-
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 -
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 -
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 -
Scientific Reports Jun 2024Frailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of...
Frailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from a Genome Wide Association Study on the Frailty Index to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples - the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67-84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty, measured via the FI, at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085-0.21) and 1.8% (β = 0.14, 95%CI, 0.10-0.17) of the variance, respectively, at age ~ 68/ ~ 70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~ 68/ ~ 70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.
Topics: Humans; Aged; Frailty; Longitudinal Studies; Aged, 80 and over; Female; Male; Multifactorial Inheritance; Genome-Wide Association Study; Aging; Birth Cohort; Risk Factors; England; Genetic Risk Score
PubMed: 38822050
DOI: 10.1038/s41598-024-63229-y -
PloS One 2024Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant...
BACKGROUND
Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications.
METHODS
We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10-3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs.
RESULTS
By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10-8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10-4.
CONCLUSIONS
Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov.
Topics: Humans; Polymorphism, Single Nucleotide; Kidney Transplantation; Graft Rejection; Female; Male; Middle Aged; Genome-Wide Association Study; Genotype; Adult; HLA Antigens; Multifactorial Inheritance; Risk Factors; Living Donors; Cohort Studies; Genetic Risk Score
PubMed: 38820342
DOI: 10.1371/journal.pone.0303446 -
Genome Medicine May 2024Polygenic prediction studies in continental Africans are scarce. Africa's genetic and environmental diversity pose a challenge that limits the generalizability of...
BACKGROUND
Polygenic prediction studies in continental Africans are scarce. Africa's genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required.
METHODS
Using the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions.
RESULTS
Polygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa's East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status.
CONCLUSIONS
Our findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.
Topics: Humans; Body Mass Index; Multifactorial Inheritance; Genome-Wide Association Study; Africa; Black People; Polymorphism, Single Nucleotide; White People; Genetic Predisposition to Disease; Gene Frequency; Gene-Environment Interaction; Linkage Disequilibrium; Male; Female
PubMed: 38816834
DOI: 10.1186/s13073-024-01348-x -
Scientific Reports May 2024We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and...
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
Topics: Humans; Machine Learning; Blood Pressure; Multifactorial Inheritance; Phenotype; Genome-Wide Association Study; Risk Factors; Male; Female; Genetic Predisposition to Disease; Models, Genetic; Hypertension; Middle Aged; Genetic Risk Score
PubMed: 38816422
DOI: 10.1038/s41598-024-62945-9 -
Translational Psychiatry May 2024Substance use disorder (SUD) is a global health problem with a significant impact on individuals and society. The presentation of SUD is diverse, involving various...
Substance use disorder (SUD) is a global health problem with a significant impact on individuals and society. The presentation of SUD is diverse, involving various substances, ages at onset, comorbid conditions, and disease trajectories. Current treatments for SUD struggle to address this heterogeneity, resulting in high relapse rates. SUD often co-occurs with other psychiatric and mental health-related conditions that contribute to the heterogeneity of the disorder and predispose to adverse disease trajectories. Family and genetic studies highlight the role of genetic and environmental factors in the course of SUD, and point to a shared genetic liability between SUDs and comorbid psychopathology. In this study, we aimed to disentangle SUD heterogeneity using a deeply phenotyped SUD cohort and polygenic scores (PGSs) for psychiatric disorders and related traits. We explored associations between PGSs and various SUD-related phenotypes, as well as PGS-environment interactions using information on lifetime emotional, physical, and/or sexual abuse. Our results identify clusters of individuals who exhibit differences in their phenotypic profile and reveal different patterns of associations between SUD-related phenotypes and the genetic liability for mental health-related traits, which may help explain part of the heterogeneity observed in SUD. In our SUD sample, we found associations linking the genetic liability for attention-deficit hyperactivity disorder (ADHD) with lower educational attainment, the genetic liability for post-traumatic stress disorder (PTSD) with higher rates of unemployment, the genetic liability for educational attainment with lower rates of criminal records and unemployment, and the genetic liability for well-being with lower rates of outpatient treatments and fewer problems related to family and social relationships. We also found evidence of PGS-environment interactions showing that genetic liability for suicide attempts worsened the psychiatric status in SUD individuals with a history of emotional physical and/or sexual abuse. Collectively, these data contribute to a better understanding of the role of genetic liability for mental health-related conditions and adverse life experiences in SUD heterogeneity.
Topics: Humans; Substance-Related Disorders; Multifactorial Inheritance; Male; Female; Adult; Phenotype; Genetic Predisposition to Disease; Middle Aged; Genome-Wide Association Study; Gene-Environment Interaction; Young Adult; Comorbidity; Mental Disorders
PubMed: 38811559
DOI: 10.1038/s41398-024-02923-x -
Nature Communications May 2024Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects....
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
Topics: Multifactorial Inheritance; Humans; Models, Genetic; Genome-Wide Association Study; Polymorphism, Single Nucleotide; Genetic Predisposition to Disease; Autoimmune Diseases; Genes, Dominant; Psoriasis
PubMed: 38811555
DOI: 10.1038/s41467-024-48654-x -
The New Phytologist Jul 2024Understanding the genetic basis of how plants defend against pathogens is important to monitor and maintain resilient tree populations. Swiss needle cast (SNC) and...
Understanding the genetic basis of how plants defend against pathogens is important to monitor and maintain resilient tree populations. Swiss needle cast (SNC) and Rhabdocline needle cast (RNC) epidemics are responsible for major damage of forest ecosystems in North America. Here we investigate the genetic architecture of tolerance and resistance to needle cast diseases in Douglas-fir (Pseudotsuga menziesii) caused by two fungal pathogens: SNC caused by Nothophaeocryptopus gaeumannii, and RNC caused by Rhabdocline pseudotsugae. We performed case-control genome-wide association analyses and found disease resistance and tolerance in Douglas-fir to be polygenic and under strong selection. We show that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection, and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identify a major transcriptional regulator of plant defense, ERF1, as the top candidate for RNC resistance. Our findings shed light on the highly polygenic architectures underlying fungal disease resistance and tolerance and have important implications for forestry and conservation as the climate changes.
Topics: Disease Resistance; Plant Diseases; Pseudotsuga; Genome-Wide Association Study; Ascomycota; Trees; Adaptation, Physiological; Multifactorial Inheritance; Gene Expression Regulation, Plant; Genes, Plant
PubMed: 38803110
DOI: 10.1111/nph.19797