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Neuroscience and Biobehavioral Reviews Oct 2023Attention-deficit/hyperactivity disorder (ADHD) co-occurs with many other psychiatric disorders and traits. In this review, we summarize and interpret the existing... (Review)
Review
Attention-deficit/hyperactivity disorder (ADHD) co-occurs with many other psychiatric disorders and traits. In this review, we summarize and interpret the existing literature on the genetic architecture of these comorbidities based on hypothesis-generating approaches. Quantitative genetic studies indicate that genetic factors play a substantial role in the observed co-occurrence of ADHD with many different disorders and traits. Molecular genetic correlations derived from genome-wide association studies and results of studies based on polygenic risk scores confirm the general pattern but provide effect estimates that are smaller than those from twin studies. The identification of the specific genetic variants and biological pathways underlying co-occurrence using genome-wide approaches is still in its infancy. The first analyses of causal inference using genetic data support causal relationships between ADHD and comorbid disorders, although bidirectional effects identified in some instances point to complex relationships. While several issues in the methodology and inferences from the results are still to be overcome, this review shows that the co-occurrence of ADHD with many psychiatric disorders and traits is genetically interpretable.
Topics: Humans; Attention Deficit Disorder with Hyperactivity; Genome-Wide Association Study; Phenotype; Risk Factors; Multifactorial Inheritance
PubMed: 37451654
DOI: 10.1016/j.neubiorev.2023.105313 -
BMC Medical Genomics Jul 2023Polygenic Risk Scores (PRS) (also known as polygenic scores, genetic risk scores or polygenic indexes) capture genetic contributions of a multitude of markers that...
Polygenic Risk Scores (PRS) (also known as polygenic scores, genetic risk scores or polygenic indexes) capture genetic contributions of a multitude of markers that characterise complex traits. Although their likely application to precision medicine remains to be established, promising advances have included their ability to stratify high risk individuals and targeted screening interventions. Current PRS have been mostly optimised for individuals of Northern European ancestries. If PRS are to become widespread as a tool for healthcare applications, more diverse populations and greater capacity for derived interventions need to be accomplished. In this editorial we aim to attract submissions from the research community that highlight current challenges in development of PRS applications at scale. We also welcome manuscripts that delve into the ethical, social and legal implications that the implementation of PRS may generate.
Topics: Humans; Genetic Predisposition to Disease; Multifactorial Inheritance; Risk Factors; Genomics; White People; Genome-Wide Association Study
PubMed: 37507694
DOI: 10.1186/s12920-023-01615-7 -
Genes Sep 2023Non-syndromic cleft lip with or without palate (NSCL/P) is a prevalent birth defect that affects 1/500-1/1400 live births globally. The genetic basis of NSCL/P is... (Review)
Review
Non-syndromic cleft lip with or without palate (NSCL/P) is a prevalent birth defect that affects 1/500-1/1400 live births globally. The genetic basis of NSCL/P is intricate and involves both genetic and environmental factors. In the past few years, various genetic inheritance models have been proposed to elucidate the underlying mechanisms of NSCL/P. These models range from simple monogenic inheritance to more complex polygenic inheritance. Here, we present a comprehensive overview of the genetic inheritance model of NSCL/P exemplified by representative genes and regions from both monogenic and polygenic perspectives. We also summarize existing association studies and corresponding loci of NSCL/P within the Chinese population and highlight the potential of utilizing polygenic risk scores for risk stratification of NSCL/P. The potential application of polygenic models offers promising avenues for improved risk assessment and personalized approaches in the prevention and management of NSCL/P individuals.
Topics: Humans; Cleft Lip; Cleft Palate; Multifactorial Inheritance; Inheritance Patterns
PubMed: 37895208
DOI: 10.3390/genes14101859 -
Trends in Genetics : TIG Jul 2021The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained... (Review)
Review
The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.
Topics: Adaptation, Physiological; Evolution, Molecular; Gene Regulatory Networks; Genetic Variation; Genetics, Population; Humans; Multifactorial Inheritance; Quantitative Trait Loci; Selection, Genetic
PubMed: 33892958
DOI: 10.1016/j.tig.2021.03.005 -
Biological Psychiatry Jul 2019Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In... (Review)
Review
Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.
Topics: Genetic Predisposition to Disease; Genetic Testing; Genome-Wide Association Study; Humans; Mental Disorders; Multifactorial Inheritance; Predictive Value of Tests; Risk Assessment
PubMed: 30737014
DOI: 10.1016/j.biopsych.2018.12.015 -
Heart (British Cardiac Society) Apr 2023
Topics: Humans; Coronary Artery Disease; Life Style; Multifactorial Inheritance; Risk Factors; Genetic Predisposition to Disease
PubMed: 36759176
DOI: 10.1136/heartjnl-2022-322057 -
American Journal of Human Genetics Sep 2021The omnigenic model was proposed as a framework to understand the highly polygenic architecture of complex traits revealed by genome-wide association studies (GWASs). I... (Review)
Review
The omnigenic model was proposed as a framework to understand the highly polygenic architecture of complex traits revealed by genome-wide association studies (GWASs). I argue that this model also explains recent observations about cross-population genetic effects, specifically the low transferability of polygenic scores and the lack of clear evidence for polygenic selection. In particular, the omnigenic model explains why the effects of most GWAS variants vary between populations. This interpretation has several consequences for the evolutionary interpretation and practical use of GWAS summary statistics and polygenic scores. First, some polygenic scores may be applicable only in populations of the same ancestry and environment as the discovery population. Second, most GWAS associations will have differing effects between populations and are unlikely to be robust clinical targets. Finally, it may not always be possible to detect polygenic selection from population genetic data. These considerations make it difficult to interpret the clinical and evolutionary meanings of polygenic scores without an explicit model of genetic architecture.
Topics: Computer Simulation; Genetic Variation; Genetics, Population; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable
PubMed: 34331855
DOI: 10.1016/j.ajhg.2021.07.003 -
ELife Oct 2021A theoretical framework predicts that using polygenic screening to select embryos against traits that depend on many genes has few benefits.
A theoretical framework predicts that using polygenic screening to select embryos against traits that depend on many genes has few benefits.
Topics: Multifactorial Inheritance; Phenotype
PubMed: 34635204
DOI: 10.7554/eLife.73193 -
Current Cardiology Reports Jul 2021Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing... (Review)
Review
PURPOSE OF THE REVIEW
Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level.
RECENT FINDINGS
Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations. With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.
Topics: Coronary Artery Disease; Genetic Predisposition to Disease; Humans; Multifactorial Inheritance; Prospective Studies; Risk Factors
PubMed: 34196841
DOI: 10.1007/s11886-021-01540-0 -
International Journal of Epidemiology Feb 2020
Topics: Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Metabolomics; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Translational Research, Biomedical
PubMed: 31828333
DOI: 10.1093/ije/dyz254