Polygenic risk modeling with latent trait-related genetic components.
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ABSTRACT:
SUBMITTER: Aguirre M
PROVIDER: S-EPMC8298449 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, this presents a significant analytic challenge due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling approaches offer a natural framework for analysis of data arising from these population-based genetic association investigations of complex diseases as they are well-suited to uncover simultaneous effects of multiple markers. In this manuscript we describe application and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and, finally, an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-infected individuals is provided for illustration.
| S-EPMC3312474 | biostudies-literature
Project description:Obesity is rapidly becoming a global epidemic. Unlike many complex human diseases, obesity is defined not just by a single trait or phenotype, but jointly by measures of anthropometry and metabolic status.We applied maximum likelihood factor analysis to identify common latent factors underlying observed covariance in multiple obesity-related measures. Both the genetic components and the mode of inheritance of the common factors were evaluated. A total of 1775 participants from 590 families for whom measures on obesity-related traits were available were included in this study.The average age of participants was 37 years, 39% of the participants were obese (body mass index >or= 30.0 kg/m(2)) and 26% were overweight (body mass index 25.0-29.9 kg/m(2)). Two latent common factors jointly accounting for over 99% of the correlations among obesity-related traits were identified. Complex segregation analysis of the age- and sex-adjusted latent factors provide evidence for a Mendelian mode of inheritance of major genetic effect with heritability estimates of 40.4 and 47.5% for the first and second factors, respectively.These findings provide a support for multivariate-based approach for investigating pleiotropic effects on obesity-related traits, which can be applied in both genetic linkage and association mapping.
| S-EPMC2830146 | biostudies-other
Project description:Progressive supranuclear palsy (PSP) is the second most common neurodegenerative Parkinsonian disorder after Parkinson's disease, and is characterized as a primary tauopathy. Leveraging the considerable clinical and neuropathologic heterogeneity associated with PSP, we measured tau neuropathology as quantitative traits to perform a genome-wide association study (GWAS) within PSP to identify genes and biological pathways that underlie the PSP disease process. In 882 PSP cases, semi-quantitative scores for phosphorylated tau-immunoreactive coiled bodies (CBs), neurofibrillary tangles (NFTs), tufted astrocytes (TAs), and tau threads were documented from 18 brain regions, and converted to latent trait (LT) variables using the R ltm package. LT analysis utilizes a multivariate regression model that links categorical responses to unobserved covariates allowing for a reduction of dimensionality, generating a single, continuous variable to account for the multiple lesions and brain regions assessed. We first tested for association with PSP LTs and the top PSP GWAS susceptibility loci. Significant SNP/LT associations were identified at rs242557 (MAPT H1c sub-haplotype) with hindbrain CBs and rs1768208 (MOBP) with forebrain tau threads. Digital microscopy was employed to quantify phosphorylated tau burden in midbrain tectum and red nucleus in 795 PSP cases and tau burdens were used as quantitative phenotypes in GWAS. Top associations were identified at rs1768208 with midbrain tectum and red nucleus tau burden. Additionally, we performed a PSP LT GWAS on an initial cohort, a follow-up SNP panel (37 SNPs, P < 10-5) in an extended cohort, and a combined analysis. Top SNP/LT associations were identified at SNPs in or near SPTBN5/EHD4, SEC13/ATP2B2, EPHB1/PPP2R3A, TBC1D8, IFNGR1/OLIG3, ST6GAL1, HK1, CALB1, and SGCZ. Finally, testing for SNP/transcript associations using whole transcriptome and whole genome data identified significant expression quantitative trait loci at rs3088159/SPTBN5/EHD4 and rs154239/GHRL. Modeling tau neuropathology heterogeneity using LTs as quantitative phenotypes in a GWAS may provide substantial insight into biological pathways involved in PSP by affecting regional tau burden.
| S-EPMC8043857 | biostudies-literature
Project description:We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55-65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.
| S-EPMC7411027 | biostudies-literature
Project description:Multiple inquiries into the genetic etiology of human traits indicated an overlap between genes underlying monogenic disorders (eg, skeletal growth defects) and those affecting continuous variability of related quantitative traits (eg, height). Extending the idea of a shared genetic basis between a Mendelian disorder and a classic polygenic trait, we performed an association study to examine the effect of 43 genes implicated in autosomal recessive cognitive disorders on intelligence in an unselected Dutch population (N=1316). Using both single-nucleotide polymorphism (SNP)- and gene-based association testing, we detected an association between intelligence and the genes of interest, with genes ELP2, TMEM135, PRMT10, and RGS7 showing the strongest associations. This is a demonstration of the relevance of genes implicated in monogenic disorders of intelligence to normal-range intelligence, and a corroboration of the utility of employing knowledge on monogenic disorders in identifying the genetic variability underlying complex traits.
| S-EPMC4592100 | biostudies-literature
Project description:BackgroundType 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these approaches by performing colocalization across GWAS traits while assessing the causality and directionality of genetic associations.MethodsWe applied colocalization between T2D and 20 related metabolic traits, across 243 loci, to obtain inferences of shared casual variants. Network-based unsupervised hierarchical clustering was performed on variant-trait associations. Partitioned polygenic risk scores (PRSs) were generated for each cluster using T2D summary statistics and validated in 21,742 individuals with T2D from 3 cohorts. Inferences of directionality and causality were obtained by applying Mendelian randomization Steiger's Z-test and further validated in a pediatric cohort without diabetes (aged 9-12 years old, n = 3866).ResultsWe identified 146 T2D loci that colocalized with at least one metabolic trait locus. T2D variants within these loci were grouped into 5 clusters. The clusters corresponded to the following pathways: obesity, lipodystrophic insulin resistance, liver and lipid metabolism, hepatic glucose metabolism, and beta-cell dysfunction. We observed heterogeneity in associations between PRSs and metabolic measures across clusters. For instance, the lipodystrophic insulin resistance (Beta - 0.08 SD, 95% CI [- 0.10-0.07], p = 6.50 × 10-32) and beta-cell dysfunction (Beta - 0.10 SD, 95% CI [- 0.12, - 0.08], p = 1.46 × 10-47) PRSs were associated to lower BMI. Mendelian randomization Steiger analysis indicated that increased T2D risk in these pathways was causally associated to lower BMI. However, the obesity PRS was conversely associated with increased BMI (Beta 0.08 SD, 95% CI 0.06-0.10, p = 8.0 × 10-33). Analyses within a pediatric cohort supported this finding. Additionally, the lipodystrophic insulin resistance PRS was associated with a higher odds of chronic kidney disease (OR 1.29, 95% CI 1.02-1.62, p = 0.03).ConclusionsWe successfully partitioned T2D genetic variants into phenotypic pathways using a colocalization first approach. Partitioned PRSs were associated to unique metabolic and clinical outcomes indicating successful partitioning of disease heterogeneity. Our work expands on previous approaches by providing stronger inferences of shared causal variants, causality, and directionality of GWAS variant-trait associations.
| S-EPMC10777532 | biostudies-literature
Project description:BackgroundObesity susceptibility genes are highly expressed in the brain suggesting that they might exert their influence on body weight through eating-related behaviors.ObjectivesTo examine whether the genetic susceptibility to obesity is mediated by eating behavior patterns.MethodsParticipants were 3977 twins (33% monozygotic, 56% females), aged 31-37 y, from wave 5 of the FinnTwin16 study. They self-reported their height and weight, eating behaviors (15 items), diet quality, and self-measured their waist circumference (WC). For 1055 twins with genome-wide data, we constructed a polygenic risk score for BMI (PRSBMI) using almost 1 million single nucleotide polymorphisms. We used principal component analyses to identify eating behavior patterns, twin modeling to decompose correlations into genetic and environmental components, and structural equation modeling to test mediation models between the PRSBMI, eating behavior patterns, and obesity measures.ResultsWe identified 4 moderately heritable (h2 = 36-48%) eating behavior patterns labeled "snacking," "infrequent and unhealthy eating," "avoidant eating," and "emotional and external eating." The highest phenotypic correlation with obesity measures was found for the snacking behavior pattern (r = 0.35 for BMI and r = 0.32 for WC; P < 0.001 for both), largely due to genetic factors in common (bivariate h2 > 70%). The snacking behavior pattern partially mediated the association between the PRSBMI and obesity measures (βindirect = 0.06; 95% CI: 0.02, 0.09; P = 0.002 for BMI; and βindirect = 0.05; 95% CI: 0.02, 0.08; P = 0.003 for WC).ConclusionsEating behavior patterns share a common genetic liability with obesity measures and are moderately heritable. Genetic susceptibility to obesity can be partly mediated by an eating pattern characterized by frequent snacking. Obesity prevention efforts might therefore benefit from focusing on eating behavior change, particularly in genetically susceptible individuals.
| S-EPMC7528566 | biostudies-literature
Project description:BackgroundPolysubstance use (PSU; lifetime use of multiple substances) is common among individuals with problematic alcohol/substance use and is associated with poor prognosis and poor physical/mental health. Furthermore, simultaneous co-use of substances, such that drug effects overlap, is also common and related to unique risks (e.g. overdose). Despite the importance of PSU, current diagnostic systems continue to conceptualize problems with alcohol/substances as class-specific constructs (e.g. Stimulant Use Disorder), which essentially ignore many unique PSU processes.MethodsThe current study modeled problems with alcohol, cannabis, stimulants, sedatives, opiates, and simultaneous co-use of these substances as a manifestation of a general substance use continuum versus as correlated class-specific constructs in a sample of young-adults(n = 2482) using confirmatory factor analysis. Utility of the models was evaluated by examining associations between the general substance use spectrum and class-specific latent factors with measures of anxiety, ADHD, adult antisocial problems, borderline symptoms, neuroticism, and intelligence in a subset of the sample(n=847).ResultsFindings supported the conceptualization of problems with all substances, including co-use of substances, as being manifestations of a general substance use spectrum, as class-specific constructs were not differentially associated with other measures of psychological dysfunction. Examination of this general substance use spectrum indicated that all substances, separately and co-use, were robustly informative of this spectrum, but tended to discriminate between different severity levels.DiscussionThe general substance use spectrum allows for integration of information from the use and co-use of all substances to provide better assessment of overall problems with substances compared to class-specific constructs.
| S-EPMC7293921 | biostudies-literature
Project description:Understanding the genetic basis of phenotypic adaptation to changing environments is an essential goal of population and quantitative genetics. While technological advances now allow interrogation of genome-wide genotyping data in large panels, our understanding of the process of polygenic adaptation is still limited. To address this limitation, we use extensive forward-time simulation to explore the impacts of variation in demography, trait genetics, and selection on the rate and mode of adaptation and the resulting genetic architecture. We simulate a population adapting to an optimum shift, modeling sequence variation for 20 QTL for each of 12 different demographies for 100 different traits varying in the effect size distribution of new mutations, the strength of stabilizing selection, and the contribution of the genomic background. We then use random forest regression approaches to learn the relative importance of input parameters in determining a number of aspects of the process of adaptation, including the speed of adaptation, the relative frequency of hard sweeps and sweeps from standing variation, or the final genetic architecture of the trait. We find that selective sweeps occur even for traits under relatively weak selection and where the genetic background explains most of the variation. Though most sweeps occur from variation segregating in the ancestral population, new mutations can be important for traits under strong stabilizing selection that undergo a large optimum shift. We also show that population bottlenecks and expansion impact overall genetic variation as well as the relative importance of sweeps from standing variation and the speed with which adaptation can occur. We then compare our results to two traits under selection during maize domestication, showing that our simulations qualitatively recapitulate differences between them. Overall, our results underscore the complex population genetics of individual loci in even relatively simple quantitative trait models, but provide a glimpse into the factors that drive this complexity and the potential of these approaches for understanding polygenic adaptation.
| S-EPMC6277123 | biostudies-literature
Project description:Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
| S-EPMC7477007 | biostudies-literature