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Journal of Affective Disorders May 2022Understanding the genetic underpinnings of antidepressant treatment response in unipolar major depressive disorder (MDD) can be useful in identifying patients at risk... (Review)
Review
BACKGROUND
Understanding the genetic underpinnings of antidepressant treatment response in unipolar major depressive disorder (MDD) can be useful in identifying patients at risk for poor treatment response or treatment resistant depression. A polygenic risk score (PRS) is a useful tool to explore genetic liability of a complex trait such as antidepressant treatment response. Here, we review studies that use PRSs to examine genetic overlap between any trait and antidepressant treatment response in unipolar MDD.
METHODS
A systematic search of literature was conducted in PubMed, Embase, and PsycINFO. Our search included studies examining associations between PRSs of psychiatric as well as non-psychiatric traits and antidepressant treatment response in patients with unipolar MDD. A quality assessment of the included studies was performed.
RESULTS
In total, eleven articles were included which contained PRSs for 30 traits. Studies varied in sample size and endpoints used for antidepressant treatment response. Overall, PRSs for attention-deficit hyperactivity disorder, the personality trait openness, coronary artery disease, obesity, and stroke have been associated with antidepressant treatment response in patients with unipolar MDD.
LIMITATIONS
The endpoints used by included studies differed significantly, therefore it was not possible to perform a meta-analysis.
CONCLUSIONS
Associations between a PRS and antidepressant treatment response have been reported for a number of traits in patients with unipolar MDD. PRSs could be informative to predict antidepressant treatment response in this population, given advances in the field. Most importantly, there is a need for larger study cohorts and the use of standardized outcome measures.
Topics: Antidepressive Agents; Depression; Depressive Disorder, Major; Humans; Multifactorial Inheritance; Risk Factors
PubMed: 35151671
DOI: 10.1016/j.jad.2022.02.015 -
Molecular Psychiatry Mar 2022Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia...
Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.
Topics: Case-Control Studies; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Schizophrenia; Exome Sequencing
PubMed: 35091668
DOI: 10.1038/s41380-021-01420-7 -
Clinical and Translational Medicine Jan 2022
Meta-Analysis
Topics: Alcohol Drinking; Genetic Loci; Genome-Wide Association Study; Heroin; Humans; Methamphetamine; Multifactorial Inheritance
PubMed: 35075802
DOI: 10.1002/ctm2.659 -
BMC Cancer Jan 2022Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value...
BACKGROUND
Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.
METHODS
We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.
RESULTS
We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.
CONCLUSIONS
Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.
Topics: Adult; Area Under Curve; Asian People; Case-Control Studies; Clinical Decision Rules; Colorectal Neoplasms; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Male; Middle Aged; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Risk Assessment; Risk Factors; White People
PubMed: 35030997
DOI: 10.1186/s12885-021-09143-2 -
Clinical Pharmacology and Therapeutics Apr 2022Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities...
Polygenic scores (PGSs) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGSs for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n = 30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGSs were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g., schizophrenia-derived PGSs for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGSs, with between 3 and 6.6 million variants included in the PGSs. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGSs deposited on the Polygenic Score Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.
Topics: Genome-Wide Association Study; Humans; Multifactorial Inheritance; Pharmacogenetics; Phenotype; Schizophrenia
PubMed: 34953075
DOI: 10.1002/cpt.2520 -
Evolution; International Journal of... Dec 2021An evolutionary model for sex differences in disease risk posits that alleles conferring higher risk in one sex may be protective in the other. These sexually...
An evolutionary model for sex differences in disease risk posits that alleles conferring higher risk in one sex may be protective in the other. These sexually antagonistic (SA) alleles are predicted to be maintained at frequencies higher than expected under purifying selection against unconditionally deleterious alleles, but there are apparently no examples in humans. Discipline-specific terminology, rather than a genuine lack of such alleles, could explain this disparity. We undertook a two-stage review of evidence for SA polymorphisms in humans using search terms from (i) evolutionary biology and (ii) biomedicine. Although the first stage returned no eligible studies, the second revealed 51 genes with sex-opposite effects; 22 increased disease risk or severity in one sex but protected the other. Those with net positive effects occurred at higher frequencies. None were referred to as SA. Our review reveals significant communication barriers to fields as a result of discipline-specific terminology.
Topics: Alleles; Biological Evolution; Female; Humans; Male; Multifactorial Inheritance; Polymorphism, Genetic; Selection, Genetic
PubMed: 34723381
DOI: 10.1111/evo.14394 -
Frontiers in Psychiatry 2021Schizophrenia is a destructive neuropsychiatric disease with a median prevalence of 4.0 per 1,000 during the whole life. Genome-wide association studies have shown the...
Schizophrenia is a destructive neuropsychiatric disease with a median prevalence of 4.0 per 1,000 during the whole life. Genome-wide association studies have shown the role of copy number variants (generally deletions) and certain alleles of common single nucleotide polymorphisms in the pathogenesis of schizophrenia. This disorder predominantly follows the polygenic inheritance model. Schizophrenia has also been linked with various alterations in the transcript and protein content of the brain tissue. Recent studies indicate that alterations in non-coding RNAs (ncRNAs) signature underlie a proportion of this dysregulation. High throughput microarray investigations have demonstrated momentous alterations in the expression of long non-coding RNAs (lncRNA) and microRNAs (miRNAs) in the circulation or post-mortem brain tissues of patients with schizophrenia compared with control samples. While Gomafu, PINT, GAS5, TCONS_l2_00021339, IFNG-AS1, FAS-AS1, PVT1, and TUG1 are among down-regulated lncRNAs in schizophrenia, MEG3, THRIL, HOXA-AS2, Linc-ROR, SPRY4-IT1, UCA1, and MALAT1 have been up-regulated in these patients. Moreover, several miRNAs, such as miR-30e, miR-130b, hsa-miR-130b, miR-193a-3p, hsa-miR-193a-3p, hsa-miR-181b, hsa-miR-34a, hsa-miR-346, and hsa-miR-7 have been shown to be dysregulated in blood or brain samples of patients with schizophrenia. Dysregulation of these transcripts in schizophrenia not only provides insight into the pathogenic processes of this disorder, it also suggests these transcripts could serve as diagnostic markers for schizophrenia. In the present paper, we explore the changes in the expression of miRNAs and lncRNAs in patients with schizophrenia.
PubMed: 34220567
DOI: 10.3389/fpsyt.2021.640463 -
Biological Reviews of the Cambridge... Apr 2021Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the...
Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or 'multi-level convergent evolution'. To investigate multi-level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long-standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi-level convergent evolution.
Topics: Biological Evolution; Multifactorial Inheritance; Phenotype
PubMed: 33306257
DOI: 10.1111/brv.12672 -
NeuroImage. Clinical 2020Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to... (Review)
Review
Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer's Disease. This paper aims to systematically review studies that examined the effect of Alzheimer's risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer's Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer's disease.
Topics: Alzheimer Disease; Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; White Matter
PubMed: 32758801
DOI: 10.1016/j.nicl.2020.102359 -
Journal of Alzheimer's Disease : JAD 2020Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are...
BACKGROUND
Late-onset Alzheimer's disease (AD) is highly heritable. The effect of many common genetic variants, single nucleotide polymorphisms (SNPs), confer risk. Variants are clustered in areas of biology, notably immunity and inflammation, cholesterol metabolism, endocytosis, and ubiquitination. Polygenic scores (PRS), which weight the sum of an individual's risk alleles, have been used to draw inferences about the pathological processes underpinning AD.
OBJECTIVE
This paper aims to systematically review how AD PRS are being used to study a range of outcomes and phenotypes related to neurodegeneration.
METHODS
We searched the literature from July 2008-July 2018 following PRISMA guidelines.
RESULTS
57 studies met criteria. The AD PRS can distinguish AD cases from controls. The ability of AD PRS to predict conversion from mild cognitive impairment (MCI) to AD was less clear. There was strong evidence of association between AD PRS and cognitive impairment. AD PRS were correlated with a number of biological phenotypes associated with AD pathology, such as neuroimaging changes and amyloid and tau measures. Pathway-specific polygenic scores were also associated with AD-related biologically relevant phenotypes.
CONCLUSION
PRS can predict AD effectively and are associated with cognitive impairment. There is also evidence of association between AD PRS and other phenotypes relevant to neurodegeneration. The associations between pathway specific polygenic scores and phenotypic changes may allow us to define the biology of the disease in individuals and indicate who may benefit from specific treatments. Longitudinal cohort studies are required to test the ability of PGS to delineate pathway-specific disease activity.
Topics: Alzheimer Disease; Genetic Predisposition to Disease; Genetic Testing; Humans; Multifactorial Inheritance; Precision Medicine
PubMed: 32250305
DOI: 10.3233/JAD-191233