Table of Contents
Overview
Definition and Scope
Quantitative genetics is defined as the study of continuous or quantitative traits, focusing on the statistical analysis of the relative contributions of genetic and environmental factors to phenotypic variation. This field has evolved significantly, particularly in the 20th century, as a response to the rediscovery of Mendelian genetics, which laid the groundwork for understanding the inheritance of traits.[2.1] The scope of quantitative genetics encompasses the examination of polygenic traits, which are influenced by multiple genes, and their heritability, which is essential for predicting and improving evolutionary and breeding outcomes. By analyzing the genetic basis of phenotypic variation, researchers can gain insights into how genes interact and contribute to the diversity of traits observed in natural populations.[3.1] Furthermore, quantitative genetics plays a crucial role in breeding programs, enabling breeders to select individuals with desirable phenotypes, thereby accelerating the improvement of populations.[3.1] In quantitative genetics, individual genotypes are often unidentified, and the traits of individuals are measured instead. This approach allows for the assignment of genotypic values to genotypes within a population, emphasizing the importance of selection in controlled populations of known ancestry.[4.1] Overall, quantitative genetics provides a theoretical framework for understanding the evolutionary processes that govern the development of phenotypic traits both within and between populations.[1.1]Importance in Genetics
Quantitative genetics is pivotal in deciphering the genetic basis of continuous traits and analyzing how genetic and environmental factors contribute to phenotypic variation. This field is fundamental for understanding evolutionary processes that shape phenotypic traits within and between populations, thereby enhancing our comprehension of adaptation in natural populations, especially amidst environmental changes.[9.1] The study of quantitative traits, such as the height of an oak tree, exemplifies the application of quantitative genetics. For instance, the sawtooth oak tree can vary in height from 40 to 60 feet, illustrating how traits can exhibit a continuous range influenced by both genetic and environmental factors.[7.1] The interaction between these factors is significant, as environmental changes can alter the expression of specific genotypes, thereby affecting the overall phenotypic distribution within a population.[8.1] Heritability is a key concept in quantitative genetics, distinguishing between narrow-sense and broad-sense heritability. Narrow-sense heritability focuses solely on additive genetic variance, while broad-sense heritability encompasses all genetic contributions, including dominance and gene interactions.[14.1] This distinction is vital for understanding the genetic architecture of polygenic traits, as it informs researchers about the potential for traits to respond to selection pressures.[18.1] Moreover, heritability estimates provide insights into the relative contributions of genetic factors to phenotypic variability observed in populations. For example, narrow-sense heritability for human height is approximately 0.8, indicating a strong genetic influence on this trait.[19.1] Understanding these heritability estimates is essential for predicting how traits may evolve in response to environmental changes, as they reflect the genetic variation available for selection.[12.1]In this section:
History
Evolution of Quantitative Genetics
The evolution of quantitative genetics can be traced back to the early 20th century, particularly with the rediscovery of Mendel's laws of inheritance in 1900, which laid the groundwork for understanding the inheritance of quantitative traits and the development of plant breeding and selection methods.[48.1] A pivotal moment in this field was Ronald Fisher's seminal 1918 paper, which not only established quantitative genetics as a discipline but also introduced the concept of variance and the statistical method of analysis of variance.[51.1] Fisher's work unified discrete Mendelian genetics with continuous biometric phenotypes, thereby quantifying the variance components of complex human characteristics.[54.1] This integration was crucial for the advancement of methodologies in contemporary quantitative genetics research. The field experienced significant growth in the late 20th century, particularly during the second International Conference on Quantitative Genetics (ICQG) held in 1987 in Raleigh, North Carolina. This conference highlighted the explosive development of new tools, such as low-density molecular markers for quantitative trait locus (QTL) mapping, and emphasized the importance of mixed-model methodologies for complex estimation issues.[51.1] The fusion of classical quantitative genetics with modern statistics and genomic data has made quantitative genetics more relevant than ever, allowing researchers to examine genetic variation responsible for complex traits involving multiple genes.[50.1] In plant breeding, quantitative genetics plays a fundamental role by focusing on identifying candidates with optimal genotypic values for specific environments.[57.1] The methodologies developed through historical breakthroughs in quantitative genetics have significantly impacted traits such as yield and disease resistance, demonstrating the practical applications of these advancements in agricultural practices.[56.1] As the field continues to evolve, the integration of genomic data with advanced computational methods, including machine learning, is expected to enhance the analysis of variance and further our understanding of the genetic basis of quantitative traits.[59.1]Basic Concepts
Polygenic Traits
Polygenic traits are characteristics that are influenced by multiple genes, leading to a continuous range of phenotypic variation. These traits, such as height, weight, and yield, are essential in understanding genetics and breeding practices. The genetic analysis of these complex traits has primarily relied on statistical measures like genetic variances and heritability, which are crucial for predicting breeding values and improving populations.[93.1] Additive genetic effects play a significant role in the expression of polygenic traits, where the contributions of individual genes are independent and cumulative.[84.1] In contrast, non-additive effects, which include dominance and epistasis, can lead to outcomes that are not merely the sum of individual gene contributions.[83.1] This distinction is vital for breeding programs, as it informs the selection of individuals with desirable phenotypes and accelerates the improvement of populations.[96.1] The integration of quantitative genetics into breeding programs has demonstrated its effectiveness in enhancing specific traits. For instance, genomic selection (GS) has improved the accuracy of predicting complex traits, facilitating the cultivation of high-yield and stress-resistant varieties.[98.1] By utilizing genome-wide marker information, GS enhances selection accuracy and shortens breeding cycles, thereby increasing genetic gains.[98.1] Moreover, understanding the genetic basis of phenotypic variation is crucial for predicting and improving evolutionary and breeding outcomes.[94.1] The application of pedigree-based prediction models, such as the additive infinitesimal model, has been central to the genetic improvement of complex traits in both plants and animals.[97.1] This comprehensive approach to polygenic traits underscores the importance of quantitative genetics in both agricultural and medical contexts, as it allows for more precise predictions and targeted improvements.[92.1]Heritability and Genetic Variation
Methodologies
Heritability is a key concept in quantitative genetics, quantifying the proportion of phenotypic variation in a trait attributable to genetic differences among individuals in a population. This understanding is crucial for predicting responses to selection in breeding programs and for understanding trait inheritance across generations. The quantitative genetics model, expressed as P = G + E, where P is the phenotype, G the genotype, and E the environmental effects, highlights that phenotypic variation arises from both genetic and environmental factors, allowing researchers to separate these components.[88.1] Quantitative traits, which vary in degree rather than kind, are influenced by multiple genes and their interactions with environmental factors. Estimating the heritability of these traits through various statistical models provides insights into the adaptive potential of populations and informs breeders in selecting individuals with desirable phenotypes to enhance genetic quality.[104.1][82.1] The study of genetic variation in quantitative traits also involves examining additive and non-additive genetic effects. Additive effects refer to the cumulative contribution of alleles, while non-additive effects involve interactions between alleles at different loci. Although often overlooked in animal breeding programs due to computational challenges, non-additive effects can significantly influence complex traits, such as fertility in livestock.[86.1] A comprehensive understanding of both genetic effects is vital for effective trait selection and breeding strategies.Quantitative Trait Loci (QTL) Analysis
Quantitative Trait Loci (QTL) analysis is a pivotal methodology within quantitative genetics that focuses on identifying the specific regions of the genome associated with quantitative traits. This approach is essential for understanding the genetic basis of phenotypic variation, as it allows researchers to link observable traits to their underlying genetic determinants. The fundamental equation used in QTL analysis is P = G + E, where P represents the phenotype, G denotes the genetic contribution, and E signifies environmental effects, which are assumed to average out to zero.[122.1] The identification of QTLs has been significantly enhanced by advancements in molecular biology and statistical genetic methods. The near completion of the human genome and the development of sophisticated analytical techniques have made it feasible for quantitative geneticists to pinpoint specific genes associated with complex traits.[122.1] This progress is complemented by the use of molecular markers, which facilitate the derivation of genetic distance relationships among different populations, thereby aiding in the identification of QTLs.[131.1] In practical applications, QTL analysis employs various statistical models to analyze trait data, particularly in inbred line crosses. For instance, linear mixed models (LMM) and functional linear mixed models (FLMM) are commonly utilized to assess the association between quantitative traits and genetic variants.[133.1] These models allow for the incorporation of fixed effects, such as those from major genes, and help in managing the complexities associated with genetic data.[132.1] Moreover, the integration of computational techniques, including machine learning, has further refined QTL analysis methodologies. These modern approaches enhance the accuracy of variant identification and improve the power of association tests, addressing the increasing demand for innovative analytical strategies in the field.[127.1] As a result, QTL analysis not only contributes to our understanding of the genetic architecture of traits but also plays a crucial role in breeding programs, where knowledge of genetic variation is essential for selecting individuals with desirable phenotypes.[121.1]Genome-Wide Association Studies (GWAS)
Genome-wide association studies (GWAS) have been significantly transformed by advancements in genome sequencing technologies, which have enhanced the identification and analysis of the genetic basis of complex quantitative traits. High-throughput sequencing techniques, particularly whole genome sequencing (WGS), have emerged as crucial tools for studying genetic variation and population structure. These methods allow researchers to reveal genetic diversity, population structure, and the loci of variation associated with economically important traits, thereby facilitating a deeper understanding of natural selection and its role in species adaptation and evolution.[138.1] Recent innovations in DNA sequencing, including third-generation sequencing technologies, have further reshaped genomic research. These advancements enable longer reads and real-time analysis, which improve the detection of genetic and epigenetic variations. Unlike earlier sequencing methods that required amplification and produced shorter reads, third-generation sequencing allows for the direct sequencing of single molecules, thereby reducing errors and preserving genomic integrity. This capability is particularly beneficial for identifying structural variations with high precision, which is essential for understanding the genetic architecture of complex traits.[139.1] Moreover, the integration of machine learning (ML) methods in GWAS has enhanced the analysis of selected features and single nucleotide polymorphisms (SNPs). Techniques such as linear regression, random forest, and support vector regression are employed for association testing, allowing researchers to explore interactions among SNPs within GWAS datasets. This approach is complemented by robust feature selection methods, including LASSO and ridge regression, which help refine the analysis by focusing on significant genetic markers.[140.1]In this section:
Applications
Agriculture and Breeding Programs
Quantitative genetics plays a pivotal role in agriculture and breeding programs by enabling the study and manipulation of traits that are essential for crop improvement. This field focuses on continuous traits, which are often associated with adaptation, reproduction, yield, form, and function, making them significant for evolutionary processes and agricultural advancements.[164.1] In plant improvement, quantitative genetics is utilized to enhance traits such as yield, disease resistance, and quality, which are critical for developing superior crop varieties.[163.1] The understanding of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes, thereby accelerating the improvement of populations.[162.1] This knowledge is particularly important in addressing global challenges such as population growth and climate change, as it ensures sustainable food production through effective crop breeding strategies.[165.1] Moreover, the interplay between genetic and environmental factors is crucial in shaping the traits of agricultural organisms. By studying gene-environment interactions, researchers can gain insights into how these factors influence the expression of traits, which is vital for optimizing breeding programs.[169.1] Genetic diversity, which is essential for the adaptability and survival of species, also plays a significant role in breeding programs aimed at enhancing crop resilience to changing environmental conditions.[171.1]Human Health and Disease Prevention
The integration of quantitative genetics into personalized medicine has revolutionized approaches to human health and disease prevention. Central to this advancement is the use of polygenic risk scores (PRS), which synthesize the effects of numerous genetic variants to estimate an individual's genetic predisposition to complex traits and diseases. PRS enhance disease prediction, refine diagnosis, guide treatment choices, and improve public health outcomes by identifying disease risk early, even before clinical symptoms appear.[198.1] Genome-wide association studies (GWAS) have been instrumental in uncovering the genetic underpinnings of complex diseases by identifying single nucleotide polymorphisms (SNPs) linked to disease risk. Although each variant may have a minor impact, their collective influence forms the genetic basis of many diseases.[196.1] The clinical application of PRS aims to transform genetic data into actionable insights, thereby enhancing risk stratification and personalized preventive care.[200.1] Despite its promise, the practical use of PRS faces challenges, such as limited applicability in non-European populations, which affects the generalizability of findings.[199.1] Additionally, translating PRS into lifetime disease risk is complex and requires careful interpretation.[200.1] Future advancements in genomic-informed risk assessments will likely involve integrated models that combine genetic factors, including PRS, with non-genetic and multiomic data, thereby improving the precision of disease prediction and management.[198.1]In this section:
Recent Advancements
Integration of Genomics and Computational Methods
Recent advancements in quantitative genetics have significantly benefited from the integration of genomics and computational methods, enhancing the understanding of complex traits. The development of genome-wide association studies (GWAS) has revolutionized the field by identifying numerous common genetic variants associated with various diseases and traits, thereby providing compelling genotype-phenotype associations.[212.1] These studies have been complemented by the integration of molecular quantitative trait loci (xQTLs), which help unveil putative causal genes underlying GWAS signals and elucidate the relevant cell types and genetic regulation mechanisms.[210.1] Moreover, the advent of high-throughput sequencing technologies has transformed marker development and genotyping processes. These technologies enable the simultaneous identification of multiple genetic markers with high sensitivity and specificity, facilitating the analysis of large-scale genomic data.[218.1] The resulting genotypic data necessitates advanced analytical tools and statistical methods to effectively interpret the complex interactions between genotypes and environmental factors.[216.1] Additionally, the application of genomic selection (GS) models has improved the accuracy of predicting complex traits, thereby accelerating the cultivation of high-yield and stress-resistant varieties. By leveraging genome-wide marker information, GS enhances selection accuracy and shortens breeding cycles, leading to significant genetic gains.[215.1] This integration of genomic data with computational methods not only streamlines breeding programs but also aids in predicting evolutionary outcomes in natural populations, particularly in response to environmental changes.[215.1]Advances in Statistical Techniques
The advancements in statistical techniques within quantitative genetics have been significantly influenced by foundational work, particularly that of Ronald Fisher. His 1918 paper introduced the quantitative genetic model, which has been recognized as a cornerstone of both basic and applied biology, comparable in importance to the discovery of the double helix by Watson and Crick in 1953.[228.1] Fisher's contributions laid the groundwork for modern methodologies in the field, particularly through his introduction of the analysis of variance (ANOVA), which quantifies the total effect of genetic factors on the variability of quantitative traits, such as human height.[229.1] This statistical approach has become a fundamental tool in contemporary research, allowing for the dissection of complex traits into their genetic components and facilitating the understanding of their inheritance patterns. The principles established by Fisher continue to influence current research methodologies, underscoring the enduring relevance of his work in the evolution of quantitative genetics.In this section:
Ethical Considerations
Responsible Use of Genetic Information
The responsible use of genetic information encompasses a range of ethical, legal, and social considerations that are critical in the context of genetic testing and research. One of the primary ethical principles involved is autonomy, which emphasizes the importance of informed consent. Patients must be fully aware of the purpose, benefits, and risks associated with genetic testing, as well as maintain control over the disclosure of their results, including any incidental findings that may arise during testing.[250.1] This is particularly challenging in vulnerable populations, such as newborns, where obtaining informed consent requires careful navigation of complex ethical issues.[252.1] Moreover, the rapid advancement of genetic knowledge often outpaces the understanding of its clinical implications, leading to potential psychological burdens for individuals and their families. Genetic information can have profound implications not only for the individual tested but also for their extended family, raising concerns about privacy and discrimination.[247.1] The predictive nature of genetic testing can create anxiety, especially when effective preventive measures are lacking, which may contribute to a deterministic view of genetics that oversimplifies the complexities of health and disease.[247.1] Legal frameworks, such as the Genetic Information Nondiscrimination Act (GINA), aim to protect individuals from discrimination based on their genetic information in health insurance and employment contexts.[254.1] However, GINA has limitations, as it does not cover all forms of discrimination and does not address the broader implications of genetic data storage and management.[257.1] The ethical dilemmas surrounding the use of genetic information extend to questions about the mandatory nature of testing, the release of results to third parties, and the protections necessary to ensure fair treatment based on genetic characteristics.[249.1] As genetic testing becomes more prevalent, the ethical and security challenges associated with the storage of genetic data also come to the forefront. Ensuring that current storage methods can adequately protect sensitive genetic information is paramount, as breaches could lead to significant consequences for individuals, including unfair treatment in accessing essential services.[248.1] Thus, the responsible use of genetic information necessitates a multifaceted approach that balances the benefits of genetic testing with the ethical obligations to protect individuals' rights and well-being.Implications for Public Health
The implementation of genetic testing in public health raises significant ethical, legal, and social implications that must be carefully considered. Each new genetic test developed prompts critical questions regarding its use, implementation, and the handling of test results. A primary concern is whether individuals should have the autonomy to choose or refuse testing, or if such testing should be mandated, as is the case with newborn screening in certain states. This dilemma highlights the tension between public health benefits and individual rights, particularly when parents may have differing opinions on testing for their newborns.[247.1] Moreover, the release of genetic test results to third parties, such as employers or insurers, necessitates robust protections to prevent discrimination based on genetic information. The ethical and legal principles of autonomy, confidentiality, privacy, and equity play a crucial role in shaping policies around genetic testing. These principles guide the development of frameworks that ensure individuals are treated fairly and that their genetic information is safeguarded against misuse.[247.1] Thus, the implications of genetic testing for public health extend beyond mere medical considerations, encompassing broader societal values and individual rights.In this section:
Future Directions
Challenges in Trait Prediction
In the field of quantitative genetics, one of the significant challenges in trait prediction arises from the complexity of traits that are controlled by multiple genes, each contributing small effects. Traits such as yield and nutritional quality are often influenced by several interrelated characteristics, which can complicate the selection process for breeders.[321.1] The selection of traits that exhibit antagonistic genetic influences can further constrain the effectiveness of breeding programs, as these correlations may limit progress in crop improvement.[321.1] To address these challenges, various methods of multiple-trait selection have been developed, each with its own effectiveness and suitability depending on the specific goals of the breeding program. For instance, tandem selection involves improving one trait at a time in a specified order, typically starting with the most important trait.[320.1] This method allows breeders to focus their efforts sequentially, but it may not always lead to optimal overall genetic improvement due to the potential for unfavorable interactions between traits. Another approach is multistage selection, which allows for the selection of different traits at various stages of cultivar development.[320.1] This method can provide a more balanced approach to trait improvement, as it enables breeders to consider multiple traits simultaneously throughout the breeding process. Additionally, recent advancements have introduced new strategies such as multi-trait LAS (MT-LAS), which aims to optimize the primary goals of a breeding program while maintaining other traits within desired ranges.[322.1] This innovative approach seeks to enhance the efficiency of trait prediction and selection in complex breeding scenarios.Potential for Personalized Medicine
The potential for personalized medicine within the realm of quantitative genetics is increasingly recognized as a significant advancement in the field. The integration of genomic technologies has enabled researchers to explore the genetic basis of complex traits and diseases, which is crucial for developing tailored therapeutic strategies. For instance, the sequencing of the human genome and initiatives like the HapMap project have facilitated numerous genome-wide association studies (GWAS) aimed at elucidating the genetic components of complex diseases, thereby enhancing our understanding of human diversity at the level of common variants.[294.1] Moreover, the application of quantitative genetics methodologies has proven essential in breeding programs, allowing for the selection of individuals with desirable phenotypes based on their genetic makeup. This knowledge accelerates the improvement of populations, which is particularly relevant in the context of personalized medicine, where treatments can be customized to an individual's genetic profile.[287.1] The study of polygenic traits and their heritability is fundamental for predicting and improving both evolutionary and breeding outcomes, further underscoring the relevance of quantitative genetics in personalized healthcare.[287.1] Advancements in high-throughput sequencing technologies and multi-omics profiling have opened new avenues for systematically investigating the intricate interaction networks that underlie complex traits.[295.1] These developments not only enhance our understanding of the genetic basis of diseases but also pave the way for the application of machine learning techniques in analyzing large genomic datasets. Machine learning can identify patterns and relevant features from extensive genomic, phenotypic, and environmental data, thereby improving the reliability of predictions related to individual responses to treatments.[302.1] As the field continues to evolve, the integration of machine learning and artificial intelligence into genomic technologies is expected to transform the analysis of complex traits in quantitative genetics. This transformation will likely lead to more precise predictions of treatment outcomes and the development of personalized therapeutic interventions tailored to the genetic profiles of individuals.[303.1] The ongoing advancements in genomic technologies and their applications in quantitative genetics hold great promise for the future of personalized medicine, enabling more effective and individualized healthcare solutions.References
[1] Quantitative Genetics - an overview | ScienceDirect Topics — Quantitative genetics is the study of continuous traits and the statistical analysis of the relative contributions of genetic and environmental effects to phenotypic variation. Quantitative genetics is the study of continuous traits and the statistical analysis of the relative contributions of genetic and environmental effects to phenotypic variation. In this article we provide a brief overview of this rapidly growing field of research, highlighting some ways in which quantitative genetic studies of natural populations are helping us to understand phenotypic evolution, and pointing some challenges and opportunities facing the field. Quantitative genetics provides theory of the evolutionary processes that govern the evolution of phenotypic traits within and between populations.
[2] PDF — Introduction to Quantitative Genetics Historical Background I Quantitative genetics is the study of continuous or quantitative traits and their underlying mechanisms. I The main principals of quantitative genetics developed in the 20th century was largely in response to the rediscovery of Mendelian genetics.
[3] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[4] Quantitative Genetics: Introduction & Key Concepts - studylib.net — Quantitative genetics, on the other hand, is a branch of genetics in which individual genotypes are unidentified, and the traits of individuals are measured. Genotypic values are assigned to genotypes in the population. Quantitative genetics emphasizes the role of selection in controlled populations of known ancestry.
[7] The Difference Between Qualitative & Quantitative Traits In Genetics — Examples of Quantitative Traits in Animals and Plants. The height of an oak tree would be an example of a quantitative trait in plants. The sawtooth oak tree, for example, ranges in height between 40 and 60 feet. That means these trees could be any height within that range from 40.1 feet to 50.76 feet to 57.01 feet.
[8] Genetic and Environmental Effects on Quantitative Traits — All of the above factors are genetic in nature, but the environment also affects quantitative traits. The primary affect of the environment is to change the value for a particular genotype. Using our example above, the value for the genotype AaBb might vary from 8-10.
[9] Quantitative Genetics - an overview | ScienceDirect Topics — Quantitative genetics is the study of continuous traits and the statistical analysis of the relative contributions of genetic and environmental effects to phenotypic variation. Quantitative genetics is the study of continuous traits and the statistical analysis of the relative contributions of genetic and environmental effects to phenotypic variation. In this article we provide a brief overview of this rapidly growing field of research, highlighting some ways in which quantitative genetic studies of natural populations are helping us to understand phenotypic evolution, and pointing some challenges and opportunities facing the field. Quantitative genetics provides theory of the evolutionary processes that govern the evolution of phenotypic traits within and between populations.
[12] Quantitative Trait Evolution and Environmental Change - PMC — From basic evolutionary theory we know that the level of genetic variation is a key factor, as is the width of selection function. A change in the environment does not only influence the phenotypic optimum but also the number of individuals in the population.
[14] Heritability (2): narrow and broad sense heritability - Arslan Zaidi — This additivity has everything to do with the difference between narrow-sense and broad-sense heritability. If broad-sense heritability is the proportion of phenotypic variance that is due to ALL genetic effects (additive + non-additive), narrow-sense heritability is the proportion of phenotypic variation only due to additive genetic effects.
[18] Narrow & Broad-Sense Heritability | Equation & Applications — The two types of heritability, broad-sense and narrow-sense, differ based on the types of genetic variance being considered. Quantitative and behavioral genetics both use heritability estimates to
[19] Estimating Trait Heritability | Learn Science at Scitable - Nature — Given its definition as a ratio of variance components, the value of heritability always lies between 0 and 1. For instance, for height in humans, narrow-sense heritability is approximately 0.8
[48] History, Contribution, and Future of Quantitative Genetics in Plant ... — The time frame of the history of quantitative genetics is similar to Mendelian genetics. The rediscovery of Mendel's laws of inheritance in 1900 was the basis for determining the inheritance of quantitative traits and for developing plant breeding and selection methods.
[50] Biometricians vs Mendelians: What a century old debate ago ... - ARCHIVE — In October 2018 I, with a group of geneticists, statisticians and mathematicians came together to celebrate the work of Ronald Fisher and his contribution to the field of quantitative genetics. Quantitative genetics is used to examine the genetic variation responsible for the emergence of complex traits, often involving multiple genes. This
[51] Special issues on advances in quantitative genetics: introduction — Fisher's (1918) classic paper on the inheritance of complex traits not only founded the field of quantitative genetics, but also coined the term variance and introduced the powerful statistical method of analysis of variance. A decade later, the second ICQG held at Raleigh, North Carolina in 1987 (Weir et al., 1988), reflected explosive growth in new tools (low-density molecular markers for early quantitative trait locus (QTL) mapping), a continued expansion of the importance of mixed-model methodology for complex estimation issues, and a growing fusion of population and quantitative genetics. Quantitative genetics has a rich past history with ecological and population genetics, and the first three papers (Anderson et al.
[54] An Early History of the Heritability Coefficient Applied to Humans ... — Fisher's 1918 paper accomplished two distinct goals: unifying discrete Mendelian genetics with continuous biometric phenotypes and quantifying the variance components of variation in complex human characteristics. The former contributed to the foundation of modern quantitative genetics; the latter was adopted by social scientists interested in the pursuit of Galtonian nature-nurture
[56] Reinventing quantitative genetics for plant breeding: something old ... — In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support
[57] Advanced Quantitative Genetics Technologies for Accelerating Plant Breeding — Kempthrome stated "Plant breeding is an applied quantitative genetics". In fact quantitative genetics is fundamental to plant breeding activities. The branch of quantitative genetics has a long-standing history with its roots tracing back to Darwin's idea of selection as the chief agent of evolutionary change.
[59] Artificial Intelligence in Genetics - PMC - PubMed Central (PMC) — This paper examines AI's potential significance in advancing precision genetic disease treatment, provides a peek at its use in genetic clinical care, examines a number of existing AI and ML uses in genetics, provides a clinician primer on critical aspects of these technologies, and makes predictions about AI's potential future applications in genetic illnesses. ML algorithms can analyze large-scale genetic sequence datasets, which can then be used to steer the development of more accurate and effective genome editing technologies by predicting probable off-target consequences . 4. Personalized medicine AI uses genomic data analysis to personalize medicines based on each patient's genetics. By granting licenses or sharing these datasets with AI-focused businesses, strong machine learning models can be developed and trained, improving the precision and applicability of genetic investigations.
[82] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[83] Additive genetic effects - Wikipedia — Non-additive effects involve dominance or epistasis, and cause outcomes that are not a sum of the contribution of the genes involved. Additive genetic effects are singularly important with regard to quantitative traits, as the sum of these effects informs the placement of a trait on the spectrum of possible outcomes.
[84] Additive Genetic Patterns vs. Nonadditive Genetic Patterns — What's the Difference? Additive genetic patterns and nonadditive genetic patterns are two different ways in which genes contribute to the expression of traits. Additive genetic patterns occur when the effects of multiple genes on a trait are independent and additive, meaning that each gene contributes a certain amount to the overall phenotype.
[86] Estimation of additive and non‐additive genetic effects for fertility ... — Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and
[88] The statistical analysis of multi-environment data: modeling genotype ... — This is no coincidence, since historically, a large part of quantitative genetics has relied on simple, yet very useful, statistical models. A notorious example is the well-known model: P = G + E, where P stands for phenotype, G for genotype and E for environment (Falconer and Mackay, 1996; Lynch and Walsh, 1998). A statistical formulation of
[92] Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data ... — The history of personalized medicine is punctuated by significant milestones in genetics, technology, and clinical applications, shifting healthcare from a one-size-fits-all approach to a more individualized understanding of the molecular basis of health and disease and effective treatment strategies . Advances in genomics and biotechnology in the 21st century are enabling more personalized approaches to medicine, predicting disease risks, and tailoring treatments to individual genetic profiles. Personalized medicine leverages these images in conjunction with genetic data to gain deeper insights into disease mechanisms in individual patients, enhancing precision in diagnosis and treatment strategies . Through the integration of genetic, molecular, and clinical data, personalized medicine enables more accurate diagnosis, precise treatment targeting, and effective disease management.
[93] Quantitative Genetics in the Genomics Era - PMC - PubMed Central (PMC) — The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. ... data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction. Keywords: Complex traits, evolution
[94] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[96] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[97] Prediction of Genetic Values of Quantitative Traits in Plant Breeding ... — PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) ( Henderson 1984 ) or in
[98] The Evolving Landscape of Genomic Selection: Insights and Innovations ... — By integrating quantitative genetics and genome prediction models, GS has improved the accuracy of predicting complex traits and accelerated the cultivation of high-yield and stress resistant varieties. By integrating genome-wide marker information, GS significantly increases selection accuracy, shortens breeding cycles, and enhances genetic gains per unit time. For example, for Years 1-3 of the breeding cycle, the composition and structure of the training population (purple) affect both selection accuracy and genetic variance, whereas the choice of genomic selection models affects the intensity of selection, prediction accuracy, and genetic variance (Adopted from Merrick et al., 2022) Additionally, the development of improved statistical models that leverage genomic information to increase prediction accuracies is critical for the effectiveness of GS-enabled breeding programs (Budhlakoti et al., 2022).
[104] An assessment of the reliability of quantitative genetics estimates in ... — Quantitative genetics approaches, and particularly animal models, are widely used to assess the genetic (co)variance of key fitness related traits and infer adaptive potential of wild populations. Despite the importance of precision and accuracy of genetic variance estimates and their potential sensitivity to various ecological and population
[121] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[122] PDF — Given the rapid advances made in molecular biology (Nature Genome Issue, February 15, 2001; Science Genome Issue, February 16, 2001), the near completion of the human genome and the development of sophisticated statistical genetic methods (e.g., Dolan et al., 1999a, 1999b; Fulker et al., 1999; Goring, 2000; Terwilliger & Zhao, 2000), the identification of specific genes, even for complex traits, has now become a realistic prospect for quantitative geneticists. 363 Twin Research October 2003 Genotyping in GenomEUtwin Decomposition of Phenotypic Variance Although Figure 1 and Table 1 lack environmental effects, quantitative geneticists assume that the individual pheno-type (P) is a function of both genetic (G) and environmental effects (E): P = G + E, where E refers to the environmental deviations, which have an expected average value of zero.
[127] Advanced Computational Approaches for Medical Genetics and Genomics — There is an unprecedented demand for novel computational methods and analytical strategies to improve the accuracy of variants identification and the power of association tests. This special issue aims to publish applications of innovative analysis pipelines and algorithms to find the better solutions of complex genetic and genomic problems in
[131] Quantitative Genetic Variation, Theory of Selection and ... - Springer — The effects of selection on quantitative traits are viewed in terms of changes in the mean phenotypes, variances and standard deviations by comparing populations derived through selection and the respective parental populations. ... Molecular markers are used to derive genetic distance relationships between and among different pure lines and
[132] A statistical framework for quantitative trait mapping. - PMC — We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts.
[133] Linear Mixed Models for Association Analysis of Quantitative Traits ... — We develop linear mixed models (LMM) and functional linear mixed models (FLMM) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effect of a major gene are modeled as a fixed effect, the
[138] Advances in Whole Genome Sequencing: Methods, Tools, and ... - MDPI — : With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Genome sequencing reveals the genetic diversity of species, population structure, domestication processes, and evolutionary and adaptive mechanisms, and identifies the loci of variation associated with economically important traits . Natural selection drives species adaptation and evolution by selecting favorable variation; so, after determining the genetic structure and ancestral components of a population, the next step is to use selective scanning methods to detect regions of the genome that have been affected by natural selection, thereby identifying candidate genes associated with economic traits or environmental adaptations. "Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics" International Journal of Molecular Sciences 26, no.
[139] Third Generation Sequencing: Revolutionizing Genomic Research — Explore how third-generation sequencing enhances genomic research with longer reads, real-time analysis, and improved detection of genetic and epigenetic variations. Unlike first- and second-generation techniques, which require amplification and produce shorter reads, this newer approach enables direct sequencing of single molecules, reducing errors and providing deeper biological insights. With its ability to generate longer reads, detect epigenetic modifications, and analyze structural variations effectively, third-generation sequencing is reshaping how scientists study genomes. Single molecule sequencing circumvents these challenges by reading extended nucleotide sequences in a single pass, preserving genomic integrity and improving structural variation resolution. Detecting structural variations with high precision is one of third-generation sequencing’s most significant contributions to genomic research.
[140] Improving genetic variant identification for quantitative traits using ... — Additionally, we utilize linear regression (LR), random forest (RF), XGBoost, and support vector regression (SVR) ML methods for association testing on selected features/SNPs. Kernel-based ML methods like SVR for association testing can identify interactions by exploring all possible combinations of SNPs within a GWAS dataset. Our proposed framework includes quality control (QC) of the data, four feature selection methods such as LASSO , ridge , elastic-net , and mutual information , followed by three association methods (i.e. LR , RF , and SVR ). For this purpose, we employed four methods, including LASSO, ridge, elastic-net, and mutual information, to select a subset of 5000 SNPs. This selection was based on the Bonferroni correction method, which adjusts the significance p-value threshold (0.05) to account for the number of tests performed .
[162] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[163] THE USE OF QUANTITATIVE GENETICS IN PLANT IMPROVEMENT - ResearchGate — In plant improvement, quantitative genetics is used to study & manipulate traits such as yield, disease resistant & quality that are important for crop improvement.
[164] Quantitative inheritance | EBSCO Research Starters — Quantitative inheritance. SIGNIFICANCE: Quantitative inheritance involves metric traits. These traits are generally associated with adaptation, reproduction, yield, form, and function.They are thus of great importance to evolution, conservation biology, psychology, and especially the improvement of agricultural organisms. The Genetics Underlying Metric Traits
[165] (PDF) The Role of Genetics and Plant Breeding for Crop Improvement ... — Genetics and plant breeding play crucial roles in driving crop improvement efforts, ensuring sustainable food production and addressing global challenges such as population growth and climate change.
[169] Gene Environment Interaction - National Human Genome Research Institute — Gene Environment Interaction Scan to visit Skip to main content Skip to navigation Skip to search Skip to slider Skip to about Skip to subscription Skip to footer About Genomics About Genomics* Introduction to Genomics Educational Resources Policy Issues in Genomics The Human Genome Project Research Funding Research Funding* Funding Opportunities Funded Programs & Projects Division and Program Directors Scientific Program Analysts Contacts by Research Area News & Events Research at NHGRI Research at NHGRI* Research Areas Research Investigators Staff Clinicians Research Projects Clinical Research Data Tools & Resources News & Events About Health About Health* Genomics & Medicine Family Health History For Patients & Families For Health Professionals Careers & Training Careers & Training* Jobs at NHGRI Training at NHGRI Funding for Research Training Professional Development Programs NHGRI Culture News & Events News & Events* News Events Social Media Broadcast Media Image Gallery Videos Press Resources About NHGRI About NHGRI* Organization NHGRI Director Mission and Vision Policies and Guidance Budget Institute Advisors Strategic Vision Leadership Initiatives Partner with NHGRI Staff Search Contact Us Breadcrumb Home About Genomics Educational Resources Talking Glossary of Genomic and Genetic Terms Gene Environment Interaction Home About Genomics Educational Resources Talking Glossary of Genomic and Genetic Terms En Español Gene Environment Interaction updated: March 14, 2025 Definition 00:00 … Gene–environment interaction refers to the interplay of genes (and, more broadly, genome function) and the physical and social environment. These interactions influence the expression of phenotypes. For example, most human traits and diseases are influenced by how one or more genes interact in complex ways with environmental factors, such as chemicals in the air or water, nutrition, ultraviolet radiation from the sun and social context. Gene–environment interactions are the situation where the impact of an environmental exposure on disease risk is different for people with different genotypes, or conversely, situations where the impact of a genotype on disease risk is different in people with different environmental exposures. Studies of gene–environment interactions can provide insights into biological mechanisms of disease and could have public health implications.
[171] Genetic diversity is crucial for the survival and adaptability of species — Genetic diversity is important because it allows species to adapt to changing environments and increase their chances of survival. Genetic diversity is important for the survival and adaptability of species, as it allows them to evolve and respond to changes in their environment. Genetic diversity is important for the survival and adaptability of a species. Genetic diversity is crucial for a species’ ability to adapt to changing environments, maintain overall fitness, and increase its chances of long-term survival. Genetic diversity is important for the adaptation and survival of species in changing environments. Genetic diversity is important for evolution as it allows for adaptability and species survival. Genetic diversity is important for the survival of species, as it allows for adaptability to changes in the environment.
[196] Influence and role of polygenic risk score in the development of 32 ... — Over the past two decades, genome-wide association studies (GWASs) have shed light on the important role of numerous single nucleotide polymorphisms (SNPs) in the development of complex diseases .Despite the considerably limited impact of each inherited variant on disease risk, they jointly form the genetic basis of many diseases [].Based on this, polygenic risk score (PRS), which
[198] Polygenic Risk Scores in Human Disease - PubMed — Herein, details on how to develop PRS are provided, followed by 5 areas in which they can be used to improve human health: (a) augmenting risk prediction, (b) refining diagnosis, (c) guiding treatment choices, (d) making clinical trials more efficient, and (e) improving public health. Summary: PRS can offer valuable information for providers and patients, including identifying risk of disease earlier in life and before the onset of clinical risk factors, guiding treatment decisions, improving public health outcomes, and making clinical trials more efficient. The future of genomic-informed risk assessments of disease is through integrated risk models that combine genetic factors including PRS, monogenic, and somatic DNA information with nongenetic risk factors such as clinical risk estimators and multiomic data.
[199] Polygenic risk scores: from research tools to clinical instruments — These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. ... Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual's PRS from a percentile of a normal distribution to a lifetime disease
[200] Polygenic risk scores in the clinic: Translating risk into action — Polygenic risk scores (PRSs) are heralded as useful tools for risk stratification and personalized preventive care, but they are clinically useful only if they can be translated into action. The risk information conveyed by a PRS must be contextualized to enable this. Best practices are evolving but …
[210] From genetic associations to genes: methods, applications, and ... — Integrating genome-wide association studies (GWASs) with molecular quantitative trait loci (xQTLs), including context-dependent (cd)-xQTLs, across multiple 'omics levels helps unveil the putative causal genes underlying GWAS signals, the relevant cell types, and the likely genetic regulation mechanisms of the prioritized genes.
[212] Benefits and limitations of genome-wide association studies — Benefits and limitations of genome-wide association studies - PubMed Search in PubMed Search in PubMed Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data. Masotti M, et al. M. et al. J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed
[215] The Evolving Landscape of Genomic Selection: Insights and Innovations ... — By integrating quantitative genetics and genome prediction models, GS has improved the accuracy of predicting complex traits and accelerated the cultivation of high-yield and stress resistant varieties. By integrating genome-wide marker information, GS significantly increases selection accuracy, shortens breeding cycles, and enhances genetic gains per unit time. For example, for Years 1-3 of the breeding cycle, the composition and structure of the training population (purple) affect both selection accuracy and genetic variance, whereas the choice of genomic selection models affects the intensity of selection, prediction accuracy, and genetic variance (Adopted from Merrick et al., 2022) Additionally, the development of improved statistical models that leverage genomic information to increase prediction accuracies is critical for the effectiveness of GS-enabled breeding programs (Budhlakoti et al., 2022).
[216] Editorial: Statistical methods for analyzing multiple environmental ... — This Research Topic highlights several novel statistical analytical tools that can effectively leverage high-dimensional data to gain a deeper understanding of genotype-environment interactions (GEI) (Elias et al., 2016; van Eeuwijk et al., 2016) and use them to predict phenotype outcomes. An alternative to predict the performance of individuals in untested environments is to use environmental covariates in either partial-least squares regressions (Crossa et al., 1999; Monteverde et al., 2019) or for genotype-specific reaction norms in random regression models (Schaeffer, 2004; Buntaran et al., 2021) or P-splines (Bustos-Korts et al., 2021). As a conclusion, this Research Topic collects a series of modern quantitative genomic methods that can effectively analyse large-scale genomic, phenomic and environmental data sets, with the aim to either predict individuals’ outcome of quantitative traits or to identify important genes that are linked to genotype by environment interactions.
[218] High-throughput sequencing for noninvasive disease detection in ... — Among currently available tests, high-throughput sequencing (HTS)-based approaches are increasingly attractive for noninvasive molecular testing. HTS-based methods can simultaneously identify multiple genetic markers with high sensitivity and specificity without individual optimization.
[228] Fisher's quantitative genetic model and the molecular genetics of ... — Fisher's classic paper on the quantitative genetic model from 1918 (Phil. Trans. R. Soc. Edin. 52: 393-433) was almost as fundamentally important to basic biology and applied biology (medicine and agriculture) as the discovery of the double helix by Watson and Crick 35 years later (1953, Nature 171: 737-738). The only reason why Fisher, Wright and Haldane were not awarded a Nobel prize
[229] Commentary: Fisher 1918: the foundation of the genetics and analysis of ... — The importance of the analysis of variance is first discussed and its meaning in terms of causes of variability. Fisher then discusses the application of the analysis of variance to quantify the total effect of 'ancestry' (genetic factors) on quantitative trait variation, with numerical examples of human height.
[247] PDF — While medical genetics shares many of the ethical and legal considerations as other areas of medicine, factors that may complicate clinical application of genetic knowledge include the following: • Rapidly advancing knowledge base that is often applied before the clinical implications are well studied • Information has direct implications for extended family, not simply the individual • Genetic information may be viewed more personally by the public than other forms of medical information, resulting in potentially significant psychological burden and heightened concerns about privacy and discrimination • The predictive nature of some forms of genetic testing may be particularly burdensome, especially when there are no effective preventive measures and may lead to an inappropriate view of genetic determinism Ethics The 4 main biomedical ethical principles that are called upon to identify, evaluate, and resolve ethical dilemmas are as follows: • Autonomy: the potential of the individual to be self-determining • Beneficence: the obligation to seek the good for the patient under all circumstances • Nonmaleficence: the obligation to avoid or refrain from intentionally harming the patient • Justice: on an individual level, we treat our patients in a fair, equitable, and appropriate manner in light of what benefits are due or owed them Legal System Page 20 of 25 On the federal level, there are several sources of laws that govern different aspects of health and medical care.
[248] A double-edged helix: The ethical consequences of widespread genetic ... — Nevertheless, as genetic testing becomes more widespread, the storage of genetic data presents significant ethical and security challenges, prompting critical questions: Are current storage methods truly capable of safeguarding such sensitive information? Before examining these risks, we must first explore the ethical dilemmas that persist even when genetic testing and data management adhere to the highest security standards. Autonomy ensures that patients give informed consent and fully understand the purpose, benefits, and risks of genetic testing while also maintaining control over which results are disclosed, including unexpected findings. A breach of genetic data could result in unofficial blacklists where high-risk individuals find themselves unable to access essential coverage even if they never develop the disease.
[249] 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.
[250] Ethical Challenges in Obtaining Informed Consent for Genetic/Genomic ... — Obtaining informed consent (IC) for genetic/genomic research is complicated because most of the ethical issues associated with genetic research are considerations to be disclosed by researchers and weighed by prospective participants during the consent process. These include complicated questions associated with return of results, management of incidental findings (results beyond the aims of
[252] Ethical issues in pediatric genetic testing and screening — Recent findings: Several professional organizations in the fields of genetics and pediatrics have published new guidance on the ethical, legal, and policy issues relevant to genetic testing in children. The roles of predictive testing for adult-onset conditions, the management of secondary findings and the role of informed consent for newborn
[254] Genetic Discrimination - National Human Genome Research Institute — The Genetic Information Nondiscrimination Act (GINA) of 2008 protects Americans from discrimination based on their genetic information in both health insurance (Title I) and employment (Title II). The Genetic Information Nondiscrimination Act (GINA) of 2008 protects Americans from discrimination based on their genetic information in both health insurance (Title I) and employment (Title II). Since wellness programs involve the exchange of health information between employees and their employers, they are relevant to the enforcement of GINA in cases where wellness programs request genetic information from employees. Since wellness programs involve the exchange of health information between employees and their employers, they are relevant to the enforcement of GINA in cases where wellness programs request genetic information from employees.
[257] The Key Features of a Genetic Nondiscrimination Policy - JAMA Network — One persistent concern is that of genetic discrimination (GD). 1 Broadly put, GD occurs when an individual or group is negatively treated, unfairly profiled, or harmed relative to the rest of the population on the basis of actual or presumed genetic characteristics. 2 Beyond its human rights implications, GD may also disincentivize individuals
[287] Understanding Quantitative Genetics: Principles and Applications — By studying the genetic basis of phenotypic variation, we can gain a better understanding of how genes interact and contribute to the diversity of traits we observe in nature. In breeding programs, knowledge of the genetic basis of quantitative traits allows breeders to select individuals with desirable phenotypes and accelerate the improvement of populations. In quantitative genetics, the study of polygenic traits and their heritability is essential for understanding the genetic basis of phenotypic variation and the process of evolution. In quantitative genetics, understanding the genetic basis of variation in traits is crucial for predicting and improving evolutionary and breeding outcomes. Quantitative genetics, the study of the heritability and variation of phenotypic traits, has important applications in medicine.
[294] The impact of next-generation sequencing technology on genetics — The sequencing of the human genome and the HapMap project have impacted the study of human disease in a significant way and are enabling many genome-wide association studies that aim to elucidate the genetic component of complex diseases. Although we understand diversity among humans reasonably well at the level of common variants (present at 5
[295] Network biology bridges the gaps between quantitative genetics and ... — With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of
[302] Machine learning algorithms translate big data into predictive breeding ... — Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect
[303] Genomic prediction using machine learning: a comparison of the ... — Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. In genomic prediction (GP) one is interested in estimating the p regression coefficients \(\beta _j\) so that genomic breeding values of non-phenotyped genotypes can be predicted from the fitted model. Predictive ability (PA; mean and range values computed across the 5-fold validation datasets and 10 replicates) of the regularized, adaptive regularized, group regularized, Bayesian regularized, ensemble, instance-based and deep learning methods, computed as the Pearson correlation coefficient between the observed breeding values (OBVs) and the predicted breeding values (PBVs), for the KWS datasets.
[320] Chapter 11: Multiple Trait Selection - Quantitative Genetics for Plant ... — Depending on the program or project goals, one of three types of multiple-trait selection can be employed. A brief description of these types follows. Multistage selection: Selection for different traits at different stages during cultivar development. Tandem selection: Selection for one trait until that trait is improved, then for a second
[321] Mitigating tradeoffs in plant breeding - ScienceDirect — Plant breeders improve complex traits controlled by multiple genes with small effects such as yield and nutritional quality. They make selections based on multiple traits, often unfavorably interrelated, which may limit the progress in crop breeding (Falconer and Mackay, 1996).Selection of traits with shared antagonistic genetic influence is functionally constrained while correlations induced
[322] Multi-trait Genomic Selection Methods for Crop Improvement — Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. ... we present a new approach for MT-GS problems to optimize the main goal of a breeding program while keeping other traits within desired ranges. This new approach, multi-trait LAS (MT-LAS), extends the ST-LAS