Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Cognitive Function in People With Familial Risk of Depression

Importance

Cognitive impairment in depression is poorly understood. Family history of depression is a potentially useful risk marker for cognitive impairment, facilitating early identification and targeted intervention in those at highest risk, even if they do not themselves have depression. Several research cohorts have emerged recently that enable findings to be compared according to varying depths of family history phenotyping, in some cases also with genetic data, across the life span.

Objective

To investigate associations between familial risk of depression and cognitive performance in 4 independent cohorts with varied depth of assessment, using both family history and genetic risk measures.

Design, Setting, and Participants

This study used data from the Three Generations at High and Low Risk of Depression Followed Longitudinally (TGS) family study (data collected from 1982 to 2015) and 3 large population cohorts, including the Adolescent Brain Cognitive Development (ABCD) study (data collected from 2016 to 2021), National Longitudinal Study of Adolescent to Adult Health (Add Health; data collected from 1994 to 2018), and UK Biobank (data collected from 2006 to 2022). Children and adults with or without familial risk of depression were included. Cross-sectional analyses were conducted from March to June 2022.

Exposures

Family history (across 1 or 2 prior generations) and polygenic risk of depression.

Main Outcomes and Measures

Neurocognitive tests at follow-up. Regression models were adjusted for confounders and corrected for multiple comparisons.

Results

A total of 57 308 participants were studied, including 87 from TGS (42 [48%] female; mean [SD] age, 19.7 [6.6] years), 10 258 from ABCD (4899 [48%] female; mean [SD] age, 12.0 [0.7] years), 1064 from Add Health (584 [49%] female; mean [SD] age, 37.8 [1.9] years), and 45 899 from UK Biobank (23 605 [51%] female; mean [SD] age, 64.0 [7.7] years). In the younger cohorts (TGS, ABCD, and Add Health), family history of depression was primarily associated with lower performance in the memory domain, and there were indications that this may be partly associated with educational and socioeconomic factors. In the older UK Biobank cohort, there were associations with processing speed, attention, and executive function, with little evidence of education or socioeconomic influences. These associations were evident even in participants who had never been depressed themselves. Effect sizes between familial risk of depression and neurocognitive test performance were largest in TGS; the largest standardized mean differences in primary analyses were -0.55 (95% CI, -1.49 to 0.38) in TGS, -0.09 (95% CI, -0.15 to -0.03) in ABCD, -0.16 (95% CI, -0.31 to -0.01) in Add Health, and -0.10 (95% CI, -0.13 to -0.06) in UK Biobank. Results were generally similar in the polygenic risk score analyses. In UK Biobank, several tasks showed statistically significant associations in the polygenic risk score analysis that were not evident in the family history models.

Conclusions and Relevance

In this study, whether assessed by family history or genetic data, depression in prior generations was associated with lower cognitive performance in offspring. There are opportunities to generate hypotheses about how this arises through genetic and environmental determinants, moderators of brain development and brain aging, and potentially modifiable social and lifestyle factors across the life span.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits

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 R ² 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 PRS-based 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.

The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

Fast and accurate Bayesian polygenic risk modeling with variational inference

The advent of large-scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction with single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods use a multiple linear regression framework to infer joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, most existing Bayesian approaches employ Markov chain Monte Carlo (MCMC) algorithms, which are computationally inefficient and do not scale favorably to higher dimensions, for posterior inference. Here, we introduce variational inference of polygenic risk scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes variational inference techniques to approximate the posterior distribution for the effect sizes. Our experiments with 36 simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that VIPRS is consistently competitive with the state-of-the-art in prediction accuracy while being more than twice as fast as popular MCMC-based approaches. This performance advantage is robust across a variety of genetic architectures, SNP heritabilities, and independent GWAS cohorts. In addition to its competitive accuracy on the "White British" samples, VIPRS showed improved transferability when applied to other ethnic groups, with up to 1.7-fold increase in R2 among individuals of Nigerian ancestry for low-density lipoprotein (LDL) cholesterol. To illustrate its scalability, we applied VIPRS to a dataset of 9.6 million genetic markers, which conferred further improvements in prediction accuracy for highly polygenic traits, such as height.

Youth Polygenic Scores, Youth ADHD Symptoms, and Parenting Dimensions: An Evocative Gene-Environment Correlation Study

Parenting practices and parental symptoms of attention-deficit/hyperactivity disorder (ADHD) have been linked to severity and course of youth ADHD. However, genetically influenced behaviors related to ADHD in youth may also influence parenting behaviors. Polygenic scores (PGS) have been widely used to quantify genetic vulnerability for ADHD but has rarely been used to examine gene-environment correlation effects. The current study examined the direct effects of youth ADHD PGS and its evocative effects on parenting behaviors via youth ADHD symptoms. 803 youth aged 6-18 years (58.5% male) completed a multistage, multi-informant assessment that included measures of parenting practices and youth and parental ADHD symptoms. A mediation model was used to evaluate direct and evocative effects. Furthermore, we examined if these evocative effects remain after controlling for parental ADHD symptoms. Sensitivity analyses across age, sex, and socioeconomic status (SES) as well as restricting ancestry groups to European only ancestry were also conducted. Results indicated that youth ADHD PGS reliably predicted youth ADHD symptoms across all models (βs ranging from 0.18 to 0.26), including across age, sex, and SES and held even with ancestry restricted to the largest group (northern European). Evocative effects emerged such that higher youth PGS significantly predicted more youth ADHD symptoms, which in turn, significantly predicted lower levels of parental involvement and higher levels of poor supervision/monitoring and inconsistent discipline. These effects remained after controlling for parent ADHD symptoms.

Rank concordance of polygenic indices

Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene-environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort

OBJECTIVE

Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes.

RESEARCH DESIGN AND METHODS

Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants.

RESULTS

We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race.

CONCLUSIONS

Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients

Inflammatory bowel disease (IBD) is a group of chronic digestive tract inflammatory conditions whose genetic etiology is still poorly understood. The incidence of IBD is particularly high among Ashkenazi Jews. Here, we identify 8 novel and plausible IBD-causing genes from the exomes of 4453 genetically identified Ashkenazi Jewish IBD cases (1734) and controls (2719). Various biological pathway analyses are performed, along with bulk and single-cell RNA sequencing, to demonstrate the likely physiological relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of Ashkenazi Jewish adult IBD displays significant overlap with very early onset-IBD genetics. Moreover, by performing biobank phenome-wide analyses, we find that IBD genes have pleiotropic effects that involve other immune responses. Finally, we show that polygenic risk score analyses based on genome-wide high impact variants have high power to predict IBD susceptibility.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Genetic contributions of noncognitive skills to academic development

Noncognitive skills such as motivation and self-regulation, predict academic achievement beyond cognitive skills. However, the role of genetic and environmental factors and of their interplay in these developmental associations remains unclear. We provide a comprehensive account of how cognitive and noncognitive skills contribute to academic achievement from ages 7 to 16 in a sample of >10,000 children from England and Wales. Results indicated that noncognitive skills become increasingly predictive of academic achievement across development. Triangulating genetic methods, including twin analyses and polygenic scores (PGS), we found that the contribution of noncognitive genetics to academic achievement becomes stronger over development. The PGS for noncognitive skills predicted academic achievement developmentally, with prediction nearly doubling by age 16, pointing to gene-environment correlation (rGE). Within-family analyses indicated both passive and active/evocative rGE processes driven by noncognitive genetics. By studying genetic effects through a developmental lens, we provide novel insights into the role of noncognitive skills in academic development.

The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Polygenic risk scores and the need for pharmacotherapy in neonatal abstinence syndrome

BACKGROUND

The aim of this study was to identify genetic variants associated with NAS through a genome-wide association study (GWAS) and estimate a Polygenic Risk Score (PRS) model for NAS.

METHODS

A prospective case-control study included 476 in utero opioid-exposed term neonates. A GWAS of 1000 genomes-imputed genotypes was performed to identify variants associated with need for pharmacotherapy for NAS. PRS models for estimating genetic predisposition were generated via a nested cross-validation approach using 382 neonates of European ancestry. PRS predictive ability, discrimination, and calibration were assessed.

RESULTS

Cross-ancestry GWAS identified one intergenic locus on chromosome 7 downstream of SNX13 exhibiting genome-wide association with need for pharmacotherapy. PRS models derived from the GWAS for a subset of the European ancestry neonates reliably discriminated between need for pharmacotherapy using cis variant effect sizes within validation sets of European and African American ancestry neonates. PRS were less effective when applying variant effect sizes across datasets and in calibration analyses.

CONCLUSIONS

GWAS has the potential to identify genetic loci associated with need for pharmacotherapy for NAS and enable development of clinically predictive PRS models. Larger GWAS with additional ancestries are needed to confirm the observed SNX13 association and the accuracy of PRS in NAS risk prediction models.

IMPACT

Genetic associations appear to be important in neonatal abstinence syndrome. This is the first genome-wide association in neonates with neonatal abstinence syndrome. Polygenic risk scores can be developed examining single-nucleotide polymorphisms across the entire genome. Polygenic risk scores were higher in neonates receiving pharmacotherapy for treatment of their neonatal abstinence syndrome. Future studies with larger cohorts are needed to better delineate these genetic associations.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

A Genomic Risk Score Identifies Individuals at High Risk for Intracerebral Hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) has an estimated heritability of 29%. We developed a genomic risk score for ICH and determined its predictive power in comparison to standard clinical risk factors.

METHODS

We combined genome-wide association data from individuals of European ancestry for ICH and related traits in a meta-genomic risk score ([metaGRS]; 2.6 million variants). We tested associations with ICH and its predictive performance in addition to clinical risk factors in a held-out validation dataset (842 cases and 796 controls). We tested associations with risk of incident ICH in the population-based UK Biobank cohort (486 784 individuals, 1526 events, median follow-up 11.3 years).

RESULTS

One SD increment in the metaGRS was significantly associated with 31% higher odds for ICH (95% CI, 1.16-1.48) in age-, sex- and clinical risk factor-adjusted models. The metaGRS identified individuals with almost 5-fold higher odds for ICH in the top score percentile (odds ratio, 4.83 [95% CI, 1.56-21.2]). Predictive models for ICH incorporating the metaGRS in addition to clinical predictors showed superior performance compared to the clinical risk factors alone (c-index, 0.695 versus 0.686). The metaGRS showed similar associations for lobar and nonlobar ICH, independent of the known APOE risk locus for lobar ICH. In the UK Biobank, the metaGRS was associated with higher risk of incident ICH (hazard ratio, 1.15 [95% CI, 1.09-1.21]). The associations were significant within both a relatively high-risk population of antithrombotic medications users, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants.

CONCLUSIONS

We developed and validated a genomic risk score that predicts lifetime risk of ICH beyond established clinical risk factors among individuals of European ancestry. Whether implementation of the score in risk prognostication models for high-risk populations, such as patients under antithrombotic treatment, could improve clinical decision making should be explored in future studies.

GWAS meta-analysis of 16 790 patients with Barrett's oesophagus and oesophageal adenocarcinoma identifies 16 novel genetic risk loci and provides insights into disease aetiology beyond the single marker level

OBJECTIVE

Oesophageal cancer (EC) is the sixth leading cause of cancer-related deaths. Oesophageal adenocarcinoma (EA), with Barrett's oesophagus (BE) as a precursor lesion, is the most prevalent EC subtype in the Western world. This study aims to contribute to better understand the genetic causes of BE/EA by leveraging genome wide association studies (GWAS), genetic correlation analyses and polygenic risk modelling.

DESIGN

We combined data from previous GWAS with new cohorts, increasing the sample size to 16 790 BE/EA cases and 32 476 controls. We also carried out a transcriptome wide association study (TWAS) using expression data from disease-relevant tissues to identify BE/EA candidate genes. To investigate the relationship with reported BE/EA risk factors, a linkage disequilibrium score regression (LDSR) analysis was performed. BE/EA risk models were developed combining clinical/lifestyle risk factors with polygenic risk scores (PRS) derived from the GWAS meta-analysis.

RESULTS

The GWAS meta-analysis identified 27 BE and/or EA risk loci, 11 of which were novel. The TWAS identified promising BE/EA candidate genes at seven GWAS loci and at five additional risk loci. The LDSR analysis led to the identification of novel genetic correlations and pointed to differences in BE and EA aetiology. Gastro-oesophageal reflux disease appeared to contribute stronger to the metaplastic BE transformation than to EA development. Finally, combining PRS with BE/EA risk factors improved the performance of the risk models.

CONCLUSION

Our findings provide further insights into BE/EA aetiology and its relationship to risk factors. The results lay the foundation for future follow-up studies to identify underlying disease mechanisms and improving risk prediction.

Associations between polygenic risk of substance use and use disorder and alcohol, cannabis, and nicotine use in adolescence and young adulthood in a longitudinal twin study

BACKGROUND

Recent well-powered genome-wide association studies have enhanced prediction of substance use outcomes via polygenic scores (PGSs). Here, we test (1) whether these scores contribute to prediction over-and-above family history, (2) the extent to which PGS prediction reflects inherited genetic variation v. demography (population stratification and assortative mating) and indirect genetic effects of parents (genetic nurture), and (3) whether PGS prediction is mediated by behavioral disinhibition prior to substance use onset.

METHODS

PGSs for alcohol, cannabis, and nicotine use/use disorder were calculated for Minnesota Twin Family Study participants (N = 2483, 1565 monozygotic/918 dizygotic). Twins' parents were assessed for histories of substance use disorder. Twins were assessed for behavioral disinhibition at age 11 and substance use from ages 14 to 24. PGS prediction of substance use was examined using linear mixed-effects, within-twin pair, and structural equation models.

RESULTS

Nearly all PGS measures were associated with multiple types of substance use independently of family history. However, most within-pair PGS prediction estimates were substantially smaller than the corresponding between-pair estimates, suggesting that prediction is driven in part by demography and indirect genetic effects of parents. Path analyses indicated the effects of both PGSs and family history on substance use were mediated via disinhibition in preadolescence.

CONCLUSIONS

PGSs capturing risk of substance use and use disorder can be combined with family history measures to augment prediction of substance use outcomes. Results highlight indirect sources of genetic associations and preadolescent elevations in behavioral disinhibition as two routes through which these scores may relate to substance use.

Dense sampling of ethnic groups within African countries reveals fine-scale genetic structure and extensive historical admixture

Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data have only been obtained from a small proportion of present-day ethnolinguistic groups. By analyzing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria, and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example, correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relate to the expansions of Bantu language-speaking peoples.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix

Existing SNP-heritability estimation methods that leverage GWAS summary statistics produce estimators that are less efficient than the restricted maximum likelihood (REML) estimator using individual-level data under linear mixed models (LMMs). Increasing the precision of a heritability estimator is particularly important for regional analyses, as local genetic variances tend to be small. We introduce a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as REML (\emph{i.e.} relative efficiency greater than 92%) but only requires summary-level statistics -- Z-scores from the marginal association tests plus the empirical LD matrix. HEELS significantly improves the statistical efficiency of the existing summary-statistics-based heritability estimators-- for instance, HEELS produces heritability estimates that are more than 3-fold and 7-times less variable than GRE and LDSC, respectively. Moreover, we introduce a unified framework to evaluate and compare the performance of different LD approximation strategies. We propose representing the empirical LD as the sum of a low-rank matrix and a banded matrix. This approximation not only reduces the storage and memory cost of using the LD matrix, but also improves the computational efficiency of the HEELS estimation. We demonstrate the statistical efficiency of HEELS and the advantages of our proposed LD approximation strategies both in simulations and through empirical analyses of the UK Biobank data.

Genetic variants associated with syncope implicate neural and autonomic processes

AIMS

Syncope is a common and clinically challenging condition. In this study, the genetics of syncope were investigated to seek knowledge about its pathophysiology and prognostic implications.

METHODS AND RESULTS

This genome-wide association meta-analysis included 56 071 syncope cases and 890 790 controls from deCODE genetics (Iceland), UK Biobank (United Kingdom), and Copenhagen Hospital Biobank Cardiovascular Study/Danish Blood Donor Study (Denmark), with a follow-up assessment of variants in 22 412 cases and 286 003 controls from Intermountain (Utah, USA) and FinnGen (Finland). The study yielded 18 independent syncope variants, 17 of which were novel. One of the variants, p.Ser140Thr in PTPRN2, affected syncope only when maternally inherited. Another variant associated with a vasovagal reaction during blood donation and five others with heart rate and/or blood pressure regulation, with variable directions of effects. None of the 18 associations could be attributed to cardiovascular or other disorders. Annotation with regard to regulatory elements indicated that the syncope variants were preferentially located in neural-specific regulatory regions. Mendelian randomization analysis supported a causal effect of coronary artery disease on syncope. A polygenic score (PGS) for syncope captured genetic correlation with cardiovascular disorders, diabetes, depression, and shortened lifespan. However, a score based solely on the 18 syncope variants performed similarly to the PGS in detecting syncope risk but did not associate with other disorders.

CONCLUSION

The results demonstrate that syncope has a distinct genetic architecture that implicates neural regulatory processes and a complex relationship with heart rate and blood pressure regulation. A shared genetic background with poor cardiovascular health was observed, supporting the importance of a thorough assessment of individuals presenting with syncope.

Bidirectional Causal Associations Between Same-Sex Attraction and Psychological Distress: Testing Moderation and Mediation Effects

Only one study has examined bidirectional causality between sexual minority status (having same-sex attraction) and psychological distress. We combined twin and genomic data from 8700 to 9700 participants in the UK Twins Early Development Study cohort at ≈21 years to replicate and extend these bidirectional causal effects using separate unidirectional Mendelian Randomization-Direction of Causation models. We further modified these models to separately investigate sex differences, moderation by childhood factors (retrospectively-assessed early-life adversity and prospectively-assessed childhood gender nonconformity), and mediation by victimization. All analyses were carried out in OpenMx in R. Same-sex attraction causally influenced psychological distress with significant reverse causation (beta = 0.19 and 0.17; 95% CIs = 0.09, 0.29 and 0.08, 0.25 respectively) and no significant sex differences. The same-sex attraction → psychological distress causal path was partly mediated by victimization (12.5%) while the reverse causal path was attenuated by higher childhood gender nonconformity (moderation coefficient = -0.09, 95% CI: -0.13, -0.04).

Genetic propensity, socioeconomic status, and trajectories of depression over a course of 14 years in older adults

Depression is one of the leading causes of disability worldwide and is a major contributor to the global burden of disease among older adults. The study aimed to investigate the interplay between socio-economic markers (education and financial resources) and polygenic predisposition influencing individual differences in depressive symptoms and their change over time in older adults, which is of central relevance for preventative strategies. The sample encompassing n = 6202 adults aged ≥50 years old with a follow-up period of 14 years was utilised from the English Longitudinal Study of Ageing. Polygenic scores for depressive symptoms were calculated using summary statistics for (1) single-trait depressive symptoms (PGS-DS_single), and (2) multi-trait including depressive symptoms, subjective well-being, neuroticism, loneliness, and self-rated health (PGS-DS_multi-trait). The depressive symptoms over the past week were measured using the eight-item Centre for Epidemiologic Studies Depression Scale. One standard deviation increase in each PGS was associated with a higher baseline score in depressive symptoms. Each additional year of completed schooling was associated with lower baseline depression symptoms (β = -0.06, 95%CI = -0.07 to -0.05, p < 0.001); intermediate and lower wealth were associated with a higher baseline score in depressive symptoms. Although there was a weak interaction effect between PGS-DSs and socio-economic status in association with the baseline depressive symptoms, there were no significant relationships of PGS-DSs, socio-economic factors, and rate of change in the depressive symptoms during the 14-year follow-up period. Common genetic variants for depressive symptoms are associated with a greater number of depressive symptoms onset but not with their rate of change in the following 14 years. Lower socio-economic status is an important factor influencing individual levels of depressive symptoms, independently from polygenic predisposition to depressive symptoms.

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.

Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms

BACKGROUND

Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture.

METHODS

We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776).

RESULTS

Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups.

CONCLUSIONS

This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.

Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans

Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.

Learning high-order interactions for polygenic risk prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin.

A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.

Significance tests for R2 of out-of-sample prediction using polygenic scores

The coefficient of determination (R²) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R² is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R² is required to test the difference between them. Here, we show how to estimate the variance and covariance of R² values to assess the 95% CI and p value of the R² difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.

An empirical Bayes approach to improving population-specific genetic association estimation by leveraging cross-population data

Populations of non-European ancestry are substantially underrepresented in genome-wide association studies (GWAS). As genetic effects can differ between ancestries due to possibly different causal variants or linkage disequilibrium patterns, a meta-analysis that includes GWAS of all populations yields biased estimation in each of the populations and the bias disproportionately impacts non-European ancestry populations. This is because meta-analysis combines study-specific estimates with inverse variance as the weights, which causes biases towards studies with the largest sample size, typical of the European ancestry population. In this paper, we propose two empirical Bayes (EB) estimators to borrow the strength of information across populations although accounting for between-population heterogeneity. Extensive simulation studies show that the proposed EB estimators are largely unbiased and improve efficiency compared to the population-specific estimator. In contrast, even though the meta-analysis estimator has a much smaller variance, it yields significant bias when the genetic effect is heterogeneous across populations. We apply the proposed EB estimators to a large-scale trans-ancestry GWAS of stroke and demonstrate that the EB estimators reduce the variance of the population-specific estimator substantially, with the effect estimates close to the population-specific estimates.

Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study

PURPOSE

This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study.

METHODS

Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years).

RESULTS

For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with P_T = 0.05 and R² = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586).

CONCLUSION

This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.

Statistical methods for cis-Mendelian randomization with two-sample summary-level data

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

Advances and Applications of Polygenic Scores for Coronary Artery Disease

Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure-as early as birth-risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Risk assessment for colorectal cancer via polygenic risk score and lifestyle exposure: a large-scale association study of East Asian and European populations

BACKGROUND

The genetic architectures of colorectal cancer are distinct across different populations. To date, the majority of polygenic risk scores (PRSs) are derived from European (EUR) populations, which limits their accurate extrapolation to other populations. Here, we aimed to generate a PRS by incorporating East Asian (EAS) and EUR ancestry groups and validate its utility for colorectal cancer risk assessment among different populations.

METHODS

A large-scale colorectal cancer genome-wide association study (GWAS), harboring 35,145 cases and 288,934 controls from EAS and EUR populations, was used for the EAS-EUR GWAS meta-analysis and the construction of candidate EAS-EUR PRSs via different approaches. The performance of each PRS was then validated in external GWAS datasets of EAS (727 cases and 1452 controls) and EUR (1289 cases and 1284 controls) ancestries, respectively. The optimal PRS was further tested using the UK Biobank longitudinal cohort of 355,543 individuals and ultimately applied to stratify individual risk attached by healthy lifestyle.

RESULTS

In the meta-analysis across EAS and EUR populations, we identified 48 independent variants beyond genome-wide significance (P < 5 × 10^-8) at previously reported loci. Among 26 candidate EAS-EUR PRSs, the PRS-CSx approach-derived PRS (defined as PRS_CSx) that harbored genome-wide variants achieved the optimal discriminatory ability in both validation datasets, as well as better performance in the EAS population compared to the PRS derived from known variants. Using the UK Biobank cohort, we further validated a significant dose-response effect of PRS_CSx on incident colorectal cancer, in which the risk was 2.11- and 3.88-fold higher in individuals with intermediate and high PRS_CSx than in the low score subgroup (P_trend = 8.15 × 10^-53). Notably, the detrimental effect of being at a high genetic risk could be largely attenuated by adherence to a favorable lifestyle, with a 0.53% reduction in 5-year absolute risk.

CONCLUSIONS

In summary, we systemically constructed an EAS-EUR PRS to effectively stratify colorectal cancer risk, which highlighted its clinical implication among diverse ancestries. Importantly, these findings also supported that a healthy lifestyle could reduce the genetic impact on incident colorectal cancer.

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.

Trans-ethnic Polygenic Risk Scores for Body Mass Index: An International Hundred K+ Cohorts Consortium Study

Background

While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort.

Methods

The PRS model was developed trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis.

Results

We show that in the absence of a well powered trans-ethnic GWAS from which to derive SNPs and effect estimates, trans-ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred to adjust the summary statistics using trans-ethnic linkage disequilibrium reference panels. The ported trans-ethnic scores outperform population specific-PRS across all non-European ancestry populations investigated including East Asians and three-way admixed Brazilian cohort.

Conclusions

Widespread use of PRS in the clinic is hampered by a lack of genotyping data in individuals of non-European ancestry for the vast majority of traits. Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans-ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three-way admixed Brazilians.

Genetic analyses implicate complex links between adult testosterone levels and health and disease

BACKGROUND

Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes.

METHODS

Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality.

RESULTS

We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes.

CONCLUSIONS

Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.

A polygenic risk score predicts atrial fibrillation in cardiovascular disease

AIMS

Interest in targeted screening programmes for atrial fibrillation (AF) has increased, yet the role of genetics in identifying patients at highest risk of developing AF is unclear.

METHODS AND RESULTS

A total of 36,662 subjects without prior AF were analyzed from four TIMI trials. Subjects were divided into quintiles using a validated polygenic risk score (PRS) for AF. Clinical risk for AF was calculated using the CHARGE-AF model. Kaplan-Meier event rates, adjusted hazard ratios (HRs), C-indices, and net reclassification improvement were used to determine if the addition of the PRS improved prediction compared with clinical risk and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Over 2.3 years, 1018 new AF cases developed. AF PRS predicted a significant risk gradient for AF with a 40% increased risk per 1-SD increase in PRS [HR: 1.40 (1.32-1.49); P < 0.001]. Those with high AF PRS (top 20%) were more than two-fold more likely to develop AF [HR 2.45 (1.99-3.03), P < 0.001] compared with low PRS (bottom 20%). Furthermore, PRS provided an additional gradient of risk stratification on top of the CHARGE-AF clinical risk score, ranging from a 3-year incidence of 1.3% in patients with low clinical and genetic risk to 8.7% in patients with high clinical and genetic risk. The subgroup of patients with high clinical risk, high PRS, and elevated NT-proBNP had an AF risk of 16.7% over 3 years. The C-index with the CHARGE-AF clinical risk score alone was 0.65, which improved to 0.67 (P < 0.001) with the addition of NT-proBNP, and increased further to 0.70 (P < 0.001) with the addition of the PRS.

CONCLUSION

In patients with cardiovascular conditions, AF PRS is a strong independent predictor of incident AF that provides complementary predictive value when added to a validated clinical risk score and NT-proBNP.

A statistical boosting framework for polygenic risk scores based on large-scale genotype data

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 L 2-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.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

Multivariate analyses of molecular genetic associations between childhood psychopathology and adult mood disorders and related traits

Ubiquitous associations have been detected between different types of childhood psychopathology and polygenic risk scores based on adult psychiatric disorders and related adult outcomes, indicating that genetic factors partly explain the association between childhood psychopathology and adult outcomes. However, these analyses in general do not take into account the correlations between the adult trait and disorder polygenic risk scores. This study aimed to further clarify the influence of genetic factors on associations between childhood psychopathology and adult outcomes by accounting for these correlations. Using a multivariate multivariable regression, we analyzed associations of childhood attention-deficit/hyperactivity disorder (ADHD), internalizing, and social problems, with polygenic scores (PGS) of adult disorders and traits including major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI), derived for 20,539 children aged 8.5-10.5 years. After correcting for correlations between the adult phenotypes, major depression PGS were associated with all three childhood traits, that is, ADHD, internalizing, and social problems. In addition, BMI PGS were associated with ADHD symptoms and social problems, while neuroticism PGS were only associated with internalizing problems and educational attainment PGS were only associated with ADHD symptoms. PGS of bipolar disorder, subjective well-being, and insomnia were not associated with any childhood traits. Our findings suggest that associations between childhood psychopathology and adult traits like insomnia and subjective well-being may be primarily driven by genetic factors that influence adult major depression. Additionally, specific childhood phenotypes are genetically associated with educational attainment, BMI and neuroticism.

Polygenic risk scores in coronary artery disease

PURPOSE OF REVIEW

Recent advances in genetics have facilitated the calculation of polygenic risk scores (PRSs) based on common genetic risk variants of coronary artery disease (CAD). Here, we provide an explanation of the genetic basis for PRSs and review recent literature investigating PRSs and the clinical utility for different aspects of CAD.

RECENT FINDINGS

CAD-based PRSs are strongly associated with atherosclerosis burden in the coronary arteries and other vascular beds. In multiple studies, PRSs have proven to be a measure of CAD risk, more powerful than most established risk factors alone, that can be used from early life to stratify individuals into varying trajectories of lifetime risk. When implemented in risk stratification models for primary prevention of cardiovascular disease, PRSs provide modest improvements in discrimination (C-index generally increasing 0-4% points) and reclassification, but yield significant clinical benefit as a risk enhancer. Additionally, data suggest possible value of PRSs for aiding decisions in other aspects of diagnostics and treatment in CAD.

SUMMARY

Once genotyped, the genetic information may be used to calculate an infinite number of PRSs and contribute to personalize medicine providing clinical value for risk stratification, diagnostics and treatment in CAD as well as in other diseases.

Interplay of ADHD Polygenic Liability With Birth-Related, Somatic, and Psychosocial Factors in ADHD: A Nationwide Study

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD) is a multifactorial neurodevelopmental disorder, yet the interplay between ADHD polygenic risk scores (PRSs) and other risk factors remains relatively unexplored. The authors investigated associations, confounding, and interactions of ADHD PRS with birth-related, somatic, and psychosocial factors previously associated with ADHD.

METHODS

Participants included a random general population sample (N=21,578) and individuals diagnosed with ADHD (N=13,697) from the genotyped Danish iPSYCH2012 case cohort, born between 1981 and 2005. The authors derived ADHD PRSs and identified 24 factors previously associated with ADHD using national registers. Logistic regression was used to estimate associations of ADHD PRS with each risk factor in the general population. Cox models were used to evaluate confounding of risk factor associations with ADHD diagnosis by ADHD PRS and parental psychiatric history, and interactions between ADHD PRS and each risk factor.

RESULTS

ADHD PRS was associated with 12 of 24 risk factors (odds ratio range, 1.03-1.30), namely, small gestational age, infections, traumatic brain injury, and most psychosocial risk factors. Nineteen risk factors were associated with ADHD diagnosis (odds ratio range, 1.20-3.68), and adjusting for ADHD PRS and parental psychiatric history led to only minor attenuations. Only the interaction between ADHD PRS and maternal autoimmune disease survived correction for multiple testing.

CONCLUSIONS

Higher ADHD PRS in the general population is associated with small increases in risk for certain birth-related and somatic ADHD risk factors, and broadly to psychosocial adversity. Evidence of gene-environment interaction was limited, as was confounding by ADHD PRS and family psychiatric history on ADHD risk factor associations. This suggests that the majority of the investigated ADHD risk factors act largely independently of current ADHD PRS to increase risk of ADHD.

Statistical Methods for Disease Risk Prediction with Genotype Data

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

Polygenic liability, stressful life events and risk for secondary-treated depression in early life: a nationwide register-based case-cohort study

BACKGROUND

In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark.

METHODS

Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions.

RESULTS

Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (β = -0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15.

CONCLUSIONS

These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals.