translational potential of genetic epidemiology, yet they also highlight ongoing challenges in effectively applying these findings to diverse populations.[250.1] To improve disease prevention strategies, it is essential to incorporate findings from genetic epidemiology into public health frameworks. This includes evaluating genetic testing, developing population interventions, and ensuring effective communication of genetic information to the public.[263.1] By addressing these areas, public health initiatives can better leverage genetic epidemiology to reduce disease incidence and improve health outcomes across different populations.Genetic Counseling and Risk Assessment
Genetic counseling and risk assessment are integral to incorporating genetic epidemiology into public health. This approach involves providing individuals with information about their genetic susceptibilities and personalized risk-reduction strategies, rather than enforcing mandatory genetic screening. It necessitates a thorough evaluation of factors such as state medical and epidemiologic data, the preparedness of health professionals, and the adequacy of laws to safeguard public access to genetic testing.[260.1] Public health officials must establish criteria for using genetic tests to predict disease probabilities, assess intervention impacts, and prevent the misuse of genetic testing.[261.1] Utilizing population-based genetic information is crucial for formulating state policies and programs that enhance individual and community health outcomes.[261.1] However, implementing genetic testing and counseling must consider health equity issues, as disparities exist in access to genomic medicine across different populations.[262.1] Ethical considerations are vital in integrating genetic epidemiology into public health policies. Issues such as privacy, informed consent, and the potential misuse of genetic information by third parties, including employers and insurers, must be carefully managed.[268.1] The ethical principles of autonomy, confidentiality, privacy, and equity are critical in determining the appropriate use of genetic tests and access to results.[268.1] Balancing public health benefits with individual privacy concerns remains a significant challenge, particularly in the context of HIV molecular epidemiology research.[269.1] To effectively identify at-risk populations, genetic epidemiology employs methodologies such as analyzing genetic variants and studying gene-environment interactions. These methods have been instrumental in understanding complex diseases and their risk factors.[270.1] Despite advancements, further research is needed, especially in diverse populations and the analysis of rare genetic variants.[271.1] Enhancing public understanding of these methodologies is essential for promoting their importance in public health and ensuring equitable access to genetic counseling and risk assessment services.References
[2] Genetic epidemiology - PMC — Genetic epidemiology is the study of the aetiology, distribution, and control of disease in groups of relatives and of inherited causes of disease in populations. 3 From its parent disciplines of genetics and epidemiology, it has inherited the key elements of studying defined populations while investigating the roles of genes and the
[3] Genetic Epidemiology - National Human Genome Research Institute — Genetic epidemiology is a field of science focused on the study of how genetic factors influence human traits, such as human health and disease. In many cases, the interaction of genes with the environment is also measured. Genetic epidemiologists seek to understand the causes, distribution and control of inherited disease in groups and the
[4] Genetic epidemiology - Wikipedia — The use of the term Genetic epidemiology emerged in the mid-1980s as a new scientific field.. In formal language, genetic epidemiology was defined by Newton Morton, one of the pioneers of the field, as "a science which deals with the etiology, distribution, and control of disease in groups of relatives and with inherited causes of disease in populations".
[5] Understanding the Genetic and Molecular Epidemiology of Diseases ... — By integrating genetic and traditional epidemiological methods, researchers can gain a more comprehensive understanding of infectious diseases, leading to improved surveillance, outbreak investigations, and the development of more effective control strategies.
[6] Population-based family studies in genetic epidemiology — Designs that involve families (the traditional strength of genetic epidemiology) and population-based sampling (the traditional strength of environmental epidemiology) allow investigation of both genes and environment, separately or together, and allow valid inference to the population. These case-control-family designs (including those involving twin pairs), can be regarded as retrospective
[7] The Evolving Field of Genetic Epidemiology: From Familial Aggregation ... — The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). Forty years ago at Johns Hopkins, we began to incorporate genetic ideas and family history information into epidemiologic studies, and since then we have mapped genetic variants, predicted risk based on genetic profiles, and tested for potential interactions between environment and genes for a multitude of complex diseases, such as chronic obstructive pulmonary disease, autism, birth defects, infection with human immunodeficiency virus, other viral infections, diabetes, cardiovascular disease, schizophrenia, inflammatory bowel disease, enteric infections, and more.
[13] Health system interventions to integrate genetic testing in routine ... — Integration of genetic testing into routine oncology care could improve access to testing. This systematic review investigated interventions and the tailored implementation strategies aimed at increasing access to genetic counselling and testing and identifying hereditary cancer in oncology. ... 15/15 clinicians were comfortable with consenting
[14] Strategies to Integrate Genomic Medicine into Clinical Care: Evidence ... — Implemented pharmacogenetic testing into clinical practice in 3 hospitals, 23 different clinics, including in academic medical centers, and primary and specialty care settings for 12 different patient populations: Use of genetic testing for drug prescribing (e.g., reduced cardiovascular adverse events); no negative outcomes
[18] Impact of Genetics on Personalized Medicine — Understanding the impact of genetics on personalized medicine is essential for developing targeted therapies and advancing healthcare innovations. By integrating genetic data into clinical practice, healthcare providers can offer individualized treatment strategies that cater to the specific needs of their patients. Overview of Personalized
[20] Genes, the environment and personalized medicine - PMC — Although not a panacea for all these problems, personalized medicine could theoretically reduce healthcare costs, as an individual's genetic or other biological information could be used to make better or earlier diagnoses of disease, apply cheaper, preventive measures to decrease disease risk, and make more efficient use of therapeutic options.
[47] The Evolving Field of Genetic Epidemiology: From Familial Aggregation ... — The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). Forty years ago at Johns Hopkins, we began to incorporate genetic ideas and family history information into epidemiologic studies, and since then we have mapped genetic variants, predicted risk based on genetic profiles, and tested for potential interactions between environment and genes for a multitude of complex diseases, such as chronic obstructive pulmonary disease, autism, birth defects, infection with human immunodeficiency virus, other viral infections, diabetes, cardiovascular disease, schizophrenia, inflammatory bowel disease, enteric infections, and more.
[48] Genetic Epidemiology and Public Health: The Evolution From ... - PubMed — Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has bee …
[49] Genetic epidemiology - Wikipedia — One definition of the field closely follows that of behavior genetics, defining genetic epidemiology as "the scientific discipline that deals with the analysis of the familial distribution of traits, with a view to understanding any possible genetic basis", and that "seeks to understand both the genetic and environmental factors and how they interact to produce various diseases and traits in humans". The British Medical Journal adopts a similar definition, "Genetic epidemiology is the study of the aetiology, distribution, and control of disease in groups of relatives and of inherited causes of disease in populations."
[50] Genetic epidemiology | Causes, Risk Factors & Outcomes | Britannica — genetic epidemiology, the study of how genes and environmental factors influence human traits and human health and disease. Khoury, Julian Little, and Wylie Burke coined the term human genome epidemiology to encompass a system of study that uses the methods of epidemiology to understand the influence of genomic variation in both health and disease, thereby going beyond the influence of individual genes, which remained the primary focus of genetic epidemiology. genome-wide association study (GWAS), systematic approach to rapidly scanning the human genome for genetic variations, with the aim of identifying associations between variants and particular diseases. Genome-wide association studies further are focused on populations or family groups; family-based studies enable researchers to apply linkage analysis, a powerful means for identifying associations between inherited genetic factors and disease.
[51] Understanding Genetic Epidemiology: The Potential Benefits and ... — In parallel with the increased rise in popularity of candidate gene studies was the publication of the first draft of the human genome by the Human Genome Project (HGP) in 2001 , which has had a lasting influence on the practice of genetic epidemiology. As environmental exposures are more amenable to intervention than genetics, gene-environment studies may not only help identify the missing heritability for complex diseases but also offer the best avenue by which genomics research can contribute to improving public health . Although warfarin and clopidogrel highlight the translational potential of genetic epidemiology research, most of the findings in genetic epidemiology to date have not made the impact on public health that many believed were possible when the human genome was first sequenced. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
[52] Ethics of Genetic Testing — The answer lies in navigating the ethical implications of genetic testing, which includes not only personal autonomy but also the social responsibility to ensure fair and just outcomes for individuals, families, and society as a whole. ... As the field of genomics advances, new ethical questions arise. The ability to screen for an ever
[53] Social, Legal, and Ethical Implications of Genetic Testing — 8Social, Legal, and Ethical Implications of Genetic Testing Each new genetic test that is developed raises serious issues for medicine, public health, and social policy regarding the circumstances under which the test should be used, how the test is implemented, and what uses are made of its results. Should people be allowed to choose or refuse the test, or should it be mandatory, as newborn screening is in some states? If test results are released to third parties such as employers or insurers, what protections should be in place to ensure that people are not treated unfairly because of their genotype? The answers to these questions depend in part on the significance given to four important ethical and legal principles: autonomy, confidentiality, privacy, and equity.
[54] A double-edged helix: The ethical consequences of widespread genetic ... — The previous section provided a brief yet crucial overview of the ethical considerations surrounding genetic testing, even within a strictly regulated framework. Nonetheless, as genetic testing becomes more mainstream—driven by the rise of whole genome sequencing and an increasing number of commercial providers—the risks of data misuse and
[56] Human Genome Project | Impact | Wellcome — Human Genome Project | Impact | Wellcome The completion of the Human Genome Project has accelerated research in genetics, leading to the discovery of new genes and pathways involved in health and disease. The Human Genome Project set a precedent for international collaboration in scientific research, with participants from various countries working together towards a common goal. The commitment to freely sharing Human Genome Project data paved the way for open science initiatives, encouraging global research and collective problem-solving. The Human Genome Project increased public awareness and understanding of genetics, prompting discussions on genetic discrimination and the societal impacts of genetic research. Insights from the Human Genome Project have helped researchers understand genetic diversity across populations, which is crucial for addressing health disparities and developing global health strategies.
[57] The Human Genome Project: Inside the 'most important biomedical ... — In 2003, the Human Genome Project produced a genome sequence that accounted for over 90% of the human genome. It was as close to complete as the technologies for sequencing DNA allowed at the time.
[61] Genes, the environment and personalized medicine - PMC — Personalized and population-level medicine based on genome-epigenome-environment interactions could provide the additional and convincing arguments needed for such sweeping public health policies. In this way, harnessing environmental and genetic data could better translate genomics into personal and public health benefits.
[62] Health is beyond genetics: on the integration of lifestyle and ... — These devices will monitor essential health metrics, environmental factors, and behavioral data, which will inform AGI-driven recommendations and provide actionable insights for both patients and providers. With 6G connectivity, these data streams will be transmitted instantly. Delays will be eliminated and real-time intervention will be enabled.
[63] Conceptual frameworks for the integration of genetic and social ... — This is far from the first call for greater integration between genetics and public health. Prior to the current -omic era of big data, Galea et al. and Diez Roux each advocated for complex systems as the path forward in epidemiology . Since these early calls, health data and bioinformatics approaches have exploded, yet cross
[95] Recommendations for the integration of genomics into clinical practice — The introduction of diagnostic clinical genome and exome sequencing (CGES) is changing the scope of practice for clinical geneticists. Many large institutions are making a significant investment in infrastructure and technology, allowing clinicians to access CGES, especially as health-care coverage begins to extend to clinically indicated genomic sequencing-based tests.
[96] Nurses Transforming Health Care Using Genetics and Genomics — In order for people to benefit from widespread genetic/genomic discoveries, nurses must be competent to obtain comprehensive family histories, identify family members at risk for developing a genomic influenced condition and for genomic influenced drug reactions, help people make informed decisions about and understand the results of their genetic/genomic tests and therapies, and refer at-risk people to appropriate health care professionals and agencies for specialized care. The goal of nursing research in clinical genetics and genomics is to improve the quality of health care for patients and families. Preparing nurses to provide genetic/genomic-based health care is an international concern and by working together, nurses are finding answers.
[97] Barriers and Facilitators to Genetic Education, Risk Assessment, and ... — Genetic education, risk assessment, and testing can save lives by facilitating the identification of pathogenic germline variants (PGVs) in cancer-susceptibility genes. ... Primary care providers are uniquely positioned to refer patients to genetic services. ... Epidemiology and mechanisms of the increasing incidence of colon and rectal cancers
[98] Statistical Genetics & Genetic Epidemiology | University of Michigan ... — Statistical genetics focuses on developing statistical methods and large-scale data sets to define the genetic basis of human traits and diseases, such as cancer, heart disease, and diabetes. Genetic epidemiology studies how genetic and environmental factors contribute to human health, disease, and traits in populations and families.
[103] Conceptual frameworks for the integration of genetic and social ... — Meanwhile, genetic epidemiology studies are often conducted without consideration of social and environmental context, limiting the public health impact of genomic discoveries. This divide endures despite shared goals and increases in interdisciplinary data due to a lack of shared theoretical frameworks and differing language.
[104] How next-generation sequencing is transforming complex disease genetics — Progress in understanding the genetics of human disease is closely tied to technological developments in DNA sequencing. Recently, next-generation technology has transformed the scale of sequencing; compared to the methods used in the Human Genome Project, modern sequencers are 50000-fold faster. Complex disease genetics presents an immediate opportunity to use this technology to move from
[106] Roche unveils a new class of next-generation sequencing with its novel ... — Roche's innovative sequencing by expansion (SBX) technology represents a leap forward in next-generation sequencing (NGS), which is playing a vital role in decoding complex diseases like cancer, immune disorders and neurodegenerative conditions Combined with an innovative, high throughput sensor module, SBX uses expanded synthetic molecules to determine the DNA sequence of a target molecule, creating an ultra-rapid, scalable and flexible technology “The science behind SBX technology represents a significant breakthrough that addresses the limitations of existing sequencing solutions,” states Matt Sause, CEO of Roche Diagnostics. With this capability we can flexibly operate across a range of throughput scales using the same sequencing system, which provides a significant advantage to users,” said Mark Kokoris, Head of Roche's SBX Technology.
[107] Next-Generation Sequencing Technology: Current Trends and ... - MDPI — NGS can sequence millions of DNA fragments at once, providing detailed information about the structure of genomes, genetic variations, gene activity, and changes in gene behavior. Abstract The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research.
[110] 15 years of GWAS discovery: Realizing the promise - PMC — Summary It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large
[111] Methods for multiancestry genome‐wide ... - Wiley Online Library — Genome-wide association studies (GWAS) have significantly enhanced our understanding of the genetic basis of complex diseases. Despite the technological advancements, gaps in our understanding remain, partly due to small effect sizes and inadequate coverage of genetic variation. Multiancestry GWAS meta-analysis (MAGMA) addresses these challenges by integrating genetic data from diverse
[120] Defining and Achieving Health Equity in Genomic Medicine — Increase representation of underrepresented groupsResearchersPrioritize recruitment of underrepresented participants over quickly reaching recruitment goalsInvestigate research questions of special interest to diverse and underserved populationsConduct clinical genomic studies in diverse healthcare settingsIncrease community engagement to build relationships, garner trust, and address local concernsFundersEncourage higher levels of inclusion in study design and review criteria for funding opportunitiesProvide investigators adequate time and resources to engage communitiesActively monitor and support researchers in reaching recruitment targetsFacilitate equal access to genomic servicesResearchersBuild on evidence base for cost-effectiveness and clinical utility of genomic testsEngage payers to promote evidence-based coverage of genomic servicesPayersCommunicate what evidence is needed to make coverage decisions about tests and genetic counseling servicesPolicymakersExplore ways to promote access to testing for underserved groups, such as through state Medicaid policiesResearch institutions, medical centers, and medical schoolsInvest resources and make it routine for health care providers to learn about genomicsIncorporate genomics into medical school curricula, continuing medical education courses, and point of care resources, among othersConduct implementation science studies to learn how to effectively integrate genomics into the clinical care of diverse groupsBuild infrastructure outside traditional settingsFunders and institutionsSupport research that strengthens infrastructure outside traditional settingsRecruit and train minority investigators
[122] Primary Care Physicians' Access to Genetic Specialists - An Impediment ... — Arguably, the best practice standards will emerge from collaboration between medical genetics and other clinical specialties. Genetics experts - board-certified genetic counselors and doctorate-level professionals - are specially trained in the use, interpretation, communication, and implementation of genetic test results.
[125] Genetics in health care: an overview of current and emerging ... - PubMed — This entails the forging of new relationships as well as an enhanced sharing of expertise and genetic information, including information regarding services. Barriers to overcome include the redistribution of roles, sharing of data and databases, and the lack of preparedness of non-genetics professionals and of the health care system in general.
[142] Statistical and Computational Methods for Genetic Diseases: An Overview ... — The paper is organized as follows: in Section 2, the study of sequence variants is described, while in Section 3 methods for association studies, meta-analysis, and expression quantitative trait loci, specifically targeted to the study of complex diseases, are discussed; finally, some conclusions are drawn in Section 4. The research in database of mutations (e.g., the Human Gene Mutation Database, HGMG—http://www.hgmd.org/) and single nucleotide polymorphisms (SNP) (e.g., http://www.ncbi.nlm.nih.gov/snp) allows linking to previous papers about the variant or linking to 1000 genome data, for example, the variant frequency. J. Bayesian statistical methods for genetic association studies. J. Genome-wide association studies for common diseases and complex traits. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits.
[143] Selection of appropriate statistical methods for data analysis — To select the appropriate statistical method, one need to know the assumption and conditions of the statistical methods, so that proper statistical method can be selected for data analysis. Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is
[144] Comprehensive guidelines for appropriate statistical analysis methods ... — For testing the significance of the regression coefficient, the null hypothesis states that “the regression coefficient is zero” and the alternative hypothesis states that “the regression coefficient is not zero.” If the null hypothesis is rejected, the conclusion is that “the regression coefficient cannot be said to statistically be zero under the significance level.” Because the calculated regression coefficient is not zero, a 1-unit change in the explanatory variable results in the changes in the response variable by the value of the regression coefficient if the other explanatory variables are held constant. Logistic regression is a statistical analysis method used to estimate a regression model that defines the linear relationship between one or more explanatory variables and a log odds ratio (logit) of a categorical response variable .
[145] Statistical Methods in Genetic Epidemiology | SpringerLink — The example of the genetics of coronary artery disease from GWAS discovery studies to the discussion of practical implications related to polygenic risk scores. ... we now want to touch on two additional topics due to their importance for statistical methods in genetic epidemiology. Sequencing studies. The development of next-generation
[146] Population-based and Family-based Designs to Analyze Rare Variants in ... — To study the genetic basis of complex diseases, two broad types of sampling designs are often used: the population-based and the family-based designs. The population-based design consists in sampling affected and unaffected individuals who are unrelated, such as population cohorts or case-control samples.
[147] Population-based family studies in genetic epidemiology — Designs that involve families (the traditional strength of genetic epidemiology) and population-based sampling (the traditional strength of environmental epidemiology) allow investigation of both genes and environment, separately or together, and allow valid inference to the population. These case-control-family designs (including those involving twin pairs), can be regarded as retrospective
[148] PDF — Summary Family-based designs are used for a variety of reasons in genetic epidemiology, including the initial estimation of the strength of genetic effects for a disease, genetic linkage analysis by which genetic causes can be sublocalized to chromosomal regions, as well as to perform association studies that are not confounded by ethnic background. This chapter describes some of the
[149] Novel approaches to the analysis of family data in genetic epidemiology — Although GWAS with unrelated samples have become one of the most common study designs currently used in human genetics, utilizing a family based design has many advantages. If a variant can be observed to co-segregate with a phenotype within a family, the evidence for its association with the disease is greatly strengthened.
[156] Infection control in the new age of genomic epidemiology — However, with improvements in sequencing technology and continuing optimization and standardization of bioinformatics algorithms, genomic epidemiology investigations can now be conducted during the course of an ongoing outbreak to provide real-time guidance for infection control and prevention interventions. 9, 17 Genomic epidemiology has
[158] Special issue on 'Genetic epidemiology of complex diseases: impact of ... — Statistical learning has become more and more significant in statistical genetics during the last years, especially in the area of high-dimensional omics data. Boulesteix et al. (2019) give an overview on statistical learning approaches in genetic epidemiology and compare regression analysis to machine learning methods.
[159] A Review for Detecting Gene-Gene Interactions Using Machine Learning ... — In conclusion, future research on producing more powerful machine learning methods is required to handle the enormous data in order to understand the genetic epidemiology of human disease.
[160] Brief Review of Regression-Based and Machine Learning Methods in ... — In genetic epidemiology, one popular use of such methods is to use genetic variants and principal components analysis to define homogeneous subsets of individuals reflecting different ethnicities within a study sample. Two popular methods in the class of unsupervised learning methods are hierarchical clustering and k -means clustering.
[167] Understanding Genetic Epidemiology: The Potential Benefits and ... — In parallel with the increased rise in popularity of candidate gene studies was the publication of the first draft of the human genome by the Human Genome Project (HGP) in 2001 , which has had a lasting influence on the practice of genetic epidemiology. As environmental exposures are more amenable to intervention than genetics, gene-environment studies may not only help identify the missing heritability for complex diseases but also offer the best avenue by which genomics research can contribute to improving public health . Although warfarin and clopidogrel highlight the translational potential of genetic epidemiology research, most of the findings in genetic epidemiology to date have not made the impact on public health that many believed were possible when the human genome was first sequenced. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
[174] Public preferences for communicating personal genomic risk information ... — These public preferences for communication strategies for genomic risk information will help to guide translation of genome‐based knowledge into improved population health. ... most studies of communicating genetic risk to date have concentrated on delivering information about ... Cancer Epidemiology Biomarkers & Prevention, 2014; 23: 566
[175] Genetic Epidemiology | Human Genetics Journal - Wiley Online Library — Yet, the public often finds it challenging to understand how genetic and environmental influences act together to impact health outcomes (Waters, Ball, and Gehlert 2017). One recent study designed an educational intervention to communicate complex GxE concepts related to eating behavior and its influence on weight through an educational video
[177] 9 Ethical Issues in Genetic Epidemiology - Oxford Academic — Ethical issues in genetic research, 6 - 8 epidemiology, 9 - 11 and genetic epidemiology 12 - 15 have been explored at length. In addition, many argue that genetic information is fundamentally similar to other kinds of health information, 16 - 18 and thus the issues and concepts addressed elsewhere in this book are applicable to genetic epidemiology.
[180] PDF — The shared nature and ownership of genetic . information Genetic conditions are family health problems. A . diagnosis or an increased chance of developing a health condition because of an inherited DNA change has implications for a person and their family. Health professionals have an ethical responsibility to prevent harm or avoid risks to
[181] Ethical issues in genetic research: disclosure and informed consent — Abstract As research to correlate genetic status with predisposition to disease has accelerated, so has the concern that participation in such studies creates the risk of genetic discrimination and emotional distress. There is a need to broaden disclosure during the consent process to ensure that potential subjects understand these risks and other issues and to address them in the consent form
[184] Gene-environment interactions within a precision environmental health ... — Gene-environment interactions within a precision environmental health framework - ScienceDirect Gene-environment interactions within a precision environmental health framework Motsinger-Reif 1, David M. Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Next article in issue No articles found. For all open access content, the Creative Commons licensing terms apply.
[186] Challenges for epidemiologic research on the verge of a new era — A broad concept of causation should underpin interpretation of epidemiological data. Many factors can influence health outcomes at different levels, scales, and time periods of a life course and these should all be considered in order to develop appropriate recommendations regarding future research requirements or policy or practice changes.
[188] Causal graphs for the analysis of genetic cohort data — Indeed, it has been argued that epidemiological data analysis depends as much on study design and background information, as on the data itself . Gene-phenotype associations, induced as a consequence of study design, are problematic in GWAS analyses because they are indistinguishable from underlying causal associations in GWAS results.
[189] Ethical considerations in the collection of genetic data from ... — For genetic epidemiology studies, subjects who either expire or who are lost to follow-up would not be enrolled, potentially eliminating an informative subset of genetic variants from analysis. Furthermore, such studies are dependent upon a robust clinical database to define genotypic-phenotypic relationships.
[190] Genetic Epidemiology: Successes and Challenges of Genome-wide ... — Sample size/Power. It has been reported that most of the common variants found in the recent GWAS are associated with ORs of only between about 1.2 and 1.5, with the mean OR of 1.36.2 This effect size translates to sample sizes of about 4000 cases and 4000 controls required to detect genetic associations with 80% statistical power if a minor allele frequency (MAF) is 10% and almost 7400 cases
[191] Editorial: Ethical and regulatory challenges in genetic and genomic ... — Respect for persons, privacy, and confidentiality are among the most important ethical and regulatory issues in this regard. It is, therefore, imperative that ethical and regulatory frameworks be in line with and able to facilitate the development of biobanks and the conduct of genetic/genomic research.
[193] A scoping review of genetics and genomics research ethics ... - PubMed — Background: Genetics and genomics research (GGR) is increasingly being conducted around the world; yet, researchers and research oversight entities in many countries have struggled with ethical challenges. A range of ethics and regulatory issues need to be addressed through comprehensive policy frameworks that integrate with local environments.
[194] Ethical Issues in Genetics and Infectious Diseases Research: An ... — Research in genetics and infectious diseases (ID) presents novel configurations of ethical, legal, and social issues (ELSIs) related to the intersection of genetics with public health regulations and the control of transmissible diseases. The issue-spotting exercise highlighted the following ELSIs: risks in reporting to government authorities, return of individual research results, and resource allocation – each taking on specific configurations based on the balance between public health and individual privacy/protection. In the current study, the issue-spotting exercise involved the exploration of possible ELSIs by a group of 20 experts from the following fields: public health, law and genomics, biobanking, genetic epidemiology, ID medicine and public health, philosophy, ethics and ID, ethics and genomics, and law and ID.
[198] Using Genetic Technologies To Reduce, Rather Than Widen, Health Disparities — Addressing this issue will require integrated strategies, including expanding genetic research, improving genetic literacy, and enhancing access to genetic technologies among minority populations
[202] Genetic architecture of quantitative traits and complex diseases — Abstract. More than 150 years after Mendel discovered the laws of heredity, the genetic architecture of phenotypic variation remains elusive. Here, we discuss recent progress in deciphering how genotypes map onto phenotypes, sources of genetic complexity, and how model organisms are illuminating general principles about the relationship between genetic and phenotypic variation.
[206] The future of genetic epidemiology - ScienceDirect — Instead, future genetic epidemiology will focus on assessing the role of those genes in the emergence and maintenance of disease 39, 40. Methods for assessing candidate genes, such as simple case/control designs, case-parent designs, haplotype-based tests, and variance component models, are beginning to receive greater attention .
[208] The future of genetic epidemiology: Trends in Genetics - Cell Press — Genetic epidemiology is a hybrid discipline whose ultimate aim is to identify and to characterize population-level factors that contribute to disease. Genetic epidemiologists often pursue this aim through the design and implementation of studies that simultaneously invoke principles in population genetics, epidemiology, molecular biology and biostatistics. However, traditional (and much
[211] Multi-omics approaches for understanding gene-environment interactions ... — By employing multi-omics and data integration techniques, it can be possible to fully understand how the interaction of genetic and environmental factors influences NCD development, progression, and treatment response.
[212] Methods for the integration of multi-omics data: mathematical aspects — Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological
[213] Applications of multi‐omics analysis in human diseases - PMC — Data annotation and storage is also a challenge for multi‐omics data integration. Integrated annotation of multi‐omics data often requires software provided by technology suppliers; however, this software may be not publicly shared. ... Zeggini E. Insights from multi‐omics integration in complex disease primary tissues. Trends Genet. 2023
[214] Multiomic Big Data Analysis Challenges: Increasing Confidence in the ... — These challenges make multiomic data analysis and interpretation a huge bottleneck for practical, routine application. ... AI has been used to explain heterogeneity of complex diseases through subgroup identification in unsupervised workflows. 76 However, ... as the number of data sets integrated in multiomics grows,
[217] Comparative Analysis of Machine Learning Techniques for Imbalanced ... — Advancements in genome sequencing technologies have significantly increased the availability of genomic data. The use of machine learning models to predict the pathogenicity or clinical significance of genetic mutations is crucial. However, genetic datasets often feature imbalanced target variables and high-cardinality, skewed predictor variables. These attributes complicate machine learning
[218] Machine learning in genetics and genomics - PMC - PubMed Central (PMC) — In addition to learning to recognize patterns in DNA sequences, machine learning can take as input data generated by other genomic assays, such as microarray or RNA-seq expression data, chromatin accessibility assays such as DNase-seq, MNase-seq, and FAIRE, or histone modification, transcription factor (TF) binding ChIP-seq data, etc. Sections 3–5 describe strategies a researcher can use to guide a machine learning model, through prior knowledge, means of integrating heterogeneous data sets and feature selection. As new technologies for generating large genomic and proteomic data sets emerge, pushing beyond DNA sequencing to mass spectrometry, flow cytometry and high-resolution imaging methods, demand will increase not only for new machine learning methods but also for experts capable of applying and adapting them to big data sets.
[219] AI in genomics and genetic analysis - Artificial Intelligence — AI in genomics and genetic analysis - Artificial Intelligence + AI in genomics and genetic analysis Discover how AI in genomics and genetic analysis revolutionizes genome sequencing, research, and personalized medicine. AI in genomics and genetic analysis is revolutionizing the future of biological research and medical advancements. The integration of AI in genetic research has revolutionized the way scientists approach genomic data. As collaborations between tech companies, research institutions, and healthcare providers continue to grow, the role of AI in genomics and genetic analysis will likely become more integrated into everyday clinical applications. While challenges surrounding data privacy and ethical considerations remain significant, the future of AI in genomics promises groundbreaking advances that will redefine the boundaries of genetic research and medicine.
[222] Understanding Genetic Epidemiology: The Potential Benefits and ... — In parallel with the increased rise in popularity of candidate gene studies was the publication of the first draft of the human genome by the Human Genome Project (HGP) in 2001 , which has had a lasting influence on the practice of genetic epidemiology. As environmental exposures are more amenable to intervention than genetics, gene-environment studies may not only help identify the missing heritability for complex diseases but also offer the best avenue by which genomics research can contribute to improving public health . Although warfarin and clopidogrel highlight the translational potential of genetic epidemiology research, most of the findings in genetic epidemiology to date have not made the impact on public health that many believed were possible when the human genome was first sequenced. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
[235] Diversity in Genomic Studies: A Roadmap to Address the Imbalance — Moreover, large-scale differences in environment and lifestyle could further limit the transferability of genetic insights (such as Polygenic Risk Score models) gained from diaspora populations to continental African populations 5. This calls for immediate measures to address the genomic studies imbalance.
[238] Human Genome Epidemiology (HuGE): Translating Advances in Human ... — To translate the results of this genetic research into opportunities for treating and preventing disease and promoting health, population-based epidemiologic studies are increasingly needed to quantify the impact of gene variants on the risk of disease, death and disability and to identify and quantify the impact of modifiable risk factors that
[239] From public health genomics to precision public health: a 20-year ... — Public health genomics is concerned with the effective and responsible translation of genomic research into population health benefits. 1 Over the past two decades, the Centers for Disease Control
[240] Public health in the genomic era: will Public Health Genomics ... — Public health agencies must, in the meantime, work out the appropriate methods to collect and monitor the results of genome-based research and technologies to identify information gaps on the population as well as on the individual level and to formulate the policy development of evidence-based strategies in this domain . The ultimate goal of public health genomics is, once we understand both the genetic and environmental factors involved in the causation of disease, and how they interact, to devise effective preventive interventions targeted at individuals with specific genotypes . Therefore, the public health community has a major role to play in raising the level of general genomic literacy, developing targeted messages about the uses of genetic information in disease prevention and coordinating communication strategies with stakeholder groups .
[247] Genetic epidemiology : methods and applications : Austin, Melissa A ... — Genetic epidemiology plays a key role in discovering genetic factors influencing health and disease, and in understanding how genes and environmental risk factors interact. There is growing interest in this field within public health, with the goal of translating the results into promoting health and preventing disease in both families and
[249] Understanding the Impact of Genetic Epidemiology on Public Health — In genetic epidemiology, understanding the factors that contribute to disease risk is essential for identifying individuals or populations with a higher likelihood of developing certain diseases. In conclusion, case-control studies play a crucial role in genetic epidemiology by providing insights into the genetic basis of diseases, assessing the influence of environmental factors, and estimating disease risk associated with specific genetic variants. In addition to understanding disease inheritance and genetic risk factors, twin studies also contribute to the field of epidemiology by providing insights into gene-environment interactions. By studying the distribution of genetic variants within populations and understanding their impact on disease risk, researchers can develop targeted prevention and treatment strategies. Genetic studies have revealed that certain mutations in genes can be associated with an increased risk of developing specific diseases.
[250] Understanding Genetic Epidemiology: The Potential Benefits and ... — In parallel with the increased rise in popularity of candidate gene studies was the publication of the first draft of the human genome by the Human Genome Project (HGP) in 2001 , which has had a lasting influence on the practice of genetic epidemiology. As environmental exposures are more amenable to intervention than genetics, gene-environment studies may not only help identify the missing heritability for complex diseases but also offer the best avenue by which genomics research can contribute to improving public health . Although warfarin and clopidogrel highlight the translational potential of genetic epidemiology research, most of the findings in genetic epidemiology to date have not made the impact on public health that many believed were possible when the human genome was first sequenced. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
[259] Genetic Epidemiology | Human Genetics Journal - Wiley Online Library — Many health conditions are affected by a complex combination of genetic and environmental risk factors. Gene-environment (GxE) interaction research explores the varying effect of environmental exposure(s) given an individual's genetic and epigenetic background for risk of complex disorders (Ritz et al. 2017).
[260] Integrating Genomics into Public Health Policy and Practice — Thus, rather than mandatory screening, another paradigm for the integration of genomics into public health could be similar to that suggested by Dr. Collins, which is to provide individuals who wish to know with information about their personal genetic susceptibilities, together with tailored risk-reduction advice. This involves assessing the state’s own medical, epidemiologic, and economic data about diseases for which genetic tests are available; the readiness and training of health professionals; the adequacy of state laws to protect the public and ensure access; laboratory proficiency; and infrastructure capacity. Genetic information is already an integral part of public health practice in the area of newborn screening and, as we move beyond the realm of rare, single-gene disorders, the system of oversight for genetic tests and the need for widespread professional and public education about genomics present challenges for public health practitioners.
[261] Genetics in public health: Rarely explored - PMC - PubMed Central (PMC) — Genetics in public health: Rarely explored - PMC Public health officials may be expected to provide criteria for 1) using genetic tests to predict the probability of disease and impact of interventions; 2) using genetic screening and services throughout the life span; and 3) preventing inappropriate uses of genetic testing. Public health policies also provide members of the public with objective guidance and information to empower them in decision making regarding the use of genetic technologies. Applied public health research into the causes of health problems, including relevant genetic factors, is the key to understand how diseases can be prevented and to reduce their burden in the community. Apply population-based genetic information to state policies and programs to improve individual and community health. Community genetics or public health genetics?
[262] Health equity in the implementation of genomics and precision medicine ... — Although we use tier 1 genomic applications as examples of evidence-based genomic medicine that require public health action, we recognize that issues of health equity in access and implementation apply to thousands of genetic disorders and to precision medicine as a whole. Here, and in Table 2, we summarize our vision and opportunities for specific public health actions that can be conducted by the Centers for Disease Control and Prevention and its many partners to help reduce disparities in the implementation of genomics and precision medicine. Public health programs in collaboration with communities and health systems could support effective implementation and improve population health outcomes by engaging communities equitably and addressing documented disparities in genomic medicine implementation.
[263] Genetics and public health: A framework for the integration of human ... — Browse content in Classical Studies Browse content in History Browse content in Law Browse content in Criminal Law Browse content in Medicine and Health Browse content in Public Health and Epidemiology Browse content in Science and Mathematics Browse content in Biological Sciences 25 Genetics, public health, and the law Khoury, Wylie Burke, and Elizabeth Thomson (eds), Genetics and Public Health in the 21st Century: Using Genetic Information to Improve Health and Prevent Disease, Oxford Monographs on Medical Genetics (New York, 2000; online edn, Oxford Academic, 1 Sept. This framework encompasses four essential public health functions (i.e., public health assessment; evaluation of genetic testing; development, implementation, and evaluation of population interventions; and communication and information dissemination) and three critical issues (i.e., partnerships and coordination; ethical, legal, and social issues; and education and training) that affect each function.
[268] Social, Legal, and Ethical Implications of Genetic Testing — 8Social, Legal, and Ethical Implications of Genetic Testing Each new genetic test that is developed raises serious issues for medicine, public health, and social policy regarding the circumstances under which the test should be used, how the test is implemented, and what uses are made of its results. Should people be allowed to choose or refuse the test, or should it be mandatory, as newborn screening is in some states? If test results are released to third parties such as employers or insurers, what protections should be in place to ensure that people are not treated unfairly because of their genotype? The answers to these questions depend in part on the significance given to four important ethical and legal principles: autonomy, confidentiality, privacy, and equity.
[269] Ensuring privacy in the study of pathogen genetics - PMC — The absence of available methods to both quantify and preserve privacy, while making use of genetic sequence data for clinical and public health benefit, remains a stumbling block for HIV molecular epidemiology research. The simple solution would be to extend the protections noted in GINA to the community level and non-host genetic information.
[270] The Evolving Field of Genetic Epidemiology: From Familial Aggregation ... — The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). Forty years ago at Johns Hopkins, we began to incorporate genetic ideas and family history information into epidemiologic studies, and since then we have mapped genetic variants, predicted risk based on genetic profiles, and tested for potential interactions between environment and genes for a multitude of complex diseases, such as chronic obstructive pulmonary disease, autism, birth defects, infection with human immunodeficiency virus, other viral infections, diabetes, cardiovascular disease, schizophrenia, inflammatory bowel disease, enteric infections, and more.
[271] Methods in Applied Genetic Epidemiology 2022 - Frontiers — Despite the huge effort made in this field, there is still much work remaining to be done, such as enhancing the study of human genetics and genomics in diverse populations, rare genetic variant analysis and gene-environment interaction. This Research Topic comprises one method article, which compares the performances of different longitudinal methods in the context of modelling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry, and four original research articles ranging from rare variant association analysis, gene by gene/environment interaction to identify rare Thalassemia variants using multiple genetic methods. This series aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in genetic epidemiology research from genetics association studies to polygenic risk score applications.