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Briefings in Bioinformatics Mar 2024Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking... (Review)
Review
Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.
Topics: Humans; Multifactorial Inheritance; Genetic Predisposition to Disease; Software; Computational Biology; Genome-Wide Association Study; Risk Factors; Risk Assessment; Genetic Risk Score
PubMed: 38770718
DOI: 10.1093/bib/bbae240 -
Progress in Neuro-psychopharmacology &... Jun 2024Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions...
Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions may affect wellbeing measures, which go beyond categorical diagnoses and reflect the health-disease continuum. We evaluated GxE effects in the UK Biobank, considering as outcomes subjective wellbeing (feeling good and functioning well) and objective measures (education and income). We estimated the polygenic risk scores (PRSs) of major depressive disorder, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder. Stressful/traumatic events during adulthood or childhood were considered as E variables, as well as social support. The addition of the PRSxE interaction to PRS and E variables was tested in linear or multinomial regression models, adjusting for confounders. We included 33 k-380 k participants, depending on the variables considered. Most PRSs and E factors showed additive effects on outcomes, with effect sizes generally 3-5 times larger for E variables than PRSs. We found some interaction effects, particularly when considering recent stress, history of a long illness/disability/infirmity, and social support. Higher PRSs increased the negative effects of stress on wellbeing, but they also increased the positive effects of social support, with interaction effects particularly for the outcomes health satisfaction, loneliness, and income (p < Bonferroni corrected threshold of 1.92e-4). PRSxE terms usually added ∼0.01-0.02% variance explained to the corresponding additive model. PRSxE effects on wellbeing involve both positive and negative E factors. Despite small variance explained at the population level, preventive/therapeutic interventions that modify E factors could be beneficial at the individual level.
Topics: Humans; Adult; Child; Depressive Disorder, Major; Genetic Risk Score; Biological Specimen Banks; UK Biobank; Multifactorial Inheritance; Risk Factors
PubMed: 38367896
DOI: 10.1016/j.pnpbp.2024.110972 -
Annual Review of Genetics Nov 2023The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine.... (Review)
Review
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.
Topics: Multifactorial Inheritance; Phenotype; Genomics; Genome; Plants
PubMed: 37562412
DOI: 10.1146/annurev-genet-022123-110824 -
Communications Biology Oct 2023Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are...
Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are evolutionary markers functioning as neurodevelopmental transcription enhancers that have been associated with brain configuration, neural information processing, and schizophrenia risk. Here, we have investigated the influence of HARs' polygenic load on neuroanatomical measures through a case-control approach (128 patients with schizophrenia and 115 controls). To this end, we have calculated the global schizophrenia Polygenic Risk Score (Global PRS) and that specific to HARs (HARs PRS). We have also estimated the polygenic burden restricted to the HARs linked to transcriptional regulatory elements active in the foetal brain (FB-HARs PRS) and the adult brain (AB-HARs PRS). We have explored the main effects of the PRSs and the PRSs x diagnosis interactions on brain regional cortical thickness (CT) and surface area (SA). The results indicate that a higher FB-HARs PRS is associated with patients' lower SA in the lateral orbitofrontal cortex, the superior temporal cortex, the pars triangularis and the paracentral lobule. While noHARs-derived PRSs show an effect on the risk, our neuroanatomical findings suggest that the human-specific transcriptional regulation during the prenatal period underlies SA variability, highlighting the role of these evolutionary markers in the schizophrenia genomic architecture.
Topics: Adult; Humans; Schizophrenia; Brain; Prefrontal Cortex; Multifactorial Inheritance; Gene Expression Regulation
PubMed: 37833414
DOI: 10.1038/s42003-023-05356-2 -
Nature Genetics Feb 2024Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These...
Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.
Topics: Humans; Transcriptome; Genome-Wide Association Study; Multifactorial Inheritance; Quantitative Trait Loci; Phenotype; Polymorphism, Single Nucleotide; Genetic Predisposition to Disease
PubMed: 38279041
DOI: 10.1038/s41588-023-01648-9 -
PLoS Computational Biology Nov 2023Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic...
Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.
Topics: Genotype; Phenotype; Genome; Biological Evolution; Multifactorial Inheritance
PubMed: 37948461
DOI: 10.1371/journal.pcbi.1011631 -
Biological Psychiatry Jul 2023The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide... (Review)
Review
The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach.
Topics: Humans; Genome-Wide Association Study; Schizophrenia; Brain; Risk Factors; Multifactorial Inheritance; Genetic Predisposition to Disease
PubMed: 36740470
DOI: 10.1016/j.biopsych.2022.10.009 -
ELife Nov 2023Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems... (Review)
Review
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
Topics: Humans; Multifactorial Inheritance; Systems Biology; Phenotype
PubMed: 37962168
DOI: 10.7554/eLife.91004 -
European Heart Journal May 2024It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2.
BACKGROUND AND AIMS
It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2.
METHODS
A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification.
RESULTS
In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC.
CONCLUSIONS
This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.
Topics: Humans; Female; Male; Middle Aged; Risk Assessment; Cardiovascular Diseases; Practice Guidelines as Topic; Aged; United Kingdom; Coronary Artery Disease; Multifactorial Inheritance; Genetic Predisposition to Disease; Risk Factors; Adult
PubMed: 38551411
DOI: 10.1093/eurheartj/ehae048 -
International Journal of Obesity (2005) Jun 2024The genetic architecture of extreme non-syndromic obesity in adults remains to be elucidated. A range of genes are known to cause monogenic obesity, but even when...
BACKGROUND/OBJECTIVE
The genetic architecture of extreme non-syndromic obesity in adults remains to be elucidated. A range of genes are known to cause monogenic obesity, but even when pathogenic mutations are present, there may be variable penetrance.
METHODS
Whole-exome sequencing (WES) was carried out on a 15-year-old male proband of Pakistani ancestry who had severe obesity. This was followed by family segregation analysis, using Sanger sequencing. We also undertook re-analysis of WES data from 91 unrelated adults with severe obesity (86% white European ancestry) from the Personalised Medicine for Morbid Obesity (PMMO) cohort, recruited from the UK National Health Service.
RESULTS
We identified an oligogenic mode of inheritance of obesity in the proband's family-this provided the impetus to reanalyze existing sequence data in a separate dataset. Analysis of PMMO participant data revealed two further patients who carried more than one rare, predicted-deleterious mutation in a known monogenic obesity gene. In all three cases, the genes involved had known autosomal dominant inheritance, with incomplete penetrance.
CONCLUSION
Oligogenic inheritance may explain some of the variable penetrance in Mendelian forms of obesity. We caution clinicians and researchers to avoid confining sequence analysis to individual genes and, in particular, not to stop looking when the first potentially-causative mutation is found.
Topics: Humans; Male; Adolescent; Obesity, Morbid; Adult; Exome Sequencing; Pedigree; Female; Genetic Predisposition to Disease; Mutation; Penetrance; United Kingdom; Pakistan; Multifactorial Inheritance
PubMed: 38297031
DOI: 10.1038/s41366-024-01476-9