Polygenic Model Research Articles

A polygenic risk score model for psoriasis based on the protein interactions of psoriasis susceptibility loci

IntroductionPolygenic Risk Scores (PRS) are an emerging tool for predicting an individual’s genetic risk to a complex trait. Several methods have been proposed to construct and calculate these scores. Here, we develop a biologically driven PRS using the UK BioBank cohort through validated protein interactions (PPI) and network construction for psoriasis, incorporating variants mapped to the interacting genes of 14 psoriasis susceptibility (PSORS) loci, as identified from previous genetic linkage studies.MethodsWe constructed the PPI network via the implementation of two major meta-databases of protein interactions, and identified variants mapped to the identified PSORS-interacting genes. We selected only European unrelated participants including individuals with psoriasis and randomly selected healthy controls using an at least 1:4 ratio to maximize statistical power. We next compared our PPI-PRS model to (i) clinical risk models and (ii) conventional PRS calculations through p-value thresholding.ResultsOur PPI-PRS model provides comparable results to both clinical risk models and conventional approaches, despite the incorporation of a limited number of variants which have not necessarily reached genome-wide significance (GWS). Exclusion of variants mapped to the HLA-C locus, an established risk locus for psoriasis resulted in highly similar associations compared to our primary model, indicating the contribution of the genetic variability mapped to non-GWS variants in PRS computations.DiscussionOur findings support the implementation of biologically driven approaches in PRS calculations in psoriasis, highlighting their potential clinical utility in risk assessment and treatment management.

Grapevine pangenome facilitates trait genetics and genomic breeding.

Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based pangenome reference (Grapepan v.1.0) from 18 newly generated phased telomere-to-telomere assemblies and 11 published assemblies. Using Grapepan v.1.0, we build a variation map with 9,105,787 short variations and 236,449 structural variations (SVs) from the resequencing data of 466 grapevine cultivars. Integrating SVs into a genome-wide association study, we map 148 quantitative trait loci for 29 agronomic traits (50.7% newly identified), with 12 traits significantly contributed by SVs. The estimated heritability improves by 22.78% on average when including SVs. We discovered quantitative trait locus regions under divergent artificial selection in metabolism and berry development between wine and table grapes, respectively. Moreover, significant genetic correlations were detected among the 29 traits. Under a polygenic model, we conducted genomic predictions for each trait. In general, our study facilitates the breeding of superior cultivars via the genomic selection of multiple traits.

Characterization of genetic landscape and novel inflammatory biomarkers in patients with adult-onset Still's disease.

Adult-onset Still's disease (AOSD) is systemic autoinflammatory disorder of unknown aetiology. Genetic studies have been limited. Here, we conducted detailed genetic and inflammatory biomarker analysis of a large AOSD cohort to investigate the underlying pathology and identify novel targets for potential treatment. We investigated AOSD cases (n=60) for rare germline and somatic variants using whole exome sequencing with virtual gene panels. Transcriptome profiles were investigated by bulk RNA sequencing whole blood. Cytokine profiling was performed on an extended patient cohort (n=106), alongside measurements of NLRP3 inflammasome activation using a custom assay, and Type I Interferon (IFN) score using a novel method. We observed higher-than-expected frequencies of rare germline variants associated with monogenic autoinflammatory disorders in AOSD cases (AOSD 38.4% vs healthy controls 20.4%), and earlier onset of putative somatic variants associated with clonal haematopoiesis of indeterminate potential. Transcriptome profiling revealed positive correlation between Still's activity score (SAS) and gene expression associated with the innate immune system. ASC/NLRP3 specks levels and Type I IFN scores were significantly elevated in AOSD cases compared to healthy controls (p=0.0001 and 0.0015 respectively), in addition to several cytokines: IL-6 (p<0.0001), IL-10 (p<0.0075), IL-12p70 (p=0.0005), IL-18 (p<0.0001), IL-23 (p<0.0001), IFN-α2 (p=0.0009), and IFNγ (p=0.0002). Our study shows considerable genetic complexity within AOSD and demonstrates the potential utility of the ASC/NLRP3 specks assay for disease stratification and targeted treatment. The enriched genetic variants identified may not, by themselves, be sufficient to cause disease but may contribute to a polygenic model for AOSD.

Predicting progression from coronary artery disease to heart failure in community cohorts

Abstract Background Approximately 10% of people develop heart failure (HF) within 5 years after an acute coronary event. Once ischaemic damage has occurred, the heart undergoes remodeling, which can lead to subclinical dysfunction and HF. However, remodeling varies considerably between patients and is difficult to predict. Purpose To assess whether adding genetic information to clinical factors improves the accuracy of predicting incident non-fatal/fatal HF within 5 years in people with coronary artery disease (CAD). Methods In the UK Biobank (UKB) [1], we developed a clinical risk model (predictors age, sex, ethnicity, body mass index, social deprivation, smoking, personal medical history, time since most recent CAD admission, lipids, HbA1C, creatinine, medications). Separately, we performed a genome-wide association meta-analysis of 13,360 (6,315 + 7,045) cases and 58,126 (37,245 + 20,881) non-cases from the UKB and deCODE [2] cohorts, relating common genetic variants to non-fatal/fatal HF or cardiomyopathy in people with CAD. For the current analysis of the UKB cohort, we derived a polygenic risk score using summary statistics from deCODE alone. We then compared risk estimates and assessed reclassification of 5-year risk using the clinical risk model with and without polygenic risk score, in UKB. Risk groups were &amp;lt;5%, 5-9.9%, 10-14.9%, ≥15%. Results Among people with CAD in the 5 years prior to assessment in UKB (n=8,880, mean age 61y, 27% women) or deCODE (n=10,113, mean age 66y, 34% women), 377 (4%) and 915 (9%) developed non-fatal/fatal HF over 5 years follow-up, respectively. In the UKB cohort, median 5-year risk with the clinical model was 3.2% (IQR 2-5.4%, C-statistic 0.72, 95% CI 0.70–0.74, Table 1) and plots of predicted versus observed risk showed very good calibration across deciles. The polygenic risk score was predictive of non-fatal/fatal HF in the UKB (hazard ratio 1.03, 95% CI 1.00 to 1.05 per standard deviation) with significance decreasing after adjusting for the prognostic index of the clinical risk model (hazard ratio 1.02, 95% CI 0.99 to 1.05 per standard deviation). Median 5-year risk by the clinical+polygenic model was 3.0% (IQR 1.9-5.0%). Net reclassification with the addition of the polygenic score improved accuracy of risk prediction for 3.6% of non-cases but deteriorated accuracy of risk prediction for 6% of cases. Overall net reclassification was deterioration of -0.025. Conclusion Our data suggest that common genetic variants may be associated with progression from CAD to HF, but that adding genetic data did not improve the accuracy of predicting progression from CAD to HF beyond established clinical predictors in the UKB cohort. Confirmation of these findings in deCODE and other large cohorts is warranted.

Semi-Supervised Triply Robust Inductive Transfer Learning

In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce target population and uses a large amount of unlabeled data simultaneously to improve the learning accuracy in the target population. Specifically, we consider a high dimensional covariate shift setting and employ two nuisance models, a density ratio model and an imputation model, to combine transfer learning and surrogate-assisted semi-supervised learning strategies effectively and achieve triple robustness. While the STRIFLE approach assumes the target and source populations to share the same conditional distribution of outcome Y given both the surrogate features S and predictors X , it allows the true underlying model of Y⏧ X to differ between the two populations due to the potential covariate shift in S and X . Different from double robustness, even if both nuisance models are misspecified or the distribution of Y⏧ S , X is not the same between the two populations when the shifted source population and the target population share enough similarities, the triply robust STRIFLE estimator can still partially use the source population when the shifted source population and the target population share enough similarities. Moreover, it is guaranteed to be no worse than the target-only surrogate-assisted semi-supervised estimator with an additional error term from transferability detection. These desirable properties of our estimator are established theoretically and verified in finite samples via extensive simulation studies. We use the STRIFLE estimator to train a Type II diabetes polygenic risk prediction model for the African American target population by transferring knowledge from electronic health records linked genomic data observed in a larger European source population. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Germline DNA Damage Repair Gene Alterations in Patients with Metachronous Breast and Colorectal Cancer.

A hereditary component of breast (BC) and colorectal cancer (CRC) has been described in approximately one-third of these tumor types. BC patients have an increased risk of developing CRC as a second primary tumor and vice versa. Germline genomic variants (NextSeq550, Illumina) were investigated in 24 unrelated BC and/or CRC patients and 7 relatives from 3 index patients. Fifty-six pathogenic or likely pathogenic variants were identified in 19 of 24 patients. We detected single-nucleotide variants (SNVs) in CRC predisposition genes (MLH1 and MUTYH) and other promising candidates (CDK5RAP3, MAD1L1, NOS3, and POLM). Eighteen patients presented SNVs or copy number variants (CNVs) in DNA damage repair genes. We also identified SNVs recently associated with BC or CRC predisposition (PABPC1, TYRO3, MAP3K1, SLC15A4, and LAMA1). The PABPC1c.1255C>T variant was detected in nine unrelated patients. Each patient presented at least one SNV/CNV in a candidate gene, and most had alterations in more than one gene, reinforcing a polygenic model for BC/CRC predisposition. A significant fraction of BC/CRC patients with a family history of these tumors harbored deleterious germline variants in DNA repair genes. Our findings can lead to strategies to improve the diagnosis, genetic counseling, and treatment of patients and their relatives.

Common variants increase risk for congenital diaphragmatic hernia within the context of de novo variants.

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly often accompanied by other structural anomalies and/or neurobehavioral manifestations. Rare de novo protein-coding variants and copy-number variations contribute to CDH in the population. However, most individuals with CDH remain genetically undiagnosed. Here, we perform integrated de novo and common-variant analyses using 1,469 CDH individuals, including 1,064 child-parent trios and 6,133 ancestry-matched, unaffected controls for the genome-wide association study. We identify candidate CDH variants in 15 genes, including eight novel genes, through deleterious de novo variants. We further identify two genomic loci contributing to CDH risk through common variants with similar effect sizes among Europeans and Latinx. Both loci are in putative transcriptional regulatory regions of developmental patterning genes. Estimated heritability in common variants is ∼19%. Strikingly, there is no significant difference in estimated polygenic risk scores between isolated and complex CDH or between individuals harboring deleterious de novo variants and individuals without these variants. The data support a polygenic model as part of the CDH genetic architecture.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2

BackgroundAs precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.ResultsThe S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.ConclusionsTaken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Differential miRNA Expression Patterns between TallyHo/JngJ Mice and Non-Diabetic SWR/J Mice

Abstract Type 2 diabetes mellitus (T2DM) increases the susceptibility of bone fragility. The underlying mechanisms have, however, remained largely unknown. MicroRNAs (miRNAs) are short single-stranded non-coding RNA molecules with utility as biomarkers due to their easy accessibility and stability in bodily fluids. Here, we aimed to use an unbiased approach to identify miRNAs dysregulated in a polygenic mouse model of T2DM. Genome-wide analysis of miRNAs in serum, bone marrow and bone from the polygenic TallyHo/JngJ (TH) mice, which recapitulate T2DM in humans, was performed. This analysis was compared to the recommended control SWR/J and a strain-matched non-diabetic control (TH-ND). When comparing TH mice with TH-ND using an adjusted P-value (FDR) cut-off of 0.2 to identify differentially expressed miRNAs, mmu-miR-466i-5p and mmu-miR-1195 were found to be up-regulated in both serum and in bone marrow. Dysregulated miRNAs were not found in bone tissue. When comparing TH-ND mice with SWR/J using the same FDR cut-off, mmu-miR-351-5p and mmu-miR-322-3p were upregulated in both bone marrow and serum, while mmu-miR-449a-5p and mmu-miR-6240 were downregulated in bone marrow and serum. Dysregulated miRNAs in bone marrow or cortical bone compared to serum between TH-ND mice and SWR/J were investigated for their cell-type enrichment to identify putative donor cells and their gene target networks. Gene target network analysis revealed genes involved in diabetes-related signaling pathways as well as in diabetic bone disease. Cell-type enrichment analysis identified hsa-miR-449a enriched in immune cells, hsa-miR-592 in hepatocytes and endothelial cells, while hsa-miR-424-3p, hsa-miR-1-3p and hsa-miR-196b-5p were enriched in mesenchymal stem cells and their derived tissues. In conclusion, our comparative miRNA profiling sheds light on differential expression patterns between SWR/J and both subgroups of TH. No differences were observed between TH and TH-ND suggesting the genetical background of SWR/J may be responsible for the change of dysregulated miRNA.

Immunogenetic Landscape in Pediatric Common Variable Immunodeficiency.

Common variable immunodeficiency (CVID) is the most common symptomatic antibody deficiency, characterized by heterogeneous genetic, immunological, and clinical phenotypes. It is no longer conceived as a sole disease but as an umbrella diagnosis comprising a spectrum of clinical conditions, with defects in antibody biosynthesis as their common denominator and complex pathways determining B and T cell developmental impairments due to genetic defects of many receptors and ligands, activating and co-stimulatory molecules, and intracellular signaling molecules. Consequently, these genetic variants may affect crucial immunological processes of antigen presentation, antibody class switch recombination, antibody affinity maturation, and somatic hypermutation. While infections are the most common features of pediatric CVID, variants in genes linked to antibody production defects play a role in pathomechanisms of immune dysregulation with autoimmunity, allergy, and lymphoproliferation reflecting the diversity of the immunogenetic underpinnings of CVID. Herein, we have reviewed the aspects of genetics in CVID, including the monogenic, digenic, and polygenic models of inheritance exemplified by a spectrum of genes relevant to CVID pathophysiology. We have also briefly discussed the epigenetic mechanisms associated with micro RNA, DNA methylation, chromatin reorganization, and histone protein modification processes as background for CVID development.

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated to large sections of a genome. By analysing allele sharing between individuals, one may calculate realized genomic relationships from single nucleotide polymorphisms (SNPs) data rather than relying on known pedigree relationships under polygenic model. The traditional approaches in genome-wide prediction (GWP) of quantitative phenotypes utilise genomic relationships in fixed global covariance modelling, possibly with some non-linear kernel mapping (for example Gaussian processes). On the other hand, the DL approaches proposed so far for GWP fail to take into account the non-Euclidean graph structure of relationships between individuals over several generations. In this paper, we propose one global convolutional neural network (GCN) and one local sub-sampling architecture (GCN-RS) that are specifically designed to perform regression analysis based on genomic relationship information. A GCN is tailored to non-Euclidean spaces and consists of several layers of graph convolutions. The GCN-RS architecture is designed to further improve the GCN's performance by sub-sampling the graph to reduce the dimensionality of the input data. Through these graph convolutional layers, the GCN maps input genomic markers to their quantitative phenotype values. The graphs are constructed using an iterative nearest neighbour approach. Comparisons show that the GCN-RS outperforms the popular Genomic Best Linear Unbiased Predictor (GBLUP) method on one simulated and three real data sets from wheat, mice and pig with a predictive improvement of 4.4% to 49.4% in terms of test mean squared error (MSE). This indicates that GCN-RS is a promising tool for genomic predictions in plants and animals. Furthermore, GCN-RS is computationally efficient, making it a viable option for large-scale applications.

Polygenic prediction of human longevity on the supposition of pervasive pleiotropy

The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31–84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11–17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models

PurposeThe HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information?MethodsA polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663).ResultsThe novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)).ConclusionThe HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

Assessing genetic and environmental components for pterygium: a nationwide study in Taiwan

This study aims to estimate the familial risks of pterygium and assess its relative contributions to environmental and genetic factors using the 2000–2017 Taiwan National Health Insurance Research Database. The marginal Cox's model and the polygenic liability model were made. In Taiwan, the prevalence rate of pterygium in 2017 was 1.64% for individuals with affected first-degree relatives, higher than the general population (1.34%). The adjusted relative risk (RR) for pterygium was highest for twins of the same sex (15.54), followed by siblings of the same sex (4.69), offsprings (3.39), siblings of the different sex (2.88), spouse (2.12), parents (1.86), twins of the different sex (1.57), respectively. The phenotypic variance of pterygium was 21.6% from additive genetic variance, 24.3% from common environmental factors shared by family members, and 54.1% from non-shared environmental factors, respectively. Sensitivity analysis by restricting those with surgical pterygium reveals that aRRs and the three components were similar to those of the overall pterygium. In summary, the prevalence rate of pterygium was higher for individuals with affected first-degree relatives than for the general population. The non-shared environmental factors account for half of the phenotypic variance of pterygium; genetic and shared environmental factors explain the rest.

Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration.

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.

Pharmacogenomic Polygenic Model of Clopidogrel Predicts Recurrent Ischemic Events in Chinese Patients With Coronary Artery Disease

Patients with coronary artery disease (CAD) need to take antiplatelet drugs regularly in order to prevent thrombosis; however, there is existing inter-individual variability in drug response. Pharmacogenomic studies indicate that drug response may also be influenced by genetic variants, and multiple genetic variants may work together. We assumed that patients carrying more risk alleles might have a worse clopidogrel drug response and that a polygenic model integrated different single variants might have the potential to explain clopidogrel drug response variability better. We aimed to investigate whether the polygenic model could be used to predict clopidogrel drug response. A total of 935 CAD patients were enrolled in the study. We investigated the association between 19 clopidogrel-related single-nucleotide polymorphisms (SNPs) and the incidence of recurrent ischemic events. Additionally, a polygenic model was constructed to assess the risk of ischemic events. There were only 2 SNPs of CYP2C8 gene (rs1934980 and rs17110453) that were nominally associated with incidence of recurrent ischemic events. We constructed a polygenic model integrated with 6 clopidogrel-related SNPs. When compared with patients carrying 6 or fewer risk alleles, patients with 7 or more risk alleles had a higher risk of ischemic events (hazard ratio = 1.87; P = 0.04). The polygenetic model may be useful for clopidogrel drug response prediction in patients with CAD.

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model.

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

Genome-wide association and polygenic risk score estimation of type 2 diabetes mellitus in Kinh Vietnamese-A pilot study.

A genome-wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic-associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10-6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62-0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.

Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias.

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.