Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk score and coronary artery disease: A meta-analysis of 979,286 participant data

BACKGROUND AND AIMS

Coronary artery disease (CAD) is a complex disease with a strong genetic basis. While previous studies have combined common single-nucleotide polymorphisms (SNPs) into a polygenic risk score (PRS) to predict CAD risk, this association is poorly characterised. We performed a meta-analysis to estimate the effect of PRS on the risk of CAD.

METHODS

Online databases were searched for studies reporting PRS and CAD. PRS computation was based on log-odds (PRS_LN), pruning or clumping and thresholding (PRS_P/C + T), Lassosum regression (PRS_Lassosum), LDpred (PRS_LDpred), or metaGRS (PRS_metaGRS). The reported odds ratio (OR), hazard ratio (HR), C-indexes and their corresponding 95% confidence interval (95% CI) were pooled in a random-effects meta-analysis.

RESULTS

Forty-nine studies were included (979,286 individuals). There was a significant association between 1-standard deviation [SD] increment in PRS and adjusted risks of both incident and prevalent CAD (OR [95% CI]: 1.67 [1.57-1.77] for PRS_metaGRS, 1.46 [1.26-1.68] for PRS_LDpred). The risk of incident CAD was highest for PRS_P/C + T (HR [95% CI]: 1.49 [1.26-1.78]), PRS_metaGRS (1.37 [1.27-1.47]), and PRS_LDpred (1.36 [1.31-1.42]). Analysis of model performance demonstrated that PRS predicted incident CAD with C-index of up to 0.71. Importantly, addition of PRS to clinical risk scores resulted in modest but statistically significant improvements in CAD risk prediction, with 1.5% observed for PRS_P/C + T (p < 0.001) and 1.6% for PRS_LDpred (p < 0.001).

CONCLUSIONS

Polygenic risk score is strongly associated with increased risks of CAD. Future prospective studies should explore the usefulness of polygenic risk scores for identifying individuals at a high risk of developing CAD.

Polygenic scores for smoking and educational attainment have independent influences on academic success and adjustment in adolescence and educational attainment in adulthood

Educational success is associated with greater quality of life and depends, in part, on heritable cognitive and non-cognitive traits. We used polygenic scores (PGS) for smoking and educational attainment to examine different genetic influences on facets of academic adjustment in adolescence and educational attainment in adulthood. PGSs were calculated for participants of the Minnesota Twin Family Study (N = 3225) and included as predictors of grades, academic motivation, and discipline problems at ages 11, 14, and 17 years-old, cigarettes per day from ages 14 to 24 years old, and educational attainment in adulthood (mean age 29.4 years). Smoking and educational attainment PGSs had significant incremental associations with each academic variable and cigarettes per day. About half of the adjusted effects of the smoking and education PGSs on educational attainment in adulthood were mediated by the academic variables in adolescence. Cigarettes per day from ages 14 to 24 years old did not account for the effect of the smoking PGS on educational attainment, suggesting the smoking PGS indexes genetic influences related to general behavioral disinhibition. In sum, distinct genetic influences measured by the smoking and educational attainment PGSs contribute to academic adjustment in adolescence and educational attainment in adulthood.

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

BACKGROUND

African ancestry individuals with comparable overall anthropometric measures to Europeans have lower abdominal adiposity. To explore the genetic underpinning of different adiposity patterns, we investigated whether genetic risk scores for well-studied adiposity phenotypes like body mass index (BMI) and waist circumference (WC) also predict other, less commonly measured adiposity measures in 2420 African American individuals from the Jackson Heart Study.

METHODS

Polygenic risk scores (PRS) were calculated using GWAS-significant variants extracted from published studies mostly representing European ancestry populations for BMI, waist-hip ratio (WHR) adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Associations between each PRS and adiposity measures including BF%, subcutaneous adiposity tissue (SAT), visceral adiposity tissue (VAT) and VAT:SAT ratio (VSR) were examined using multivariable linear regression, with or without BMI adjustment.

RESULTS

In non-BMI adjusted models, all phenotype-PRS were found to be positive predictors of BF%, SAT and VAT. WHR-PRS was a positive predictor of VSR, but BF% and BMI-PRS were negative predictors of VSR. After adjusting for BMI, WHR-PRS remained a positive predictor of BF%, VAT and VSR but not SAT. WC-PRS was a positive predictor of SAT and VAT; BF%-PRS was a positive predictor of BF% and SAT only.

CONCLUSION

These analyses suggest that genetically driven increases in BF% strongly associate with subcutaneous rather than visceral adiposity and BF% is strongly associated with BMI but not central adiposity-associated genetic variants. How common genetic variants may contribute to observed differences in adiposity patterns between African and European ancestry individuals requires further study.

Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar

Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.

Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

Background

Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.

Methods

We searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function.

Results

Here, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network.

Conclusions

Our study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00939-2.

De novo variation in bipolar disorder

Bipolar disorder (BD) is a common, highly heritable disorder that affects 1-2% of the world's population. To date, most genetic studies of BD have focused on common gene variation, and while robustly associated loci have been identified, a substantial proportion of the heritability remains missing and could be partially attributable to rare variation. In this study, we apply a de novo paradigm in BD to identify newly arisen variants that have yet to undergo natural selection and may represent highly pathogenic variants. We performed whole genome sequencing of 97 trios of Ashkenazi Jewish descent, selecting "simplex" families with no family history of BD and an early age of onset. We found a total of 6882 de novo variants (an average of 70.9 ± 12.9 S.D. variants per trio), including 107 variants within protein-coding genes. We combined our exonic variations with the results of 79 previously published BD trios, identifying 20 loss-of-function (LoF) and 77 missense damaging de novo variants in BD. These variants showed significant enrichment for constrained genes and for genes located to the postsynaptic density (PSD) (all Bonferroni corrected p < 0.05). Pathway analyses showed enrichment in several pathways, including "Phosphoinositides (PI) and their downstream targets" (Bonferroni p = 4.2 × 10-6), a pathway prominently featured in lithium's hypothesized mechanism of action. In addition, while we found overall evidence for transmission of common variant polygenic risk of BD in our full sample (pTDT p = 2.21 × 10-4), specific trios with LoF variants showed no evidence of polygenic transmission. In sum, our findings support the de novo paradigm as a contributor to the genetic architecture of BD and provide evidence that constrained genes, as well as genes within the PSD and PI pathway harbor rare variation associated with BD.

Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

Medical Records-Based Genetic Studies of the Complement System

BACKGROUND

Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts.

METHODS

We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network.

RESULTS

In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; β=0.20; 95% CI, 0.14 to 0.25; P=1.52x10-11) and chr.19p13.3 (C3 locus; rs11569470-G; β=0.19; 95% CI, 0.13 to 0.24; P=1.29x10-8). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; β=0.40; 95% CI, 0.34 to 0.45; P=4.58x10-35). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (β=-0.36; 95% CI, -0.42 to -0.30; P=2.98x10-22) and C4-AL-BS (β=0.25; 95% CI, 0.21 to 0.29; P=8.11x10-23). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation.

CONCLUSIONS

We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.

Genetic insights into biological mechanisms governing human ovarian ageing

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

Polygenic Risk Score of Adolescent Idiopathic Scoliosis for Potential Clinical Use

Adolescent idiopathic scoliosis (AIS) is a common disease causing three-dimensional spinal deformity in as many as 3% of adolescents. Development of a method that can accurately predict the onset and progression of AIS is an immediate need for clinical practice. Because the heritability of AIS is estimated as high as 87.5% in twin studies, prediction of its onset and progression based on genetic data is a promising option. We show the usefulness of polygenic risk score (PRS) for the prediction of onset and progression of AIS. We used AIS genomewide association study (GWAS) data comprising 79,211 subjects in three cohorts and constructed a PRS based on association statistics in a discovery set including 31,999 female subjects. After calibration using a validation data set, we applied the PRS to a test data set. By integrating functional annotations showing heritability enrichment in the selection of variants, the PRS demonstrated an association with AIS susceptibility (p = 3.5 × 10^-40 with area under the receiver-operating characteristic [AUROC] = 0.674, sensitivity = 0.644, and specificity = 0.622). The decile with the highest PRS showed an odds ratio of as high as 3.36 (p = 1.4 × 10^-10 ) to develop AIS compared with the fifth in decile. The addition of a predictive model with only a single clinical parameter (body mass index) improved predictive ability for development of AIS (AUROC = 0.722, net reclassification improvement [NRI] 0.505 ± 0.054, p = 1.6 × 10^-8 ), potentiating clinical use of the prediction model. Furthermore, we found the Cobb angle (CA), the severity measurement of AIS, to be a polygenic trait that showed a significant genetic correlation with AIS susceptibility (rg = 0.6, p = 3.0 × 10^-4 ). The AIS PRS demonstrated a significant association with CA. These results indicate a shared polygenic architecture between onset and progression of AIS and the potential usefulness of PRS in clinical settings as a predictor to promote early intervention of AIS and avoid invasive surgery. © 2021 American Society for Bone and Mineral Research (ASBMR).

The role of parental genotype in predicting offspring years of education: evidence for genetic nurture

Similarities between parent and offspring are widespread in psychology; however, shared genetic variants often confound causal inference for offspring outcomes. A polygenic score (PGS) derived from genome-wide association studies (GWAS) can be used to test for the presence of parental influence that controls for genetic variants shared across generations. We use a PGS for educational attainment (EA3; N ≈ 750 thousand) to predict offspring years of education in a sample of 2517 twins and both parents. We find that within families, the dizygotic twin with the higher PGS is more likely to attain higher education (unstandardized β = 0.32; p < 0.001). Additionally, however, we find an effect of parental genotype on offspring outcome that is independent of the offspring's own genotype; this raises the variance explained in offspring years of education from 9.3 to 11.1% (∆R² = 0.018, p < 0.001). Controlling for parental IQ or socioeconomic status substantially attenuated or eliminated this effect of parental genotype. These findings suggest a role of environmental factors affected by heritable characteristics of the parents in fostering offspring years of education.

Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

The distribution of common-variant effect sizes

The genetic effect-size distribution of a disease describes the number of risk variants, the range of their effect sizes and sample sizes that will be required to discover them. Accurate estimation has been a challenge. Here I propose Fourier Mixture Regression (FMR), validating that it accurately estimates real and simulated effect-size distributions. Applied to summary statistics for ten diseases (average [Formula: see text]), FMR estimates that 100,000-1,000,000 cases will be required for genome-wide significant SNPs to explain 50% of SNP heritability. In such large studies, genome-wide significance becomes increasingly conservative, and less stringent thresholds achieve high true positive rates if confounding is controlled. Across traits, polygenicity varies, but the range of their effect sizes is similar. Compared with effect sizes in the top 10% of heritability, including most discovered thus far, those in the bottom 10-50% are orders of magnitude smaller and more numerous, spanning a large fraction of the genome.

Genetic Risk for Overall Cancer and the Benefit of Adherence to a Healthy Lifestyle

Cancer site-specific polygenic risk scores (PRS) effectively identify individuals at high risk of individual cancers, but the effectiveness of PRS on overall cancer risk assessment and the extent to which a high genetic risk of overall cancer can be offset by a healthy lifestyle remain unclear. Here, we constructed an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific PRSs. Lifestyle was determined according to smoking, alcohol consumption, physical activity, body mass index, and diet. Cox regression by sex was used to analyze associations of genetic and lifestyle factors with cancer incidence using UK Biobank data (N = 442,501). Compared with participants at low genetic risk (bottom quintile of CPRS), those at intermediate (quintiles 2 to 4) or high (top quintile) genetic risk had HRs of 1.27 (95% confidence interval, 1.21-1.34) or 1.91 (1.81-2.02) for overall cancer, respectively, for men, and 1.21 (1.16-1.27) or 1.62 (1.54-1.71), respectively, for women. A joint effect of genetic and lifestyle factors on overall cancer risk was observed, with HRs reaching 2.99 (2.45-3.64) for men and 2.38 (2.05-2.76) for women with high genetic risk and unfavorable lifestyle compared with those with low genetic risk and favorable lifestyle. Among participants at high genetic risk, the standardized 5-year cancer incidence was significantly reduced from 7.23% to 5.51% for men and from 5.77% to 3.69% for women having a favorable lifestyle. In summary, individuals at high genetic risk of overall cancer can be identified by CPRS, and risk can be attenuated by adopting a healthy lifestyle. SIGNIFICANCE: A new indicator of cancer polygenic risk score measures genetic risk for overall cancer, which could identify individuals with high cancer risk to facilitate decision-making about lifestyle modifications for personalized prevention.

A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.

Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores

Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies

Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

Implementation and implications for polygenic risk scores in healthcare

Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.

Integration of Multimodal Data for Deciphering Brain Disorders

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

Distinction between the effects of parental and fetal genomes on fetal growth

Birth weight is a common measure of fetal growth that is associated with a range of health outcomes. It is directly affected by the fetal genome and indirectly by the maternal genome. We performed genome-wide association studies on birth weight in the genomes of the child and parents and further analyzed birth length and ponderal index, yielding a total of 243 fetal growth variants. We clustered those variants based on the effects of transmitted and nontransmitted alleles on birth weight. Out of 141 clustered variants, 22 were consistent with parent-of-origin-specific effects. We further used haplotype-specific polygenic risk scores to directly test the relationship between adult traits and birth weight. Our results indicate that the maternal genome contributes to increased birth weight through blood-glucose-raising alleles while blood-pressure-raising alleles reduce birth weight largely through the fetal genome.

Monogenic and polygenic causes of low and extremely low LDL-C levels in patients referred to specialty lipid clinics: Genetics of low LDL-C

BACKGROUND

In clinical setting, current standard-of-care does not include genetic testing for patients with low (<50 mg/dL) and extremely low (<20 mg/dL) levels of serum low-density lipoprotein-cholesterol (LDL-C).

OBJECTIVE

We aimed identify the underlying molecular cause - both monogenic and polygenic - of low and extremely low LDL-C levels in a cohort of patients presenting to specialty lipid clinics.

METHODS

Whole exome sequencing was done in patients with low or extremely low LDL-C not due to any secondary causes.

RESULTS

Nine patients (4 women), ranging in age from 25 to 63 years old, with low or extremely low LDL-C levels were evaluated. Median LDL-C was 16 mg/dL (range undetectable - 43), total cholesterol 82 mg/dL (42 - 101), triglycerides 35 mg/dL (19-239), and high-density lipoprotein-cholesterol 45 mg/dL (24-81). Of nine patients, two carried known pathogenic variants in APOB (one stop-gain, one deletion; LDL-C range undetectable -10 mg/dL); three patients had novel APOB heterozygous mutations (two frameshift deletions and one splice site; LDL-C range undectable-13 mg/dL); two had heterozygous APOB frameshift deletions previously reported as variants of unknown significance (LDL-C 18 mg/dL in both patients); one (LDL-C 43 mg/dL) had two heterozygous mutations in PCSK9, both previously reported to be benign; and one patient (LDL-C 16 mg/dL) had the APO E2/E2 genotype along with several variants of unknown significance in genes associated with triglycerides. No patients had an LDL-C polygenic risk score below the 5th percentile (range 26th percentile to 93rd percentile).

CONCLUSION

We found APOB mutations to be the most common molecular defect in patients presenting to lipid clinics with low or extremely low LDL-C . Whether clinical genetic testing and LDL-C polygenic risk scores have any utility - other than diagnostic purposes - for such patients remains unclear. In addition, further efforts may be needed to better reclassify pathogenicity of variants of unknown significance.

Phenotypic and genetic associations between anhedonia and brain structure in UK Biobank

Anhedonia is a core symptom of multiple psychiatric disorders and has been associated with alterations in brain structure. Genome-wide association studies suggest that anhedonia is heritable, with a polygenic architecture, but few studies have explored the association between genetic loading for anhedonia-indexed by polygenic risk scores for anhedonia (PRS-anhedonia)-and structural brain imaging phenotypes. Here, we investigated how anhedonia and PRS-anhedonia were associated with brain structure within the UK Biobank cohort. Brain measures (including total grey/white matter volumes, subcortical volumes, cortical thickness (CT) and white matter integrity) were analysed using linear mixed models in relation to anhedonia and PRS-anhedonia in 19,592 participants (9225 males; mean age = 62.6 years, SD = 7.44). We found that state anhedonia was significantly associated with reduced total grey matter volume (GMV); increased total white matter volume (WMV); smaller volumes in thalamus and nucleus accumbens; reduced CT within the paracentral cortex, the opercular part of inferior frontal gyrus, precentral cortex, insula and rostral anterior cingulate cortex; and poorer integrity of many white matter tracts. PRS-anhedonia was associated with reduced total GMV; increased total WMV; reduced white matter integrity; and reduced CT within the parahippocampal cortex, superior temporal gyrus and insula. Overall, both state anhedonia and PRS-anhedonia were associated with individual differences in multiple brain structures, including within reward-related circuits. These associations may represent vulnerability markers for psychopathology relevant to a range of psychiatric disorders.

Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity

Large-scale human exome sequencing can identify rare protein-coding variants with a large impact on complex traits such as body adiposity. We sequenced the exomes of 645,626 individuals from the United Kingdom, the United States, and Mexico and estimated associations of rare coding variants with body mass index (BMI). We identified 16 genes with an exome-wide significant association with BMI, including those encoding five brain-expressed G protein-coupled receptors (CALCR, MC4R, GIPR, GPR151, and GPR75). Protein-truncating variants in GPR75 were observed in ~4/10,000 sequenced individuals and were associated with 1.8 kilograms per square meter lower BMI and 54% lower odds of obesity in the heterozygous state. Knock out of Gpr75 in mice resulted in resistance to weight gain and improved glycemic control in a high-fat diet model. Inhibition of GPR75 may provide a therapeutic strategy for obesity.

A novel transcriptional risk score for risk prediction of complex human diseases

Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.

Improved genetic prediction of complex traits from individual-level data or summary statistics

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.

Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank

OBJECTIVE

Socioeconomic factors have been suggested to influence the effect of education- and intelligence-associated genetic variants. However, results from previous studies on the interaction between socioeconomic status and education or intelligence have been inconsistent. The authors sought to assess these interactions in the UK Biobank cohort of 500,000 participants.

METHODS

The authors assessed the effect of socioeconomic deprivation on education- and intelligence-associated genetic variants by estimating the single-nucleotide polymorphism (SNP) heritability for fluid intelligence, educational attainment, and years of education in subsets of UK Biobank participants with different degrees of social deprivation, using linkage disequilibrium score regression. They also generated polygenic scores with LDpred and tested for interactions with social deprivation.

RESULTS

SNP heritability increased with socioeconomic deprivation for fluid intelligence, educational attainment, and years of education. Polygenic scores were also found to interact with socioeconomic deprivation, where the effects of the scores increased with increasing deprivation for all traits.

CONCLUSIONS

These results indicate that genetics have a larger influence on educational and cognitive outcomes in more socioeconomically deprived U.K. citizens, which has serious implications for equality of opportunity.

Polygenic Liability and Recurrence of Depression in Patients With First-Onset Depression Treated in Hospital-Based Settings

This cohort study examines the association of polygenic risk score for major depression with risk of recurrence in individuals diagnosed with unipolar depression in hospital-based settings and estimates the absolute risk of recurrence based on polygenic risk.

Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians

Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke's pseudo R2 by 40%, 41%, and 5%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC) of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677, 0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.

Discriminating bipolar depression from major depressive disorder with polygenic risk scores

BACKGROUND

Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis.

METHODS

Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status.

RESULTS

We found a strong association between a BD diagnosis and PRSs drawn from BD (R2 = 3.5%, p = 4.94 × 10-12) and schizophrenia (R2 = 3.2%, p = 5.71 × 10-11) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19-5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10-4).

CONCLUSIONS

Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset

Motivation

Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RápidoPGS, a flexible and fast method to compute PGS requiring summary-level Genome-wide association studies (GWAS) datasets only, with little computational requirements and no test data required for parameter tuning.

Results

We show that RápidoPGS performs slightly less well than two out of three other widely used PGS methods (LDpred2, PRScs and SBayesR) for case–control datasets, with median r² difference: -0.0092, -0.0042 and 0.0064, respectively, but up to 17 000-fold faster with reduced computational requirements. RápidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog.

Availability and implementation

Our method is available with a GPL license as an R package from CRAN and GitHub.

Supplementary information

Supplementary data are available at Bioinformatics online.

Statistical genetics and polygenic risk score for precision medicine

The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future.

Polygenic Risk Scores

As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.

A Polygenic Risk Score to Predict Future Adult Short Stature Among Children

CONTEXT

Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height.

OBJECTIVE

To develop a polygenic risk score for adult height and evaluate its clinical utility.

DESIGN

A polygenic risk score was constructed based on meta-analysis of genomewide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

SUBJECTS

Participants included 442 599 genotyped White British individuals in the UK Biobank and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

Standing height was measured using stadiometer; Standing height 2 SDs below the sex-specific population average was considered as short stature.

RESULTS

Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height or only one of the child's parent's height could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood.

CONCLUSIONS

A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.

Genome-wide analysis in 756,646 individuals provides first genetic evidence that ACE2 expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5×10^-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

Role of Common Genetic Variants for Drug-Resistance to Specific Anti-Seizure Medications

Objective: Resistance to anti-seizure medications (ASMs) presents a significant hurdle in the treatment of people with epilepsy. Genetic markers for resistance to individual ASMs could support clinicians to make better-informed choices for their patients. In this study, we aimed to elucidate whether the response to individual ASMs was associated with common genetic variation. Methods: A cohort of 3,649 individuals of European descent with epilepsy was deeply phenotyped and underwent single nucleotide polymorphism (SNP)-genotyping. We conducted genome-wide association analyses (GWASs) on responders to specific ASMs or groups of functionally related ASMs, using non-responders as controls. We performed a polygenic risk score (PRS) analyses based on risk variants for epilepsy and neuropsychiatric disorders and ASM resistance itself to delineate the polygenic burden of ASM-specific drug resistance. Results: We identified several potential regions of interest but did not detect genome-wide significant loci for ASM-specific response. We did not find polygenic risk for epilepsy, neuropsychiatric disorders, and drug-resistance associated with drug response to specific ASMs or mechanistically related groups of ASMs. Significance: This study could not ascertain the predictive value of common genetic variants for ASM responder status. The identified suggestive loci will need replication in future studies of a larger scale.

Contribution of genetic ancestry and polygenic risk score in meeting vitamin B12 needs in healthy Brazilian children and adolescents

Polymorphisms in genes related to the metabolism of vitamin B12 haven’t been examined in a Brazilian population. To (a) determine the correlation between the local genetic ancestry components and vitamin B12 levels using ninety B12-related genes; (b) determine associations between these genes and their SNPs with vitamin B12 levels; (c) determine a polygenic risk score (PRS) using significant variants. This cross-sectional study included 168 children and adolescents, aged 9–13 years old. Total cobalamin was measured in plasma. Genotyping arrays and whole exome data were combined to yield ~ 7000 SNPs in 90 genes related to vitamin B12. The Efficient Local Ancestry Inference was used to estimate local ancestry for African (AFR), Native American, and European (EUR). The association between the genotypes and vitamin B12 levels were determined with generalized estimating equation. Vitamin B12 levels were driven by positive (EUR) and negative (AFR, AMR) correlations with genetic ancestry. A set of 36 variants were used to create a PRS that explained 42% of vitamin level variation. Vitamin B12 levels are influenced by genetic ancestry and a PRS explained almost 50% of the variation in plasma cobalamin in Brazilian children and adolescents.

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

Testing and estimation of X-chromosome SNP effects: Impact of model assumptions

Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRS_Metabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PL_Metabo ); validation of two comprehensive polygenic risk scores: GRS_Gola based on Metabochip data, and GRS_Khera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDI_rel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRS_Metabo and PL_Metabo , GRS_Gola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDI_rel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRS_Khera yielded slightly worse prediction results than GRS_Gola .

Eleven genomic loci affect plasma levels of chronic inflammation marker soluble urokinase-type plasminogen activator receptor

Soluble urokinase-type plasminogen activator receptor (suPAR) is a chronic inflammation marker associated with the development of a range of diseases, including cancer and cardiovascular disease. The genetics of suPAR remain unexplored but may shed light on the biology of the marker and its connection to outcomes. We report a heritability estimate of 60% for the variation in suPAR and performed a genome-wide association meta-analysis on suPAR levels measured in Iceland (N = 35,559) and in Denmark (N = 12,177). We identified 13 independently genome-wide significant sequence variants associated with suPAR across 11 distinct loci. Associated variants were found in and around genes encoding uPAR (PLAUR), its ligand uPA (PLAU), the kidney-disease-associated gene PLA2R1 as well as genes with relations to glycosylation, glycoprotein biosynthesis, and the immune response. These findings provide new insight into the causes of variation in suPAR plasma levels, which may clarify suPAR's potential role in associated diseases, as well as the underlying mechanisms that give suPAR its prognostic value as a unique marker of chronic inflammation.

Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA2DS2-VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS).

METHODS

Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability.

RESULTS

Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%-3.0%]) and fit (χ2P=0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06-1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, -0.018).

CONCLUSIONS

In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

Translating genetic association of lipid levels for biological and clinical application

PURPOSE OF REVIEW

This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction.

RECENT FINDINGS

Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes-PCSK9, LDLR, and APOB-with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction.

CONCLUSIONS

Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.

Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents

BACKGROUND

Childhood obesity is a complex multifaceted condition, which is influenced by genetics, environmental factors, and their interaction. However, these interactions have mainly been studied in twin studies and evidence from population-based cohorts is limited. Here, we analyze the interaction of an obesity-related genome-wide polygenic risk score (PRS) with sociodemographic and lifestyle factors for BMI and waist circumference (WC) in European children and adolescents.

METHODS

The analyses are based on 8609 repeated observations from 3098 participants aged 2-16 years from the IDEFICS/I.Family cohort. A genome-wide polygenic risk score (PRS) was calculated using summary statistics from independent genome-wide association studies of BMI. Associations were estimated using generalized linear mixed models adjusted for sex, age, region of residence, parental education, dietary intake, relatedness, and population stratification.

RESULTS

The PRS was associated with BMI (beta estimate [95% confidence interval (95%-CI)] = 0.33 [0.30, 0.37], r2 = 0.11, p value = 7.9 × 10-81) and WC (beta [95%-CI] = 0.36 [0.32, 0.40], r2 = 0.09, p value = 1.8 × 10-71). We observed significant interactions with demographic and lifestyle factors for BMI as well as WC. Children from Southern Europe showed increased genetic liability to obesity (BMI: beta [95%-CI] = 0.40 [0.34, 0.45]) in comparison to children from central Europe (beta [95%-CI] = 0.29 [0.23, 0.34]), p-interaction = 0.0066). Children of parents with a low level of education showed an increased genetic liability to obesity (BMI: beta [95%-CI] = 0.48 [0.38, 0.59]) in comparison to children of parents with a high level of education (beta [95%-CI] = 0.30 [0.26, 0.34]), p-interaction = 0.0012). Furthermore, the genetic liability to obesity was attenuated by a higher intake of fiber (BMI: beta [95%-CI] interaction = -0.02 [-0.04,-0.01]) and shorter screen times (beta [95%-CI] interaction = 0.02 [0.00, 0.03]).

CONCLUSIONS

Our results highlight that a healthy childhood environment might partly offset a genetic predisposition to obesity during childhood and adolescence.

Greater genetic risk for adult psychiatric diseases increases vulnerability to adverse outcome after preterm birth

Preterm birth is an extreme environmental stress associated with an increased risk of later cognitive dysfunction and mental health problems. However, the extent to which preterm birth is modulated by genetic variation remains largely unclear. Here, we test for an interaction effect between psychiatric polygenic risk and gestational age at birth on cognition at age four. Our sample comprises 4934 unrelated individuals (2066 individuals born < 37 weeks, 918 born <  = 34 weeks). Genome-wide polygenic scores (GPS's) were calculated for each individual for five different psychiatric pathologies: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder. Linear regression modelling was used to estimate the interaction effect between psychiatric GPS and gestational age at birth (GA) on cognitive outcome for the five psychiatric disorders. We found a significant interaction effect between Schizophrenia GPS and GA (β = 0.038 ± 0.013, p = 6.85 × 10-3) and Bipolar Disorder GPS and GA (β = 0.038 ± 0.014, p = 6.61 × 10-3) on cognitive outcome. Individuals with greater genetic risk for Schizophrenia or Bipolar Disorder are more vulnerable to the adverse effects of birth at early gestational age on brain development, as assessed by cognition at age four. Better understanding of gene-environment interactions will inform more effective risk-reducing interventions for this vulnerable population.

Genome-wide polygenic risk score for retinopathy of type 2 diabetes

Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04-1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02-1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03-1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00-1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03-2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.

Polygenic Risk Score-Enhanced Risk Stratification of Coronary Artery Disease in Patients With Stable Chest Pain

BACKGROUND

Polygenic risk scores (PRSs) are associated with coronary artery disease (CAD), but the clinical potential of using PRSs at the single-patient level for risk stratification has yet to be established. We investigated whether adding a PRS to clinical risk factors (CRFs) improves risk stratification in patients referred to coronary computed tomography angiography on a suspicion of obstructive CAD.

METHODS

In this prespecified diagnostic substudy of the Dan-NICAD trial (Danish study of Non-Invasive testing in Coronary Artery Disease), we included 1617 consecutive patients with stable chest symptoms and no history of CAD referred for coronary computed tomography angiography. CRFs used for risk stratification were age, sex, symptoms, prior or active smoking, antihypertensive treatment, lipid-lowering treatment, and diabetes. In addition, patients were genotyped, and their PRSs were calculated. All patients underwent coronary computed tomography angiography. Patients with a suspected ≥50% stenosis also underwent invasive coronary angiography with fractional flow reserve. A combined end point of obstructive CAD was defined as a visual invasive coronary angiography stenosis >90%, fractional flow reserve <0.80, or a quantitative coronary analysis stenosis >50% if fractional flow reserve measurements were not feasible.

RESULTS

The PRS was associated with obstructive CAD independent of CRFs (adjusted odds ratio, 1.8 [95% CI, 1.5-2.2] per SD). The PRS had an area under the curve of 0.63 (0.59-0.68), which was similar to that for age and sex. Combining the PRS with CRFs led to a CRF+PRS model with area under the curve of 0.75 (0.71-0.79), which was 0.04 more than the CRF model (P=0.0029). By using pretest probability (pretest probability) cutoffs at 5% and 15%, a net reclassification improvement of 15.8% (P=3.1×10^-4) was obtained, with a down-classification of risk in 24% of patients (211 of 862) in whom the pretest probability was 5% to 15% based on CRFs alone.

CONCLUSIONS

Adding a PRS improved risk stratification of obstructive CAD beyond CRFs, suggesting a modest clinical potential of using PRSs to guide diagnostic testing in the contemporary clinical setting. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717.