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Trends in Genetics : TIG Oct 2018Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification... (Review)
Review
Accurate prediction of complex traits requires using a large number of DNA variants. Advances in statistical and machine learning methodology enable the identification of complex patterns in high-dimensional settings. However, training these highly parameterized methods requires very large data sets. Until recently, such data sets were not available. But the situation is changing rapidly as very large biomedical data sets comprising individual genotype-phenotype data for hundreds of thousands of individuals become available in public and private domains. We argue that the convergence of advances in methodology and the advent of Big Genomic Data will enable unprecedented improvements in complex-trait prediction; we review theory and evidence supporting our claim and discuss challenges and opportunities that Big Data will bring to complex-trait prediction.
Topics: Big Data; Genome-Wide Association Study; Genomics; Genotype; Humans; Models, Genetic; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Quantitative Trait Loci
PubMed: 30139641
DOI: 10.1016/j.tig.2018.07.004 -
Journal of Molecular and Cellular... Jan 2019Morphology underlies subdivision of the primary/heritable sarcomeric cardiomyopathies (CMs) into hypertrophic (HCM) and dilated (DCM). Next-generation DNA-sequencing... (Review)
Review
Morphology underlies subdivision of the primary/heritable sarcomeric cardiomyopathies (CMs) into hypertrophic (HCM) and dilated (DCM). Next-generation DNA-sequencing (NGS) has identified important disease-variants, improving CM diagnosis, management, genetic screening, and prognosis. Although monogenic (Mendelian) analyses directly point at downstream studies, they disregard coexisting genomic variations and gene-by-gene interactions molding detailed CM-phenotypes. In-place of polygenic models, in accounting for observed defective genotype-phenotype correlations, fuzzy concepts having gradations of significance and unsharp domain-boundaries are invoked, including pleiotropy, genetic-heterogeneity, incomplete penetrance, and variable expressivity. HCM and DCM undoubtedly entail cooperativity of unidentified/elusive causative genomic-variants. Modern genomics can exploit comprehensive electronic/digital health records, facilitating consideration of multifactorial variant-models. Genome-wide association studies entailing high-fidelity solid-state catheterization, multimodal-imaging, molecular cardiology, systems biology and bioinformatics, will decipher accurate genotype-phenotype correlations and identify novel therapeutic-targets, fostering personalized medicine/cardiology. This review surveys successes and challenges of genetic/genomic approaches to CMs, and their impact on current and future clinical care.
Topics: Biological Variation, Population; Biomechanical Phenomena; Cardiomyopathies; Humans; Multifactorial Inheritance; Sarcomeres; Translational Research, Biomedical
PubMed: 30423317
DOI: 10.1016/j.yjmcc.2018.10.024 -
Aging Cell Jul 2022Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly...
Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle-aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long-lived individuals. We further stratified PRSs into cell-type groups and significance-level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p-values (p ≤ 1x10 ) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10 < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade-offs between complex diseases and longevity.
Topics: Aged, 80 and over; Alleles; Diabetes Mellitus, Type 2; Humans; Longevity; Middle Aged; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 35754110
DOI: 10.1111/acel.13654 -
Human Genomics Jul 2021Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics... (Review)
Review
Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.
Topics: Genetic Diseases, Inborn; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Phenotype; Risk Factors
PubMed: 34284826
DOI: 10.1186/s40246-021-00339-y -
Journal of Medical Genetics Oct 2013Digenic inheritance (DI) is the simplest form of inheritance for genetically complex diseases. By contrast with the thousands of reports that mutations in single genes... (Review)
Review
Digenic inheritance (DI) is the simplest form of inheritance for genetically complex diseases. By contrast with the thousands of reports that mutations in single genes cause human diseases, there are only dozens of human disease phenotypes with evidence for DI in some pedigrees. The advent of high-throughput sequencing (HTS) has made it simpler to identify monogenic disease causes and could similarly simplify proving DI because one can simultaneously find mutations in two genes in the same sample. However, through 2012, I could find only one example of human DI in which HTS was used; in that example, HTS found only the second of the two genes. To explore the gap between expectation and reality, I tried to collect all examples of human DI with a narrow definition and characterise them according to the types of evidence collected, and whether there has been replication. Two strong trends are that knowledge of candidate genes and knowledge of protein-protein interactions (PPIs) have been helpful in most published examples of human DI. By contrast, the positional method of genetic linkage analysis, has been mostly unsuccessful in identifying genes underlying human DI. Based on the empirical data, I suggest that combining HTS with growing networks of established PPIs may expedite future discoveries of human DI and strengthen the evidence for them.
Topics: Epistasis, Genetic; Genetic Linkage; Genetics, Medical; Humans; Inheritance Patterns; Multifactorial Inheritance; Research Design
PubMed: 23785127
DOI: 10.1136/jmedgenet-2013-101713 -
Psychological Medicine Oct 2021Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has... (Review)
Review
Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.
Topics: Comorbidity; Genome-Wide Association Study; Genomics; Humans; Mental Disorders; Multifactorial Inheritance; Mutation; Phenotype; Polymorphism, Single Nucleotide; Risk Factors
PubMed: 33729112
DOI: 10.1017/S0033291721000829 -
Schizophrenia Research May 2024Schizophrenia is a highly heritable, severe mental illness characterized by hallucinations, delusions, social withdrawal, and cognitive dysfunction present in ∼1% of... (Review)
Review
Schizophrenia is a highly heritable, severe mental illness characterized by hallucinations, delusions, social withdrawal, and cognitive dysfunction present in ∼1% of populations across cultures. There have been recent major advancements in our understanding of the genetic architecture of schizophrenia. Both rare, highly penetrant genetic variants as well as common, low-penetrant genetic variants can predispose individuals to schizophrenia and can impact the way people metabolize psychoactive medications used to treat schizophrenia. However, the impact of these findings on the clinical management of schizophrenia remains limited. This review highlights the few places where genetics currently informs schizophrenia management strategies, discusses major limitations, and reviews promising areas of genetics research that are most likely to impact future schizophrenia care. Specifically, I focuss on psychiatric genetic counseling, genetic testing strategies, pharmacogenetics, polygenic risk, and genetics-guided treatment. Lastly, I emphasize important ethical considerations in the clinical use of genetics for schizophrenia management, including the exacerbation of healthcare inequalities and unintended consequences of new genetic technologies.
Topics: Humans; Schizophrenia; Pharmacogenetics; Genetic Testing; Genetic Counseling; Multifactorial Inheritance; Genetic Predisposition to Disease
PubMed: 37813777
DOI: 10.1016/j.schres.2023.09.042 -
International Journal of Molecular... Sep 2021Syncope, defined as a transient loss of consciousness caused by transient global cerebral hypoperfusion, affects 30-40% of humans during their lifetime. Vasovagal... (Review)
Review
Syncope, defined as a transient loss of consciousness caused by transient global cerebral hypoperfusion, affects 30-40% of humans during their lifetime. Vasovagal syncope (VVS) is the most common cause of syncope, the etiology of which is still unclear. This review summarizes data on the genetics of VVS, describing the inheritance pattern of the disorder, candidate gene association studies and genome-wide studies. According to this evidence, VVS is a complex disorder, which can be caused by the interplay between genetic factors, whose contribution varies from monogenic Mendelian inheritance to polygenic inherited predisposition, and external factors affecting the monogenic (resulting in incomplete penetrance) and polygenic syncope types.
Topics: Genetic Predisposition to Disease; Humans; Inheritance Patterns; Multifactorial Inheritance; Syncope, Vasovagal
PubMed: 34638656
DOI: 10.3390/ijms221910316 -
American Journal of Human Genetics Jul 2023In polygenic score (PGS) analysis, the coefficient of determination (R) is a key statistic to evaluate efficacy. R is the proportion of phenotypic variance explained by...
In polygenic score (PGS) analysis, the coefficient of determination (R) is a key statistic to evaluate efficacy. R is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (h, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R. However, in real data analyses R has been reported to exceed h, which occurs in parallel with the observation that h estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific h exist, or if genetic correlations between cohorts are less than one, h estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R will be greater than h and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
Topics: Humans; Genome-Wide Association Study; Polymorphism, Single Nucleotide; Multifactorial Inheritance; Phenotype; Computer Simulation
PubMed: 37379836
DOI: 10.1016/j.ajhg.2023.06.006 -
The Journal of Headache and Pain Apr 2018The latest Genome-Wide Association Study identified 38 genetic variants associated with migraine. In this type of studies the significance level is very difficult to... (Review)
Review
BACKGROUND
The latest Genome-Wide Association Study identified 38 genetic variants associated with migraine. In this type of studies the significance level is very difficult to achieve (5 × 10) due to multiple testing. Thus, the identified variants only explain a small fraction of the genetic risk. It is expected that hundreds of thousands of variants also confer an increased risk but do not reach significance levels. One way to capture this information is by constructing a Polygenic Risk Score. Polygenic Risk Score has been widely used with success in genetics studies within neuropsychiatric disorders. The use of polygenic scores is highly relevant as data from a large migraine Genome-Wide Association Study are now available, which will form an excellent basis for Polygenic Risk Score in migraine studies.
RESULTS
Polygenic Risk Score has been used in studies of neuropsychiatric disorders to assess prediction of disease status in case-control studies, shared genetic correlation between co-morbid diseases, and shared genetic correlation between a disease and specific endophenotypes.
CONCLUSION
Polygenic Risk Score provides an opportunity to investigate the shared genetic risk between known and previously unestablished co-morbidities in migraine research, and may lead to better and personalized treatment of migraine if used as a clinical assistant when identifying responders to specific drugs. Polygenic Risk Score can be used to analyze the genetic relationship between different headache types and migraine endophenotypes. Finally, Polygenic Risk Score can be used to assess pharmacogenetic effects, and perhaps help to predict efficacy of the Calcitonin Gene-Related Peptide monoclonal antibodies that soon become available as migraine treatment.
Topics: Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Migraine Disorders; Multifactorial Inheritance
PubMed: 29623444
DOI: 10.1186/s10194-018-0856-0