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Introduction: Research has yet to examine the interplay between indices of environmental risk and resilience processes and genetic predisposition for epigenetic aging in predicting early adolescent depressive symptoms. In the current study we examine whether adverse life events and parental acceptance moderate polygenic predisposition for GrimAge epigenetic aging in predicting trajectories of depressive symptoms across early adolescence. Method: Using data from the Adolescent Brain Development Study (ABCD, N = 11,875), we created polygenic scores for GrimAge, and examined whether exposure to adverse life events and parental acceptance moderated the relation between genetic risk and depressive symptom trajectories from age 10/11 to 12/13 using growth mixture modelling. We examined models separately in European American (EA), African American (AA), and Latinx (LX) subgroups of ABCD. Results: In the EA and AA subgroups, adverse life events moderated polygenic scores for GrimAge such that there was increased likelihood of membership in a higher vs. lower depression trajectory. Discussion: We extend literature by identifying genetic contributions to epigenetic aging as a depression diathesis in adolescence. Findings also highlight the detrimental role of adverse life events in exacerbating genetic risk for the development of depression in adolescence. [ABSTRACT FROM AUTHOR]

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is one of the most destructive fungal diseases threatening global wheat production. Exploring powdery mildew resistance (Pm) gene(s) and dissecting the molecular mechanism of the host resistance are critical to effectively and reasonably control this disease. Durum wheat (Triticum turgidum L. var. durum Desf.) is an important gene donor for wheat improvement against powdery mildew. In this study, a resistant durum wheat accession W762 was used to investigate its potential resistance component(s) and profile its expression pattern in responding to Bgt invasion using bulked segregant RNA-Seq (BSR-Seq) and further qRT-PCR verification. Genetic analysis showed that the powdery mildew resistance in W762 did not meet monogenic inheritance and complex genetic model might exist within the population of W762 × Langdon (susceptible durum wheat). After BSR-Seq, 6,196 consistently different single nucleotide polymorphisms (SNPs) were called between resistant and susceptible parents and bulks, and among them, 763 SNPs were assigned to the chromosome arm 7B. Subsequently, 3,653 differentially expressed genes (DEGs) between resistant and susceptible parents and bulkswere annotated and analyzed by GeneOntology (GO), Cluster of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The potential regulated genes were selected and analyzed their temporal expression patterns following Bgt inoculation. As a result, nine disease-related genes showed distinctive expression profile after Bgt invasion and might serve as potential targets to regulate the resistance against powdery mildew in W762. Our study could lay a foundation for analysis of the molecular mechanism and also provide potential targets for the improvement of durable resistance against powdery mildew. [ABSTRACT FROM AUTHOR]

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches--linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results. [ABSTRACT FROM AUTHOR]

Introduction: Rare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer susceptibility for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD susceptibility, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability. Methods: In a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability. Results: Bottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that susceptibility for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD. Discussion: Our findings suggest that cognitive ability itself may not be the factor driving the underlying liability for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits. [ABSTRACT FROM AUTHOR]

Introduction: T cells, known for their ability to respond to an enormous variety of pathogens and other insults, are increasingly recognized as important mediators of pathology in neurodegeneration and other diseases. T cell gene expression phenotypes can be regulated by disease-associated genetic variants. Many complex diseases are better represented by polygenic risk than by individual variants. Methods: We first compute a polygenic risk score (PRS) for Alzheimer's disease (AD) using genomic sequencing data from a cohort of Alzheimer's disease (AD) patients and age-matched controls, and validate the AD PRS against clinical metrics in our cohort. We then calculate the PRS for several autoimmune disease, neurological disorder, and immune function traits, and correlate these PRSswith T cell gene expression data from our cohort. We compare PRS-associated genes across traits and four T cell subtypes. Results: Several genes and biological pathways associated with the PRS for these traits relate to key T cell functions. The PRS-associated gene signature generally correlates positively for traits within a particular category (autoimmune disease, neurological disease, immune function) with the exception of stroke. The traitassociated gene expression signature for autoimmune disease traits was polarized towards CD4+ T cell subtypes. Discussion: Our findings show that polygenic risk for complex disease and immune function traits can have varying effects on T cell gene expression trends. Several PRS-associated genes are potential candidates for therapeutic modulation in T cells, and could be tested in in vitro applications using cells from patients bearing high or low polygenic risk for AD or other conditions. [ABSTRACT FROM AUTHOR]

Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia (SCZ) is a heritable disorder with a polygenic architecture, and the gut microbiota seems to be involved in its development and outcome. In this study, we investigate the interplay between genetic risk and gut microbial markers. Methods: We included 159 first-episode, drug-naïve SCZ patients and 86 healthy controls. The microbial composition of feces was characterized using the 16S rRNA sequencing platform, and five microbial a-diversity indices were estimated [Shannon, Simpson, Chao1, the Abundance-based Eoverage Estimator (ACE), and a phylogenetic diversity-based estimate (PD)]. Polygenic risk scores (PRS) for SCZ were constructed using data from large-scale genome-wide association studies. Effects of microbial α-diversity, microbial abundance, and PRS on SCZ were evaluated via generalized linear models. Results: We confirmed that PRS was associated with SCZ (OR = 2.08, p = 1.22×10-5) and that scores on the Shannon (OR = 0.29, p = 1.15×10-8) and Simpson (OR = 0.29, p = 1.25×10-8) indices were inversely associated with SCZ risk. We found significant interactions (p < 0.05) between PRS and a-diversity indices (Shannon, Simpson, and PD), with the effects of PRS being larger in those exhibiting higher diversity compared to those with lower diversity. Moreover, the PRS effects were larger in individuals with a high abundance of the genera Romboutsia, Streptococcus, and Anaerostipes than in those with low abundance (p < 0.05). All three of these genera showed protective effects against SCZ. Conclusion: The current findings suggest an interplay between the gut microbiota and polygenic risk of SCZ that warrants replication in independent samples. Experimental studies are needed to determine the underpinning mechanisms. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is the second most common neurodegenerative disease with currently no cure. Most PD cases are sporadic, and about 5–10% of PD cases present a monogenic inheritance pattern. Mutations in more than 20 genes are associated with genetic forms of PD. Mitochondrial dysfunction is considered a prominent player in PD pathogenesis. Post-translational modifications (PTMs) allow rapid switching of protein functions and therefore impact various cellular functions including those related to mitochondria. Among the PD-associated genes, Parkin, PINK1, and LRRK2 encode enzymes that directly involved in catalyzing PTM modifications of target proteins, while others like a-synuclein, FBXO7, HTRA2, VPS35, CHCHD2, and DJ-1, undergo substantial PTM modification, subsequently altering mitochondrial functions. Here, we summarize recent findings on major PTMs associated with PDrelated proteins, as enzymes or substrates, that are shown to regulate important mitochondrial functions and discuss their involvement in PD pathogenesis. We will further highlight the significance of PTM-regulated mitochondrial functions in understanding PD etiology. Furthermore, we emphasize the potential for developing important biomarkers for PD through extensive research into PTMs. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a complex genetic architecture, showing monogenic, oligogenic, and polygenic inheritance. In this study, we describe the case of a 71 years-old man diagnosed with ALS with atypical clinical features consisting in progressive ocular ptosis and sensorineural deafness. Genetic analyses revealed two heterozygous variants, in the SOD1 (OMIM*147450) and the TBK1 (OMIM*604834) genes respectively, and furthermore mitochondrial DNA (mtDNA) sequencing identified the homoplasmic m.14484T>C variant usually associated with Leber’s Hereditary Optic Neuropathy (LHON). We discuss how all these variants may synergically impinge on mitochondrial function, possibly contributing to the pathogenic mechanisms which might ultimately lead to the neurodegenerative process, shaping the clinical ALS phenotype enriched by adjunctive clinical features. [ABSTRACT FROM AUTHOR]

Introduction: Porcine congenital splay leg syndrome (PCS) is a major birth defect in piglets, resulting in lameness and high mortality rates. The multifactorial pathogenesis of PSC is not well understood but includes a polygenic inheritance. Methods: Here, in addition to morphological investigations, we characterized the expression of myogenic genes and functional (proliferation and differentiation) properties of myogenic precursor/satellite cells (SATCs) in 1 day-old PCS piglets, non-affected littermates (LCs), and piglets from PCS-free healthy litters (HCs). In addition, PCS phenotypes were related to the SNP Homer1_rs325197091 within the Homer1 locus, which has been identified as a potential hereditary cause of PCS. Results and discussion: Samples from musculus semitendinosus (ST) of PCS piglets had a higher proportion of type II fibers, reflecting myofiber immaturity. In addition, myofiber atrophy, a lower number of myonuclei per fiber (ST), and a higher apoptotic activity (in ST and longissimus dorsi muscle; LD) were found in the PCS group. A higher proportion of cycling committed myoblasts (Pax7+/Ki67+ cells) occurred in samples from PCS-affected piglets, and on the other hand, the mRNA expression of genes involved in differentiation (muscle differentiation 1; MyoD, myogenin; MyoG) was repressed compared with HCs. Cultured SATCs from PCS-affected animals showed a temporal shift in peak expression of Pax7, MyoD, and MyoG toward days 3 and 4 of their 7 days differentiation regime. In vitro experiments with isolated SATCs confirmed the lower differentiation potential and the delayed progression of the myogenic processes in cells from piglets with PCS phenotype. In addition, Pax7 and desmin were differently expressed in Homer1_ rs325197091 genotype variants (GG, GA, and AA). Both genes showed the lowest expression in the homozygous AA-variant, which was most frequently found in PCS-affected animals. The homozygous AA-variant was also associated with lower expression of the truncated Homer1-subtype 205. Thus, we hypothesize that in PCS, the balance between Homer1 proteins and its signaling functions is changed in a way detrimental to the myogenic differentiation program. Our results demonstrated direct negative effects of the Homer1 AA genotype on Pax7 expression, but the exact mode of action still needs to be elucidated. [ABSTRACT FROM AUTHOR]

The adrenal glands are small endocrine glands located on top of each kidney, producing hormones regulating important functions in our body like metabolism and stress. There are several underlying causes for adrenal insufficiency, where an autoimmune attack by the immune system is the most common cause. A number of genes are known to confer early onset adrenal disease in monogenic inheritance patterns, usually genetic encoding enzymes of adrenal steroidogenesis. Autoimmune primary adrenal insufficiency is usually a polygenic disease where our information recently has increased due to genome association studies. In this review, we go through the physiology of the adrenals before explaining the different reasons for adrenal insufficiency with a particular focus on autoimmune primary adrenal insufficiency. We will give a clinical overview including diagnosis and current treatment, before giving an overview of the genetic causes including monogenetic reasons for adrenal insufficiency and the polygenic background and inheritance pattern in autoimmune adrenal insufficiency. We will then look at the autoimmune mechanisms underlying autoimmune adrenal insufficiency and how autoantibodies are important for diagnosis. We end with a discussion on how to move the field forward emphasizing on the clinical workup, early identification, and potential targeted treatment of autoimmune PAI. [ABSTRACT FROM AUTHOR]

Introduction: The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D. Methods: Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method. Results: Both RFS (OR: 1.11; 95% CI: 1.03-1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D. Conclusion: A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D. [ABSTRACT FROM AUTHOR]

Introduction: Spontaneous rupture of tendons and ligaments is common in several species including humans. In horses, degenerative suspensory ligament desmitis (DSLD) is an important acquired idiopathic disease of a major energy-storing tendon-like structure. DSLD risk is increased in several breeds, including the Peruvian Horse. Affected horses have often been used for breeding before the disease is apparent. Breed predisposition suggests a substantial genetic contribution, but heritability and genetic architecture of DSLD have not been determined. Methods: To identify genomic regions associated with DSLD, we recruited a reference population of 183 Peruvian Horses, phenotyped as DSLD cases or controls, and undertook a genome-wide association study (GWAS), a regional window variance analysis using local genomic partitioning, a signatures of selection (SOS) analysis, and polygenic risk score (PRS) prediction of DSLD risk. We also estimated trait heritability from pedigrees. Results: Heritability was estimated in a population of 1,927 Peruvian horses at 0.22 ± 0.08. After establishing a permutation-based threshold for genome-wide significance, 151 DSLD risk single nucleotide polymorphisms (SNPs) were identified by GWAS. Multiple regions of enriched local heritability were identified across the genome, with strong enrichment signals on chromosomes 1, 2, 6, 10, 13, 16, 18, 22, and the X chromosome. With SOS analysis, there were 66 genes with a selection signature in DSLD cases that was not present in the control group that included the TGFB3 gene. Pathways enriched in DSLD cases included proteoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways that included the hedgehog signaling pathway. The best PRS predictive performance was obtained when we fitted 1% of top SNPs using a Bayesian Ridge Regression model which achieved the highest mean of R² on both the probit and logit liability scales, indicating a strong predictive performance. Discussion: We conclude that within-breed GWAS of DSLD in the Peruvian Horse has further confirmed that moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations in novel tendinopathy candidate genes influencing disease risk. Pathways enriched with DSLD risk variants include ones that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, signal transduction pathways. [ABSTRACT FROM AUTHOR]

Background: Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Methods: Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. Results: In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. Conclusion: We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence suggests a potential link between psychological distress (anxiety and depression) and lung cancer risk, however, it is unclear whether other factors such as tobacco smoking and genetic susceptibility modify the association. Methods: We included 405,892 UK Biobank participants free of cancer at baseline. Psychological distress was measured using the Patient Health Questionnaire-4 (PHQ-4). A polygenic risk score (PRS) was calculated using 18 lung cancer-associated genetic loci. Multivariable Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During a median follow-up of 7.13 years, 1754 lung cancer cases were documented. The higher score of psychological distress was associated with an increased risk of lung cancer (HRper 1-SD= 1.07, 95% CI: 1.02-1.11) after adjustment for smoking and other confounders. Mediation analysis revealed that 16.8% (95% CI: 13.0%-20.6%) of the distress-lung cancer association was mediated by smoking. Compared with never smokers with no distress, participants with heavy smoking and high distress had the highest risk of lung cancer (HR=18.57, 95% CI: 14.51-23.76). Both multiplicative and additive interactions were observed between smoking and psychological distress in lung cancer. Furthermore, the greatest relative increase in risk was observed among those with high genetic risk and high distress (HR=1.87, 95%CI: 1.50-2.33), and there was a significant additive interaction between the PRS and psychological distress. Conclusion: Our results indicate that psychological distress was associated with an elevated risk of incident lung cancer, and such relation was modified by tobacco smoking and genetic susceptibility. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy. [ABSTRACT FROM AUTHOR]

Pharmacogenomics (PGx) aims at tailoring drug therapy by considering patient genetic makeup. While drug dosage guidelines have been extensively based on single gene mutations (single nucleotide polymorphisms) over the last decade, polygenic risk scores (PRS) have emerged in the past years as a promising tool to account for the complex interplay and polygenic nature of patients' genetic predisposition affecting drug response. Even though PRS research has demonstrated convincing evidence in disease risk prediction, the clinical utility and its implementation in daily care has yet to be demonstrated, and pharmacogenomics is no exception; usual endpoints include drug efficacy or toxicity. Here, we review the general pipeline in PRS calculation, and we discuss some of the remaining barriers and challenges that must be undertaken to bring PRS research in PGx closer to patient care. Besides the need in following reporting guidelines and larger PGx patient cohorts, PRS integration will require close collaboration between bioinformatician, treating physicians and genetic consultants to ensure a transparent, generalizable, and trustful implementation of PRS results in real-world medical decisions. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is adults' most common and fatally malignant brain tumor. The heterogeneity is the leading cause of treatment failure. However, the relationship between cellular heterogeneity, tumor microenvironment, and GBM progression is still elusive. Methods: Integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) of GBM were conducted to analyze the spatial tumor microenvironment. We investigated the subpopulation heterogeneity of malignant cells through gene set enrichment analyses, cell communications analyses, and pseudotime analyses. Significantly changed genes of the pseudotime analysis were screened to create a tumor progress-related gene risk score (TPRGRS) using Cox regression algorithms in the bulkRNA-sequencing(bulkRNA-seq) dataset. We combined the TPRGRS and clinical characteristics to predict the prognosis of patients with GBM. Furthermore, functional analysis was applied to uncover the underlying mechanisms of the TPRGRS. Results: GBM cells were accurately charted to their spatial locations and uncovered their spatial colocalization. The malignant cells were divided into five clusters with transcriptional and functional heterogeneity, including unclassified malignant cells and astrocyte-like, mesenchymal-like, oligodendrocytes-progenitor-like, and neural-progenitor-like malignant cells. Cell-cell communications analysis in scRNA-seq and stRNA-seq identified ligand-receptor pairs of the CXCL, EGF, FGF, and MIF signaling pathways as bridges implying that tumor microenvironment may cause malignant cells' transcriptomic adaptability and disease progression. Pseudotime analysis showed the differentiation trajectory of GBM cells from proneural to mesenchymal transition and identified genes or pathways that affect cell differentiation. TPRGRS could successfully divide patients with GBM in three datasets into high- and low-risk groups, which was proved to be a prognostic factor independent of routine clinicopathological characteristics. Functional analysis revealed the TPRGRS associated with growth factor binding, cytokine activity, signaling receptor activator activity functions, and oncogenic pathways. Further analysis revealed the association of the TPRGRS with gene mutations and immunity in GBM. Finally, the external datasets and qRT-PCR verified high expressions of the TPRGRS mRNAs in GBM cells. Conclusion: Our study provides novel insights into heterogeneity in GBM based on scRNA-seq and stRNA-seq data. Moreover, our study proposed a malignant cell transition-based TPRGRS through integrated analysis of bulkRNA-seq and scRNA-seq data, combined with the routine clinicopathological evaluation of tumors, which may provide more personalized drug regimens for GBM patients. [ABSTRACT FROM AUTHOR]

Intraocular pressure (IOP) is the only modifiable risk factor for glaucoma, the leading cause of irreversible blindness worldwide. In this review, we summarize the findings of genome-wide association studies (GWASs) of IOP published in the past 10 years and prior to December 2022. Over 190 genetic loci and candidate genes associated with IOP have been uncovered through GWASs, although most of these studies were conducted in subjects of European and Asian ancestries. We also discuss how these common variants have been used to derive polygenic risk scores for predicting IOP and glaucoma, and to infer causal relationship with other traits and conditions through Mendelian randomization. Additionally, we summarize the findings from a recent large-scale exome-wide association study (ExWAS) that identified rare variants associated with IOP in 40 novel genes, six of which are drug targets for clinical treatment or are being evaluated in clinical trials. Finally, we discuss the need for future genetic studies of IOP to include individuals from understudied populations, including Latinos and Africans, in order to fully characterize the genetic architecture of IOP. [ABSTRACT FROM AUTHOR]

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R2 value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRSbased prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity. [ABSTRACT FROM AUTHOR]

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of postsurgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors. [ABSTRACT FROM AUTHOR]

Intraocular pressure (IOP) is the only modifiable risk factor for glaucoma, the leading cause of irreversible blindness worldwide. In this review, we summarize the findings of genome-wide association studies (GWASs) of IOP published in the past 10 years and prior to December 2022. Over 190 genetic loci and candidate genes associated with IOP have been uncovered through GWASs, although most of these studies were conducted in subjects of European and Asian ancestries. We also discuss how these common variants have been used to derive polygenic risk scores for predicting IOP and glaucoma, and to infer causal relationship with other traits and conditions through Mendelian randomization. Additionally, we summarize the findings from a recent large-scale exome-wide association study (ExWAS) that identified rare variants associated with IOP in 40 novel genes, six of which are drug targets for clinical treatment or are being evaluated in clinical trials. Finally, we discuss the need for future genetic studies of IOP to include individuals from understudied populations, including Latinos and Africans, in order to fully characterize the genetic architecture of IOP. [ABSTRACT FROM AUTHOR]

Background: Common polygenic risk and de novo variants (DNVs) capture a small proportion of autism spectrum disorder (ASD) liability, and ASD phenotypic heterogeneity remains difficult to explain. Integrating multiple genetic factors contribute to clarifying the risk and clinical presentation of ASD. Methods: In our study, we investigated the individual and combined effects of polygenic risk, damaging DNVs (including those in ASD risk genes), and sex among 2,591 ASD simplex families in the Simons Simplex Collection. We also explored the interactions among these factors, along with the broad autism phenotypes of ASD probands and their unaffected siblings. Finally, we combined the effects of polygenic risk, damaging DNVs in ASD risk genes, and sex to explain the total liability of ASD phenotypic spectrum. Results: Our findings revealed that both polygenic risk and damaging DNVs contribute to an increased risk for ASD, with females exhibiting higher genetic burdens than males. ASD probands that carry damaging DNVs in ASD risk genes showed reduced polygenic risk. The effects of polygenic risk and damaging DNVs on autism broad phenotypes were inconsistent; probands with higher polygenic risk exhibited improvement in some behaviors, such as adaptive/cognitive behaviors, while those with damaging DNVs exhibited more severe phenotypes. Siblings with higher polygenic risk and damaging DNVs tended to have higher scores on broader autism phenotypes. Females exhibited more severe cognitive and behavioral problems compared to males among both ASD probands and siblings. The combination of polygenic risk, damaging DNVs in ASD risk genes, and sex explained 1-4% of the total liability of adaptive/cognitive behavior measurements. Conclusion: Our study revealed that the risk for ASD and the autism broad phenotypes likely arises from a combination of common polygenic risk, damaging DNVs (including those in ASD risk genes), and sex. [ABSTRACT FROM AUTHOR]

Genetic data plays an increasingly important role in modern medicine. Decrease in the cost of sequencing with subsequent increase in imputation accuracy, and the accumulation of large amounts of high-quality genetic data enable the creation of polygenic risk scores (PRSs) to perform genotype-phenotype associations. The accuracy of phenotype prediction primarily depends on the overall trait heritability, Genome-wide association studies cohort size, and the similarity of genetic background between the base and the target cohort. Here we utilized 8,664 high coverage genomic samples collected across Russia by "Evogen", a Russian biomedical company, to evaluate the predictive power of PRSs based on summary statistics established on cohorts of European ancestry for basic phenotypic traits, namely height and BMI. We have demonstrated that the PRSs calculated for selected traits in three distinct Russian populations, recapitulate the predictive power from the original studies. This is evidence that GWAS summary statistics calculated on cohorts of European ancestry are transferable onto at least some ethnic groups in Russia. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach and further commonly used methods. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types—we illustrate this for the prediction of binary traits. [ABSTRACT FROM AUTHOR]

Introduction: As gene identification efforts have advanced in psychiatry, so have aspirations to use genome-wide polygenic information for prevention and intervention. Although polygenic risk scores (PRS) for substance use and psychiatric outcomes are not yet available in clinical settings, individuals can access their PRS through online direct-to-consumer resources. One of these widely used websites reports that alcohol use disorder is the third most requested PRS out of >1,000 conditions. However, data indicate that there are misunderstandings about complex genetic concepts, with a lower understanding of PRS being associated with a more negative impact of receiving polygenic risk information. There is a need to develop and evaluate educational tools to increase understanding of PRS. Methods: We conducted a randomized controlled trial to evaluate the impact of web-based educational information on understanding of PRS for alcohol use disorder. A total of 325 college students (70.4% female; 43.6% White; mean age = 18.9 years) from an urban, diverse university completed the study. Results: Overall, participants were highly satisfied with the educational information. Results from a one-way ANOVA indicated that there was a significant increase in overall understanding of PRS for alcohol use disorder (p-value < 0.001), among individuals who received educational information about PRS and alcohol use disorder, as compared to receiving no accompanying information (adj. p-value < 0.001), or educational information about alcohol use disorder only (adj. p-value < 0.001). Discussion: These findings suggest that the web-based educational tool could be provided alongside polygenic risk information in order to enhance understanding and interpretation of the information. [ABSTRACT FROM AUTHOR]

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a clinically heterogeneous class of neurodevelopmental conditions with a strong, albeit complex, genetic basis. The genetic architecture of ASD includes different genetic models, from monogenic transmission at one end, to polygenic risk given by thousands of common variants with small effects at the other end. The mitochondrial DNA (mtDNA) was also proposed as a genetic modifier for ASD, mostly focusing on maternal mtDNA, since the paternal mitogenome is not transmitted to offspring. We extensively studied the potential contribution of mtDNA in ASD pathogenesis and risk through deep next generation sequencing and quantitative PCR in a cohort of 98 families. While the maternally-inherited mtDNA did not seem to predispose to ASD, neither for haplogroups nor for the presence of pathogenic mutations, an unexpected influence of paternal mtDNA, apparently centered on haplogroup U, came from the Italian families extrapolated from the test cohort (n = 74) when compared to the control population. However, this result was not replicated in an independent Italian cohort of 127 families and it is likely due to the elevated paternal age at time of conception. In addition, ASD probands showed a reduced mtDNA content when compared to their unaffected siblings. Multivariable regression analyses indicated that variants with 15%-5% heteroplasmy in probands are associated to a greater severity of ASD based on ADOS-2 criteria, whereas paternal super-haplogroups H and JT were associated with milder phenotypes. In conclusion, our results suggest that the mtDNA impacts on ASD, significantly modifying the phenotypic expression in the Italian population. The unexpected finding of protection induced by paternal mitogenome in term of severity may derive from a role of mtDNA in influencing the accumulation of nuclear de novo mutations or epigenetic alterations in fathers' germinal cells, affecting the neurodevelopment in the offspring. This result remains preliminary and needs further confirmation in independent cohorts of larger size. If confirmed, it potentially opens a different perspective on how paternal noninherited mtDNA may predispose or modulate other complex diseases. [ABSTRACT FROM AUTHOR]

Background: Growing evidence supports that alterations in the gutmicrobiota play an essential role in the etiology of anxiety, depression, and other psychiatric disorders. However, the potential effect of oral microbiota on mental health has received little attention. Methods: Using the latest genome-wide association study (GWAS) summary data of the oral microbiome, polygenic risk scores (PRSs) of 285 salivary microbiomes and 309 tongue dorsum microbiomes were conducted. Logistic and linear regressionmodels were applied to evaluate the relationship between salivary-tongue dorsum microbiome interactions with anxiety and depression. Two-sampleMendelian randomization (MR) was utilized to compute the causal effects between the oral microbiome, anxiety, and depression. Results: We observed significant salivary-tongue dorsum microbiome interactions related to anxiety and depression traits. Significantly, one common interaction was observed to be associated with both anxiety score and depression score, Centipeda periodontii SGB 224 x Granulicatella uSGB 3289 (P depressionscore = 1.41 x 10-8, P anxietyscore = 5.10 x 10-8). Furthermore, we detected causal effects between the oral microbiome and anxiety and depression. Importantly, we identified one salivary microbiome associated with both anxiety and depression in both the UKB database and the Finngen public database, Eggerthia (P IVW-majordepression-UKB = 2.99 x 10-6, P IVW-Self-reportedanxiety/panicattacks-UKB = 3.06 x 10-59, P IVW-depression-Finngen = 3.16 x 10-16, P IVW-anxiety-Finngen = 1.14 x 10-115). Conclusion: This study systematically explored the relationship between the oral microbiome and anxiety and depression, which could help improve our understanding of disease pathogenesis and propose new diagnostic targets and early intervention strategies. [ABSTRACT FROM AUTHOR]

Barley leaf rust (BLR), caused by Puccinia hordei, is best controlled through genetic resistance. An efficient resistance breeding program prioritizes the need to identify, characterize, and map new sources of resistance as well as understanding the effectiveness, structure, and function of resistance genes. In this study, three mapping populations were developed by crossing Israelian barley lines "AGG-396," "AGG-397," and "AGG-403" (carrying unknown leaf rust resistance) with a susceptible variety "Gus" to characterize and map resistance. Genetic analysis of phenotypic data from rust testing F3s with a P. hordei pathotype 5457 P+ revealed monogenic inheritance in all three populations. Targeted genotyping-by-sequencing of the three populations detected marker trait associations in the same genomic region on the short arm of chromosome 2H between 39 and 57 Mb (AGG-396/Gus), 44 and 64Mb (AGG-397/Gus), and 31 and 58Mb (AGG-403/Gus), suggesting that the resistance in all three lines is likely conferred by the same locus (tentatively designated RphAGG396). Two Kompetitive allelespecific PCR (KASP) markers, HvGBSv2-902 and HvGBSv2-932, defined a genetic distance of 3.8cM proximal and 7.1cM distal to RphAGG396, respectively. To increase the marker density at the RphAGG396 locus, 75 CAPS markers were designed between two flanking markers. Integration of marker data resulted in the identification of two critical recombinants and mapping RphAGG396 between markers- Mloc-28 (40.75Mb) and Mloc-41 (41.92Mb) narrowing the physical window to 1.17Mb based on the Morex v2.0 reference genome assembly. To enhance map resolution, 600 F2s were genotyped with markers- Mloc-28 and Mloc-41 and nine recombinants were identified, placing the gene at a genetic distance of 0.5 and 0.2cM between the two markers, respectively. Two annotated NLR (nucleotide-binding domain leucine-rich repeat) genes (r2.2HG0093020 and r2.2HG0093030) were identified as the best candidates for RphAGG396. A closely linked marker was developed for RphAGG396 that can be used for marker-assisted selection. [ABSTRACT FROM AUTHOR]

Background: Genetic factors increase the individual risk of colorectal cancer (CRC); however, the extent to which a healthy lifestyle can offset increased genetic risk is unknown. This study investigated whether a healthy lifestyle is associated with lower CRC risk, regardless of genetic risk. Methods: We recruited 390,365 participants without cancer at baseline (2006– 2010) from the UK Biobank. The primary outcome was CRC incidence. A healthy lifestyle score constructed using 16 factors of six dimensions (smoking, drinking, body mass index, diet, exercise, and sleep) was categorized into three risk categories: favorable, intermediate, and unfavorable. To calculate the polygenic risk scores (PRSs) of UK Biobank participants, we extracted 454,678 single nucleotide polymorphisms (SNPs) from the UK Biobank and FinnGen Biobank after quality control. Cox proportional hazards regression was performed to evaluate the associations and was expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Results: During a median follow-up of 10.90 years, 4,090 new CRC cases were reported in the UK Biobank. The “best-fit” PRSs were constructed using 59 SNPs based on the UK Biobank cohort and FinnGen genome-wide association study summary data (R2 = 0.23%) and were divided into low (lowest quintile), intermediate (including second–fourth quintile), and high (highest quintile) genetic risk categories. The multivariate-adjusted Cox model revealed that participants with favorable lifestyles had HRs of 0.66 (95% CI = 0.60–0.72) for developing CRC vs. those with unfavorable lifestyles; low genetic risk was associated with a decreased risk of CRC (HR = 0.67, 95% CI =0.61–0.74) compared with those with high genetic risk. The HRs for low genetic risk participants with favorable lifestyles were 0.44 (95% CI =0.36–0.55) vs. participants with high genetic risk and unfavorable lifestyles. Among the participants with low, intermediate, or high genetic risk, the HRs of favorable vs. unfavorable lifestyles were 0.74, 0.64, and 0.72 (all p< 0.05). Conclusions: Low genetic risk and a favorable lifestyle were significantly associated with a decreased risk of CRC. A favorable lifestyle was associated with a lower CRC risk, regardless of genetic risk. [ABSTRACT FROM AUTHOR]

As an important predictor of adulthood psychopathology, self-reported childhood abuse appears heritable and is associated with brain abnormalities. However, the specific genetic mechanisms behind these brain alterations remain largely unknown. This study recruited young adults who reported different degrees of childhood abuse from the community. In order to fully understand the influence of genes on brain changes related to self-reported childhood abuse, various experiments were conducted in this study. Firstly, volume changes of gray matter and white matter related to childhood abuse were investigated by using advanced magnetic resonance imaging techniques. After sequencing the whole exons, we further investigated the relationship between polygenic risk score, brain volume alterations, and childhood abuse score. Furthermore, transcription-neuroimaging association analysis was used to identify risk genes whose expressions were associated with brain volume alterations. The gray matter volumes of left caudate and superior parietal lobule, and white matter volumes of left cerebellum and right temporal lobe-basal ganglia region were significantly correlated with the childhood abuse score. More importantly, brain volume changes mediated the influence of polygenic risk on self-reported childhood abuse. Additionally, transcription-neuroimaging association analysis reported 63 risk genes whose expression levels were significantly associated with childhood abuse-related brain volume changes. These genes are involved in multiple biological processes, such as nerve development, synaptic transmission, and cell construction. Combining data from multiple perspectives, our work provides evidence of brain abnormalities associated with childhood abuse, and further indicates that polygene genetic risk and risk gene expression may affect the occurrence of childhood abuse by brain regulation, which provides insights into the molecularpathology and neuromechanism of childhood adversity. Paying attention to the physical and mental health of high-risk children may be a fundamental way to prevent childhood abuse and promote lifelong mental health. [ABSTRACT FROM AUTHOR]

Type 2 diabetes (T2D) with increasing prevalence is a significant global public health challenge. Obesity, unhealthy diet, and low physical activity are one of the major determinants of the rise in T2D prevalence. In addition, family history and genetic risk of diabetes also play a role in the process of developing T2D. Therefore, solutions for the early identification of individuals at high risk for T2D for early targeted detection of T2D, prevention, and intervention are highly preferred. Recently, novel genomic-based polygenic risk scores (PRSs) have been suggested to improve the accuracy of risk prediction supporting the targeting of preventive interventions to those at highest risk for T2D. Therefore, the aim of the present study was to assess the cost-utility of an additional PRS testing information (as a part of overall risk assessment) followed by a lifestyle intervention and an additional medical therapy when estimated 10-year overall risk for T2D exceeded 20% among Finnish individuals screened as at the highrisk category (i.e., 10%-20% 10-year overall risk of T2D) based on traditional risk factors only. For a cost-utility analysis, an individual-level state-transition model with probabilistic sensitivity analysis was constructed. A 1-year cycle length and a lifetime time horizon were applied in the base-case. A 3% discount rate was used for costs and QALYs. Cost-effectiveness acceptability curve (CEAC) and estimates for the expected value of perfect information (EVPI) were calculated to assist decision makers. The use of the targeted PRS strategy reclassified 12.4 percentage points of individuals to be very high-risk individuals who would have been originally classified as high risk using the usual strategy only. Over a lifetime horizon, the targeted PRS was a dominant strategy (i.e., less costly, more effective). One-way and scenario sensitivity analyses showed that results remained dominant in almost all simulations. However, there is uncertainty, since the probability (EVPI) of cost-effectiveness at a WTP of 0€/QALY was 63.0% (243€) indicating the probability that the PRS strategy is a dominant option. In conclusion, the results demonstrated that the PRS provides moderate additional value in Finnish population in risk screening leading to potential cost savings and better quality of life when compared with the current screening methods for T2D risk. [ABSTRACT FROM AUTHOR]

Background: Familial hypercholesterolemia (FH) is clearly underdiagnosed and undertreated. The aim of this present study is to assess the benefits of FH screening through a joint national program implemented between clinical laboratories and lipid units. Methods: All clinical laboratory tests from 1 January 2017 to 31 December 2018 were reviewed, and those with LDL cholesterol (LDL-C) levels >250 mg/dl were identified in subjects >18 years of age of both sexes. Once secondary causes had been ruled out, the treating physician was contacted and advised to refer the patient to an LU to perform the Dutch Lipid Clinic Network score and to request genetic testing if the score was ≥6 points. Next Generation Sequencing was used to analyse the promoter and coding DNA sequences of four genes associated with FH (LDLR, APOB, PCSK9, APOE) and two genes that have a clinical overlap with FH characteristics (LDLRAP1 and LIPA). A polygenic risk score based on 12 variants was also obtained. Results: Of the 3,827,513 patients analyzed in 26 centers, 6,765 had LDL-C levels >250 mg/dl. Having ruled out secondary causes and known cases of FH, 3,015 subjects were included, although only 1,205 treating physicians could be contacted. 635 patients were referred to an LU and genetic testing was requested for 153 of them. This resulted in a finding of sixty-seven pathogenic variants for FH, 66 in the LDLR gene and one in APOB. The polygenic risk score was found higher in those who had no pathogenic variant compared to those with a pathogenic variant. Conclusion: Despite its limitations, systematic collaboration between clinical laboratories and lipid units allows for the identification of large numbers of patients with a phenotypic or genetic diagnosis of FH, which will reduce their vascular risk. This activity should be part of the clinical routine. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) leverage the genetic contribution of an individual's genotype to a complex trait by estimating disease risk. Traditional PRS prediction methods are predominantly for the European population. The accuracy of PRS prediction in non-European populations is diminished due to much smaller sample size of genome-wide association studies (GWAS). In this article, we introduced a novel method to construct PRS for non-European populations, abbreviated as TL-Multi, by conducting a transfer learning framework to learn useful knowledge from the European population to correct the bias for non-European populations. We considered non-European GWAS data as the target data and European GWAS data as the informative auxiliary data. TL-Multi borrows useful information from the auxiliary data to improve the learning accuracy of the target data while preserving the efficiency and accuracy. To demonstrate the practical applicability of the proposed method, we applied TL-Multi to predict the risk of systemic lupus erythematosus (SLE) in the Asian population and the risk of asthma in the Indian population by borrowing information from the European population. TL-Multi achieved better prediction accuracy than the competing methods, including Lassosum and meta-analysis in both simulations and real applications. [ABSTRACT FROM AUTHOR]

One important confounder in genome-wide association studies (GWASs) is population genetic structure, which may generate spurious associations if not properly accounted for. This may ultimately result in a biased polygenic risk score (PRS) prediction, especially when applied to another population. To explore this matter, we focused on principal component analysis (PCA) and asked whether a population genetics informed strategy focused on PCs derived from an external reference population helps in mitigating this PRS transferability issue. Throughout the study, we used two complex model traits, height and body mass index, and samples from UK and Estonian Biobanks. We aimed to investigate 1) whether using a reference population (1000G) for computation of the PCs adjusted for in the discovery cohort improves the resulting PRS performance in a target set from another population and 2) whether adjusting the validation model for PCs is required at all. Our results showed that any other set of PCs performed worse than the one computed on samples from the same population as the discovery dataset. Furthermore, we show that PC correction in GWAS cannot prevent residual population structure information in the PRS, also for non-structured traits. Therefore, we confirm the utility of PC correction in the validation model when the investigated trait shows an actual correlation with population genetic structure, to account for the residual confounding effect when evaluating the predictive value of PRS. [ABSTRACT FROM AUTHOR]

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients. [ABSTRACT FROM AUTHOR]

Background: Effective identification of high-risk rheumatoid arthritis (RA) individuals is still a challenge. Whether the combined effects of multiple previously reported genetic loci together with lifestyle factors can improve the prediction of RA risk remains unclear. Methods: Based on previously reported results and a large-scale Biobank dataset, we constructed a polygenic risk score (PRS) for RA to evaluate the combined effects of the previously identified genetic loci in both case-control and prospective cohorts. We then evaluated the relationships between several lifestyles and RA risk and determined healthy lifestyles. Then, the joint effects of healthy lifestyles and genetic risk on RA risk were evaluated. Results: We found a positive association between PRS and RA risk (OR = 1.407, 95% confidence interval (CI) = 1.354~1.463; HR = 1.316, 95%CI = 1.257~1.377). Compared with the low genetic risk group, the group with intermediate or high genetic risk had a higher risk (OR = 1.347, 95% CI = 1.213~1.496; HR = 1.246, 95% CI = 1.108~1.400) (OR = 2.169, 95% CI = 1.946~2.417; HR = 1.762, 95% CI = 1.557~1.995). After adjusting for covariates, we found protective effects of three lifestyles (no current smoking, regular physical activity, and moderate bodymass index) on RA risk and defined themas healthy lifestyles. Compared with the individuals with low genetic risks and favorable lifestyles, those with high genetic risks and unfavorable lifestyles had as high as OR of 4.637 (95%CI = 3.767~5.708) and HR of 3.532 (95%CI = 2.799~4.458). Conclusions: In conclusion, the integration of PRS and lifestyles can improve the prediction of RA risk. High RA risk can be alleviated by adopting healthy lifestyles but aggravated by adopting unfavorable lifestyles. [ABSTRACT FROM AUTHOR]

Background: Few studies have investigated the associations of childhood growth trajectories with the prenatal metabolic risks of mothers and their interaction with children's genetic susceptibility. Objective: To investigate the effects of gestational metabolic syndrome (GMS) risks and children's polygenic risk scores (PRSs), and their interaction effect on the BMI trajectory and obesity risk of offspring from birth to 6 years of age. Methods: A total of 2,603 mother-child pairs were recruited from the Ma'anshan birth cohort (Anhui Province of China) study. Data on maternal prepregnancy obesity, gestational weight gain (GWG), gestational diabetes mellitus (GDM), and hypertensive disorders of pregnancy (HDP) were used to evaluate maternal GMS risk. In addition, 1,482 cord blood samples were used to genotype 11 candidate single-nucleotide polymorphisms (SNPs) to calculate children's PRSs. The latent class growth model using the longitudinal BMI-for-age z scores (BMIz) was applied to validly capture the BMIz growth trajectory. Results: Maternal GMS status was associated with higher BMIz scores and with an increased risk of overweight/obesity. Positive relationships were revealed between PRS and the risk of overweight/obesity among girls. Additionally, maternal GMS significantly interacted with the child's PRS on BMIz scores and the risk of overweight/obesity among girls. Hierarchical BMI trajectory graphs by different exposure groups showed consistent findings, and both boys' and girls' BMIz trajectories were divided into three groups. Among girls, the higher the GMS risk or PRS they had, the higher the probability of being in the high BMIz trajectory group. Conclusions: Maternal GMS status increased BMIz scores and the risk of obesity in both boys and girls and elevated the child's BMI trajectory from birth to 6 years of age among girls. PRSs were significantly associated with children's BMI trajectory and the risk of obesity and modified the associations between maternal GMS status and obesity biomarkers only among girls. Thus, regarding childhood obesity, steps should be taken to decrease maternal metabolic risks before and during pregnancy, and sex discrepancies should be noted to identify high-risk populations after birth to hierarchically manage them. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual's susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care. [ABSTRACT FROM AUTHOR]

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease that can have profound differences in survival outcomes. A variety of powerful prognostic factors and models have been constructed; however, the development of more accurate prognosis prediction and targeted treatment for DLBCL still faces challenges. An explosion of research on super-enhancer (SE)–associated genes provide the possibility to use in prognostication for cancer patients. Here, we aimed to establish a novel effective prognostic model using SE-associated genes from DLBCL. Methods: A total of 1,105 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. A total of 11 SE-associated genes (BCL2, SPAG16, PXK, BTG1, LRRC37A2, EXT1, TGFBR2, ANKRD12, MYCBP2, PAX5, and MYC) were initially screened and identified by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis. Finally, a risk score model based on these 11 genes was constructed. Results: Kaplan–Meier (K–M) curves showed that the low-risk group appeared to have better clinical survival outcomes. The excellent performance of the model was determined via time-dependent receiver operating characteristic (ROC) curves. A nomogram based on the polygenic risk score was further established to promote reliable prognostic prediction. This study proposed that the SE-associated-gene risk signature can effectively predict the response to chemotherapy in DLBCL patients. Conclusion: A novel and reliable SE-associated-gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of overall survival was developed, which may assist clinicians in the treatment of DLBCL. [ABSTRACT FROM AUTHOR]

Growth differentiation factor 15 (GDF-15) levels have been revealed as a robust biomarker for metformin use. We conducted Mendelian randomization (MR) analysis to explore the association between GDF-15 and gallstone disease to inform potential therapeutic effects targeting GDF-15. Four genetic variants associated with GDF-15 levels at p < 5 × 10–8 were selected as instrumental variables from a genome-wide association meta-analysis including 21,758 individuals. Two-sample MR analysis was conducted using summary-level data from UK Biobank (10,520 gallstone cases and 350,674 controls) and FinnGen consortium (19,023 gallstone cases and 195,144 controls). Polygenic risk score analysis using individual-level data in UK biobank was performed to complement the MR findings by examining the non-linearity of the association. Diabetic complications were taken as positive controls to validate the therapeutic effect of targeting GDF-15. Linear and nonlinear associations between genetically predicted GDF-15 levels and gallstones were estimated with stratification by the diabetic status. In the two-sample MR analysis, the odds ratio (OR) of gallstones was 1.09 (95% confidence interval (CI), 1.03–1.15; p = 0.001) for one standard deviation increase in genetically predicted GDF-15 levels in the meta-analysis of two datasets. Polygenic risk score analysis found this association to be U-shaped (p = 0.037). The observed association was predominantly seen in nondiabetic population (OR = 1.11, 95% CI: 1.01–1.21; p = 0.003). An inverse association between genetically predicted GDF-15 levels and diabetic complications (OR = 0.77, 95% CI: 0.62–0.96; p = 0.023) was observed, validating the potential therapeutic effects of targeting GDF-15 levels. This MR study indicates that the increased risk of gallstone disease should be taken into account when considering GDF-15 as a therapeutic target for diabetic complications. [ABSTRACT FROM AUTHOR]

Receiving polygenic risk estimates of future disease through health care or direct-to-consumer companies is expected to become more common in the coming decades. However, only a limited number of studies have examined if such estimates might evoke an adverse psychosocial reaction in receivers. The present study utilized data from a sub-section of a personalized medicine project (the P5 study) that combines genomic and traditional health data to evaluate participants' risk for certain common diseases. We investigated how communication of future disease risk estimates related to type 2 diabetes and coronary heart disease influenced respondents' risk perception, self-efficacy, disease-related worry, and other emotions. A randomized controlled trial was conducted, where the experimental group (n = 714) received risk estimates based on traditional and polygenic risk factors and the control group (n = 649) based solely on traditional risk factors. On average, higher disease risk was associated with higher perceived risk (p s, <0.001, ηp2 = 0.087–0.071), worry (p s <0.001, ηp2 = 0.061–0.028), lower self-efficacy (p <0.001, ηp2 = 0.012), less positive emotions (p s <0.04, ηp2 = 0.042–0.005), and more negative emotions (p s <0.048, ηp2 = 0.062–0.006). However, we found no evidence that adding the polygenic risk to complement the more traditional risk factors would induce any substantive psychosocial harm to the recipients (p s >0.06). [ABSTRACT FROM AUTHOR]

Central areolar choroidal dystrophy (CACD) is a rare hereditary disease that mainly affects the macula, resulting in progressive and usually profound visual loss. Being part of congenital retinal dystrophies, it may have an autosomal dominant or recessive inheritance and, until now, has no effective treatment. Given the shortage of genotypic information about the disease, this work systematically reviews the literature for CACD-causing genes. Three independent researchers selected 33 articles after carefully searching and filtering the Scielo, Pubmed, Lilacs, Web of Science, Scopus, and Embase databases. Mutations of six genes (PRPH2, GUCA1A, GUCY2D, CDHR1, ABCA4 , and TTLL5) are implicated in the monogenic dominant inheritance of CACD. They are functionally related to photoreceptors (either in the phototransduction process, as in the case of GUCY2D , or the recovery of retinal photodegradation in photoreceptors for GUCA1A, or the formation and maintenance of specific structures within photoreceptors for PRPH2). The identified genetic variants do not explain all observed clinical features, calling for further whole-genome and functional studies for this disease. A network analysis with the CACD-related genes identified in the systematic review resulted in the identification of another 20 genes that may influence CACD onset and symptoms. Furthermore, an enrichment analysis allowed the identification of 13 transcription factors and 4 long noncoding RNAs interacting with the products of the previously mentioned genes. If mutated or dysregulated, they may be directly involved in CACD development and related disorders. More than half of the genes identified by bioinformatic tools do not appear in commercial gene panels, calling for more studies about their role in the maintenance of the retina and phototransduction process, as well as for a timely update of these gene panels. [ABSTRACT FROM AUTHOR]

Background: Maternal smoking during pregnancy has adverse health effects on the offspring, including lower birth weight and increased risk for obesity. These outcomes are also influenced by common genetic polymorphisms. We aimed to investigate the combined effect of maternal smoking during pregnancy and genetic predisposition on birth weight and body mass index (BMI)-related traits in 1,086 children of the Human Early Life Exposome (HELIX) project. Methods: Maternal smoking during pregnancy was self-reported. Phenotypic traits were assessed at birth or at the age of 8 years. Ten polygenic risk scores (PRSs) per trait were calculated using the PRSice v2 program. For birth weight, we estimated two sets of PRSs based on two different base GWAS summary statistics: PRS-EGG, which includes HELIX children, and PRS-PanUK, which is completely independent. The best PRS per trait (highest R 2) was selected for downstream analyses, and it was treated in continuous or categorized into three groups. Multivariate linear regression models were applied to evaluate the association of the explanatory variables with the traits of interest. The combined effect was evaluated by including an interaction term in the regression models and then running models stratified by the PRS group. Results: BMI-related traits were correlated among them but not with birth weight. A similar pattern was observed for their PRSs. On average, the PRSs explained ∼4% of the phenotypic variation, with higher PRS values related to higher trait values (p -value <5.55E-08). Sustained maternal smoking was associated with lower birth weight and higher BMI and related traits (p -value <2.99E-02). We identified a gene by environment (GxE) interaction for birth weight between sustained maternal smoking and the PRS-EGG in three groups (p -value interaction = 0.01), which was not replicated with the PRS-PanUK (p -value interaction = 0.341). Finally, we did not find any statistically significant GxE interaction for BMI-related traits (p -value interaction >0.237). Conclusion: Sustained maternal smoking and the PRSs were independently associated with birth weight and childhood BMI-related traits. There was low evidence of GxE interactions. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, P obesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, P extremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, P obesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, P extremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations. [ABSTRACT FROM AUTHOR]

Objective: Whether fasting insulin (FI) plays a role in cancer risk remains unclear. This study aimed to investigate the association between FI and cancer risk and to explore its potential mediator role in the association between type 2 diabetes mellitus (T2DM) and cancer. Methods: Two-sample Mendelian randomization (TSMR) analysis was performed to evaluate the effect of FI on overall and 14 site-specific cancers using genome-wide association study (GWAS) summary-level data from Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) and consortia of 14 site-specific cancers. The primary MR approach was conducted by using the random-effect inverse-variance weighted (IVW) method, and sensitivity analyses were implemented by adopting weighted-median, weighted-mode, MR-Egger, and MR-PRESSO tests. Polygenic risk score analysis was executed by using individual-level data from UK Biobank to validate the findings from TSMR analyses. Multivariable Mendelian randomization (MVMR) was carried out to estimate the mediation effect of FI on the association between T2DM and cancer. Results: TSMR study suggested that genetically determined high FI levels were associated with increased risk of colorectal cancer (odds ratio (OR) = 1.87, 95% CI: 1.23–2.84, p = 0.003) and endometrial cancer (OR = 1.89, 95% CI: 1.08–3.01, p = 0.008), but not associated with overall cancer risk or the other 12 studied cancer sites. Polygenic risk score analysis successfully replicated the association between genetic liability to high FI levels and the increased risk of colorectal and endometrial cancers. MVMR and MR mediation analyses detected an intermediary effect of FI and quantified that FI mediated 21.3% of the association between T2DM and endometrial cancer. Conclusions: This study demonstrated that FI levels are associated with the risk of colorectal and endometrial cancers, and FI was found to play an intermediary role in the association between T2DM and endometrial cancer. The associations between FI and other cancers need to be further studied. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder (BD) with co-occurring attention deficit-hyperactivity disorder (ADHD) is associated with an unfavorable course of illness. We aimed to identify potential clinical and genetic correlates of BD with and without ADHD. Methods: Among patients with BD (N = 2,198) enrolled in the Mayo Clinic Bipolar Biobank we identified those with ADHD diagnosed in childhood (BD+cADHD; N = 350), those with adult-onset attention deficit symptoms (BD+aAD; N = 254), and those without ADHD (N = 1,594). We compared the groups using linear or logistic regression adjusting for age, sex, and recruitment site. For genotyped patients (N = 1,443), logistic regression was used to compare ADHD and BD polygenic risk scores (PRSs) between the BD groups, as well as to non-BD controls (N = 777). Results: Compared to the non-ADHD BD group, BD+cADHD patients were younger, more often men and had a greater number of co-occurring anxiety and substance use disorders (all p < 0.001). Additionally, BD+cADHD patients had poorer responses to lithium and lamotrigine (p = 0.005 and p = 0.007, respectively). In PRS analyses, all BD patient subsets had greater genetic risk for BD and ADHD when compared to non-BD controls (p < 0.001 in all comparisons). BD+cADHD patients had a higher ADHD-PRS than non-ADHD BD patients (p = 0.012). However, BD+aAD patients showed no evidence of higher ADHD-PRS than non-ADHD BD patients (p = 0.38). Conclusions: BD+cADHD was associated with a greater number of comorbidities and reduced response to mood stabilizing treatments. The higher ADHD PRS for the BD+cADHD group may reflect a greater influence of genetic factors on early presentation of ADHD symptoms. [ABSTRACT FROM AUTHOR]

Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising. Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer. Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205). Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations. [ABSTRACT FROM AUTHOR]