Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Genes Related to Education Predict Frailty Among Older Adults in the United States

OBJECTIVE

This article expands on research that links education and frailty among older adults by considering the role of genes associated with education.

METHOD

Data come from a sample of 7,064 non-Hispanic, white adults participating in the 2004-2012 waves of the Health and Retirement Study. Frailty was measured with two indices: (a) The Frailty Index which corresponds to a deficit accumulation model; and (b) The Paulson-Lichtenberg Frailty Index which corresponds to the biological syndrome/phenotype model. Genes associated with education were quantified using an additive polygenic score. Associations between the polygenic score and frailty indices were tested using a series of multilevel models, controlling for multiple observations for participants across waves.

RESULTS

Results showed a strong and negative association between genes for education and frailty symptoms in later life. This association exists above and beyond years of completed education and we demonstrate that this association becomes weaker as older adults approach their 80s.

DISCUSSION

The results contribute to the education-health literature by highlighting new and important pathways through which education might be linked to successful aging.

Imputed gene expression risk scores: a functionally informed component of polygenic risk

Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

Are there causal relationships between attention-deficit/hyperactivity disorder and body mass index? Evidence from multiple genetically informed designs

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and body mass index (BMI) are associated. However, it remains unclear whether this association reflects causal relationships in either direction or confounding. Here, we implemented genetically informed methods to examine bidirectional causality and potential confounding.

METHODS

Three genetically informed methods were employed: (i) cross-lagged twin-differences analyses to assess bidirectional effects of ADHD symptoms and BMI at ages 8, 12, 14 and 16 years in 2386 pairs of monozygotic twins from the Twins Early Development Study (TEDS); (ii) within- and between-family ADHD and BMI polygenic score (PS) analyses in 3320 pairs of dizygotic TEDS twins; and (iii) two-sample bidirectional Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) on ADHD (N = 55,374) and BMI (N = 806,834).

RESULTS

Mixed results were obtained across the three methods. Twin-difference analyses provided little support for cross-lagged associations between ADHD symptoms and BMI over time. PS analyses were consistent with bidirectional relationships between ADHD and BMI, with plausible time-varying effects from childhood to adolescence. MR findings also suggested bidirectional causal effects between ADHD and BMI. Multivariable MR indicated the presence of substantial confounding in bidirectional relationships.

CONCLUSIONS

The three methods converged to highlight multiple sources of confounding in the association between ADHD and BMI. PS and MR analyses suggested plausible causal relationships in both directions. Possible explanations for mixed causal findings across methods are discussed.

Advances in Genomics Research of Blood Pressure Responses to Dietary Sodium and Potassium Intakes

More than half of US adults have hypertension by 40 years of age and a subsequent increase in atherosclerotic cardiovascular disease risk. Dietary sodium and potassium are intricately linked to the pathophysiology of hypertension. However, blood pressure responses to dietary sodium and potassium, phenomena known as salt and potassium sensitivity of blood pressure, respectively, are heterogenous and normally distributed in the general population. Like blood pressure, salt and potassium sensitivity are complex phenotypes, and previous research has shown that up to 75% of individuals experience a blood pressure change in response to such dietary minerals. Previous research has also implicated both high salt sensitivity and low salt sensitivity (or salt resistance) of blood pressure to an increased risk of hypertension and potentially atherosclerotic cardiovascular disease risk. Given the clinical challenges required to accurately measure the sodium and potassium response phenotypes, genomic characterization of these traits has become of interest for hypertension prevention initiatives on both the individual and population levels. Here, we review advances in human genomics research of blood pressure responses to dietary sodium and potassium by focusing on 3 main areas, including the phenotypic characterization of salt sensitivity and resistance, clinical challenges in diagnosing such phenotypes, and the genomic mechanisms that may help to explain salt and potassium sensitivity and resistance. Through this process, we hope to further underline the value of leveraging genomics and broader multiomics for characterizing the blood pressure response to sodium and potassium to improve precision in lifestyle approaches for primordial and primary atherosclerotic cardiovascular disease prevention.

Association between the polygenic liabilities for prostate cancer and breast cancer with biochemical recurrence after radical prostatectomy for localized prostate cancer

Prostate and breast cancers are hormone-related malignancies and are characterized by a complex interplay of hundreds of susceptibility loci throughout the genome. Prostate cancer could be inhibited by eliminating androgens through castration or estrogen administration, thus facilitating long-term treatment of prostate cancer; however, the role of estrogen in prostate cancer remains unclear. This study aimed to determine whether polygenic risk scores (PRSs) comprising combinations of genome-wide susceptibility variants influence the clinical outcomes of prostate cancer patients. The study subjects were recruited from four medical centers in Taiwan, and genome-wide genotyping data were obtained from 643 prostate cancer patients. We derived the PRS for prostate cancer (PRS-PC) and for breast cancer (PRS-BC) for each patient. The association between the PRS-PC/PRS-BC at the age of prostate cancer onset and recurrence within seven years was evaluated using a regression model adjusted for population stratification components. A higher PRS-PC was associated with an earlier onset age for prostate cancer (beta in per SD increase in PRS = -0.89, P = 0.0008). In contrast, a higher PRS-BC was associated with an older onset age for prostate cancer (beta = 0.59, P = 0.02). PRS-PC was not associated with the risk of recurrence (hazard ratio = 1.03, P = 0.67), whereas a higher PRS-BC was associated with a low recurrence risk (hazard ratio = 0.86, P = 0.03). These results indicate that the genetic predisposition to breast cancer is associated with a low risk of prostate cancer recurrence. Further studies are warranted to explore the role of breast cancer susceptibility variants and estrogen signaling in prostate cancer progression.

Polygenic risk scores for low-density lipoprotein cholesterol and familial hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal dominant monogenic disorder characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C) and an increased risk of premature coronary artery disease (CAD). Recently, it has been shown that a high polygenic risk score (PRS) could be an independent risk factor for CAD in FH patients of European ancestry. However, it is uncertain whether PRS is also useful for risk stratification of FH patients in East Asia. We recruited and genotyped clinically diagnosed FH (CDFH) patients from the Kanazawa University Mendelian Disease FH registry and controls from the Shikamachi Health Improvement Practice genome cohort in Japan. We calculated PRS from 3.6 million variants of each participant (imputed from the 1000 Genome phase 3 Asian dataset) for LDL-C (PRS_LDLC) using a genome-wide association study summary statistic from the BioBank Japan Project. We assessed the association of PRS_LDLC with LDL-C and CAD among and within monogenic FH, mutation negative CDFH, and controls. We tested a total of 1223 participants (376 FH patients, including 173 with monogenic FH and 203 with mutation negative CDFH, and 847 controls) for the analyses. PRS_LDLC was significantly higher in mutation negative CDFH patients than in controls (p = 3.1 × 10^-13). PRS_LDLC was also significantly linked to LDL-C in controls (p trend = 3.6 × 10^-4) but not in FH patients. Moreover, we could not detect any association between PRS_LDLC and CAD in any of the groups. In conclusion, mutation negative CDFH patients demonstrated significantly higher PRS_LDLC than controls. However, PRS_LDLC may have little additional effect on LDL-C and CAD among FH patients.

Cardiovascular Risk Factor Trajectories Since Childhood and Cognitive Performance in Midlife: The Cardiovascular Risk in Young Finns Study

BACKGROUND

Cardiovascular risk factors, such as high blood pressure, adverse serum lipids, and elevated body mass index in midlife, may harm cognitive performance. It is important to note that longitudinal accumulation of cardiovascular risk factors since childhood may be associated with cognitive performance already since childhood, but the previous evidence is scarce. We studied the associations of cardiovascular risk factors from childhood to midlife, their accumulation, and midlife cognitive performance.

METHODS

From 1980, a population-based cohort of 3596 children (3-18 years of age) have been repeatedly followed up for 31 years. Blood pressure, serum lipids, and body mass index were assessed in all follow-ups. Cardiovascular risk factor trajectories from childhood to midlife were identified using latent class growth mixture modeling. Cognitive testing was performed in 2026 participants 34 to 49 years of age using a computerized test. The associations of the cardiovascular risk factor trajectories and cognitive performance were studied for individual cardiovascular risk factors and cardiovascular risk factor accumulation.

RESULTS

Consistently high systolic blood pressure (β=-0.262 SD [95% CI, -0.520 to -0.005]) and serum total cholesterol (β=-0.214 SD [95% CI, -0.365 to -0.064]) were associated with worse midlife episodic memory and associative learning compared with consistently low values. Obesity since childhood was associated with worse visual processing and sustained attention (β=-0.407 SD [95% CI, -0.708 to -0.105]) compared with normal weight. An inverse association was observed for the cardiovascular risk factor accumulation with episodic memory and associative learning (P for trend=0.008; 3 cardiovascular risk factors: β=-0.390 SD [95% CI, -0.691 to -0.088]), with visual processing and sustained attention (P for trend<0.0001; 3 cardiovascular risk factors: β=-0.443 SD [95% CI, -0.730 to -0.157]), and with reaction and movement time (P for trend=0.048; 2 cardiovascular risk factors: β=-0.164 SD [95% CI, -0.318 to -0.010]).

CONCLUSIONS

Longitudinal elevated systolic blood pressure, high serum total cholesterol, and obesity from childhood to midlife were inversely associated with midlife cognitive performance. It is important to note that the higher the number of cardiovascular risk factors, the worse was the observed cognitive performance. Therefore, launching preventive strategies against cardiovascular risk factors beginning from childhood might benefit primordial promotion of cognitive health in adulthood.

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

Background

Machine learning approaches for predicting disease risk from high-dimensional whole genome sequence (WGS) data often result in unstable models that can be difficult to interpret, limiting the identification of putative sets of biomarkers. Here, we design and validate a graph-based methodology based on maximum flow, which leverages the presence of linkage disequilibrium (LD) to identify stable sets of variants associated with complex multigenic disorders.

Results

We apply our method to a previously published logistic regression model trained to identify variants in simple repeat sequences associated with autism spectrum disorder (ASD); this L₁-regularized model exhibits high predictive accuracy yet demonstrates great variability in the features selected from over 230,000 possible variants. In order to improve model stability, we extract the variants assigned non-zero weights in each of 5 cross-validation folds and then assemble the five sets of features into a flow network subject to LD constraints. The maximum flow formulation allowed us to identify 55 variants, which we show to be more stable than the features identified by the original classifier.

Conclusion

Our method allows for the creation of machine learning models that can identify predictive variants. Our results help pave the way towards biomarker-based diagnosis methods for complex genetic disorders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00262-x.

Risk Stratification and Clinical Utility of Polygenic Risk Scores in Ophthalmology

Translational Relevance

Common genetic variants can be used to effectively stratify the risk of disease development and progression and may be used to guide screening, triaging, monitoring, or treatment thresholds.

Mechanisms of parent-child transmission of tobacco and alcohol use with polygenic risk scores: Evidence for a genetic nurture effect

Parent-child similarity is a function of genetic and environmental transmission. In addition, genetic effects not transmitted to offspring may drive parental behavior, thereby affecting the rearing environment of the child. Measuring genetic proclivity directly, through polygenic risk scores (PRSs), provides a way to test for the effect of nontransmitted parental genotype, on offspring outcome, termed a genetic nurture effect-in other words, if and how parental genomes might affect their children through the environment. The current study used polygenic risk scores for smoking initiation, cigarettes per day, and drinks per week to predict substance use in a sample of 3,008 twins, assessed prospectively from age 17-29, and their parents, from the Minnesota Center for Twin and Family Research. Mixed-effects models were used to test for a genetic nurture effect whereby parental PRSs predict offspring tobacco and alcohol use after statistically adjusting for offspring's own PRS. Parental smoking initiation PRS predicted offspring cigarettes per day at age 24 (β = .103, 95% CI [.03, .17]) and alcohol use at age 17 (β = .091, 95% CI [.04, .14]) independent of shared genetics. There was also a suggestive independent association between the parent PRS and offspring smoking at age 17 (β = .096; 95% CI [.02, .17]). Mediation analyses provided some evidence for environmental effects of parental smoking, alcohol use, and family socioeconomic status. These findings, and more broadly the molecular genetic method used, have implications on the identification of environmental effects on developmental outcomes such as substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being

Resilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (0.71) and showed a strong environmental correlation (0.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.

Chronic migraine: Genetics or environment?

BACKGROUND

The transition from episodic migraine to chronic migraine, migraine chronification, is usually a gradual process, which involves multiple risk factors. To date, studies of the genetic risk factors for chronic migraine have focused primarily on candidate-gene approaches using healthy individuals as controls.

AIMS AND METHODS

In this study, we used a large cohort of migraine families and unrelated migraine patients (n > 2200) with supporting genotype and whole-genome sequencing data. We evaluated whether there are any genetic variants, common or rare, with a specific association to chronic migraine compared with episodic migraine.

RESULTS

We found no aggregation of chronic migraine in families with a clustering of migraine. No specific rare variants gave rise to migraine chronification, and migraine chronification was not associated with a higher polygenic risk score. Migraine chronification was not associated with allelic associations with an odds ratio above 2.65. Assessment of effect sizes with genome-wide significance below an odds ratio of 2.65 requires a genome-wide association study of at least 7500 chronic migraine patients.

CONCLUSION

Our results suggest that migraine chronification is caused by environmental factors rather than genetic factors.

Childhood trauma, life-time self-harm, and suicidal behaviour and ideation are associated with polygenic scores for autism

Autistic individuals experience significantly elevated rates of childhood trauma, self-harm and suicidal behaviour and ideation (SSBI). Is this purely the result of negative environmental experiences, or does this interact with genetic predisposition? In this study we investigated if a genetic predisposition for autism is associated with childhood trauma using polygenic scores (PGS) and genetic correlations in the UK Biobank (105,222 < N < 105,638), and tested potential mediators and moderators of the association between autism, childhood trauma and SSBI. Autism PGS were significantly associated with childhood trauma (max R2 = 0.096%, P < 2 × 10-16), self-harm ideation (max R2 = 0.108%, P < 2 × 10-16), and self-harm (max R2 = 0.13%, P < 2 × 10-16). Supporting this, we identified significant genetic correlations between autism and childhood trauma (rg = 0.36 ± 0.05, P = 8.13 × 10-11), self-harm ideation (rg = 0.49 ± 0.05, P = 4.17 × 10-21) and self-harm (rg = 0.48 ± 0.05, P = 4.58 × 10-21), and an over-transmission of PGS for the two SSBI phenotypes from parents to autistic probands. Male sex negatively moderated the effect of autism PGS on childhood trauma (β = -0.023 ± 0.005, P = 6.74 × 10-5). Further, childhood trauma positively moderated the effect of autism PGS on self-harm score (β = 8.37 × 10-3 ± 2.76 × 10-3, P = 2.42 × 10-3) and self-harm ideation (β = 7.47 × 10-3 ± 2.76 × 10-3, P = 6.71 × 10-3). Finally, depressive symptoms, quality and frequency of social interactions, and educational attainment were significant mediators of the effect of autism PGS on SSBI, with the proportion of effect mediated ranging from 0.23 (95% CI: 0.09-0.32) for depression to 0.008 (95% CI: 0.004-0.01) for educational attainment. Our findings identify that a genetic predisposition for autism is associated with adverse life-time outcomes, which represent complex gene-environment interactions, and prioritizes potential mediators and moderators of this shared biology. It is important to identify sources of trauma for autistic individuals in order to reduce their occurrence and impact.

Evaluation of polygenic prediction methodology within a reference-standardized framework

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.

Genetic scores for adult subcortical volumes associate with subcortical volumes during infancy and childhood

Individual differences in subcortical brain volumes are highly heritable. Previous studies have identified genetic variants that underlie variation in subcortical volumes in adults. We tested whether those previously identified variants also affect subcortical regions during infancy and early childhood. The study was performed within the Generation R study, a prospective birth cohort. We calculated polygenic scores based on reported GWAS for volumes of the accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus. Participants underwent cranial ultrasound around 7 weeks of age (range: 3-20), and we obtained metrics for the gangliothalamic ovoid, a predecessor of the basal ganglia. Furthermore, the children participated in a magnetic resonance imaging (MRI) study around the age of 10 years (range: 9-12). A total of 340 children had complete data at both examinations. Polygenic scores primarily associated with their corresponding volumes at 10 years of age. The scores also moderately related to the diameter of the gangliothalamic ovoid on cranial ultrasound. Mediation analysis showed that the genetic influence on subcortical volumes at 10 years was only mediated for 16.5-17.6% of the total effect through the gangliothalamic ovoid diameter at 7 weeks of age. Combined, these findings suggest that previously identified genetic variants in adults are relevant for subcortical volumes during early life, and that they affect both prenatal and postnatal development of the subcortical regions.

Identifying therapeutic drug targets using bidirectional effect genes

Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10-8). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

Copy Number Variant Analysis and Genome-wide Association Study Identify Loci with Large Effect for Vesicoureteral Reflux

BACKGROUND

Vesicoureteral reflux (VUR) is a common, familial genitourinary disorder, and a major cause of pediatric urinary tract infection (UTI) and kidney failure. The genetic basis of VUR is not well understood.

METHODS

A diagnostic analysis sought rare, pathogenic copy number variant (CNV) disorders among 1737 patients with VUR. A GWAS was performed in 1395 patients and 5366 controls, of European ancestry.

RESULTS

Altogether, 3% of VUR patients harbored an undiagnosed rare CNV disorder, such as the 1q21.1, 16p11.2, 22q11.21, and triple X syndromes ((OR, 3.12; 95% CI, 2.10 to 4.54; P=6.35×10-8) The GWAS identified three study-wide significant and five suggestive loci with large effects (ORs, 1.41-6.9), containing canonical developmental genes expressed in the developing urinary tract (WDPCP, OTX1, BMP5, VANGL1, and WNT5A). In particular, 3.3% of VUR patients were homozygous for an intronic variant in WDPCP (rs13013890; OR, 3.65; 95% CI, 2.39 to 5.56; P=1.86×10-9). This locus was associated with multiple genitourinary phenotypes in the UK Biobank and eMERGE studies. Analysis of Wnt5a mutant mice confirmed the role of Wnt5a signaling in bladder and ureteric morphogenesis.

CONCLUSIONS

These data demonstrate the genetic heterogeneity of VUR. Altogether, 6% of patients with VUR harbored a rare CNV or a common variant genotype conferring an OR >3. Identification of these genetic risk factors has multiple implications for clinical care and for analysis of outcomes in VUR.

Polygenic scores for dyslipidemia: the emerging genomic model of plasma lipoprotein trait inheritance

PURPOSE OF REVIEW

Contemporary polygenic scores, which summarize the cumulative contribution of millions of common single-nucleotide variants to a phenotypic trait, can have effects comparable to monogenic mutations. This review focuses on the emerging use of 'genome-wide' polygenic scores for plasma lipoproteins to define the etiology of clinical dyslipidemia, modify the severity of monogenic disease, and inform therapeutic options.

RECENT FINDINGS

Polygenic scores for low-density lipoprotein cholesterol (LDL-C), triglycerides, and high-density lipoprotein cholesterol are associated with severe hypercholesterolemia, hypertriglyceridemia, or hypoalphalipoproteinemia, respectively. These polygenic scores for LDL-C or triglycerides associate with risk of incident coronary artery disease (CAD) independent of polygenic scores designed specifically for CAD and may identify individuals that benefit most from lipid-lowering medication. Additionally, the severity of hypercholesterolemia and CAD associated with familial hypercholesterolemia-a common monogenic disorder-is modified by these polygenic factors. The current focus of polygenic scores for dyslipidemia is to design predictive polygenic scores for diverse populations and determining how these polygenic scores could be implemented and standardized for use in the clinic.

SUMMARY

Polygenic scores have shown early promise for the management of dyslipidemias, but several challenges need to be addressed before widespread clinical implementation to ensure that potential benefits are robust and reproducible, equitable, and cost-effective.

Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status

IMPORTANCE

Combining information on polygenic risk scores (PRSs) with other known risk factors could potentially improve the identification of risk of depression in the general population. However, to our knowledge, no study has estimated the association of PRS with the absolute risk of depression, and few have examined combinations of the PRS and other important risk factors, including parental history of psychiatric disorders and socioeconomic status (SES), in the identification of depression risk.

OBJECTIVE

To assess the individual and joint associations of PRS, parental history, and SES with relative and absolute risk of early-onset depression.

DESIGN, SETTING, AND PARTICIPANTS

This case-cohort study included participants from the iPSYCH2012 sample, a case-cohort sample of all singletons born in Denmark between May 1, 1981, and December 31, 2005. Hazard ratios (HRs) and absolute risks were estimated using Cox proportional hazards regression for case-cohort designs.

EXPOSURES

The PRS for depression; SES measured using maternal educational level, maternal marital status, and paternal employment; and parental history of psychiatric disorders (major depression, bipolar disorder, other mood or psychotic disorders, and other psychiatric diagnoses).

MAIN OUTCOMES AND MEASURES

Hospital-based diagnosis of depression from inpatient, outpatient, or emergency settings.

RESULTS

Participants included 17 098 patients with depression (11 748 [68.7%] female) and 18 582 (9429 [50.7%] male) individuals randomly selected from the base population. The PRS, parental history, and lower SES were all significantly associated with increased risk of depression, with HRs ranging from 1.32 (95% CI, 1.29-1.35) per 1-SD increase in PRS to 2.23 (95% CI, 1.81-2.64) for maternal history of mood or psychotic disorders. Fully adjusted models had similar effect sizes, suggesting that these risk factors do not confound one another. Absolute risk of depression by the age of 30 years differed substantially, depending on an individual's combination of risk factors, ranging from 1.0% (95% CI, 0.1%-2.0%) among men with high SES in the bottom 2% of the PRS distribution to 23.7% (95% CI, 16.6%-30.2%) among women in the top 2% of PRS distribution with a parental history of psychiatric disorders.

CONCLUSIONS AND RELEVANCE

This study suggests that current PRSs for depression are not more likely to be associated with major depressive disorder than are other known risk factors; however, they may be useful for the identification of risk in conjunction with other risk factors.

A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits

The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.

Common and Rare Variant Prediction and Penetrance of IBD in a Large, Multi-ethnic, Health System-based Biobank Cohort

BACKGROUND AND AIMS

Polygenic risk scores (PRS) may soon be used to predict inflammatory bowel disease (IBD) risk in prevention efforts. We leveraged exome-sequence and single nucleotide polymorphism (SNP) array data from 29,358 individuals in the multiethnic, randomly ascertained health system-based BioMe biobank to define effects of common and rare IBD variants on disease prediction and pathophysiology.

METHODS

PRS were calculated from European, African American, and Ashkenazi Jewish (AJ) reference case-control studies, and a meta-GWAS run using all three association datasets. PRS were then combined using regression to assess which combination of scores best predicted IBD status in European, AJ, Hispanic, and African American cohorts in BioMe. Additionally, rare variants were assessed in genes associated with very early-onset IBD (VEO-IBD), by estimating genetic penetrance in each BioMe population.

RESULTS

Combining risk scores based on association data from distinct ancestral populations improved IBD prediction for every population in BioMe and significantly improved prediction among European ancestry UK Biobank individuals. Lower predictive power for non-Europeans was observed, reflecting in part substantially lower African IBD case-control reference sizes. We replicated associations for two VEO-IBD genes, ADAM17 and LRBA, with high dominant model penetrance in BioMe. Autosomal recessive LRBA risk alleles are associated with severe, early-onset autoimmunity; we show that heterozygous carriage of an African-predominant LRBA protein-altering allele is associated with significantly decreased LRBA and CTLA-4 expression with T-cell activation.

CONCLUSIONS

Greater genetic diversity in African populations improves prediction across populations, and generalizes some VEO-IBD genes. Increasing African American IBD case-collections should be prioritized to reduce health disparities and enhance pathophysiological insight.

Negative selection on complex traits limits phenotype prediction accuracy between populations

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide association studies are done in European populations, which reduces the accuracy of predictions via polygenic scores in non-European populations. Here, we use population genetic models to show that human demographic history and negative selection on complex traits can result in population-specific genetic architectures. For traits where alleles with the largest effect on the trait are under the strongest negative selection, approximately half of the heritability can be accounted for by variants in Europe that are absent from Africa, leading to poor performance in phenotype prediction across these populations. Further, under such a model, individuals in the tails of the genetic risk distribution may not be identified via polygenic scores generated in another population. We empirically test these predictions by building a model to stratify heritability between European-specific and shared variants and applied it to 37 traits and diseases in the UK Biobank. Across these phenotypes, ∼30% of the heritability comes from European-specific variants. We conclude that genetic association studies need to include more diverse populations to enable the utility of phenotype prediction in all populations.

On polygenic risk scores for complex traits prediction

Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome-wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high-dimensional but sparsity free setting where the triplet ( n , p , m ) → ( ∞ , ∞ , ∞ ) with n , p , m being the sample size, the number of assayed single-nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out-of-sample (prediction) R-squared for PRS. These results help understand the widespread observed gap between the in-sample heritability (or partial R-squared due to the genetic features) estimate and the out-of-sample R-squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p-value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the m / n ratio. For highly polygenic traits with a large m / n ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobank database.

Effect of Education on Myopia: Evidence from the United Kingdom ROSLA 1972 Reform

Purpose

Cross-sectional and longitudinal studies have consistently reported an association between education and myopia. However, conventional observational studies are at risk of bias due to confounding by factors such as socioeconomic position and parental educational attainment. The current study aimed to estimate the causal effect of education on refractive error using regression discontinuity analysis.

Methods

Regression discontinuity analysis was applied to assess the influence on refractive error of the raising of the school leaving age (ROSLA) from 15 to 16 years introduced in England and Wales in 1972. For comparison, a conventional ordinary least squares (OLS) analysis was performed. The analysis sample comprised 21,548 UK Biobank participants born in a nine-year interval centered on September 1957, the date of birth of those first affected by ROSLA.

Results

In OLS analysis, the ROSLA 1972 reform was associated with a -0.29 D (95% confidence interval [CI]: -0.36 to -0.21, P < 0.001) more negative refractive error. In other words, the refractive error of the study sample became more negative by -0.29 D during the transition from a minimum school leaving age of 15 to 16 years of age. Regression discontinuity analysis estimated the causal effect of the ROSLA 1972 reform on refractive error as -0.77 D (95% CI: -1.53 to -0.02, P = 0.04).

Conclusions

Additional compulsory schooling due to the ROSLA 1972 reform was associated with a more negative refractive error, providing additional support for a causal relationship between education and myopia.

Genetic Risk Stratification: A Paradigm Shift in Prevention of Coronary Artery Disease

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CAD is a pandemic that can be prevented. Conventional risk factors are inadequate to detect who is at risk early in the asymptomatic stage.

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Genetic risk for CAD can be determined at birth, and those at highest genetic risk have been shown to respond to lifestyle changes and statin therapy with a 40% to 50% reduction in cardiac events.

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Genetic risk stratification for CAD should be brought to the bedside in an attempt to prevent this pandemic disease.

Coronary artery disease (CAD) is a pandemic disease that is highly preventable as shown by secondary prevention. Primary prevention is preferred knowing that 50% of the population can expect a cardiac event in their lifetime. Risk stratification for primary prevention using the American Heart Association/American College of Cardiology predicted 10-year risk based on conventional risk factors for CAD is less than optimal. Conventional risk factors such as hypertension, cholesterol, and age are age-dependent and not present until the sixth or seventh decade of life. The genetic risk score (GRS), which is estimated from the recently discovered genetic variants predisposed to CAD, offers a potential solution to this dilemma. The GRS, which is derived from genotyping the population with a microarray containing these genetic risk variants, has indicated that genetic risk stratification based on the GRS is superior to that of conventional risk factors in detecting those at high risk and who would benefit most from statin therapy. Studies performed in >1 million individuals confirmed genetic risk stratification is superior and primarily independent of conventional risk factors. Prospective clinical trials based on risk stratification for CAD using the GRS have shown lifestyle changes, physical activity, and statin therapy are associated with 40% to 50% reduction in cardiac events in the high genetic risk group (20%). Genetic risk stratification has the advantage of being innate to an individual’s DNA, and because DNA does not change in a lifetime, it is independent of age. Genetic risk stratification is inexpensive and can be performed worldwide, providing risk analysis at any age and thus has the potential to revolutionize primary prevention.

Estimating the effective sample size in association studies of quantitative traits

The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment

BACKGROUND

To better characterize brain-based mechanisms of polygenic liability for psychopathology and psychological traits, we extended our previous report (Liu et al. Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. Psychological Medicine, 2017), focused solely on schizophrenia, to test the association between multivariate psychophysiological candidate endophenotypes (including novel measures of θ/δ oscillatory activity) and a range of polygenic scores (PGSs), namely alcohol/cannabis/nicotine use, an updated schizophrenia PGS (containing 52 more genome-wide significant loci than the PGS used in our previous report) and educational attainment.

METHOD

A large community-based twin/family sample (N = 4893) was genome-wide genotyped and imputed. PGSs were constructed for alcohol use, regular smoking initiation, lifetime cannabis use, schizophrenia, and educational attainment. Eleven endophenotypes were assessed: visual oddball task event-related electroencephalogram (EEG) measures (target-related parietal P3 amplitude, frontal θ, and parietal δ energy/inter-trial phase clustering), band-limited resting-state EEG power, antisaccade error rate. Principal component analysis exploited covariation among endophenotypes to extract a smaller number of meaningful dimensions/components for statistical analysis.

RESULTS

Endophenotypes were heritable. PGSs showed expected intercorrelations (e.g. schizophrenia PGS correlated positively with alcohol/nicotine/cannabis PGSs). Schizophrenia PGS was negatively associated with an event-related P3/δ component [β = -0.032, nonparametric bootstrap 95% confidence interval (CI) -0.059 to -0.003]. A prefrontal control component (event-related θ/antisaccade errors) was negatively associated with alcohol (β = -0.034, 95% CI -0.063 to -0.006) and regular smoking PGSs (β = -0.032, 95% CI -0.061 to -0.005) and positively associated with educational attainment PGS (β = 0.031, 95% CI 0.003-0.058).

CONCLUSIONS

Evidence suggests that multivariate endophenotypes of decision-making (P3/δ) and cognitive/attentional control (θ/antisaccade error) relate to alcohol/nicotine, schizophrenia, and educational attainment PGSs and represent promising targets for future research.

Improving reporting standards for polygenic scores in risk prediction studies

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Improving reporting standards for polygenic scores in risk prediction studies

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians

Background

The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear.

Methods

We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients.

Results

Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10^− 103 < P < 1.3 × 10^− 75) and 3-year lipid changes (1.4 × 10^− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10^− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10^− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, P_trend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10^− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association.

Conclusions

The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00831-z.

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

BACKGROUND

Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses.

OBJECTIVES

We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning.

METHODS

We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison.

RESULTS

Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction.

CONCLUSIONS

There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

An Improved Genome-Wide Polygenic Score Model for Predicting the Risk of Type 2 Diabetes

Polygenic risk score (PRS) has been shown to be predictive of disease risk such as type 2 diabetes (T2D). However, the existing studies on genetic prediction for T2D only had limited predictive power. To further improve the predictive capability of the PRS model in identifying individuals at high T2D risk, we proposed a new three-step filtering procedure, which aimed to include truly predictive single-nucleotide polymorphisms (SNPs) and avoid unpredictive ones into PRS model. First, we filtered SNPs according to the marginal association p-values (p≤ 5× 10^-2) from large-scale genome-wide association studies. Second, we set linkage disequilibrium (LD) pruning thresholds (r ²) as 0.2, 0.4, 0.6, and 0.8. Third, we set p-value thresholds as 5× 10^-2, 5× 10^-4, 5× 10^-6, and 5× 10^-8. Then, we constructed and tested multiple candidate PRS models obtained by the PRSice-2 software among 182,422 individuals in the UK Biobank (UKB) testing dataset. We validated the predictive capability of the optimal PRS model that was chosen from the testing process in identifying individuals at high T2D risk based on the UKB validation dataset (n = 274,029). The prediction accuracy of the PRS model evaluated by the adjusted area under the receiver operating characteristics curve (AUC) showed that our PRS model had good prediction performance [AUC = 0.795, 95% confidence interval (CI): (0.790, 0.800)]. Specifically, our PRS model identified 30, 12, and 7% of the population at greater than five-, six-, and seven-fold risk for T2D, respectively. After adjusting for sex, age, physical measurements, and clinical factors, the AUC increased to 0.901 [95% CI: (0.897, 0.904)]. Therefore, our PRS model could be useful for population-level preventive T2D screening.

Polygenic risk modeling with latent trait-related genetic components

Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex

Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.

Polygenic risk score validation using Korean genomes of 265 early-onset acute myocardial infarction patients and 636 healthy controls

Background

The polygenic risk score (PRS) developed for coronary artery disease (CAD) is known to be effective for classifying patients with CAD and predicting subsequent events. However, the PRS was developed mainly based on the analysis of Caucasian genomes and has not been validated for East Asians. We aimed to evaluate the PRS in the genomes of Korean early-onset AMI patients (n = 265, age ≤50 years) following PCI and controls (n = 636) to examine whether the PRS improves risk prediction beyond conventional risk factors.

Results

The odds ratio of the PRS was 1.83 (95% confidence interval [CI]: 1.69–1.99) for early-onset AMI patients compared with the controls. For the classification of patients, the area under the curve (AUC) for the combined model with the six conventional risk factors (diabetes mellitus, family history of CAD, hypertension, body mass index, hypercholesterolemia, and current smoking) and PRS was 0.92 (95% CI: 0.90–0.94) while that for the six conventional risk factors was 0.91 (95% CI: 0.85–0.93). Although the AUC for PRS alone was 0.65 (95% CI: 0.61–0.69), adding the PRS to the six conventional risk factors significantly improved the accuracy of the prediction model (P = 0.015). Patients with the upper 50% of PRS showed a higher frequency of repeat revascularization (hazard ratio = 2.19, 95% CI: 1.47–3.26) than the others.

Conclusions

The PRS using 265 early-onset AMI genomes showed improvement in the identification of patients in the Korean population and showed potential for genomic screening in early life to complement conventional risk prediction.

Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups

Systemic lupus erythematosus (SLE), a worldwide autoimmune disease with high heritability, shows differences in prevalence, severity and age of onset among different ancestral groups. Previous genetic studies have focused more on European populations, which appear to be the least affected. Consequently, the genetic variations that underlie the commonalities, differences and treatment options in SLE among ancestral groups have not been well elucidated. To address this, we undertake a genome-wide association study, increasing the sample size of Chinese populations to the level of existing European studies. Thirty-eight novel SLE-associated loci and incomplete sharing of genetic architecture are identified. In addition to the human leukocyte antigen (HLA) region, nine disease loci show clear ancestral differences and implicate antibody production as a potential mechanism for differences in disease manifestation. Polygenic risk scores perform significantly better when trained on ancestry-matched data sets. These analyses help to reveal the genetic basis for disparities in SLE among ancestral groups.

A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis

Iron is essential for many biological functions and iron deficiency and overload have major health implications. We performed a meta-analysis of three genome-wide association studies from Iceland, the UK and Denmark of blood levels of ferritin (N = 246,139), total iron binding capacity (N = 135,430), iron (N = 163,511) and transferrin saturation (N = 131,471). We found 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. Variants at DUOX2, F5, SLC11A2 and TMPRSS6 associate with iron deficiency anemia, while variants at TF, HFE, TFR2 and TMPRSS6 associate with iron overload. A HBS1L-MYB intergenic region variant associates both with increased risk of iron overload and reduced risk of iron deficiency anemia. The DUOX2 missense variant is present in 14% of the population, associates with all iron homeostasis biomarkers, and increases the risk of iron deficiency anemia by 29%. The associations implicate proteins contributing to the main physiological processes involved in iron homeostasis: iron sensing and storage, inflammation, absorption of iron from the gut, iron recycling, erythropoiesis and bleeding/menstruation.

Genome-wide association study of pediatric obsessive-compulsive traits: shared genetic risk between traits and disorder

Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10-8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p's < 0.01). OC traits were highly, but not significantly, genetically correlated with OCD (rg = 0.71, p = 0.062). We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.

Polygenic Risk of Psychiatric Disorders Exhibits Cross-trait Associations in Electronic Health Record Data From European Ancestry Individuals

BACKGROUND

Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting.

METHODS

We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories.

RESULTS

Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder.

CONCLUSIONS

We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.

Predictive validity of genome-wide polygenic scores for alcohol use from adolescence to young adulthood

BACKGROUND

Adolescence is a critical period for experimenting with alcohol, and these early experiences have long-term influences on alcohol-related behaviours throughout adulthood. This study examined the utility of genome-wide polygenic scores (GPS) for predicting alcohol use during adolescence and young adulthood.

METHODS

We used GPS based on the Genome-wide association study and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) study on drinks per week to predict alcohol use in a longitudinal, UK-representative sample of unrelated adolescents aged 16 through to 22 years (N_max = 3390).

RESULTS

At age 16, the GSCAN GPS predicted variance in alcohol consumption on a typical day (0.58 %), intake frequency (0.89 %), and hazardous drinking (i.e. ≥6 units at one occasion) (1.07 %). At age 22, the predictive power of the GPS had increased, explaining variance in alcohol consumption (0.61 %), intake frequency (1.69 %), and hazardous drinking (1.19 %).

CONCLUSIONS

The predictive validity of GPS for phenotypic alcohol use was evident in adolescence and increased in young adulthood. The findings suggest that GPS, which are available from birth, may be potentially useful for identifying individuals at risk for harmful and hazardous alcohol use. However, because the overall effect sizes were small, the utility of the GPS that are currently available is limited for the prediction of individual-level alcohol use.

Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later

Background

Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI).

Methods

A cross-validated logistic regression (LR) algorithm was trained either on ~ 440K European ancestry individuals from the UK Biobank (UKB), or the full UKB population, including as features different combinations of conventional established-at-birth risk factors (ancestry, sex) and risk factors that are non-fixed over an individual’s lifespan (age, BMI, hypertension, hyperlipidemia, diabetes, smoking, family history), with and without also including PRS. The algorithm was trained separately with IHD, MI, and early MI as prediction labels.

Results

When LR was trained using risk factors established-at-birth, adding the four PRS significantly improved the area under the curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73), as well as for early MI (0.70 to 0.79). When LR was trained using all risk factors, adding the four PRS only resulted in a significantly higher disease prevalence in the 98th and 99th percentiles of both the IHD and MI scores.

Conclusions

PRS improve cardiovascular risk stratification early in life when knowledge of later-life risk factors is unavailable. However, by middle age, when many risk factors are known, the improvement attributed to PRS is marginal for the general population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00828-8.

Genetic and clinical analyses of psychosis spectrum symptoms in a large multiethnic youth cohort reveal significant link with ADHD

Psychotic symptoms are not only an important feature of severe neuropsychiatric disorders, but are also common in the general population, especially in youth. The genetic etiology of psychosis symptoms in youth remains poorly understood. To characterize genetic risk for psychosis spectrum symptoms (PS), we leverage a community-based multiethnic sample of children and adolescents aged 8-22 years, the Philadelphia Neurodevelopmental Cohort (n = 7225, 20% PS). Using an elastic net regression model, we aim to classify PS status using polygenic scores (PGS) based on a range of heritable psychiatric and brain-related traits in a multi-PGS model. We also perform univariate PGS associations and evaluate age-specific effects. The multi-PGS analyses do not improve prediction of PS status over univariate models, but reveal that the attention deficit hyperactivity disorder (ADHD) PGS is robustly and uniquely associated with PS (OR 1.12 (1.05, 1.18) P = 0.0003). This association is driven by subjects of European ancestry (OR = 1.23 (1.14, 1.34), P = 4.15 × 10^-7) but is not observed in African American subjects (P = 0.65). We find a significant interaction of ADHD PGS with age (P = 0.01), with a stronger association in younger children. The association is independent of phenotypic overlap between ADHD and PS, not indirectly driven by substance use or childhood trauma, and appears to be specific to PS rather than reflecting general psychopathology in youth. In an independent sample, we replicate an increased ADHD PGS in 328 youth at clinical high risk for psychosis, compared to 216 unaffected controls (OR 1.06, CI(1.01, 1.11), P = 0.02). Our findings suggest that PS in youth may reflect a different genetic etiology than psychotic symptoms in adulthood, one more akin to ADHD, and shed light on how genetic risk can be investigated across early disease trajectories.

Polygenic Risk Score for Low-Density Lipoprotein Cholesterol Is Associated With Risk of Ischemic Heart Disease and Enriches for Individuals With Familial Hypercholesterolemia

BACKGROUND

The clinical implications of a polygenic risk score (PRS) for LDL-C (low-density lipoprotein cholesterol) are not well understood, both within the general population and individuals with familial hypercholesterolemia (FH).

METHODS

We developed the LDL-C PRS using Least Absolute Shrinkage and Selection Operator regression in 377 286 White British participants from UK Biobank and tested its association with LDL-C according to FH variant carrier status in another 41 748 whole-exome sequenced individuals. Next, we tested for an enrichment of FH variant carriers among individuals with severe hypercholesterolemia and low LDL-C PRS. Last, we contrasted the effect of the LDL-C PRS, measured LDL-C and FH variant carrier status on risk of ischemic heart disease among 3010 cases and 38 738 controls.

RESULTS

Among the 41 748 whole-exome sequenced White British individuals, 1-SD increase in the LDL-C PRS was associated with elevated LDL-C among both FH variant carriers (0.34 [95% CI, 0.22-0.47] mmol/L) and noncarriers (0.42 [95% CI, 0.42-0.43] mmol/L). Among individuals with severe hypercholesterolemia, FH variant carriers were enriched in those with a low LDL-C PRS (odds ratio, 2.20 [95% CI, 1.66-2.71] per SD). Each SD increase in the LDL-C PRS was associated with risk of ischemic heart disease to the comparable magnitude as measured LDL-C (odds ratio, 1.24 [95% CI, 1.20-1.29] and odds ratio, 1.15 [95% CI, 1.09-1.23], respectively). The LDL-C PRS was not strongly associated with other traditional ischemic heart disease risk factors.

CONCLUSIONS

An LDL-C PRS could be used to identify individuals with a higher probability of harboring FH variants. The association between ischemic heart disease and the LDL-C PRS was comparable to measured LDL-C, likely because the PRS reflects lifetime exposure to LDL-C levels.

Interactions Between Enhanced Polygenic Risk Scores and Lifestyle for Cardiovascular Disease, Diabetes, and Lipid Levels

BACKGROUND

Both lifestyle and genetic factors confer risk for cardiovascular diseases, type 2 diabetes, and dyslipidemia. However, the interactions between these 2 groups of risk factors were not comprehensively understood due to previous poor estimation of genetic risk. Here we set out to develop enhanced polygenic risk scores (PRS) and systematically investigate multiplicative and additive interactions between PRS and lifestyle for coronary artery disease, atrial fibrillation, type 2 diabetes, total cholesterol, triglyceride, and LDL-cholesterol.

METHODS

Our study included 276 096 unrelated White British participants from the UK Biobank. We investigated several PRS methods (P+T, LDpred, PRS continuous shrinkage, and AnnoPred) and showed that AnnoPred achieved consistently improved prediction accuracy for all 6 diseases/traits. With enhanced PRS and combined lifestyle status categorized by smoking, body mass index, physical activity, and diet, we investigated both multiplicative and additive interactions between PRS and lifestyle using regression models.

RESULTS

We observed that healthy lifestyle reduced disease incidence by similar multiplicative magnitude across different PRS groups. The absolute risk reduction from lifestyle adherence was, however, significantly greater in individuals with higher PRS. Specifically, for type 2 diabetes, the absolute risk reduction from lifestyle adherence was 12.4% (95% CI, 10.0%-14.9%) in the top 1% PRS versus 2.8% (95% CI, 2.3%-3.3%) in the bottom PRS decile, leading to a ratio of >4.4. We also observed a significant interaction effect between PRS and lifestyle on triglyceride level.

CONCLUSIONS

By leveraging functional annotations, AnnoPred outperforms state-of-the-art methods on quantifying genetic risk through PRS. Our analyses based on enhanced PRS suggest that individuals with high genetic risk may derive similar relative but greater absolute benefit from lifestyle adherence.

Maltreatment timing, HPA axis functioning, multigenic risk, and depressive symptoms in African American youth: Differential associations without moderated mediation

Utilizing a large (N = 739), ancestrally homogenous sample, the current study aimed to better understand biological risk processes involved in the development of depressive symptoms in maltreated, African American children age 8-12 years. Maltreatment was independently coded from Child Protective Services records and maternal report. Self-reported depressive symptoms were attained in the context of a week-long, summer research camp. DNA was acquired from buccal cell or saliva samples and genotyped for nine polymorphisms in four hypothalamic-pituitary-adrenal (HPA)-axis-related genes: FKBP5, NR3C1, NR3C2, and CRHR1. Salivary cortisol samples were collected each morning (9 a.m.) and late afternoon (4 p.m.) throughout the week to assess HPA functioning. Results revealed that experiences of maltreatment beginning prior to age 5 were most predictive of depressive symptoms, whereas maltreatment onset after age 5 was most predictive of HPA axis dysregulation (blunted daytime cortisol patterns). Multigenic risk did not relate to HPA functioning, nor did it moderate the relationship between maltreatment and HPA activity. There was no mediation of the relationship between maltreatment and depressive symptoms by HPA dysfunction. Results are interpreted through a developmental psychopathology lens, emphasizing the principle of equifinality while carefully appraising racial differences. Implications for future research, particularly the need for longitudinal studies, and important methodological considerations are discussed.