-
Current Psychiatry Reports Feb 2020Attention deficit hyperactivity disorder (ADHD) shows high heritability in formal genetic studies. In our review article, we provide an overview on common and rare... (Review)
Review
PURPOSE OF REVIEW
Attention deficit hyperactivity disorder (ADHD) shows high heritability in formal genetic studies. In our review article, we provide an overview on common and rare genetic risk variants for ADHD and their link to clinical practice.
RECENT FINDINGS
The formal heritability of ADHD is about 80% and therefore higher than most other psychiatric diseases. However, recent studies estimate the proportion of heritability based on singlenucleotide variants (SNPs) at 22%. It is a matter of debate which genetic mechanisms explain this huge difference. While frequent variants in first mega-analyses of genome-wideassociation study data containing several thousand patients give the first genome-wide results, explaining only little variance, the methodologically more difficult analyses of rare variants are still in their infancy. Some rare genetic syndromes show higher prevalence for ADHD indicating a potential role for a small number of patients. In contrast, polygenic risk scores (PRS) could potentially be applied to every patient. We give an overview how PRS explain different behavioral phenotypes in ADHD and how they could be used for diagnosis and therapy prediction. Knowledge about a patient's genetic makeup is not yet mandatory for ADHD therapy or diagnosis. PRS however have been introduced successfully in other areas of clinical medicine, and their application in psychiatry will begin within the next years. In order to ensure competent advice for patients, knowledge of the current state of research is useful forpsychiatrists.
Topics: Attention Deficit Disorder with Hyperactivity; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 32108282
DOI: 10.1007/s11920-020-1141-x -
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 -
Proceedings of the National Academy of... Aug 2020The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's...
The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.
Topics: Genetic Variation; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Phenotype; Quantitative Trait, Heritable
PubMed: 32753378
DOI: 10.1073/pnas.2005634117 -
Journal of the American College of... Jul 2019
Topics: Humans; Hypercholesterolemia; Hyperlipoproteinemia Type II; Multifactorial Inheritance
PubMed: 31345426
DOI: 10.1016/j.jacc.2019.06.006 -
European Journal of Human Genetics :... Jan 2021Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic...
Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.
Topics: Biomarkers; Body Composition; Cytochrome P-450 Enzyme System; Female; Glucuronosyltransferase; Humans; Male; Mendelian Randomization Analysis; Multifactorial Inheritance; Sex Characteristics; Testosterone
PubMed: 32873964
DOI: 10.1038/s41431-020-00712-w -
Biological Psychiatry Jan 2023Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging...
BACKGROUND
Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits.
METHODS
Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects).
RESULTS
Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease.
CONCLUSIONS
Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.
Topics: Humans; Genetic Heterogeneity; Genetic Predisposition to Disease; Multifactorial Inheritance; Brain; DNA Copy Number Variations; Psychiatry; Genome-Wide Association Study
PubMed: 36372570
DOI: 10.1016/j.biopsych.2022.08.024 -
The Lancet. Psychiatry Oct 2022Private companies have begun offering services to allow parents undergoing in-vitro fertilisation to screen embryos for genetic risk of complex diseases, including... (Review)
Review
Private companies have begun offering services to allow parents undergoing in-vitro fertilisation to screen embryos for genetic risk of complex diseases, including psychiatric disorders. This procedure, called polygenic embryo screening, raises several difficult scientific and ethical issues, as discussed in this Personal View. Polygenic embryo screening depends on the statistical properties of polygenic risk scores, which are complex and not well studied in the context of this proposed clinical application. The clinical, social, and ethical implications of polygenic embryo screening have barely been discussed among relevant stakeholders. To our knowledge, the International Society of Psychiatric Genetics is the first professional biomedical organisation to issue a statement regarding polygenic embryo screening. For the reasons discussed in this Personal View, the Society urges caution and calls for additional research and oversight on the use of polygenic embryo screening.
Topics: Cognition; Humans; Mental Disorders; Multifactorial Inheritance; Phenotype; Risk Factors
PubMed: 35931093
DOI: 10.1016/S2215-0366(22)00157-2 -
PLoS Genetics May 2021The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study...
The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.
Topics: Computer Simulation; Datasets as Topic; Genome-Wide Association Study; Genotype; Humans; Models, Genetic; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Precision Medicine; Reproducibility of Results; Twin Studies as Topic; Twins; United Kingdom
PubMed: 33945532
DOI: 10.1371/journal.pgen.1009021 -
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 -
Behavior Genetics May 2021Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in...
Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents' nontransmitted alleles, one can estimate the effect of "genetic nurture"-a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to (a) account for the influence of different types of assortative mating, (b) estimate the total variation due to additive genetic effects and their covariance with the familial environment (i.e., the full genetic nurture effect), and (c) model situations where a parental trait influences a different offspring trait. By utilizing structural equation modeling techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.
Topics: Alleles; Gene-Environment Interaction; Genotype; Humans; Maternal Inheritance; Models, Genetic; Models, Theoretical; Multifactorial Inheritance; Parent-Child Relations; Parents; Paternal Inheritance; Phenotype; Statistics as Topic; Twins
PubMed: 33387133
DOI: 10.1007/s10519-020-10032-w