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Nature Communications Jun 2024Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods...
Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.
Topics: Humans; Coronary Artery Disease; Male; Female; Middle Aged; Electronic Health Records; Aged; Risk Assessment; Risk Factors; Adult; Genetic Predisposition to Disease; Hydroxymethylglutaryl-CoA Reductase Inhibitors; United Kingdom; Longitudinal Studies; Multifactorial Inheritance
PubMed: 38849421
DOI: 10.1038/s41467-024-49296-9 -
Scientific Reports Jun 2024Speech-in-noise (SIN) perception is a primary complaint of individuals with audiometric hearing loss. SIN performance varies drastically, even among individuals with...
Speech-in-noise (SIN) perception is a primary complaint of individuals with audiometric hearing loss. SIN performance varies drastically, even among individuals with normal hearing. The present genome-wide association study (GWAS) investigated the genetic basis of SIN deficits in individuals with self-reported normal hearing in quiet situations. GWAS was performed on 279,911 individuals from the UB Biobank cohort, with 58,847 reporting SIN deficits despite reporting normal hearing in quiet. GWAS identified 996 single nucleotide polymorphisms (SNPs), achieving significance (p < 5*10) across four genomic loci. 720 SNPs across 21 loci achieved suggestive significance (p < 10). GWAS signals were enriched in brain tissues, such as the anterior cingulate cortex, dorsolateral prefrontal cortex, entorhinal cortex, frontal cortex, hippocampus, and inferior temporal cortex. Cochlear cell types revealed no significant association with SIN deficits. SIN deficits were associated with various health traits, including neuropsychiatric, sensory, cognitive, metabolic, cardiovascular, and inflammatory conditions. A replication analysis was conducted on 242 healthy young adults. Self-reported speech perception, hearing thresholds (0.25-16 kHz), and distortion product otoacoustic emissions (1-16 kHz) were utilized for the replication analysis. 73 SNPs were replicated with a self-reported speech perception measure. 211 SNPs were replicated with at least one and 66 with at least two audiological measures. 12 SNPs near or within MAPT, GRM3, and HLA-DQA1 were replicated for all audiological measures. The present study highlighted a polygenic architecture underlying SIN deficits in individuals with self-reported normal hearing.
Topics: Humans; Genome-Wide Association Study; Polymorphism, Single Nucleotide; Male; Female; Speech Perception; Multifactorial Inheritance; Adult; Noise; Middle Aged; Self Report; Aged; Hearing; Young Adult
PubMed: 38849415
DOI: 10.1038/s41598-024-63972-2 -
Nature Communications Jun 2024Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color...
Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
Topics: Adult; Female; Humans; Male; Middle Aged; Adaptation, Physiological; Chromosome Mapping; Genome-Wide Association Study; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Skin Pigmentation; Ultraviolet Rays; East Asian People
PubMed: 38849341
DOI: 10.1038/s41467-024-49031-4 -
Scientific Reports Jun 2024The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC)...
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
Topics: Humans; Macular Degeneration; Israel; Genome-Wide Association Study; Female; Male; Genetic Predisposition to Disease; Aged; Polymorphism, Single Nucleotide; Risk Factors; Middle Aged; Case-Control Studies; Aged, 80 and over; Multifactorial Inheritance; Jews; Genotype
PubMed: 38844476
DOI: 10.1038/s41598-024-63065-0 -
Neurology Jun 2024The World Health Organization recently released a novel metric for healthy aging: intrinsic capacity (IC). The relationship between IC and the incidence of dementia, and...
BACKGROUND AND OBJECTIVES
The World Health Organization recently released a novel metric for healthy aging: intrinsic capacity (IC). The relationship between IC and the incidence of dementia, and its subtypes, is unknown. We aimed to analyze the relationship between IC and the incidence of dementia and its subtypes. Moreover, we tested whether genetic susceptibility to dementia could be modified by IC.
METHODS
This cohort study involved 366,406 participants from the UK Biobank between 2006 and 2010. We analyzed 7 factors that reflected functional status across 4 IC domains to compute a comprehensive IC deficit score. Cox models were used to elucidate the relationship between the IC deficit score and the incidence of dementia.
RESULTS
Among the 366,406 participants, 5,207 cases of dementia were documented, encompassing 2,186 and 1,175 cases of Alzheimer disease (AD) and vascular dementia (VD), respectively. Compared with participants with an IC score of 0, individuals with an IC score of 4+ had a markedly elevated risk of dementia (hazard ratio [HR] 2.17, 95% CI 1.92-2.45). In the joint analysis, for participants with a high polygenic risk score (PRS) and an IC score of 4 or more, the HR of all-cause dementia was 8.11 (95% CI 6.28-10.47) compared with individuals with a low PRS and an IC score of 0. Similar results were seen in the AD and VD groups.
DISCUSSION
In summary, IC is associated with a higher risk of dementia, particularly in those combined with genetically predisposed to dementia.
Topics: Humans; Female; Male; United Kingdom; Aged; Apolipoproteins E; Biological Specimen Banks; Multifactorial Inheritance; Middle Aged; Dementia; Prospective Studies; Genotype; Genetic Predisposition to Disease; Alzheimer Disease; Cohort Studies; Incidence; Risk Factors; Healthy Aging; Dementia, Vascular; Genetic Risk Score; UK Biobank
PubMed: 38843484
DOI: 10.1212/WNL.0000000000209452 -
Briefings in Bioinformatics May 2024In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the...
In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.
Topics: Humans; Precision Medicine; Alzheimer Disease; Disease-Free Survival; Machine Learning; Proportional Hazards Models; Multifactorial Inheritance; Male; Female; Multiomics
PubMed: 38836403
DOI: 10.1093/bib/bbae267 -
Nature Genetics Jun 2024While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological...
While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.
Topics: Humans; Genome-Wide Association Study; Schizophrenia; Linkage Disequilibrium; Multifactorial Inheritance; Models, Genetic; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Genetic Predisposition to Disease; Chromosome Mapping; Computer Simulation; Quantitative Trait, Heritable
PubMed: 38831010
DOI: 10.1038/s41588-024-01771-1 -
Translational Psychiatry Jun 2024There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major...
There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.
Topics: Humans; Male; Female; Adult; Magnetic Resonance Imaging; Bipolar Disorder; Depressive Disorder, Major; Schizophrenia; Psychotic Disorders; Gray Matter; Middle Aged; Genome-Wide Association Study; Factor Analysis, Statistical; Brain; Psychopathology; Multifactorial Inheritance; Cerebral Cortex
PubMed: 38830892
DOI: 10.1038/s41398-024-02936-6 -
Biodemography and Social Biology 2024Polygenic scores (PGS) are broadly misconstrued as reflecting direct causal genetic effects on their respective phenotypes. While this assumption might be accurate for...
Polygenic scores (PGS) are broadly misconstrued as reflecting direct causal genetic effects on their respective phenotypes. While this assumption might be accurate for some anthropometric traits like height, more complex traits such as educational attainment show very large indirect effects that stem from many sources. One unexplored source of confounding is the possibility of evocative gene-environment correlation (rGE). Using data from the National Longitudinal Study of Adolescent to Adult Health, we examine the relationship between interviewer assessments of respondent appearance as a function of education PGS. We show a bivariate association between educational PGS and 1) perceived grooming, 2) physical attractiveness, and 3) personality. We then regress years of education on the educational PGS and show that very little of the association (~1-2%) is mediated by attractiveness or personality but 7.5% of the baseline association is confounded with how others may perceive grooming. These results highlight the importance of social-behavioral mechanisms that may link specific genotypes to successful transitions through high school and college and continue to bridge research from the social and biological sciences.
Topics: Humans; Female; Male; Multifactorial Inheritance; Longitudinal Studies; Educational Status; Adolescent; Adult; Personality; Young Adult; Gene-Environment Interaction; Phenotype
PubMed: 38828740
DOI: 10.1080/19485565.2024.2355891 -
The Journal of Clinical Investigation Jun 2024Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional... (Review)
Review
Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.
Topics: Humans; Substance-Related Disorders; Genome-Wide Association Study; Genetic Predisposition to Disease; Multifactorial Inheritance
PubMed: 38828723
DOI: 10.1172/JCI172881