Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Association of Childhood Exposure to Nitrogen Dioxide and Polygenic Risk Score for Schizophrenia With the Risk of Developing Schizophrenia

IMPORTANCE

Schizophrenia is a highly heritable psychiatric disorder, and recent studies have suggested that exposure to nitrogen dioxide (NO2) during childhood is associated with an elevated risk of subsequently developing schizophrenia. However, it is not known whether the increased risk associated with NO2 exposure is owing to a greater genetic liability among those exposed to highest NO2 levels.

OBJECTIVE

To examine the associations between childhood NO2 exposure and genetic liability for schizophrenia (as measured by a polygenic risk score), and risk of developing schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS

Population-based cohort study including individuals with schizophrenia (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code F20) and a randomly selected subcohort. Using national registry data, all individuals born in Denmark between May 1, 1981, and December 31, 2002, were followed up from their 10th birthday until the first occurrence of schizophrenia, emigration, death, or December 31, 2012, whichever came first. Statistical analyses were conducted between October 24, 2018, and June 17, 2019.

EXPOSURES

Individual exposure to NO2 during childhood estimated as mean daily exposure to NO2 at residential addresses from birth to the 10th birthday. Polygenic risk scores were calculated as the weighted sum of risk alleles at selected single-nucleotide polymorphisms based on genetic material obtained from dried blood spot samples from the Danish Newborn Screening Biobank and on the Psychiatric Genomics Consortium genome-wide association study summary statistics file.

MAIN OUTCOMES AND MEASURES

The main outcome was schizophrenia. Weighted Cox proportional hazards regression models were fitted to estimate adjusted hazard ratios (AHRs) for schizophrenia with 95% CIs according to the exposures.

RESULTS

Of a total of 23 355 individuals, 11 976 (51.3%) were male and all had Danish-born parents. During the period of the study, 3531 were diagnosed with schizophrenia. Higher polygenic risk scores were correlated with higher childhood NO2 exposure (ρ = 0.0782; 95% CI, 0.065-0.091; P < .001). A 10-μg/m3 increase in childhood daily NO2 exposure (AHR, 1.23; 95% CI, 1.15-1.32) and a 1-SD increase in polygenic risk score (AHR, 1.29; 95% CI, 1.23-1.35) were independently associated with increased schizophrenia risk.

CONCLUSIONS AND RELEVANCE

These findings suggest that the apparent association between NO2 exposure and schizophrenia is only slightly confounded by a higher polygenic risk score for schizophrenia among individuals living in areas with greater NO2. The findings demonstrate the utility of including polygenic risk scores in epidemiologic studies.

Association of Childhood Exposure to Nitrogen Dioxide and Polygenic Risk Score for Schizophrenia With the Risk of Developing Schizophrenia

IMPORTANCE

Schizophrenia is a highly heritable psychiatric disorder, and recent studies have suggested that exposure to nitrogen dioxide (NO2) during childhood is associated with an elevated risk of subsequently developing schizophrenia. However, it is not known whether the increased risk associated with NO2 exposure is owing to a greater genetic liability among those exposed to highest NO2 levels.

OBJECTIVE

To examine the associations between childhood NO2 exposure and genetic liability for schizophrenia (as measured by a polygenic risk score), and risk of developing schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS

Population-based cohort study including individuals with schizophrenia (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code F20) and a randomly selected subcohort. Using national registry data, all individuals born in Denmark between May 1, 1981, and December 31, 2002, were followed up from their 10th birthday until the first occurrence of schizophrenia, emigration, death, or December 31, 2012, whichever came first. Statistical analyses were conducted between October 24, 2018, and June 17, 2019.

EXPOSURES

Individual exposure to NO2 during childhood estimated as mean daily exposure to NO2 at residential addresses from birth to the 10th birthday. Polygenic risk scores were calculated as the weighted sum of risk alleles at selected single-nucleotide polymorphisms based on genetic material obtained from dried blood spot samples from the Danish Newborn Screening Biobank and on the Psychiatric Genomics Consortium genome-wide association study summary statistics file.

MAIN OUTCOMES AND MEASURES

The main outcome was schizophrenia. Weighted Cox proportional hazards regression models were fitted to estimate adjusted hazard ratios (AHRs) for schizophrenia with 95% CIs according to the exposures.

RESULTS

Of a total of 23 355 individuals, 11 976 (51.3%) were male and all had Danish-born parents. During the period of the study, 3531 were diagnosed with schizophrenia. Higher polygenic risk scores were correlated with higher childhood NO2 exposure (ρ = 0.0782; 95% CI, 0.065-0.091; P < .001). A 10-μg/m3 increase in childhood daily NO2 exposure (AHR, 1.23; 95% CI, 1.15-1.32) and a 1-SD increase in polygenic risk score (AHR, 1.29; 95% CI, 1.23-1.35) were independently associated with increased schizophrenia risk.

CONCLUSIONS AND RELEVANCE

These findings suggest that the apparent association between NO2 exposure and schizophrenia is only slightly confounded by a higher polygenic risk score for schizophrenia among individuals living in areas with greater NO2. The findings demonstrate the utility of including polygenic risk scores in epidemiologic studies.

Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders

Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals, a disease characteristic termed variable expressivity. Recently, the aggregate effect of common variation, quantified as polygenic scores (PGSs), has emerged as an effective tool for predictions of disease risk and trait variation in the general population. Here, we measure the effect of PGSs on 11 RGDs including four sex-chromosome aneuploidies (47,XXX; 47,XXY; 47,XYY; 45,X) that affect height; two copy-number variant (CNV) disorders (16p11.2 deletions and duplications) and a Mendelian disease (melanocortin 4 receptor deficiency (MC4R)) that affect BMI; and two Mendelian diseases affecting cholesterol: familial hypercholesterolemia (FH; LDLR and APOB) and familial hypobetalipoproteinemia (FHBL; PCSK9 and APOB). Our results demonstrate that common, polygenic factors of relevant complex traits frequently contribute to variable expressivity of RGDs and that PGSs may be a useful metric for predicting clinical severity in affected individuals and for risk stratification.

Migraine polygenic risk score associates with efficacy of migraine-specific drugs

Objective

To assess whether the polygenic risk score (PRS) for migraine is associated with acute and/or prophylactic migraine treatment response.

Methods

We interviewed 2,219 unrelated patients at the Danish Headache Center using a semistructured interview to diagnose migraine and assess acute and prophylactic drug response. All patients were genotyped. A PRS was calculated with the linkage disequilibrium pred algorithm using summary statistics from the most recent migraine genome-wide association study comprising ∼375,000 cases and controls. The PRS was scaled to a unit corresponding to a twofold increase in migraine risk, using 929 unrelated Danish controls as reference. The association of the PRS with treatment response was assessed by logistic regression, and the predictive power of the model by area under the curve using a case-control design with treatment response as outcome.

Results

A twofold increase in migraine risk associates with positive response to migraine-specific acute treatment (odds ratio [OR] = 1.25 [95% confidence interval (CI) = 1.05–1.49]). The association between migraine risk and migraine-specific acute treatment was replicated in an independent cohort consisting of 5,616 triptan users with prescription history (OR = 3.20 [95% CI = 1.26–8.14]). No association was found for acute treatment with non–migraine-specific weak analgesics and prophylactic treatment response.

Conclusions

The migraine PRS can significantly identify subgroups of patients with a higher-than-average likelihood of a positive response to triptans, which provides a first step toward genetics-based precision medicine in migraine.

Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development

BACKGROUND

The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present.

METHODS

The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits-LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events.

RESULTS

The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P<1×10-8. Three loci were replicated at Bonferroni significance and 7 loci at nominal significance (P<0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes (TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB, and MMP11) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 - 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score).

CONCLUSIONS

We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.

A flexible and parallelizable approach to genome-wide polygenic risk scores

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.

A role for vitamin D and omega-3 fatty acids in major depression? An exploration using genomics

Trials testing the effect of vitamin D or omega-3 polyunsaturated fatty acid (n3-PUFA) supplementation on major depressive disorder (MDD) reported conflicting findings. These trials were inspired by epidemiological evidence suggesting an inverse association of circulating 25-hydroxyvitamin D (25-OH-D) and n3-PUFA levels with MDD. Observational associations may emerge from unresolved confounding, shared genetic risk, or direct causal relationships. We explored the nature of these associations exploiting data and statistical tools from genomics. Results from genome-wide association studies on 25-OH-D (N = 79 366), n3-PUFA (N = 24 925), and MDD (135 458 cases, 344 901 controls) were applied to individual-level data (>2000 subjects with measures of genotype, DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) lifetime MDD diagnoses and circulating 25-OH-D and n3-PUFA) and summary-level data analyses. Shared genetic risk between traits was tested by polygenic risk scores (PRS). Two-sample Mendelian Randomization (2SMR) analyses tested the potential bidirectional causality between traits. In individual-level data analyses, PRS were associated with the phenotype of the same trait (PRS 25-OH-D p = 1.4e - 20, PRS n3-PUFA p = 9.3e - 6, PRS MDD p = 1.4e - 4), but not with the other phenotypes, suggesting a lack of shared genetic effects. In summary-level data analyses, 2SMR analyses provided no evidence of a causal role on MDD of 25-OH-D (p = 0.50) or n3-PUFA (p = 0.16), or for a causal role of MDD on 25-OH-D (p = 0.25) or n3-PUFA (p = 0.66). Applying genomics tools indicated that shared genetic risk or direct causality between 25-OH-D, n3-PUFA, and MDD is unlikely: unresolved confounding may explain the associations reported in observational studies. These findings represent a cautionary tale for testing supplementation of these compounds in preventing or treating MDD.

Extreme Polygenicity of Complex Traits Is Explained by Negative Selection

Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (Me), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate Me, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread ∼4× more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.

The moderating role of SES on genetic differences in educational achievement in the Netherlands

Parental socioeconomic status (SES) is a strong predictor of children's educational achievement (EA), with an increasing effect throughout development. Inequality in educational outcomes between children from different SES backgrounds exists in all Western countries. It has been proposed that a cause of this inequality lies in the interplay between genetic effects and SES on EA, which might depend on society and the equality of the education system. This study adopted two approaches, a classical twin design and polygenic score (PGS) approach, to address the effect of parental SES on EA in a large sample of 12-year-old Dutch twin pairs (2479 MZ and 4450 DZ twin pairs with PGSs for educational attainment available in 2335 children) from the Netherlands Twin Register (NTR). The findings of this study indicated that average EA increased with increasing parental SES. The difference in EA between boys and girls became smaller in the higher SES groups. The classical twin design analyses based on genetic covariance structure modeling pointed to lower genetic, environmental, and thus phenotypic variation in EA at higher SES. Independent from a child's PGS, parental SES predicted EA. However, the strength of the association between PGS and EA did not depend on parental SES. In a within-family design, the twin with a higher PGS scored higher on EA than the co-twin, demonstrating that the effect of the PGS on EA was at least partly independent from parental SES. To conclude, EA depended on SES both directly and indirectly, and SES moderated the additive genetic and environmental components of EA. Adding information from PGS, in addition to parental SES, improved the prediction of children's EA.

Extreme inbreeding in a European ancestry sample from the contemporary UK population

In most human societies, there are taboos and laws banning mating between first- and second-degree relatives, but actual prevalence and effects on health and fitness are poorly quantified. Here, we leverage a large observational study of ~450,000 participants of European ancestry from the UK Biobank (UKB) to quantify extreme inbreeding (EI) and its consequences. We use genotyped SNPs to detect large runs of homozygosity (ROH) and call EI when >10% of an individual's genome comprise ROHs. We estimate a prevalence of EI of ~0.03%, i.e., ~1/3652. EI cases have phenotypic means between 0.3 and 0.7 standard deviation below the population mean for 7 traits, including stature and cognitive ability, consistent with inbreeding depression estimated from individuals with low levels of inbreeding. Our study provides DNA-based quantification of the prevalence of EI in a European ancestry sample from the UK and measures its effects on health and fitness traits.

Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene-region aggregation techniques

Sex differences in rates of depression are thought to contribute to sex differences in smoking initiation (SI) and number of cigarettes smoked per day (CPD). One hypothesis is that women smoke as a strategy to cope with anxiety and depression, and have difficulty quitting because of concomitant changes in hypothalamic-pituitary-adrenocortical (HPA) axis function during nicotine withdrawal states. Despite evidence of biological ties, research has not examined whether genetic factors that contribute to depression-smoking comorbidity differ by sex. We utilized two statistical aggregation techniques-polygenic scores (PGSs) and sequence kernel association testing-to assess the degree of pleiotropy between these behaviors and moderation by sex in the Health and Retirement Study (N = 8,086). At the genome-wide level, we observed associations between PGSs for depressive symptoms and SI, and measured SI and depressive symptoms (all p < .01). At the gene level, we found evidence of pleiotropy in FKBP5 for SI (p = .028), and sex-specific pleiotropy in females in NR3C2 (p = .030) and CHRNA5 (p = .025) for SI and CPD, respectively. Results suggest bidirectional associations between depression and smoking may be partially accounted for by shared genetic factors, and genetic variation in genes related to HPA-axis functioning and nicotine dependence may contribute to sex differences in SI and CPD.

Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior

Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered. We performed a genome-wide association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior. In aggregate, all tested genetic variants accounted for 8 to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual's sexual behavior. Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior. Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.

Comparing Within- and Between-Family Polygenic Score Prediction

Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

Polygenic risk scores in psychiatry: Will they be useful for clinicians?

Major psychiatric disorders are heritable but they are genetically complex. This means that, with certain exceptions, single gene markers will not be helpful for diagnosis. However, we are learning more about the large number of gene variants that, in combination, are associated with risk for disorders such as schizophrenia, bipolar disorder, and other psychiatric conditions. The presence of those risk variants may now be combined into a polygenic risk score (PRS). Such a score provides a quantitative index of the genomic burden of risk variants in an individual, which relates to the likelihood that a person has a particular disorder. Currently, such scores are quite useful in research, and they are telling us much about the relationships between different disorders and other indices of brain function. In the future, as the datasets supporting the development of such scores become larger and more diverse and as methodological developments improve predictive capacity, we expect that PRS will have substantial clinical utility in the assessment of risk for disease, subtypes of disease, and even treatment response. Here, we provide an overview of PRS in general terms (including a glossary suitable for informed non-geneticists) and discuss the use of PRS in psychiatry, including their limitations and cautions for interpretation, as well as their applications now and in the future.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

Analysis of polygenic risk score usage and performance in diverse human populations

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10⁻⁶), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

Predominant participation of European-ancestry individuals in genetic studies has hindered the better understanding of genetic risk in non-European ancestry individuals. Here, Duncan et al. quantify polygenic risk score use and performance in worldwide populations.

GWEHS: A Genome-Wide Effect Sizes and Heritability Screener

During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations.

Predicting Polygenic Risk of Psychiatric Disorders

Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

PRSice-2: Polygenic Risk Score software for biobank-scale data

BACKGROUND

Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required.

RESULTS

Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power.

CONCLUSION

PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set-based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.

Genome-wide association study implicates CHRNA2 in cannabis use disorder

Cannabis is the most frequently used illicit psychoactive substance worldwide; around one in ten users become dependent. The risk for cannabis use disorder (CUD) has a strong genetic component, with twin heritability estimates ranging from 51 to 70%. Here we performed a genome-wide association study of CUD in 2,387 cases and 48,985 controls, followed by replication in 5,501 cases and 301,041 controls. We report a genome-wide significant risk locus for CUD (P = 9.31 × 10^-12) that replicates in an independent population (P_replication = 3.27 × 10^-3, P_{meta-analysis} = 9.09 × 10^-12). The index variant (rs56372821) is a strong expression quantitative trait locus for cholinergic receptor nicotinic α2 subunit (CHRNA2); analyses of the genetically regulated gene expression identified a significant association of CHRNA2 expression with CUD in brain tissue. At the polygenic level, analyses revealed a significant decrease in the risk of CUD with increased load of variants associated with cognitive performance. The results provide biological insights and inform on the genetic architecture of CUD.

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.

Associations between an educational attainment polygenic score with educational attainment in an African American sample

Polygenic propensity for educational attainment has been associated with higher education attendance, academic achievement and criminal offending in predominantly European samples; however, less is known about whether this polygenic propensity is associated with these outcomes among African Americans. Using an educational attainment polygenic score (EA PGS), the present study examined whether this score was associated with post-secondary education, academic achievement and criminal offending in an urban, African American sample. Three cohorts of participants (N = 1050; 43.9% male) were initially recruited for an elementary school-based universal prevention trial in a Mid-Atlantic city and followed into young adulthood. Standardized tests of reading and math achievement were administered in first grade. At age 20, participants reported on their level of education attained, and records of incarceration were obtained from Maryland's Criminal Justice Information System. In young adulthood, DNA was collected and extracted from blood or buccal swabs and genotyped. An EA PGS was created using results from a large-scale genome-wide association study on educational attainment. A higher EA PGS was associated with a greater log odds of post-secondary education. The EA PGS was not associated with reading achievement, although a significant relationship was found with math achievement in the third cohort. These findings contribute to the dearth of molecular genetics work conducted in African American samples and highlight that polygenic propensity for educational attainment is associated with higher education attendance.

Genomic prediction of cognitive traits in childhood and adolescence

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment, with highly genetically correlated traits, to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at ages 12 and 16, we show that we can now predict up to 11% of the variance in intelligence and 16% in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. We found that multi-trait genomic methods were effective in boosting predictive power. Prediction accuracy varied across polygenic score approaches, however results were similar for different multi-trait and polygenic score methods. We discuss general caveats of multi-trait methods and polygenic score prediction, and conclude that polygenic scores for educational attainment and intelligence are currently the most powerful predictors in the behavioural sciences.

Common Polygenic Variations for Psychiatric Disorders and Cognition in Relation to Brain Morphology in the General Pediatric Population

OBJECTIVE

This study examined the relation between polygenic scores (PGSs) for 5 major psychiatric disorders and 2 cognitive traits with brain magnetic resonance imaging morphologic measurements in a large population-based sample of children. In addition, this study tested for differences in brain morphology-mediated associations between PGSs for psychiatric disorders and PGSs for related behavioral phenotypes.

METHOD

Participants included 1,139 children from the Generation R Study assessed at 10 years of age with genotype and neuroimaging data available. PGSs were calculated for schizophrenia, bipolar disorder, major depression disorder, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, intelligence, and educational attainment using results from the most recent genome-wide association studies. Image processing was performed using FreeSurfer to extract cortical and subcortical brain volumes.

RESULTS

Greater genetic susceptibility for ADHD was associated with smaller caudate volume (strongest prior = 0.01: β = -0.07, p = .006). In boys, mediation analysis estimates showed that 11% of the association between the PGS for ADHD and the PGS attention problems was mediated by differences in caudate volume (n = 535), whereas mediation was not significant in girls or the entire sample. PGSs for educational attainment and intelligence showed positive associations with total brain volume (strongest prior = 0.5: β = 0.14, p = 7.12 × 10^-8; and β = 0.12, p = 6.87 × 10^-7, respectively).

CONCLUSION

The present findings indicate that the neurobiological manifestation of polygenic susceptibility for ADHD, educational attainment, and intelligence involve early morphologic differences in caudate and total brain volumes in childhood. Furthermore, the genetic risk for ADHD might influence attention problems through the caudate nucleus in boys.

Validation of Genome-Wide Polygenic Risk Scores for Coronary Artery Disease in French Canadians

BACKGROUND

Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality worldwide. Given the healthcare risks and societal impacts associated with CAD, their clinical management would benefit from improved prevention and prediction tools. Polygenic risk scores (PRS) based on an individual's genome sequence are emerging as potentially powerful biomarkers to predict the risk to develop CAD. Two recently derived genome-wide PRS have shown high specificity and sensitivity to identify CAD cases in European-ancestry participants from the UK Biobank. However, validation of the PRS predictive power and transferability in other populations is now required to support their clinical utility.

METHODS

We calculated both PRS (GPSCAD and metaGRSCAD) in French-Canadian individuals from 3 cohorts totaling 3639 prevalent CAD cases and 7382 controls and tested their power to predict prevalent, incident, and recurrent CAD. We also estimated the impact of the founder French-Canadian familial hypercholesterolemia deletion ( LDLR delta >15 kb deletion) on CAD risk in one of these cohorts and used this estimate to calibrate the impact of the PRS.

RESULTS

Our results confirm the ability of both PRS to predict prevalent CAD comparable to the original reports (area under the curve=0.72-0.89). Furthermore, the PRS identified about 6% to 7% of individuals at CAD risk similar to carriers of the LDLR delta >15 kb mutation, consistent with previous estimates. However, the PRS did not perform as well in predicting an incident or recurrent CAD (area under the curve=0.56-0.60), maybe because of confounding because 76% of the participants were on statin treatment. This result suggests that additional work is warranted to better understand how ascertainment biases and study design impact PRS for CAD.

CONCLUSIONS

Collectively, our results confirm that novel, genome-wide PRS is able to predict CAD in French Canadians; with further improvements, this is likely to pave the way towards more targeted strategies to predict and prevent CAD-related adverse events.

Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective

With the advent of very large scale genome‐wide association studies (GWASs), the promise of Mendelian randomization (MR) has begun to be fulfilled. However, whilst GWASs have provided essential information on the single nucleotide polymorphisms (SNPs) associated with modifiable risk factors needed for MR, the availability of large numbers of SNP instruments raises issues of how best to use this information and how to deal with potential problems such as pleiotropy. Here we provide commentary on some of the recent advances in the MR analysis, including an overview of the different genetic architectures that are being uncovered for a variety of modifiable risk factors and how users ought to take that into consideration when designing MR studies.

Estimating variance components in population scale family trees

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.

Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.

Using DNA to predict educational trajectories in early adulthood

At the end of compulsory schooling, young adults decide on educational and occupational trajectories that impact their subsequent employability, health and even life expectancy. To understand the antecedents to these decisions, we follow a new approach that considers genetic contributions, which have largely been ignored before. Using genomewide polygenic scores (EA3) from the most recent genomewide association study of years of education in 1.1 million individuals, we tested for genetic influence on early adult decisions in a United Kingdom-representative sample of 5,839 at 18 years of age. EA3 significantly predicted educational trajectories in early adulthood (Nagelkerke R2 = 10%), χ2(4) = 571.77, p < .001, indicating that young adults partly adapt their aspirations to their genetic propensities-a concept known as gene-environment correlation. Compared to attending university, a 1 standard deviation increase in EA3 was associated on average with a 51% reduction in the odds of pursuing full-time employment (OR = .47; 95% CI [.43, .51]); an apprenticeship (OR = .49; 95% CI [.45, .54]); or not going in education, employment, or training (OR = .50; 95% CI [.41, .60]). EA3 associations were attenuated when controlling for previous academic achievement and family socioeconomic status. Overall this research illustrates how DNA-based predictions offer novel opportunities for studying the sociodevelopmental structures of life outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.

Polygenic prediction via Bayesian regression and continuous shrinkage priors

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction

In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent "related," and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.

Clinical use of current polygenic risk scores may exacerbate health disparities

Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.

Unraveling the association between depression and telomere length using genomics

OBJECTIVE

While there is robust evidence for a cross-sectional association between depression and shorter telomere length, suggestive of advanced biological aging, the nature of this association remains unclear. Here, we tested whether both traits share a common genetic liability with novel methods using genomics.

METHODS

Data were from 2032 participants of the Netherlands Study of Depression and Anxiety (NESDA) with genome-wide genetic information and multiple waves of data on DSM-IV lifetime depression diagnosis, depression severity, neuroticism and telomere length. Polygenic risk scores (PRS) for both traits were built using summary results from the largest genome-wide association studies (GWAS) on depression (59,851 cases and 113,154 controls) and telomere length (37,684 samples). Additionally, a PRS for neuroticism was built (337,000 samples). Genetic overlap between the traits was tested using PRS for same- and cross-trait associations. Furthermore, GWAS summary statistics were used to estimate the genome-wide genetic correlation between traits.

RESULTS

In NESDA data, the PRS for depression was associated with lifetime depression (odds ratio = 1.36; p = 6.49e-7) and depression severity level (β = 0.13; p = 1.24e-8), but not with telomere length. Similar results were found for the PRS for neuroticism. Conversely, the PRS for telomere length was associated with telomere length (β = 0.07; p = 8.42e-4) and 6-year telomere length attrition rate (β = 0.04; p = 2.15e-2), but not with depression variables. In summary-level analyses, the genetic correlation between the traits was small and not significant (rg=-0.08; p = .300).

CONCLUSION

The use of genetic methods in this paper indicated that the established phenotypic association between telomere length and depression is unlikely due to shared underlying genetic vulnerability. Our findings suggest that short telomeres in depressed patients may simply represent a generic marker of disease or may originate from non-genetic environmental factors.

Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction

BACKGROUND

The relative prevalence and clinical importance of monogenic mutations related to familial hypercholesterolemia and of high polygenic score (cumulative impact of many common variants) pathways for early-onset myocardial infarction remain uncertain. Whole-genome sequencing enables simultaneous ascertainment of both monogenic mutations and polygenic score for each individual.

METHODS

We performed deep-coverage whole-genome sequencing of 2081 patients from 4 racial subgroups hospitalized in the United States with early-onset myocardial infarction (age ≤55 years) recruited with a 2:1 female-to-male enrollment design. We compared these genomes with those of 3761 population-based control subjects. We first identified individuals with a rare, monogenic mutation related to familial hypercholesterolemia. Second, we calculated a recently developed polygenic score of 6.6 million common DNA variants to quantify the cumulative susceptibility conferred by common variants. We defined high polygenic score as the top 5% of the control distribution because this cutoff has previously been shown to confer similar risk to that of familial hypercholesterolemia mutations.

RESULTS

The mean age of the 2081 patients presenting with early-onset myocardial infarction was 48 years, and 66% were female. A familial hypercholesterolemia mutation was present in 36 of these patients (1.7%) and was associated with a 3.8-fold (95% CI, 2.1-6.8; P<0.001) increased odds of myocardial infarction. Of the patients with early-onset myocardial infarction, 359 (17.3%) carried a high polygenic score, associated with a 3.7-fold (95% CI, 3.1-4.6; P<0.001) increased odds. Mean estimated untreated low-density lipoprotein cholesterol was 206 mg/dL in those with a familial hypercholesterolemia mutation, 132 mg/dL in those with high polygenic score, and 122 mg/dL in those in the remainder of the population. Although associated with increased risk in all racial groups, high polygenic score demonstrated the strongest association in white participants ( P for heterogeneity=0.008).

CONCLUSIONS

Both familial hypercholesterolemia mutations and high polygenic score are associated with a >3-fold increased odds of early-onset myocardial infarction. However, high polygenic score has a 10-fold higher prevalence among patients presents with early-onset myocardial infarction.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov . Unique identifier: NCT00597922.

Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Interrogating the Genetic Determinants of Tourette's Syndrome and Other Tic Disorders Through Genome-Wide Association Studies

OBJECTIVE

Tourette's syndrome is polygenic and highly heritable. Genome-wide association study (GWAS) approaches are useful for interrogating the genetic architecture and determinants of Tourette's syndrome and other tic disorders. The authors conducted a GWAS meta-analysis and probed aggregated Tourette's syndrome polygenic risk to test whether Tourette's and related tic disorders have an underlying shared genetic etiology and whether Tourette's polygenic risk scores correlate with worst-ever tic severity and may represent a potential predictor of disease severity.

METHODS

GWAS meta-analysis, gene-based association, and genetic enrichment analyses were conducted in 4,819 Tourette's syndrome case subjects and 9,488 control subjects. Replication of top loci was conducted in an independent population-based sample (706 case subjects, 6,068 control subjects). Relationships between Tourette's polygenic risk scores (PRSs), other tic disorders, ascertainment, and tic severity were examined.

RESULTS

GWAS and gene-based analyses identified one genome-wide significant locus within FLT3 on chromosome 13, rs2504235, although this association was not replicated in the population-based sample. Genetic variants spanning evolutionarily conserved regions significantly explained 92.4% of Tourette's syndrome heritability. Tourette's-associated genes were significantly preferentially expressed in dorsolateral prefrontal cortex. Tourette's PRS significantly predicted both Tourette's syndrome and tic spectrum disorders status in the population-based sample. Tourette's PRS also significantly correlated with worst-ever tic severity and was higher in case subjects with a family history of tics than in simplex case subjects.

CONCLUSIONS

Modulation of gene expression through noncoding variants, particularly within cortico-striatal circuits, is implicated as a fundamental mechanism in Tourette's syndrome pathogenesis. At a genetic level, tic disorders represent a continuous spectrum of disease, supporting the unification of Tourette's syndrome and other tic disorders in future diagnostic schemata. Tourette's PRSs derived from sufficiently large samples may be useful in the future for predicting conversion of transient tics to chronic tic disorders, as well as tic persistence and lifetime tic severity.

A Genetic Investigation of the Well-Being Spectrum

The interrelations among well-being, neuroticism, and depression can be captured in a so-called well-being spectrum (3-phenotype well-being spectrum, 3-WBS). Several other human traits are likely linked to the 3-WBS. In the present study, we investigate how the 3-WBS can be expanded. First, we constructed polygenic risk scores for the 3-WBS and used this score to predict a series of traits that have been associated with well-being in the literature. We included information on loneliness, big five personality traits, self-rated health, and flourishing. The 3-WBS polygenic score predicted all the original 3-WBS traits and additionally loneliness, self-rated health, and extraversion (R² between 0.62% and 1.58%). Next, using LD score regression, we calculated genetic correlations between the 3-WBS and the traits of interest. From all candidate traits, loneliness and self-rated health were found to have the strongest genetic correlations (r_g = - 0.79, and r_g= 0.64, respectively) with the 3-WBS. Lastly, we use Genomic SEM to investigate the factor structure of the proposed spectrum. The best model fit was obtained for a two-factor model including the 5-WBS traits, with two highly correlated factors representing the negative- and positive end of the spectrum. Based on these analyses we propose to include loneliness and self-rated health in the WBS and use a 5-phenotype well-being spectrum in future studies to gain more insight into the determinants of human well-being.

Efficient Implementation of Penalized Regression for Genetic Risk Prediction

Polygenic risk scores (PRS) combine many single-nucleotide polymorphisms into a score reflecting the genetic risk of developing a disease. Privé, Aschard, and Blum present an efficient implementation of penalized logistic regression...

Polygenic Risk Scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The “Clumping+Thresholding” (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T. In this paper, we present an efficient method for the joint estimation of SNP effects using individual-level data, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. We also provide an implementation of penalized linear regression for quantitative traits. We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. Overall, we find that PLR achieves equal or higher predictive performance than C+T in most scenarios considered, while being scalable to biobank data. In particular, we find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, in simulations, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC values of 89% and of 82.5%. Applying penalized linear regression to 350,000 individuals of the UK Biobank, we predict height with a larger correlation than with the best prediction of C+T (∼65% instead of ∼55%), further demonstrating its scalability and strong predictive power, even for highly polygenic traits. Moreover, using 150,000 individuals of the UK Biobank, we are able to predict breast cancer better than C+T, fitting PLR in a few minutes only. In conclusion, this paper demonstrates the feasibility and relevance of using penalized regression for PRS computation when large individual-level datasets are available, thanks to the efficient implementation available in our R package bigstatsr.

Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.

Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R² = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Generalizing polygenic risk scores from Europeans to Hispanics/Latinos

Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL, n = 12 , 803 ). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women's Health Initiative (WHI, n = 3 , 582 ). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non-EA GWAS results to estimate weights improved results.

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

The Genetics of Externalizing Problems

Externalizing problems generally refer to a constellation of behaviors and/or disorders characterized by impulsive action and behavioral disinhibition. Phenotypes on the externalizing spectrum include psychiatric disorders, nonclinical behaviors, and personality characteristics (e.g. alcohol use disorders, other illicit substance use, antisocial behaviors, risky sex, sensation seeking, among others). Research using genetic designs including latent designs from twin and family data and more recent designs using genome-wide data reveal that these behaviors and problems are genetically influenced and largely share a common genetic etiology. Large-scale gene-identification efforts have started to identify robust associations between genetic variants and these phenotypes. However, there is still considerable work to be done. This chapter provides an overview of the current state of research into the genetics of behaviors and disorders on the externalizing spectrum.

Polygenic risk scores: a biased prediction?

A new study highlights the biases and inaccuracies of polygenic risk scores (PRS) when predicting disease risk in individuals from populations other than those used in their derivation. The design bias of workhorse tools used for research, particularly genotyping arrays, contributes to these distortions. To avoid further inequities in health outcomes, the inclusion of diverse populations in research, unbiased genotyping, and methods of bias reduction in PRS are critical.

Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.